Sensory and Related Techniques for Evaluation of Dairy Foods-2008.pdf

Course Compendium

Sensory and Related Techniques for Evaluation of

Dairy Foods

23rd Short Course

Organized under the aegis of Centre of Advanced Studies

in Dairy Technology

17th June, 2008 to 7th July, 2008

Course Director Dr. Dharam Pal

Course Coordinator Dr. Ashish Kumar Singh

Centre of Advanced Studies Dairy Technology Division

National Dairy research Institute Karnal 132001 (Haryana), India

2008

Published by

Dr. A. A. Patel Head, Dairy Technology Division

Director, CAS

Course Director Dr. Dharam Pal

Course Coordinator Dr. Ashish Kumar Singh

Editing and Compilation Dr. Dharam Pal Dr. V.K. Gupta Dr. R.R.B. Singh

Dr. Mrs. Latha Sabikhi Dr. A.K. Singh

Dr. Sumit Arora

All Right Reserved ©

No part of this lecture compendium may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photography, recording or any other information storage and retrieval system without the written permission from the Director, NDRI, Karnal

Cover Design and Page Layout Mr. Avneet Rajoria & Mr. Ramesh Modi

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ii

Committees for the Course Organization

ORGANIZING COMMIITTEE

Dr A. A. Patel (Director, CAS) Dr. S. Singh

Dr. G. K. Goyal Dr. V. K. Gupta

Dr. S.K. Kanawjia Dr. D. K. Thompkinson

Dr. Dharam Pal (Course Director) Dr. Ashish Kumar Singh (Course Coordinator)

RECEPTION COMMITTEE TECHNICAL COMIITTEE

Dr. G. K. Goyal (Chairman) Dr. V. K. Gupta (Chairman) Dr. Ashish Kumar Singh Dr. R. R. B. Singh Mr. Ram Swaroop Dr. (Mrs.) Latha Sabikhi

HOSPITALITY COMMITTEE PURCHASE COMMITTEE

Dr. S. K. Kanawjia Dr. D. K. Thompkinson (Chairman) (Chairman) Dr. (Mrs.) Latha Sabikhi Mr. F. C. Garg Mr. Lahiri Singh Mr. M. K. Trehan

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Short Course on

Sensory and Related Techniques for Evaluation of Dairy Foods

17th June - 7 July, 2008

17.06.2008 (TUESEDAY) 10.:00 AM- 10.15 AM Registration Dr. Ashish Kumar

Singh 10.15 AM-10.50 AM Visit to ATIC Mr. M. K. Trehan 11.00 AM-11.45 AM Inauguration of Course 11.45 AM-1.00 PM Visit to Experimental Dairy Plant Mr. M. K. Trehan 1.00 PM- 2.00 PM Lunch 2.15 PM -3.30 PM Visit to Model Dairy Mr. M. K. Trehan 3.30 PM- 3.45 PM Tea 3.45 PM- 4.30 PM Visit to Library Mr. M. K. Trehan

18.06.2008 (WEDNESDAY) 9:45 AM-10.45 AM Requirements for Sensory Evaluation of

Foods (Theory) Dr. Dharam Pal

10.45 AM-1.00 PM Determination of Taste threshold (Practical)

Dr. Dharam Pal Mr. Ramswaroop

1.00 PM – 2.00 PM Lunch 2.15 PM- 3.15 PM Determination of Odour threshold

(Practical) Dr. Dharam Pal Mr. Ramswaroop

3.15 PM - 3.30 PM Tea 3.30 PM – 4.30 PM Library Consultation -

19.06.2008 (THURSDAY) 9:45 AM- 10.45 AM Sensory Methods and their Applications in

Evaluating Quality of Foods (Theory) Dr. Dharam Pal

10.45 AM- 1.00 PM Sensory Evaluation of Milk (Theory & Practical)

Dr. Dharam Pal Mr. Ramswaroop

1.00 PM- 2.00 PM Lunch 2.15 PM- 3.15 PM Sensory Evaluation of Dried Milk and

Milk Products (Theory) Dr. V. K. Gupta

3.15 PM - 3.30 PM Tea 3.30 PM – 4.30 PM Library Consultation

20.06.2008 (FRIDAY) 9:45 AM- 1.00 PM Determination of Water Activity of Foods

(Theory & Practical) Dr. R. R. B. Singh Mr. Avneet

Course Programme

iv

Rajoria 1.00 PM – 2.00 PM Lunch 2.15 PM- 3.15 PM Principles of Good Laboratory Practice

(Theory) Dr. Rajan Sharma

3.15 PM-3.30 PM Tea 3.30 PM-4.30 PM Library Consultation -

21.06.2008 (SATURDAY) 9:45 AM- 10.45 AM Chemistry of Flavour Development in

Cheese (Theory) Dr. Sumit Arora

10.45 AM-1.00 PM Microstructure of Dairy Products (Theory & Practical)

Dr. S. K. Tomar

1.00 PM – 2.00 PM Lunch 2.15 PM- 3.15 PM Consumer Acceptance Studies (Theory) Dr. (Mrs.) Latha

Sabikhi 3.15 PM - 3.30 PM Tea 3.30 PM – 4.30 PM Colour Measurement of Foods (Practical) Dr. Ashish Kumar

Singh Mr. .Avneet Rajoria

23.06.2008 (MONDAY) 9:45 AM- 1.00 PM Sensory Attributes of Ice-cream and

Frozen Dessert (Theory & Practical) Mr. F. C. Garg Mr. Ramswaroop

1.00 PM – 2.00 PM Lunch 2.15 PM- 3.15 PM Viscoelastic Characteristics of Foods

(Theory) Dr. G. R. Patil

3.15 PM -4.30 PM Sensory Evaluation of Milk Chocolate (Practical)

Dr. Ashish Kumar Singh Mr. Modi Ramesh

24.06.2008 (TUESDAY) 9:45 AM- 10.45 AM Statistical Techniques for Analysis of

Sensory Data (Theory) Dr. R. Malhotra

10.45 AM-1.00 PM Sensory Evaluation of Dried Milks (Practical)

1.00 PM – 2.00 PM Lunch 2.15 PM- 4.30 PM Texture Measurement of Dahi & Yoghurt

(Practical) Dr. Dharam Pal Mr. N. Raju

25.06.2008 (WEDNESDAY) 9:45 AM- 10.45 AM Analytical techniques for Characterization

of Flavoring Compounds in Dairy Products (Theory)

Dr. Rajesh Bajaj

10.45 AM-1.00 PM Descriptive Sensory Analysis of foods (Practical)

Dr. Ashish Kumar Singh

1.00 PM – 2.00 PM11 Lunch 2.15 PM- 4.30 PM Sensory evaluation of Paneer and Un-

ripened Cheeses (Theory & Practical) Dr. S. K. Kanawjia

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26.06.2008 (THURSDAY) 9:45 AM- 1.00 PM Sensory and Rheological Properties of

Fermented Milks (Theory & Practical) Dr. (Mrs.) Latha Sabikhi Mr. Ram Swaroop

1.00 PM – 2.00 PM Lunch 2.15 PM- 4.30 PM Sensory Evaluation of Khoa & Khoa based

Sweets (Theory & Practical) Mr. F. C. Garg Mr. Ram Swaroop

27.06.2008 (FRIDAY) 9:45 AM- 10.45 AM Biosensors in Chemical Quality

Assessment of Dairy and Food Products (Theory)

Dr. Rajan Sharma

10.45 AM-11.15 AM Nutritional and Therapeutic Assessment Techniques for Dairy Products (Theory)

Dr. (Mrs.) Suman Kapila

11.15 AM-11.30 AM Tea break 11.30 AM-1.00 PM Texture Profile Analysis of Dairy Products

(Practical) Dr. R. R. B. Singh Mr. Avneet Rajoria

1.00 PM – 2.00 PM Lunch 2.15 PM- 4.30 PM Sensory Characteristics of Milk Protein

Products (Theory) Dr. V. K. Gupta

28.06.2008 (SATURDAY) 9:45 AM- 1.00 PM Statistical Software for Analysis of

Sensory Data (Theory & Practical) Dr. R. Malhotra

1.00 PM – 2.00 PM Lunch 2.15 PM-4.30 PM Sensory Characteristics of Concentrated

and UHT Milk (Theory & Practical) Dr. R. R. B. Singh

30.06.2008 (MONDAY) 9:45 AM- 10.45 AM Influence of Packaging Materials on

Sensory Quality of Dairy Products (Theory)

Dr. G. K. Goyal

10.45 AM-1.00 PM Testing of Packaging Materials for Dairy Foods (Practical)

Dr. G. K. Goyal Mr. Ram Swaroop

1.00 PM – 2.00 PM Lunch 2.15 PM- 3.15 PM Chemistry of Quality Attributes in Heat

Processed Dairy products (Theory) Dr. (Mrs.) Bimlesh Mann

3.15 PM-3.30 PM Tea

3.30 PM-4.30 PM Library Consultation

1.07.2008 (TUESDAY) 9:45 AM- 1.00 PM Descriptive Sensory Analysis of Dairy

Foods (Theory & Practical) Dr. Ashish Kumar Singh & Ms. Rekha Chawla

1.00 PM – 2.00 PM Lunch 2.15 PM- 4.30 PM Role of Starter and Adjunct Cultures on

Quality Characteristics of Fermented Dairy Products (Theory)

Dr. Rameshwar Singh

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2.07.2008 (WEDNESDAY) 9:45 AM- 10.45 AM Role of Primary Senses in Sensory

Evaluation of Foods (Theory) Prof. V. K. Joshi

10.45 AM- 11.00 AM Tea 11.00 AM- 1.00 PM Techniques for Sensory Evaluation of

Beverages (Theory & Practical) Prof. V. K. Joshi

1.00 PM – 2.00 PM Lunch 2.15 PM- 4.30 PM Judging Contest for Participants Dr. Dharam Pal

Mr. Ramswaroop

3.07.2008 (THURSDAY) 9:45 AM- 1.00 PM Sensory Characteristics of Fat-rich Dairy

Products (Theory & Practical) Dr. A. A. Patel Mr. Ramswaroop

1.00 PM – 2.00 PM Lunch 2.15 PM- 3.15 PM Emerging Concepts in Sweet Taste

(Theory)

Dr. Sumit Arora

3.15 PM-3.30 PM Tea 3.30 PM- 4.30 PM Rheological & Textural Characteristics of

Solid Foods (Theory) Dr. A. A. Patel

4.07.2008 (FRIDAY) 9:45 AM- 1.00 PM Basic Concepts of Rheology and Texture

Measurement of Foods (Theory) Dr. D. S. Sogi

1.00 PM – 2.00 PM Lunch 2.15 PM- 4.30 PM Properties of Food Powders (Theory &

Practical) Dr. R. R. B. Singh Mr. Avneet Rajoria

5.07.2008 (SATURDAY) 9:45 AM- 10.45 AM Concept of Colour Measurement and

Sampling Techniques for Quality Evaluation of Food (Theory)

Dr. S. N. Jha

10.45 AM- 1.00 PM Sensory Characteristics of Ripened Varieties of Cheeses (Theory & Practical)

Dr. S. Singh

1.00 PM – 2.00 PM Lunch 2.15 PM- 3.15 PM Nondestructive methods for Quality

Evaluation of Dairy and Food Products (Theory)

Dr. S. N. Jha

3.15 PM - 3.30 PM Tea & Discussion 3.30 PM –4.30 PM

7.07.2008 (MONDAY) 9:45 AM- 10.45 AM Course Evaluation

10.45 AM-11.15 AM Interaction with the Course Faculty 11.15 AM-1. 00 PM 1.00 PM – 2.00 PM Lunch 2.30 PM- 3.30 PM Valedictory function

Foreword Dr. A.K. Srivastava i

Committees for Course the Course Organization

ii

Course Programme iii

1 Requirements for Sensory Evaluation of Foods

Dr. Dharam Pal 1

2 Sensory Methods and their Applications in Evaluating Quality of Foods

Dr. Dharam Pal 10

3 Sensory Evaluation of Milk Dr. A.K. Singh 18

4 Sensory Characteristics of Fresh Cheese Dr. S.K. Kanawjia 25

5 Sensory Attributes of Ice Cream Mr. F.C. Garg 33

6 Sensory Evaluation of Dairy Products with Special Emphasis on Flavour Lexicon

V. Pathak & Z.F. Bhat 40

7 Sensory Attributes of Concentrated Milk and their Evaluation

Dr. R.R.B. Singh 48

8 Sensory Attributes of Fermented Milk Products

Dr. Latha Sabikhi 56

9 Application of e-tongue in monitoring

Sensory quality of foods

Dr. S.K. Kanawjia 65

10 Role of Packaging Materials In Enhancing Sensory Quality of Dairy Products

Dr. G.K. Goyal

77

11 Consumer Acceptance Studies Dr. Latha Sabikhi 83

12 Chemistry of Flavour Development In Cheese

Dr. Sumit Arora 86

13 Analytical Techniques for Characterization of Flavouring Compounds In Dairy Products

Dr. Rajesh Kumar, Dr. R. B. Sangwan and Dr. Bimlesh Mann

97

14 Sensory Attributes of Milk Protein Products

Dr. Vijay Kumar Gupta

104

15 Sensory Evaluation of Dried Milk and Milk Products


110

16 Application of Rheology in Quality Assurance in Food Processing

Dr. Dalbir Singh Sogi

116

CONTENTS

17 Nondestructive Methods for Quality Evaluation of Dairy and Food Products

Dr. S. N. Jha

126

18 Good Laboratory Practices – Genesis & Concept

Dr. Rajan Sharma 135

19 Chemistry of Quality Attributes in Heat Processed Dairy Products

Dr. (Mrs.) Bimlesh Mann

144

20 Determination of Sorption Isotherms and Generation of Sorption Data

Dr. R. R. B. Singh

155

21 Biosensor in Chemical Quality Assessment of Dairy and Food Products

Dr. Rajan Sharma

165

22 Soft Computing Models with Applications to Dairying

Dr. A. K. Sharma 177

23 Microstructure of Cultured Dairy Products: An Update

Dr. Sudhir Tomar 184

24 Nutritional and Therapeutic Assessment for Functional Dairy Products

Dr. Suman Kapila 196

25 Viscoelastic Behaviour of Foods Dr. G. R. Patil 208

26 Fundamentals of Rheology Dr. Dalbir Singh Sogi 214

27 Switching Sweeteners – A Sweet Approach

Dr. Sumit Arora

221

28 Concept of Colour Measurement and Sampling Techniques for Quality Evaluation of Food

Dr. S. N. Jha

227

29 Sensory Evaluation of Ripened Varieties of Cheese

Dr. S. Singh 245

30 Statistical Techniques for Analysis of Sensory Data

Dr. Ravinder Malhotra 254

31 Descriptive Sensory Analysis Dr. A. K. Singh 267

Sensory and Related Techniques for Evaluation of Dairy Foods 1

Dr. Dharam Pal Principal Scientist

Division of Dairy Technology National Dairy Research Institute, Karnal.

1.0 INTRODUCTION

A number of quality assurance procedures are used to examine and maintain quality of a dairy product. The testing starts from reception of raw material, for example, milk, to close examination of finalized product. These tests are physical, chemical, microbiological, instrumental and sensory. In our country, the dairy industry so far considers the chemical and microbiological quality as the sole criteria of deciding food quality. With the availability of more milk, increased competition and consumers’ awareness about quality, the significance of sensory evaluation is being realized and it is emerging as an important analytical tool in fast growing dairy industry.

Sensory evaluation may be defined as a scientific discipline used to evoke, measure, analyze and interpret results of those characteristics of foods and materials as they are perceived by the senses of sight, smell, taste, touch and hearing.

The sensory evaluation is very important in product evaluation on account of following advantages:

i) It is a simple analytical tool,

ii) It identifies the presence or absence of perceptible differences in terms of flavour, texture, colour and appearance,

iii) These important quality attributes are measured in a fast and quantifiable manner employing sensory techniques. The use of chemical and instrumental methods for examining sensory characteristics are time consuming, complicated and expensive,

iv) It enables identification of a particular problem or defect that cannot be detected by other analytical techniques,

v) Sensory evaluation techniques help in ensuring that the consumers get a non defective and enjoyable product.

In recent years, the competition in food/dairy corporate has tremendously increased. The food processing companies are making very fast changes in their existing product in terms of ingredients, value addition, packaging etc. or developing new products to grab larger global market share. In all these situations, sensory

REQUIREMENTS FOR SENSORY EVALUATION OF FOODS


evaluation plays a critical role. The various applications of sensory evaluation are given as below:

• Inspection of raw materials • New product development • improvement/reformulation of existing product • Cost reduction • Quality assurance • Selection of packaging material • Shelf life studies • Establishing analytical/ instrumental/ sensory relationships

2.0 REQUIREMENTS

A successful implementation of sensory evaluation programme requires following three major components:

• Proper laboratory facilities • Sensory panels and their rigorous training programmes • Statistician

2.1 Laboratory Set Up

Many designs of the sensory evaluation laboratory are available. The sensory laboratory set up normally consists of a reception cum briefing room, panel booths and preparation room. Sensory evaluation should be conducted in quiet and well lit rooms free from any odours. The dominant motive of constructional details should be to have comfort for concentrated prolonged testing and ease of cleaning. Pleasing neutral shades and maintenance of comfortable temperature and humidity conditions of the whole area or at least the panel room are desirable. The testing area where booths are located should be separated from sample preparation room and wash room and store by a complete partition.

2.1.1 Reception and briefing room It should be so designed as to ensure maintenance of pleasant attitudes and minimize traffic to the booths. Panel members shall assemble here, register, receive the evaluation card and briefed about the test. 2.1.2 Testing booths/area:

This is the area where panel members carry out actual sensory evaluation of dairy products. Testing area shall be located separately but in the immediate vicinity of the preparation area. This area is normally divided in small booths (number of booths between 5 to 10) so that each panel member can independently evaluate the product. Following conditions have to be maintained in testing area for obtaining best results:

- The temperature and relative humidity shall be constant, controllable and comfortable for evaluators. A temperature of about 20oC and 62% relative humidity are considered to be optimum.


- Noise level shall be kept to a minimum during the tests. The movement of persons shall also be restricted in the area.

- The testing area shall keep free from odours. A slight positive pressure may be created in the testing area to reduce inflow of odorous air from other area.

- Lighting is very important in all sensory testing. It is particularly important in colour examination of dairy products. Lighting particularly in testing booths shall be uniform, shadow free, controllable and of sufficient intensity to permit effective evaluation of the colour and appearance of samples. In most cases, lights having a correlated colour temperature of 6500 K (or 110 candle foot light) are desirable. In order to mask differences in colour and other appearance characteristics special lighting devices, such as a dimmer device, colored lamps/filters or sodium vapour lamps, may be provided.

- The size of each testing booth shall be sufficiently large to accommodate the samples, utensils, sink, rinsing agents and score sheet/card. An area of 0.9 m wide and 0.6 m deep is considered optimum for this purpose. The height of working space in the booth should be appropriate to allow comfort to the evaluator.

- A counter on the serving/distribution area side shall be provided. Openings, covered by sliding doors, of convenient size may be provided for supplying samples into the booths from the serving counter. A system, such as light bulb on the counter side, is devised for evaluator to signal to the operator when he is ready for a sample.

2.1. 3 Preparation room It shall be suitably separated from the testing room and should be equipped for preparing and serving food samples. The room should have the facility for cooking range hot and cold storage cabinet. The ventilation should be proper and the cooking odours should not penetrate the panel booth area. The samples shall be passed to the test booths through hatch in the partition. The hatch on the service counter should preferably be constructed in such a manner that there shall be no recognition of individual or either side of partition. The laboratory facility should be flexible enough to handle enough to handle current and future testing activities as well as to provide a workable environment for the staff. The use of computers has been recommended for sensory evaluation work. In that case, sensory evaluation laboratory should include space for data processing equipment. 3.0 SELECTION OF SENSORY PANELISTS Analysis of sensory properties of food involves the use of human subjects in the laboratory/processing plant environment. The sensitivity and experience of an evaluator (panelist) influence the accuracy of results. The evaluator should work like a calibrated instrument and provide reproducible results. The selection of most stable


and sensitive panel members and their training, is therefore, very essential for efficient conduct of sensory analysis of dairy products.

3.1 Types of panel

The sensory panels are classified into three categories viz., trained, semi trained and consumer panel. The panelists are selected and trained by the sensory leader/coordinator depending on the type of the product.

Trained Panel: They should be carefully selected and trained, and need not be expert panelists. The trained panel should be used to establish the intensity of a sensory character or overall quality of a food. A trained panel should comprise of small number of members varying from 5 to 10 and may be used in all developmental, processing and storage studies. A small highly trained panel will give more reliable results than a large untrained panel.

Semi-Trained Panel (D&C Panel): This type of panel should be constituted from persons normally familiar with quality of milk and different classes of dairy products. This panel is capable of discriminating differences and communicating their reactions, though it may not have been formally trained. In a semi-trained panel individual variations can be balanced out by involving greater number of panelists. The panel, should normally consist of about 25 to 30 members, and should be used as a preliminary screening programme to select a few products for large scale consumer trials.

Consumer Panel: The members of the consumer or untrained panel should be selected at random and ensure due representation to different age, sex, race and income groups in the potential consumer population in the market area. More than 80 members are required to constitute a consumer panel.

Two channels can be adopted for screening and selection of sensory panel members. First, from the quality control/research laboratory and second source is from the processing unit. Another option is to have a mixed source i.e. some of the members from quality control laboratory/research laboratory and the remaining from processing sections. Normally double the numbers of panelists finally required are selected. For example, if 7 members are needed in the final panel at least 15 should be initially screened

3.1.2 Qualification for Screening a Panelist

Interest and motivation: Candidates who are interested in sensory analysis and have investigating curiosity are likely to be more motivated and will do better jobs.

Attitudes to foods: Candidates having strong liking or disliking towards a dairy product should not be screened.

Knowledge and aptitude: The evaluators should have capacity to concentrate and to remain unaffected by external influences. He should have knowledge about basic aspects and principles of milk and its processing into products.


Health: Candidates should be in good general health. They shall not suffer from any disabilities, which may affect their senses, or from any allergies or illness and shall not take medication, which might impair their sensory capacities.

Ability to Communicate: The ability of candidates to communicate and describe the sensations they perceive when judging a food product is particularly important.

Availability: Candidates shall be available to attend both training and subsequent evaluation. Personnel who travel frequently or have heavy workloads are often unsuited for sensory work.

Table 1. Examples of materials/substances and their concentration for identification/ matching test

Taste or odour Material

Concentration in water (taste material) or ethanol* (odorous material) at room temperature

(g/litre)

Taste

Sweet Sucrose 16

Acid/ sour Tartaric acid or citric acid 1

Bitter Caffeine 0.5

Salty Sodium chloride 5

Astringent Tannic acid or potassium aluminium sulfate (alum)

1 0.5

Metallic Ferrous sulfate**, hydrates, FeSO4.7H2O

0.01

Odour

Lemon, fresh Citral (C10H15O) 1 x 10-3

Vanilla Vanillin (C8H8O3) 1 x 10-3

Thyme Thymol (C10H14O) 5 x 10-4

Floral, Jasmine Benzyl acetate (C8H12O2) 1 x 10-3 * Stock solutions are prepared with ethanol, but the final dilution is made with water and shall not contain more than 2% of alcohol.

** To mask yellow colour, present the solutions in closed opaque containers or under dim or colouring light.

Test for Detection of Basic Taste: Solutions of four basic taste solutions, namely sweet, sour, salt and bitter are prepared of the concentration as shown in table 2 below:


Table 2. Concentration of taste solutions used to examine the acuity of candidates

Material Taste Quality Concentration in water at room temperature

Caffaine Bitter 0.27 g/ litre

Citric Acid Sour 0.60 g/ litre

Sodium Chloride Salt 2 g/ litre

Sucrose Sweet 12 g/ litre 3.1.3 Screening and Selection

Sensory panelists can be screened and selected by adopting several tests. The followings are the most commonly used tests: • determine impairment of primary senses (colour, vision, ageusia and anosmia) • matching test for taste and odour substances • ability to detect basic taste and odour acuity • determine ability to characterized texture • performance in comparison with other candidates

Colour Vision: Candidates with abnormal colour vision or colour blindness are unsuitable for judging of dairy products. Assessment of colour vision can be carried out by a qualified optician.

Matching Test: Samples of sapid and/ or olfactory materials, depending on the nature of product for which the panel members are to be trained later, at well above threshold levels of the expected panelists are prepared. The examples of these materials are given in table 1. Each sample is allotted a different, random, three digit code number. Candidates are presented with one sample of each type and are allowed to familiarize themselves with them. They are then presented with a series of the same materials labelled with different code numbers. They may be asked to match each of them with one of the original set and describe the sensation they are experiencing. For the substances and their concentration given in table 1, candidates who make fewer than 80% correct answers should not be chosen as selected panelists.

These test materials along with blank (water) are presented to the candidates and asked them to detect the taste quality. Preferably candidates should have 100% correct responses as the concentrations test materials are at the super threshold level. Inability to detect differences and identify the taste quality after several repetitions indicate that the candidates have poor sensitivity and unsuitable to judge the samples on the basis of taste.

Odour Recognition Test: Candidates are presented many (about 10 in each lot) odoriferous substances. Some of these materials are familiar (those we use daily such as tea, coffee, onion, garlic, curd, orange, spices, etc.) and others unfamiliar (table 3). The odorous food materials may be presented preferably in form of liquid extract or as such (in a test tube in invisible form). The concentration should be above the


recommended threshold level. Candidates are graded according to correct answers. Those recognize less than 65% of odorous substances/odour are unsuitable as panelist for this type of test.

Table 3. Examples of unfamiliar odorous material for odour recognition test

Material Name most commonly associated with the odour

Benzaldehyde Bitter almonds, cherry, …..

Octene-3-Ol Mushroom, ……

Phenyl-2-ethyl acetate Floral, ……

Diallyl sulphide Garlic, ……

Camphor Camphor, medicine, ……

Menthol Peppermint, ……

Butyric acid Rancid butter, ……

Acetic acid Vinegar, ……

Isoamyl acetate Fruit, banana, ……

Thymol Spices, ……

Vanillin Vanilla, ……

Textural characterization: This type of test is highly beneficial for selecting the panelists for judging the dairy products where texture is an important attribute like cheese, paneer, butter, ice cream, khoa etc. In this test, all range of products having typical texture (table 4) is given to the candidates. They have to arrange these products according to the nature and level of textural properties, such as hard, elastic (spongy), adhesive (sticky/pasty), brittle, gummy, cohesive, chewy etc. A satisfactory level of success in this task can be specified only in relation to the products used. Candidates who achieve less than 65% of the maximum score are unsuitable.

Table 4. Food products with typical textural attributes

Food product Textural attribute most commonly associated Carrot (raw) Hard, crunchy Butter Soft Toffee Gummy Meat/ Paneer Chewy Biscuit Brittle Rasogolla Spongy Oranges Juicy, cellular particles Chest nut puree Pasty Semolina Grainy Salt Gritty/ coarse


3.1.4 Training

The purpose of training is to increase sensory acuity of panelists and provide them with rudimentary knowledge of procedures used in sensory evaluation. Training also develops the ability of panel members to detect, recognize and describe sensory stimuli related to food products. A general step-wise approach for training in food/dairy product is summarized as below:

a) Sensory panelists (assessors or evaluators) should be explained the basic requirements of sensory evaluation i.e. what they should do and what not to do.

b) Assessors shall be acquainted with the:

- desirable and undesirable attributes of the product - correct terminology - use of score card - scoring technique/ sequence of observations

c) Samples used for training and testing shall be characteristic of their origin, style and quality, and representative of the range generally found in the market (all defects may be simulated in the samples under laboratory conditions). A reference (having most desirable characters) is always provide with test samples.

d) Difficulties of the test are so adjust the that the group as a whole will find difference between the samples, but some panelists will fail.

e) Start with the large group and reject those who are insensitive or under perform.

f) Finally a trained panel comprising of 5-6 members is retained.

4.0 STATISTICIAN

The role of a statistician is very important in sensory evaluation programme at each stage starting from designing of experiments till drawing valid conclusions from the sensory data. A statistician help in planning of experiment coding and presentation of samples, statistical analysis of data by applying appropriate design and interpretation of results. Though, now a days several softwares are available for statistical analysis, the role of statistician in tabulation of data and drawing of inferences is equally important.

5.0 SAMPLING REQUIREMENTS

i) Sampling should be carried out by a trained and experienced person and it is essential that the sample should be representative of the lot.

ii) A procedure of sample preparation which is most likely to bring out the difference in the particular quality attribute under evaluation shall be selected. Care shall be taken that no loss of flavor occurs and no foreign tastes or odours are imported by the procedure during preparation, storage, serving, etc. Depending upon the nature of the material and aim of the test, the need for a medium in testing auxiliary items should be decided. Foods like hot sauce, spices, vinegar, etc. may require dilution with some medium because of their intense physiological efforts.


iii) The panelist should be allowed to have sufficient sample necessary to make judgment. Unless, only one sample is to be tested, full normal serving quantities shall not be served even though the material is available.

iv) The temperature of serving should be close to that recommended for the food product. The samples shall be served in utensils of the same type and appropriate size, shape and colour and they shall not import any taste or odour to the sample. The test should be carried out at least one hour before or after lunch.

v) Use of materials which are likely to vitiate results such as smoking, chewing, pan (betel-vine) and taking intoxicants by a panelist should have a time lapse of at least half-an-hour before the test. Use of strong odoriferous substances such as cosmetics, flowers, hair oil should be avoided.

vi) The number of samples served in any session shall depend upon the nature of the test product and upon the evaluation method use. In case the test product exert mild sensory effects, large number of the products exerting strong prolonged sensory effects, the number of samples may be reduced to less than 5.

vii) Since coding is necessary to obscure the identity of the sample, a multiple digit code generated from a table of random numbers should be used. Avoid constant use of certain codes or a set of codes to expedite tabulation of results.

6.0 EVALUATION CARD

The evaluation card should be clearly printed and the matter should be arranged in logical sequence for examination which is expected under each test. Appropriate terminology without ambiguity shall be used. The evaluation card should be simple, brief, easy to follow and record what is exactly required. Due weightage should be given to all the sensory attributes. 7.0 REFERENCES

Amerine, M. A., Pangborn, R. M. and Roessler, E. B. (1965). Principles of Sensory Evaluation of Food Academic Press, New York.

ASTM (1986). Manual on Sensory Testing Methods. STP 434, American Society for Testing and Materials, Philadelphia, U.S.A.

Bodyfelt, F.W., Tobias, J. and Trout, G.M. (1988). The Sensory Evaluation of Dairy Products, AVI Publ. Co., New York.

Dharam Pal, Sachdeva, S. and Singh, S. (1995). Methods for determination of sensory quality of foods: A critical appraisal. J. Fd. Sci. Technol. 32 (5):357-367.

Russell, G. M. (1984). Some basic consideration in computerizing the sensory laboratory. Food Technol. 38(9): 67-70.

Stone, H. and Sidel, J. (1993). Sensory Evaluation Practices, Academic Press, Inc. London.


SENSORY METHODS AND THEIR APPLICATIONS IN EVALUATING QUALITY OF FOODS

Dr. Dharam Pal Principal Scientist

Division of Dairy Technology National Dairy Research Institute, Karnal.

1.0 INTRODUCTION

Sensory tests are conducted to meet the following purposes: • Select qualified judges and study human perception of food attributes. • Correlate sensory with chemical and physical measurement. • Study processing effects, maintain quality, evaluate raw material

selection, establish storage stability or reduce costs. • Evaluate quality or • Determine consumer reaction.

Each of these purposes requires appropriate tests. There are a substantial number of test methods and new methods continue to be developed. Stone and Sidel (1993) have classified these methods into following categories.

S.No. Category Test Type

1. Discriminative Paired comparison, Duo trio, Triangle, Dual standard, Multiple standard, etc.

2. Descriptive Flavor profile, Texture profile and Quantative Descriptive Analysis (QDA)

3. Affective Acceptance/ preference: 9-points Hedonic scale, Consumer studies

4. Others Scoring, Ranking 2.0 DISCRIMINATIVE TESTING

This is one of the most useful analytical tools available to the sensory professionals. It is on the basis of a perceived difference between two products that one can justify proceeding to a descriptive test in order to identify the basis for the difference. Within this general class is variety of specific methods as given in the table above.

The main objective of all these methods is to answer a simple question. “Are the products perceived as different”? Obviously the response to this question can have major consequences. If the conclusions from a discrimination test are to be accepted by management as reliable, valid and believable, then it is important that each test be


conducted with proper consideration for all aspects of the test design, product preparation and handling, implementation, data analysis and interpretation.

2.1 Paired Comparison Test The paired comparison procedure is the earliest example of the application of

discrimination testing to food and beverage evaluation. It has also been used successfully for determinations of threshold for basic taste solutions. The paired comparison test is a two product test, and the panelist task is to indicate the one that has more of a designated characteristic such as sweetness, tenderness or skinniness. This method is also identified as a directional paired comparison test, the “directional” component altering the panelist to a specific type of paired test. The paired comparison test is relatively easy to organize and to implement. The two coded products (AA, BB, AB and BA) are served simultaneously and the subject has to decide whether “there is difference” or “there is no difference”. Requiring a “difference” response in all cases has been found to give better results.

Another version of the paired test is the A-not-A procedure. The subject is presented with a single sample for evaluation, which is then replaced by a second sample. The subject then makes a decision as to whether the products are same (or different). This particular test procedure has considerable merit in those situations where non test variables such as a colour difference may influence results. 2.2 Duo-trio and Triangle test

These tests have been discussed earlier. The Duo-trio test is suitable for products that have relatively intense taste, odour and / or kinaesthetic effects such that the sensitivity is significantly reduced. It lends itself to use for quality control and for selection of judges for superior discrimination. The chance probability associated with the duo-trio test is identical with that of the other two product tests. Whenever products are being compared with a current franchise (i.e. product now being manufactured), the duo-trio, constant-reference test method, is most appropriate.

The chance probability associated with the three product (triangle) test is only 1/3, which accounts for its claim of greater sensitivity. The triangle test is more difficult test because the subject must recall the sensory characteristics of two products before evaluating a third and then making a decision. In fact, the test can be viewed as a combination of three paired tests (A-B, A-C and B-C). Products that have intense flavours and aromas that are spicy and/or are difficult to remove from palate, or that have physiological effects (distilled beverages) usually preclude the use of the triangle test.

2.3 Multiple Sample Test Tests involving more than 3 stimuli are classified as multiple sample tests. They may have equal (symmetrical) or unequal (asymmetrical) numbers of each stimulus. When they are applied as true difference tests, the judge is required to separate the samples into two groups or like samples. When they are applied as


directional tests, the judge is asked to identity the groups of higher or lower intensity or a given criterion. Difference test designs involving more than three stimuli have had only limited use. The limitation is based on the increase in psychological complexity and physiological fatigue which accompanies an increase in the number of stimuli. In addition, large quantities of samples are required and more time is needed for observer to make a decision, these tests appear to be most applicable to visual discrimination, where the judge does not rely on memory and fatigue is almost non-existent. 2.4 Dual Standard Test The dual standards method was proposed for use in quality control situations. The subject is served four products; two are identified as references A and B and two are coded. The subjects must match the reference product with the coded product. The designation of the two references could reflect quality control limits or current production and product outside the limit. 2.5 Multiple Standards Test

This test was developed for odour evaluation when a non-uniform standard was to be compared with an unknown. Any numbers of the questionable standards are presented simultaneously with the unknown and the subject is asked to designate the one which is most different. The chance probability of identifying the unknown correctly is ones over the total number of samples involved.

The literature provides a somewhat conflicting selection of conclusions regarding the sensitivity of the various test methods; some sensory professionals suggest that the triangle is more sensitive than the duo-trio and the paired tests, while others have arrived at contrary conclusions. The various difference tests can be ranked in terms of increasing sensitivity as: paired, dual standard, duo-trio, triangle and multiple standard (Amerine et al, 1965). Recently Stone and Sidel (1993) have concluded that all discrimination tests are equally sensitive. 3.0 DESCRIPTIVE ANALYSIS

Descriptive analysis is a sensory methodology that provides quantitative descriptions of products based on the perceptions of a group of qualified subjects. It is a total sensory description taking into account all sensations that are perceived – visual, auditory, olfactory, kinaesthetic and so on- when the product is evaluated. Descriptive analysis results provide complete sensory descriptions of an array of products and provide a basis for determining those sensory attributes that are important to acceptance. The results enable one to relate specific process variables to specific changes in some of the sensory attributes of a product. From the product development view point, descriptive information is essential in finding out those product variables that are different and from which one can establish cause and effect relationships.


A descriptive test involves relatively few subjects who have been screened. Screening should be product category specific as is the subsequent training effort. Training is primarily focused on development of descriptive language which is used as a basis for scoring the product. Apart from this the other important activities that are part of training include the grouping of attributes by modality (i.e. appearance attributes, aroma attributes and so on), listening them by occurrence, developing a definition for each attribute, identifying helpful references for use during training, and familiarizing the subjects with the scoring procedure. There are numerous applications for descriptive analysis, including monitoring competition, storage stability / shelf life, product development, quality control, physical / chemical and sensory correlation, etc.

Depending up on the test methods used the training can be quite different. Some of the descriptive methods described in the literature are summarized here.

3.1 Flavour Profile

The flavor profile method is the only formal qualitative descriptive procedure and is probably the most well known of sensory test methods. This utilizes a panel of four to six screened subjects who first examine and then discuss the product in an open session. Once agreement is reached on the description of the product the panel leader summarizes the results in report form. The method has considerable appeal because results could be obtained rapidly and would obviate the need for statistics.

3.2 Texture Profile

This method represents advancement in descriptive analysis with respect to development of the descriptive terminology, the scales for recording intensities and the word/product anchors for each scale category. In developing the method, the objective was to eliminate problems of subject variability, allow direct comparison of results with known materials and provide a relationship with instrument measures. There is considerable appeal to the direct link between specific instrumental measures of these rheological properties of a product and the responses of a panel of specific sensory attributes, for example, texturometer units and hardness sensory ratings. However, separation of texture from other sensory properties of a product such as colour, aroma, tests and so forth limits the total perception of the product’s sensory properties.

3.3 Quantitative Descriptive Analysis

The quantitative descriptive analysis (QDA) method was developed with an approach that was primarily behavioural in orientation with a consensus approach to language development, use of replication for assessing subject and attribute sensitivity, and for identifying specific product differences and defined statistical analysis. The development of method evolved from a number of considerations to ensure that it would:


• Be responsive to all the sensory properties of a product • Rely on a limited number of subjects for each test • Use subjects qualified before participation • Be able to evaluate multiple product in individual booths • Use a language development process free from leader influence • Be quantitative and use a repeated trials design • Have a useful data analysis system

In a QDA test, the subjects evaluate all of the products on an attribute by attribute basis on more than a single occasion.

4.0 OTHER METHODS Many more descriptive methods have been described in the literature which is

more or less on the lines of the test methods discussed above. The spectrum descriptive analysis, for example, involves extensive training activities, reflecting the basic flavor and texture profile procedures, with particular reliance on training the subjects with specific standards of specified intensities. Free choice profiling is another approach in which no subject screening or training are required and the subject can use any words they want to describe the products being evaluated. The time advantage may, however, actually not be there since the experimenter requires spending time explaining the testing procedures to the subjects. 4.1 Scoring

The most frequently used of all sensory testing systems is scoring because of its diversity, apparent simplicity and ease of statistical analysis. Scoring methods have most extensively been used by the dairy industry for product development and improvements, shelf life studies and assessing suitability of packaging materials. Score cards based on 100 points generally used for judging and grading of dairy products. Most recently 25 points score cards have been suggested (Bodyfelt, et al, 1988). It is believed that numerical rating tests give more complete information than either ranking tests or descriptive rating tests, but the judges must be trained. Since there is no indication of liking to the test product, palatability norms should be established. The score card must be properly developed giving due weightage to all the sensory attributes.

4.2 Ranking

Ranking tests require that judges arrange a series of two or more samples in an ascending or descending order of intensity of a specific attribute. In ranking test for quality, the usual objective is to select one or two if the best samples rather than to test all samples thoroughly. Ranking is often used for screening inferior from superior experimental samples in product development and occasionally in training judges.

Samples may be ranked in order of degree of acceptability or in order of general quality, or by specific attributes of colour, volume, texture or flavour intensity. Judges should be thoroughly familiar with all aspects of the sample


characterization under consideration. This may not be easily achieved in practice, because stimuli may vary along several dimensions simultaneously, complicating the interpretation of the criteria used to differentiate. This problem arises not only in ranking tests but in most methods. 5.0 AFFECTIVE/ACCPTANCE TESTING

Acceptance testing, a valuable and necessary component of every sensory programme is performed at consumer’s levels. It refers to measuring liking or preference for a product. Preference can be measured directly by comparison of two or more products with each other, that is, which one of the two or more products is scored significantly higher than another product in a multiproduct test, or which product is scored higher than another by significantly more people. The two methods most frequently used to directly measure preference and acceptance are the paired comparison test and the nine point hedonic scale. Other methods are either modifications of these two methods or are types of quality scales: for example, excellent to poor and palatable to unpalatable. 5.1 Hedonic Scale

The nine point hedonic scale has been used extensively since its development with a wide variety of products and with considerable success. The scale is easily understood by naïve consumers with minimal instruction and the product differences are reproducible with different groups of subjects. The results from use of this scale are most informative since computations will yield means, variance measures and frequency distributions, all by order of presentation and magnitude of difference between products by subject and by panel and the data can be converted to ranks as well, which yields product preferences. As an example of the scale is given below:

Like extremely : 9 Like very much : 8 Like moderately : 7 Like slightly : 6 Neither liked nor disliked : 5 Dislike slightly : 4 Dislike moderately : 3 Dislike very much : 2 Dislike extremely : 1

The sensory acceptance test is a very cost-effective resource that has a major role to play in the development of successful product. Properly used, it can have a significant impact on the growth and long term development of sensory evaluation.

5.2 Consumer evaluation

With the increase in competition, availability of many brands of same product in the market and the choice of consumers, it is highly desirable for the food/dairy industry to study the acceptance/preference and needs of consumers. In some cases it


is possible to create markets for certain dairy products when none existed earlier. In many other situations, such as alterations in existing formulations, change in packaging materials, use of some additives or adoption of a new technology, the food processor has to go to consumers with their product to study their acceptance/preference. While conducting the consumer studies, the sensory leader/organizer should consider all the factors that are important in achieving the desired results. Some of these factors are: clear objectives, target population, start and completion dates, representative test samples, number of products number of responses per sampling, sample coding procedures, questionnaire, instruction on serving and pre-screening, data analysis and processing procedure, and proposed reporting schedule. As far as preference and acceptance of consumers is concerned the factors are grouped into two categories viz. 1) the attitude of the dairy product and 2) of the consumer.

Attitude of the Dairy Product: This is related to the product itself in respect of availability; utility; convenience; price; storage stability/ requirements; safety and nutritional value; and sensory properties, which of course is very important.

Attitude of the Consumer: Religion preference; nationality and race; age and sex; education, socio-economics; psychological motivation such as symbolism of food, advertising, etc. and physiological motivation, such as thirst, hunger, deficiencies and pathological conditions.

While designing consumer studies and interpreting the results, the role of above factors may be considered.

Questionnaire for Consumer Studies: A well developed questionnaire for obtaining desired information, including preference, from the consumers is very important. The important considerations for developing a questionnaire are that it should be:

• simple and clear • realistic • use appropriate terms • avoid stereotype answers

One example of such a questionnaire for seeking consumer opinion on Control milk sample (A) and on experimental milk sample fortified with Vit A. (B) is given below:

1. Prefer sample A……………. Prefer sample B …………….

2. Why do you prefer the sample of your choice (Tick mark one or more):

Preferred milk sample has

Richer taste ……………. Sweeter taste …………….


Smoother body ……………. Rich consistency ……………. Other …………….

3. If you prefer to buy the preferred sample, how much more (if any) per litre would you be willing to pay:

25 paise ……………. 50 paise ……………. Re. 1 ……………. None …………….

The above questionnaire shows the relationship between preference for milk and willingness to pay more for the preferred sample.

6.0 REFERENCES

Amerine, M. A., Pangborn, R. M. and Roessler, E. B. (1965). Principles of Sensory Evaluation of Food, Academic Press, New York.

ASTM (1986). Manual on Sensory Testing Methods. STP 434, American Society for Testing and Materials, Philadelphia, U.S.A.

Bodyfelt, F. W., Tobias, J. and Trout, G. M. (1988). The Sensory Evaluation of Dairy Products, AVI Publishing Co. New York.

Dharam Pal, Sachdeva, S. and Singh, S. (1995). Methods for determination of sensory quality of foods: A critical appraisal. J. Fd. Sci. Technol. 32 (5):357-367.

Stone, H. and Sidel, J. (1993). Sensory Evaluation Practices, Academic Press Inc. London.


Dr. Ashish Kumar Singh

Senior Scientist Dairy Technology Division

NDRI, Karnal

1. INTRODUCTION

The sensory evaluation of milk is of utmost importance. Packaged and retail sale of fresh milk comprises a major share of Indian Dairy industry (both in the organized and unorganized sectors). Since fluid milk is consumed by most everyone everyday it is being assessed dairy for its quality. If the flavour of milk is not appealing or appetizing less of it will be consumed.

The sensory characteristics of any dairy product are dependent on the quality attributes of milk ingredients used. FINISHED MILK PRODUCTS CAN NOT BE BETTER THAN THE INGREDIENTS FROM WHICH THEY ARE MADE. If the raw milk supply is properly assessed for its sensory quality all off-flavour defects due to raw milk could be minimized if not eliminated.

Among dairy product judges the scoring or differentiation of milk into different quality classes demands keener, more fully developed senses of smell and taste than in the sensory evaluation of other dairy products. Many of the off-flavours present in fluid milk are more delicate, less volatile or more elusive than those present in other milk products.

Milk, may be raw or pasteurized, skim or whole, toned or double toned, standardized or full fat, cow or buffalo. For the purpose of present discussion, milk would mean PASTEURIZED, STANDARDIZED (MIXED) MILK unless otherwise specified. Pasteurization is effected by heating the milk to 72oC for 15 sec or 63oC for 30 min in HTST or LTLT respectively.

Pasteurized milk commonly possesses some degree of a heated or cooked flavour especially immediately after processing, but the intensity of cooked flavour diminishes during storage. The flavour of milk is affected by:

a. heating-up and cooling time.

b. temperature difference between the product and heating medium

c. velocity of the product in a continuous system

SENSORY EVALUATION OF MILK


d. occurrence of product ‘burn on’ and

e. direct Vs indirect heating methods.

The flavour of pasteurized unhomogenized milk undergoes flavour changes during storage as below:

HEATED--> NORMAL--> FLAT--> METALLIC--> OXIDISED

The extent of flavour deterioration depends on the storage time, session of the year, type of roughage fed to the cow and buffaloes and relative levels of cupric or ferric ions.

2. MILK SCORE CARD

The original score card (100 point scale) developed by the ADSA has been extensively modified and is presented on the next page. Bacterial counts, milk temperature and sedimentation test are important data to be provided by lab. Evaluation of the container/closure is also a valid quantity criterion that should be evaluated when required. Flavour on the new score card is evaluated on a 10-point scale. 100 point score card can still be used provided the milk has a bacterial content of 20,000 ml and a maximum temperature of 7.2oC. Familiarity with the score and use of scorecard guide is important for milk product judging.

SCORE CARD FOR MILK

Product _______________ Date__________

-------------------------------------------------------------------------------------------------

Sample number

FLAVOUR 10 Criticism 1 2 3 4

acid Score astringent barmy NO CRITICISM bitter 10 cooked cowy feed NORMAL RANGE fermented/fruity 1-10 flat foreign garlic/ onion UNSALEABLE lacks freshness malty oxidized light-induced oxidized metal induced rancid


salty unclean ------------------------------------------------------------------------------------------------------------

SEDIMENT 3

------------------------------------------------------------------------------------------------------

PACKAGE 5

------------------------------------------------------------------------------------------------------

dented/defective

NO CRITICISM dirty inside/outside

5 leaky/not full

NORMAL RANGE heat seal defective

1-5 illegible printing

UNSALEABLE labeling/code incorrect

0

------------------------------------------------------------------------------------------------------

BACTERIA 5

-------------------------------------------------------------------------------------------------------

TEMPERATURE 2

-------------------------------------------------------------------------------------------------------

TOTAL SCORE

Signature of the judge

Fig. 1 The modified ADSA scorecard for milk

3. JUDGING OF MILK & MILK PRODUCTS

Table 1. Suggested scoring guide for flavour for milk

Intensity of flavour defect

Slight Moderate Definite Strong

Pronounced Astringent 8 7 6 - -

Barmy 5 4 3 2 1-0


Bitter 5 4 3 2 1-0

Cooked 9 8 7 6 5-0

Cowy 6 5 4 3 2-0

Feed 9 8 7 6 5-0

Fermented/fruity 3 2 1 0 0

Flat 9 8 7 - -

Foreign 3 2 1 0 0

Garlic / onion 5 4 3 2 1-0

High acid 3 2 1 0 0

Lacks freshness 8 7 6 0 0

Malty 5 4 3 2 1-0

Metallic 5 4 3 2 1-0

Oxidized

Light induced 6 5 4 3 2-0

Metal induced 5 4 3 2 1-0

Rancid 4 3 2 1 0

Salty 8 7 6 2 4-0

Unclean 3 2 1 0 0

3. SOME MILK SCORING TECHNIQUES

3.1 Preparation of samples for evaluation

This depends on the purpose or objectives of evaluation, number of participants and the quality criteria to be assessed. If several persons are to judge the milk samples for flavour, container and closure, sediment and other criteria then several containers of each individual lot of milk must be provided. The sediment test/bacterial count of each sample should be provided.

3.2 Order of examination and scoring

3.2.1 Sediment test


It should be performed first. The kind, amount and size of sediment particles should be carefully observed and scored against a chart or mental image.

3.2.2 Closure

Closure should be carefully observed. Now a days bottles or cartons (not used in India) are not the usual packaging material. The milk is being packaged polyethylene sachets. Hence the evaluator must see that the packaging properly sealed to prevent leakage/pilferage.

3.2.3 Container

Container as stated above, since plastic bags is now in vogue; these should be examined for extent of fullness, cleanliness and freedom from cuts/nicks/pinholes from leakage.

3.2.4 Flavour

The milk should be properly tempered between 13 to 18oC preferably 15.5oC. Milk samples should be poured into clean, odourless glasses or paper/plastic cups. 10 to 15 ml milk should be poured and a sip taken, rolled around the mouth and flavour sensation noted and then expectorated. Sometimes, any aftertaste may be enhanced by drawing a breath of fresh air very slowly through the mouth and then exhaling through the nose slowly. A full WHITE of air should be taken soon after the sample is placed in the container for any off-odour that may be present.

3.3 Evaluation temperature

Pasteurized milk should at 7.2oC but lower than 4.4oC is preferred. A 2-point scale may be used. If the temperature is above 7.2oC the sample may be scored ZERO. Samples at 4.4oC or below should be scored a perfect or 2 score.

3.4 Evaluation of sediment

Consumers want that the milk should be free from foreign matter. A 3-point scale may be employed. Presence of any sediment is serious and should receive a ZERO score. One Possible scoring system could be:

No sediment 3

< 0.02 mg/disc 2

0.025mg/disc 1

>0.025mg/disc 0

3.5 Evaluation of milk flavour


Typically the flavour of milk should be PLEASANTLY SWEET AND POSSESS NEITHER A FORETASTE NOR AN AFTERTASTE other then that imparted by the natural richness due to milk fat and milk solids.

When milk clearly exhibits the soc-called TASTE there is usually something WRONG with the flavour of the milk sample. Thus milk is considered to have a defect if it has an odour, fore-or after-taste and does not leave the mouth in clean, sweet, pleasant condition, following tasting. The scoring guide lists more frequently observed off-flavours. The defects should be described while scoring.

4. UNDESIREABLE FLAVOURS

4.1 Acid

Sour detected by taste and smell-due to microbial conversion of lactose to lactic acid, which imparts a tingling effect.

4.2 Astringent

Not common in milk.

4.3 Barny

Transmitted off-flavour due to poor ventilation, foul smelling environment. Perceived by sniffling and tasting. Characteristic aftertaste.

4.4 Bitter

Associated with other defects like astringency, rancidity due to weeds and microbial growth specially psychrotrophs..

4.5 Cooked

Heat-induced defect appears when milk is heated to 76oC or more. There are 4 types of heat induced flavours: COOKED/SULPHUROUS; HEATED OR RICH; CARAMELISED and SCORCHED Heated and cooked flavors are easily identified, reaction time is quick, and sensation remains after expectoration. Cooked flavour may also be noted through smell.

4.6 Cowy (acetone)

Distinct, persistent unpleasant, medicinal chemical aftertaste with acetone bodies in milk i.e. ketosis in cows.

4.7 Feed

Imparts aromatic taints to milk when fed ½-3 hours prior to milking. The off-flavour is aromatic sometimes pleasant (e.g. alfa-alfa), detected by smell varies with feed. To prevent such feeds should not be fed 3 hours prior to milking.

4.8 Fermented/Fruity

Due to microbes, resembles vinegar, pineapple, apple. Found in old pasteurized milk, due to growth of Pseudomonas sp. (P. fragii).


4.9 Flat

Flat taste/mouthfeel—lack of richness.

4.10 Foreign

Smelled or tasted, due to chemicals/detergents, disinfectants, sanitizers, exposure to fumes of petrol, diesel, kerosene, insecticides, ointments, medication to cows etc.

4.11 Garlic/onion (weedy)

Pungent odour and persistent aftertaste.

4.12 Lacks freshness (stale)

Taste reaction indicates loss of fine pleasing taste. Slightly chalky. May be ‘forerunner’ of either oxidized or rancid off-flavour or off-flavour caused by pshychrotrophs.

4.13 Malty

Flavour definite or pronounced, suggestive of malt caused by the growth of S lactis var. Maltigenes at > 18.2oC for 2-3 hours can be smelled or tasted. Bacterial population in millions, followed by acid/sour taste.

4.14 Metal-induced oxidized off-flavour

Due to lipid oxidation-metal catalyzed. Metallic, oily, cardboardy, cappy, stale, tallowy, painty and fishy are used to describe this off-flavour. The off-flavour is quickly perceived in the mouth and has a relatively short adaptation time.

4.15 Light-induced oxidized off-flavour

Described as burnt, burnt protein, burnt feathers, cabbagy, medicinal or chemical-like, light-activated or sunlight flavour or sunshine flavour, light catalyzed lipid oxidation as well as protein degradation both are involved. It requires riboflavin that is naturally present in milk. Homogenized milk is more susceptible but is resistant to oxidized off-flavour (due to lipid oxidation) the opposite is true for non-homogenized milk.

4.16 Rancid

Extremely unpleasant, due to volatile fatty acids formed through enzymatic hydrolysis of fat. Soapy, bitter and unclean aftertaste. Flavour is nauseating and revolting.

4.17 Salty

Perceived quickly in the mouth

4.18 Unclean

Due to growth/activity of psychrotrophs at 7.2oC


Dr. S. K. Kanawjia, Sanjeev Kumar** and Hitesh Gahane* Principal Scientist, Division of Dairy Technology

NDRI, Karnal **Ph.D. Scholar and * M.Tech (DT)

1. Introduction

Cheese, the nature’s wonder food and the classical product of biotechnology, is highly nutritious food with good keeping quality, enriched pre-digested protein with fat, calcium, phosphorus, riboflavin and other vitamins, available in a concentrated form. It is the most important category of fermented foods, has been reported to have therapeutic, anticholesterolemic, anticarcinogenic and anticariogenic properties beyond their basic nutritive value. They, contributing to a variety in our gustative desire, have been recognized to provide important nutrients and considered superior over non-fermented dairy products in terms of nutritional attributes as the micro flora present produce simple compounds like lactic acid, amino acids and free fatty acids that are easily assimilable. Some of the cheese flora has been reported to inhibit the growth of certain toxin-producing bacteria in the intestine.

Soft unripened cheeses are commonly known as “Fresh Cheese” and are made by coagulating either whole milk, partly skimmed milk, skim milk or cream; eliminating a large part of the liquid portion (whey) and retaining the coagulated milk solids. The amount of water retained in the curd greatly influences the relative softness of unripened cheese made from milk having constant casein-to-fat ratio. Softness of cheese also depends on the extent of protein hydrolysis salt content and the amount of milk fat in cheese. Soft unripened cheese derive their flavour mainly from the culture and the cream dressing. Cottage cheese, Cream cheese, Mozzarella cheese, Ricotta cheese, paneer etc are some of the common varieties of fresh cheese. They differ from each other in their method of manufacture with respect to type of milk, treatment given to milk, type of culture, amount of culture method of coagulation, cutting of curd, cooking of curd, pressing of curd etc.

Consequently, they differ in sensory as well as chemical attributes. The desirable sensory attributes of fresh cheeses, defects and their probable causes and remedies with special reference to cottage cheese are described in this lecture note.

2. Scenario of Cheese Production in India

Scenario of cheese production in India is quite bright because of the facts that cheese has all the beneficial attributes of an ideal dairy product and the emergence of

SENSORY CHARACTERISTICS OF FRESH CHEESE


new global economic reforms based on globalization and liberalization in the marketing arena that has unfastened the door to the Indian dairy industry to penetrate the international cheese market. There has been a steady increase in the consumption of cheese in most countries worldwide, the annual growth rate in cheese consumption being over 3 per cent with an acceleration being expected due to worldwide trend of adopting “Western” consumption habits with a high level of cheese in the diet list. In India cheese, production has been accelerating quite steadily, being 1000 tonnes in 1980 to 40000 tonnes in 2007, against the world production of 16 million tonnes.

Till about 8-10 years back, the only major regional/national players in the cheese market were Amul, Verka, and Vijaya – all from the cooperative sector. These plants are continuing to expand their cheese manufacturing capacities slowly in tune with the demand growth for cheese. Many new players like Dynamix and Dabur, and entrepreneurs, such as Vadilal, Vintage, Chaudhary’s Miraj, Kodai, etc have ventured in cheese production in recent years. At present Amul has targeted opening of 3000 Pizza retail franchise outlets all over the country by the year-end, which would boost the annual sets of Mozzarella cheese to 5000 tonnes.

3. Cottage Cheese

Cottage cheese is a fresh, soft, unripened cheese made from sweet, pasteurized skim milk by lactic culture with or without the addition of rennet. The curd is cut and cooked to facilitate whey expulsion and development of proper curd consistency. When the curd has attained the desired consistency, whey is drained off, curd is washed and salted. Subsequently, the curd is dressed with cream in the case of creamed cottage cheese which contains 4% fat. Cottage cheese contains 80% moisture.

3.1 Desired sensory attributes

3.1.1 Appearance and colour

The curd particles of cottage cheese should be distinctly separate and uniform in size and shape. The cheese should possess moderately glossy sheen and creamy white colour. The cream dressing should be reasonably viscous and foam free, and bulk of it should adhere to the curd particles. The excess dressing should form only a uniform and smooth coating on the curd particles. Free cream, free whey, lack of uniformity and the presence of lumps or curd dust are considered as common appearance defects in cottage cheese caused mostly by faulty method of manufacture viz., excessive cooking, insufficient washing, cutting of curd at too high or too low pH, rapid cooking, uneven cutting or cutting with a faulty knife or aggressive stirring low TS milk, excessive heat treatment of skim milk, use of excessive coagulator, severe stirring or orugh handling of curd during cooking etc. Appropriate corrective measures during manufacture of cottage cheese eliminate these defects.


3.1.2 Body and texture of cottage cheese

Ideally, creamed cottage cheese should have a tender body, and smooth and meat like texture. Curd particles should maintain their shape and individual identity but should not be too firm, rubbery or too soft. Smooth, meaty and tender curd particles exhibit good capillary desired for complete absorption of cream dressing common body & texture defects are listed below:

3.1.2.1 Too firm body:

Firm or rubbery bodied curd particles of cottage cheese resist crushing between tongue and roof of the mouth. This defect occurs due to over use of rennet or other milk coagulator; cooking of curd at too high temperature and for too long; or cutting of curd at a pH more than 4.7

3.1.2.2 Mealy/Grainy/Gritty:

Presence of this defect gives a corn meal like sensation in the mouth when masticated curd is pressed by the tongue against the roof. Also, a dry rough and serrated curd mass is observed when the washed curd particles of creamed cottage cheese are kneaded and smeared between the forefinger and thumb. The defect may be caused by overdeveloping the acid during curd formation; retention of too low moisture, non-uniform cutting of coagulum, uneven heating, too rapid cooking, inadequate stirring, and curd particles coming in contact milk extremely hot surfaces during cooking.

3.1.2.3 Gelatinous:

Gelatinous cheese has a jelly-like and sticky character. Often this defect is associated with a bitter flavour and translucent appearance. This defect is caused by psychotropic bacteria.

3.1.2.4 Weak/soft/mushy:

This defect is characteristic of high moisture, low-solid cottage cheese. It is caused by faulty manufacturing methods, which favour retention of whey in the curd. On storage such cheese may become pasty and bitter.

3.1.2.5 Over stabilized dressing:

When this defect occurs the creamed cottage cheese appears dry and some individual curd particles are surrounded by a thick, pasty, coating. This usually happens due to the use of excessive amount of non-fat dry milk, stabilizers and/or emulsifiers.


3.1.3 Flavour:

Cottage cheese should have a fresh, clean, pleasant delicate (balanced culture) flavour that cleans up well immediately after the sample has been eliminated from the mouth. This flavor is made up of characteristic curd flavour and its acidity, volatile products by lactic acid organisms. Addition of cream and salt enhance the flavour of creamed cottage cheese. The probable cottage cheese being high perishable product is proving to the development of specific flavour defects as discussed below:

3.1.3.1 Acid/high acid/sour:

Acid taste is clean and sharp while sour taste is pronounced and may be associated with other bacterial defects like fruity, fermented etc. Excessive acid development and/or insufficient washings of the curd cause this defect. Such product is sometimes also criticized for flavour defect like “whey taint”.

3.1.3.2 Bitter:

Bitter flavour is characterized by its relatively slow reaction time; taste at or near the back of the tongue only; freedom from astringency; and persistence after expectorating the sample. The defect is most frequently encountered in old cottage cheese or in the sample stored at a temperature favourable for the growth of Pseudomonas organisms.

3.1.3.3 Flat:

Absence of characteristic flavour or aroma is termed as flat flavour. A dry, unsalted and washed rennet curd yields a distinctly flat taste during the intermediary stages of oxidized flavour development.

3.1.3.4 Lacks freshness:

The flavour of cottage cheese is its best immediately after manufacture. Cottage cheese progressively deteriorates in flavour during storage. Often this defect is referred as storage flavour because the aroma of cheese is similar to that of the refrigerator in which it was stored.

3.1.3.5 Fruity/Fermented:

This defect is characterized by the presence of a pleasant aromatic flavour suggestive of pine apple, apple, banana or strawberry and distinctive lingering aftertaste. The cottage cheese stored at elevated or favourable temperatures for the psychrotrophic bacteria may develop this defect.


3.1.3.6 Yeasty:

Yeasty and vinegar like flavours have a peculiar aromatic quality in addition to high acidity. Yeasts and various other contaminants including psychrotrophic bacteria are generally responsible for causing this flavour defect.

Other flavour defects in cottage cheese include malty, musty, oxidized, rancid, salty and unclean flavours.

4. Cream Cheese

Cream cheese is a soft, unripened cheese made by coagulating cream (12-30% milk fat) either by lactic acid bacteria aided by milk coagulating enzymes or by direct acidification followed by removal of whey by centrifugation or pressing the curd in cloth bags. The fat content in the final product varies from 3 to 40%. Neufchatel cheese is a similar product made from whole milk of high fat contents. It contains about 20-25% fat.

4.1 Desirable sensory attributes

4.1.1 Flavour:

Cream cheese should have a full rich, clean and milk acidic flavour. Neufchatel type cheese may have a moderate acid taste. More common flavour defects in various types of cream cheese may be flat, sour or too high acid, metallic, yeasty and unclean after taste.

4.1.2 Body and texture:

Soft yet sufficiently firm body to retain its shape is the characteristic of cream cheese. The texture should be somewhat buttery and silky smooth. It should possess both spreading as well as slicing characteristics. Cream cheese prepared from cream containing 16% fat exhibits most desirable body and texture properties. In such cheeses the moisture and fact content may vary in the ranges of 50-54% and 37-42%, respectively. Cream containing less fat yields a cream cheese which is criticized as having grainy texture and crumbly body. Increased fat content of cream (20%) results in excessive smoothness and stickiness. Other body and texture defects of cream cheese include coarse, grainy, too firm and too soft.

5. Mozzarella Cheese

It is a soft unripened variety of cheese of Italian origin. It is produced from whole or partly skimmed milk to which small amounts of starter or organic acids are added, followed by rennet extract. The curd is cut, allowed to firm up in the warm whey with occasional stirring and the whey is drained off. When the curd has developed the desired plasticity and fibrous texture and the whey acidity 0.65-0.70% LA, it is milded. The curd pieces are immersed in hot water kneaded, stretched and


moulded. Salting of cheese is done by dipping the cheese in brine solution for few days. The cheese can be consumed after the brine treatment is complete.

5.1 Desirable sensory attributes

5.1.1 Colour and appearance:

Mozzarella cheese should have a uniform white to light cream color. Faulty manufacturing method and microbial contamination may sometimes cause colour defects in the product. Use of too high salt may cause discoloration. Development of browning may be caused by using starter culture containing only Thermophilus. Contamination with Psuedomonas spp. Causes development of superficial reddish marks.

5.1.2 Body and Texture:

Mozzarella cheese should have a soft, elastic, waxy and moist body with typical structure of pulled curd cheese. It should have a fibrous texture with no gas holes. It should possess a good slicing as well as melting properties. Use of too high salt or growth of Lactobacillus casei may cause poor melting quality. Undesirable microbial contamination may cause development of defects, like pigmentation, hole formation and other textural defects. Rapid evaporation of moisture from the surface leads to the development of granular texture.

5.1.3 Flavour:

Bland, pleasant but mildly acidic with slightly salty taste is the characteristic of mozzarella cheese. Buffalo milk cheese is a more piquant and aromatic than cow milk cheese. Microbial contamination, particularly with Pseudomonas species may lead to the development of flavour defects like putrid smell, bitter flavour etc. Other flavour defects may be of absorbed or chemical nature as in the case of cottage cheese.

6. Ricotta Cheese

It is yet another variety of soft unripened cheese of Italian origin. In the manufacture of ricotta cheese, mixture of whey and skim milk is acidified to a critical pH with lactic acid, acetic acid or acid whey powder and then heated. The resulting curd is recovered and over filled in perforated tin containers, cooled and allowed to drain free whey. Cheese is now ready for consumption. Ricotta cheese made from whole milk is consumed directly while made from skim milk or whey skim milk mixture is highly suited for pastry manufacture.

Ricotta cheese from whole milk resembles highly creamed, cottage cheese but has a softer and more fragile texture. A mixture of skim milk whey yields a firmer and drier product which lacks its distinctive nutty flavour. In general ricotta cheese is soft, and creamy with a delicate, pleasant and slight caramel flavour.


Ricotta cheese is highly susceptible to spoilage due to microbial contamination leading to flavour defects like sour, fermented, fruity etc. Excessive gas formation may also cause blowing of the lid of the container.

7. Paneer

Paneer is an indigenous milk product made by coagulating heated milk preferably buffalo milk (6% fat) acid solution and/or sour whey. The whey is drained and the curd filled in hoops and pressed. The pressure is removed after 10-15 and the paneer is cut into pieces and immersed in chilled water for cooling.

7.1 Desirable sensory characteristics

7.1.1 Colour and appearance:

Paneer should have uniform white colour with greenish tinge if made from buffalo milk and light yellow it prepared from cow milk. Paneer may develop colour and appearance defects as listed below:

7.1.1.1 Dull:

This defect is recognized by its dead, unattractive appearance and suggest lack of cleanliness in manufacture.

7.1.1.2 Dry surface:

Use of milk containing excessive amount of fat gives paneer with dry surface and unattractive appearance.

7.1.1.3 Surface skin:

Exposure of paneer while hot to the atmosphere causes rapid evaporation of moisture from the surface resulting into the formation an undesirable yellow skin on the surface.

7.1.1.4 Visible dirt/ Foreign Matter:

This defect may occur due to improper straining of milk, use of dirty water, dirty, windy surrounding, poor packaging and careless handling of paneer.

7.1.1.5 Mouldy surface:

Long storage of product in humid atmosphere coupled with higher moisture content flavours development of moulds on the paneer surface.

7.1.2 Body and texture

The body of paneer should neither be too firm nor too soft. It should remain retain its shape. The texture of the high grade paneer should be compact, smooth elastic and velvety.

Paneer develops body and texture defects due to faulty manufacturing methods and microbial contamination. Excessive retention of moisture due to low coagulation temperature, delayed straining or incorrect pH of coagulation often gives a paneer


with soft body and pasty texture. Low moisture content in paneer caused by higher coagulation temperature, incorrect pH at coagulation, use of low fat milk, yield hard and rubbery bodied paneer. Such paneer may also have a mealy texture. Frozen storage of paneer causes crumbly body and coarse/mealy texture in paneer.

7.1.3 Flavour

Flavour of paneer is a characteristic blend of the flavour of heated milk curt, and acid. The flavour of the high-grade paneer should be pleasant, mildly acidic, slight sweet and nutty. Common flavour defects observed in paneer are similar to those as observed in other fresh cheese and can be eliminated by following proper manufacturing, method, sanitation, packaging, storage and handling.

8. References

Bodyfelt, F.W.; Tobias, J. and Trout, G.M. 1988. The Sensory Evaluation of Dairy

Products. Van Nostrand Reinhold, NY.

Dharam Pal and Gupta, S.K. 1985. Sensory evaluation of Indian Milk Products.

Indian Dairyman, 37: 465

Kanawjia, S.K. and Singh, S. 1996. Sensory and Textural changes in Paneer during

storage. Buffalo J. (Thailand) 12: 329-32

Khurana, H. and Kanawjia S. K. 2007. Recent trends in fermented milks. Current

Nutrition Food Sci. (USA) 3: 91-108

Makkal, S. and Kanawjia S. K. 2003. Preservation of Cottage Cheese: A review

Indian J. Dairy Sci. 56: 1-12

Nelson, J.N. and Trout, G.M. 1964. Judging of Dairy Products. Olsen Publ. Co.

Milwaukee. Wis., USA.

Tiwari, B.D. 1996. Sensory attributes of fresh cheese. Compendium: Sensory

evaluation and rheology of milk and milk products. CAS, DT Division, NDRI, Karnal:

52-57.


Dr. F.C. Garg Dairy Technology Division

NDRI, Karnal

1. Introduction Ice cream is a delicious, wholesome nutritious frozen dairy food. It has

evolved over a period spanning about five centuries. The great technological progress made in the field of dairying in the nineteenth century such as the development of centrifugal separator, mechanical refrigeration, better understanding of chemistry and bacteriology provided stimulus to the development of a large ice-cream industry that we see today. Ice cream has occupied a unique place in the diet of western people and is gaining steadily in popularity all over the world. For instance, the annual production of ice cream in USA has reached more than 3770 million litres. Other countries ranking high in annual production of Ice cream and related products are Japan: (750 million litres), Canada (476), Australia (331), and UK (218). India is the third largest producer of ice cream in the world with production of over 513 million litres annually. It has been estimated that 0.6 per cent of total milk production in our country is utilized for making ice cream and Kulfi. The ice cream industry in India is expanding very fast witnessing an estimated growth rate of 25 to 35 per cent per year. Production of excellent quality Ice cream is essential to the success and progress of the ice-cream industry. The quality of ice cream is judged by the consumer on the basis of its sensory attributes i.e. flavour, body and texture, melting behaviour, colour and the appearance of package or container. Besides, the product should also comply with legal standards with regard to its chemical composition and bacteriological quality. Ice cream not possessing desirable sensory properties cause diminished consumer goodwill, sales and income to the manufacturer.

2. Factors Affecting Sensory Attributes of Ice-cream

The quality of ice cream depends not only on composition of ice-cream, but also on the quality of raw materials used, methods of manufacture, distribution and sale of the product – these factors are under the control of the ice cream maker. A full knowledge of the factors by which the quality may be attained or controlled is therefore, essential for the production of ice cream possessing desirable sensory attributes.

There are many differing concepts of ‘perfect ice-cream’. Individual preferences can cause large variations in what people consider to be ice cream of highest quality. Some prefer ice cream with a low fat content, while others will want high fat. Some will like very smooth textured ice-cream, others may prefer it be not too smooth. Variations exist in the required sweetness level and so on. Therefore,

SENSORY ATTRIBUTES OF ICE-CREAM


desirable sensory attributes of ice cream can be best explained by giving details of defects and faults which may be found in ice cream, and show how these faults occur and how they may be overcome.

3. Judging of Ice-cream

The available methods of determining the sensory attributes of ice cream rely mainly on tasting and using a scorecard. Such scorecards give maxima for various aspects of the ice-cream quality such as flavour, texture, body and colour. The American Dairy Association has stipulated a scorecard for ice cream, which carries a maximum score of 10 for flavour, 5 for body and 5 for colour and appearance, 3 for melting quality and 2 for bacterial content. The recommended scoring guide is given in Table 1.

Table 1. The ADSA scoring guide for sensory defects of ice cream

Intensity of Defect

____________________________________________________________________

Criticisms Slight Definite Pronounced

Flavour

Acid (sour) 4 2 0

Cooked 9 7 5

Flavouring:

Lacks flavoring 9 8 7

Too high 8 8 7

Unnatural 8 6 4

Lacks fine flavour 9 8 7

Lack freshness 8 7 6

Metallic 6 4 2

Old ingredient 6 4 2

Oxidized 6 4 1

Rancid 4 2 0

Salty 8 7 5

Storage 7 6 4


Sweetener:

Lacks 9 8 7

Too high 9 8 7

Syrup Flavour 9 7 5

Whey 7 6 4

Body and texture

Coarse/Icy 4 2 1

Crumbly (brittle, friable) 4 3 2

Fluffy (foamy) 3 2 1

Gummy (pasty, sticky) 4 2 1

Sandy 2 1 0

Soggy (heavy, pudding-like) 4 3 2

Weak (watery) 4 2 1

Table 2. Flavour defects of ice-cream, their causes and remedies

Defects Cause Prevention

A High flavour Presence of large

amount of flavouring

material

Adding right amount of flavoring

material.

B Low flavour Presence of insufficient

amount of flavouring

material

Adding right amount of flavoring

material.

C Acid flavour Presence of an excessive

amount of lactic acid

(developed)

Using fresh dairy products Prompt, efficient cooling

of mix Avoiding prolonged

storage of mix at high storage temperature

D Bitter flavour Use of inferior products Using true flavour extract Avoiding use of dairy


products stored for long period at low temperature

Using products free from off flavour

E Cooked

flavour

Overheating the mix.

Using overheated concentrated dairy products

Carefully controlling pasteurization process

Using conc. Products free of cooked flavour

F Flat flavour Use of insufficient

flavour, sugar or milk

solids

Using right amount of these ingredients

G Metallic

flavour

Copper contamination

Bacterial action

Avoiding copper contamination of mix during processing.

Avoiding use of products have metal flavour.

H Unnatural

flavour

Flavour not typical to ice

cream

Using high quality flavouring products.

Using high quality dairy and non-dairy products.

I Oxidized

flavour

Using oxidized flavoured dairy products.

Metallic contamination.

Using fresh dairy products.

Using only stainless steel equipment.

Using antioxidants. Pasteurizing the mix at

high temperatures.

When ice cream is being judged organoleptically it is important that the serving temperature should be correct. If it is too cold the palate will be deadened, and it will not be possible either to enjoy the ice cream or to judge any of its sensory characteristics. If it is too warm it will have melted partially judging of body and texture will be almost impossible. A consumer judges the quality or sensory attributes of ice cream on the basis of several characteristics – these are flavour, body, texture and appearance of the product and the package.

3.1 Flavour

Ice cream is a mixture of fat, sugar and milk solids-not-fat together with added flavour and colour. An increase in total solids increases the richness of the ice cream and normally improves the flavour, texture and body. Some if these ingredients have marked flavour, others are more nearly neutral or bland. However, the flavour of no single ingredient should predominate, but each should blend together to form a


harmonious whole, creamy sweet sensation with a slight flavour, leaving a pleasant after taste which must not be excessive. Many possible flavour defects may arise due to use of faulty ingredients. The more common flavour defects are given in Table 2.

Table 3. Body and texture defects of ice cream

Name Causes Prevention

A Crumbly body Low T.S. Content

Insufficient Stabilizer

Excessive overrun

Improper homogenization

Increasing T.S. Content Increasing Stabilizer Decreasing overrun Proper homogenization

B Soggy body Low overrun High sugar

content Excessive

amount of stabilizer

Proper overrun Optimum suggest

content Right amount of

stabilizer

C Shrunken body

(The ice cream

shrinks away

from the sides

and top of the

container)

Fluctuating temperatures during storage

Excessive overrun

Protein instability

Rough transportation

Avoiding fluctuating temperature during storage

Reducing overrun Avoiding high acidity in

mix Avoiding rough

transportation.

D Weak body Low T.S. content Insufficient

stabilizer

Increasing T.S. content Increasing stabilizer

E Buttery texture Improper homogenization

High fat content Slow freezing

Proper homogenization Optimum fat content Fast freezing

F Coarse or ice

texture

Low T.S. content Insufficient

stabilizer Slow freezing Slow hardening Insufficient

ageing

Increasing T.S. content Increasing stabilizer Fast freezing Fast hardening Sufficient ageing Avoiding heat shocking Avoiding prolonged


Heat shocking Prolonged

storage

storage

G Fluffy texture Excessive overrun

Low T.S. content High emulsifier

content

Decreasing overrun Increasing T.S. content Decreasing emulsifier

content

H Sandy texture High M.S.N.F. (Lactose) content

Fluctuating temp. in retail cabinets

Long storage Period

Decreasing M.S.N.F. (Lactose) content

Avoiding fluctuating temperatures in retail cabinets

Reducing storage periods

3.2 Body and texture

Both the body and texture of ice cream may be determined readily by the senses of sight and touch. The desired body in ice cream is that which is firm, has substance, responds readily to dipping and melts down at ordinary temperatures to a creamy consistency. The desired texture is that which is fine, smooth, velvety and carries the appearance of creaminess throughout. The possible body and texture defects which may be encountered in ice cream are presented in Table 3.

3.3 Melting Quality

High quality ice cream should show little resistance toward melting when it is exposed to room temperature. During melting the mix should drain away as rapidly as it melts and form a smooth, uniform homogeneous liquid. Any variations from this behaviour may lead the consumer to be suspicious of its quality. The defects in melting quality frequently observed in judging ice cream are given in Table 4.

Table 4. Melting quality defects of ice cream

Name Causes Prevention

a) Curdy meltdown High acidity of mix. Using fresh dairy products

b) Slow melting *Excessive amount of

stabilizer

*Improper homogenization.

* Reducing the amount of

stabilizer.

* Proper homogenization


c) Whey leakage * Poor quality dairy

products

* Improperly balanced mix.

* Improperly stabilized mix.

* Using fresh dairy products

* Balancing the mix

properly.

* Using more effective

stabilizer

d) Foamy melt

down

* Excessive overrun.

* Excessive amount of

emulsifier.

* Reducing overrun

* Reducing amount of

emulsifier.

3.4 Colour

The colour of the ice cream should be attractive and pleasing. The ideal colour is characteristic of the flavour, true in shade and neither too pale nor too intense. For example, vanilla ice cream should have a creamish yellow to white colour. Uniform, natural colour is desirable in ice cream. Excessive colour is the result of adding too much artificial colour to the mix. An uneven colour results if the colour is not properly added and also if care is not exercised when changing flavours. An unnatural colour is caused by (a) carelessness in adding the colour, (b) improper use of colours, or (c) use of foreign materials.

4. Conclusion

Therefore, an excellent quality of Ice-cream can be made only from good mix ingredients properly balanced to produce a desirable composition along with proper processing, freezing, hardening and distribution, under proper sanitary conditions. All these factors are important and must be carefully controlled if the ice cream having desirable sensory attributes is to be produced. It must be remembered that product inferiority constitutes one of the greatest menaces to the success and progress of the ice cream industry. The consumer has learnt to depend upon Ice cram as a safe, enjoyable, energy-giving, nourishing and refreshing food.

References

Arbuckle, W.S. (1986) Ice cream, 4th Edn., Van Nostrand Reinhold Co. NY

Bodyfelt, F.W., Tobias, J. and Trout G.M. (1988) The sensory evaluation of Dairy

products. Van Nostrand Reinhold, NY.

Hyde, K.A. and Rothwell, J. (1973) Ice cream. Churchull Livingstone, Edinburgh,

UK.


SENSORY EVALUATION OF DAIRY PRODUCTS WITH SPECIAL EMPHASIS ON FLAVOUR LEXICON

V. Pathak and Z. F. Bhat Associate Proffesor & Head

Division of Livestock Products Technology F.V.Sc. & A.H., SKUAST-J

Introduction:

Sensory evaluation of a food refers to its scientific evaluation through the application of human senses. It involves development and use of principles and methods for measuring human responses to products and ingredients. These principles and methods have broad application for a variety of products. The common element in these tasks is the use of humans as evaluators. It is this link, the human evaluator, that suggests sensory evaluation's proximity to, if not reliance on, the behavioral and social sciences. Sensory evaluation complies with the principles of science and is different from organoleptic evaluation as the results are often reproducible and comparable. This new field is founded principally on the behavioral and social sciences, rather than chemistry, microbiology, and engineering, the principal scientific fields in which traditional dairy scientists have been trained. Behavioral research in perception, learning, cognition, psychophysics, and psychometrics, to mention only a few, provides the basis for the principles and methods the sensory scientist uses today.

One thing in common to all sensory assessment methods is that they use humans as the measuring instrument. Even though sophisticated and highly sensitive measuring instruments such as gas chromatographs, mass spectrometers, nuclear magnetic resonance spectrometers, IR and UV spectrophotometers, etc., are now available, the importance of sensory analysis has grown rather than diminished. The problem with instruments is that one instrument will analyse only single component at a time which does not fulfill our requirement as we are interested in getting total sensory impression of a processed food product at the same time.

The dairy industry has come a long way since the early 1900s, when it began developing techniques for judging dairy products to stimulate interest and education in dairy science. Since then sensory analysis techniques have developed into powerful tools for understanding how the appearance, flavor and texture attributes of dairy products drive consumer preferences. In the traditional methods that emerged, judging and grading dairy products normally involved one or two trained “experts” assigning quality scores on the appearance, flavor and texture of the products based on the presence or absence of predetermined defects. Modern sensory techniques can help dairy processors develop new products that are highly appealing to consumers; enable processors to optimize a product’s flavor, texture and color to attract specific target audiences as well as accurately monitor product quality; can help determine variations in sensory attributes associated with processing variables, geographic region of production, production season, etc.


Anatomical Structures involved:

Tongue: The four basic tastes are: sweet, salty, sour and bitter. The papillae for sweet are more concentrated on tip of tongue, the saline at tip and edge, the sour at the edge, and the bitter at the back. The papillae for bitter are especially deep, and so this sensation takes longer to perceive but tends to linger.

Nose:

We sense odour at the ‘Regio Olfactory’ (R) which lies at the top of the inside of the nose. The nose has two openings; the nostrils are anterior nares, the openings at back of throat are the posterior nares or choana. In ordinary breathing, the inspired air does not stream through the upper part of the nasal chamber. A definite sniff is required for air to reach this area and so this technique is called ‘sniffing’. The air whirls in the upper passage creating a multiplication effect.

Skin:

While tasting, we not only perceive the four basic tastes but also warmth, cold, pain, tactile and pressure sensations. These belong to the cutaneous senses.

In contrast to the cutaneous senses, the kinesthetic sense is a muscle or power sense. It gives us a sensation of resistance.

Ear:

Sound sensations perceived while tasting, like crackly in the case of crisp cookies (biscuits) and crunchy in case of chips (crisps).

Eye:

The colour sense-part of the sense of vision such as brightness, colour, shape and happenings (events).

History:

Exact sensory of food began in about 1940 in Scandinavian countries with the development of triangle test, a difference test method. At the same time, independent and analogous studies were being carried out in the United States of America. Not until 1950 did European countries start to employ sensory analysis. The first book on sensory analysis was written in 1957 by Tilgner in the polish language and translated into Eastern languages (Czech, Hungarian and Russian). The first published descriptive sensory technique is the Flavor Profile Method (FPM) developed in the 1950s by Arthur D. Little Inc. (Pangborn 1989; Lawless and Heymann 1998; Meilgaard and others 1999). Refinements and variations in FPM occurred in the 1970s with the development of Quantitative Descriptive Analysis (QDA) and the Spectrum™ method of descriptive analysis. Today, descriptive analysis has gained


wide acceptance as one of the most important tools for studying issues related to flavor, appearance and texture, as well as a way to guide product development efforts.

Classification of sensory tests:

There are several types of procedures, depending on the specific objective of the evaluation. Also, there are different approaches for classifying the procedures. Many described procedures are currently accepted procedures for such tasks as detecting differences between samples, descriptive analysis of flavor characteristics, quantitative estimates of flavor intensities, rating quality of a product in relation to pre-established standards, identifying preferences, and measuring consumer acceptance.

There are four primary types of tests, and these may be classified as Affective, Discrimination, Descriptive and Quality tests. Classification of sensory tests into one of these four categories depends on

• The test objective • The criteria for respondent selection • The specific task required of each respondent

Affective Tests

These tests measure the subjective attitudes, such as product acceptance and preference and the task is to indicate preference or acceptance by either selecting, ranking, or scoring samples. The paired-preference and 9-point hedonic scale are popular examples of these types of tests and respondents are usually consumers who are selected on their current or potential use of the product. In laboratory situations, consumer demographics often are substituted in favor of accessible respondents whose preference and acceptance behavior satisfactorily correlate with those of the target consumer population. Laboratory-type acceptance tests can be done with 25 to 50 respondents. In field studies where the target population is used, minimum numbers are increased by 75 to 200 or more.

Discrimination Tests

Discrimination tests are designed whether samples are detectably different from one another. The most frequently used discrimination test methods are the sequential, paired difference, duo-trio, and triangle tests. The discrimination test is a small panel test, used in a laboratory environment. Using between 12 and 20 qualified subjects, it is possible to make reliable and valid decisions, with each subject providing replicate judgments. Alternatively, one could use 24 to 40 subjects and no replication.

Descriptive Tests


The objective of descriptive tests is to describe sensory properties of products and measure the perceived intensity of those properties. The most popular descriptive methods include classical and modified flavor profile, texture profile and quantitative descriptive analysis (QDA). Typically, 6 to 12 subjects may be used to evaluate a product. Subjects for descriptive tests are screened for their sensory acuity and are then trained to perform the descriptive task. The QDA requires replicate judgments to monitor quantitatively subject performance throughout the course of the test. Results from the descriptive test provide information about product similarities and differences in quantitative terms, thus facilitating development efforts.

Quality Tests

These provide a score or grade to summarize a product's proximity to a standard. The standard may be a written specification or an actual product selected to embody these specifications. The subject's task differs, as does the quality test itself, depending on the specific industry and product. In most quality tests developed by the dairy industry, subjects also use a single scale consisting of numbers anchored by different word descriptors, or use a procedure that includes a checklist of deficiencies and assignment of a final grade depending on the specific number and kind of deficiencies in the product. A low score on a quality test is intended to indicate deficiencies, as they are defined by experts.

Flavor lexicons:

A flavor lexicon is a set of word descriptors that describes the flavor of a product or commodity, which is then applied or practiced using descriptive sensory analysis techniques. It provides a source list to describe a category of products, such as commodities or finished products. The descriptive panel produces its own list to describe the product array under study but a lexicon provides a source of possible terms with references and definitions for clarification. For development of a representative flavor lexicon several steps, including appropriate product frame-of-reference collection, language generation and designation of definitions and references are essential before a final descriptor list can be determined. Flavor lexicons, once developed, can be used to record and define product flavor, compare products and determine storage stability, as well as to study correlations of sensory data with consumer liking/acceptability and chemical flavor data. Flavor lexicons should be discriminating and descriptive and the language should be developed from a broad representative sample set that exhibits all the potential variability within the product.

Application:

Flavor Lexicons have been used for a variety of purposes as:

• They are widely used to describe and discriminate among products within a category.


• They are widely used in industry for comparing and monitoring products and product consistency and for profiling new and competitive products.

• Used in quality control, with relationships to instrumental or consumer responses.

• A powerful research tool with numerous applications across many commodities and commercial products.

• Several different flavor lexicons have been used in cheese to document aroma and flavor development, the effects of fat reduction, and the effects of different starter or adjunct bacteria (Muir and others 1995a; Piggot and Mowat 1991; Roberts and Vickers 1994)

• Several groups have identified and used descriptive sensory analysis to differentiate fluid milks to determine the effects of fat content, storage, and other processing conditions on milk flavor and aroma perception (Lawless and Claassen 1993; Phillips and others 1995; Phillips and Barbano 1997; Watson and McEwan 1995; Chapman and others 2001; Bom Frost and others 2001).

• Flavor lexicon for chocolate ice cream has been used to discern the effects of milk fat, cocoa butter, and fat replacers on sensory properties of chocolate ice cream (Prindiville and others 1999, 2000)

• Descriptive analysis lexicons have also been used for numerous other products including fermented milks, spoiled milk aroma, yogurt etc.

Descriptive Analysis Techniques:

Quantitative Descriptive Analysis (QDA) and the SpectrumTM method of descriptive analysis differ markedly from FPM as they are designed to take measurements from individual panelists and then generate a panel average, rather than generation of a group consensus profile as with FPM. However, all 3 techniques involve screening panelists for sensitivity to flavor and aroma and discriminatory ability which is followed by extensive training to generate a group of individuals who can function analogously to an instrument to evaluate flavor of products. Usually fewer panel members are used (minimum 4) in FPM and the panel leader along with other panel member’s work together to generate the language and the method for sample presentation and evaluation.

After the panelists are screened for sensory acuity and trained extensively (minimum 60 hr), panelists work as a group to generate a consensus profile of the sensory properties of the product and the intensity of each as well as overall amplitude of the product, as well as a measure of balance and blend is evaluated. The Attributes are evaluated in order of appearance and the scale used for character (descriptor) notes is a 4 point scale (0-not present, 1-slight, 2-moderate, 3-strong) with half point designations in between, for a total of 7 points of discrimination.

In contrast to FPM, the panel leader in QDA is a sensory professional, rather than a member of the panel (ideally 8 to 12 prescreened individuals) and does not


participate in the discussion to generate the flavor attributes, but facilitates the process to generate the language to describe the product. The order of appearance of the attributes and, thus, orders of evaluation for each descriptor and definitions for each descriptor are generated and additional products, which may further clarify terms, may also be used. Data is collected on scorecards using line scales anchored on each end and the marks are converted to numerical scores by measuring the responses on the scale with a ruler, digitizer, or computerized system. Unlike FPM the data can be analyzed statistically and is traditionally graphically represented using a web plot.

A universal intensity scale is usually used in the SpectrumTM method whereas product specific scaling can also be applied. The panel size is similar to QDA and the panelists are screened for cognitive, descriptive and sensory discrimination ability, interest, and availability. By this method, panelists score intensities in the same manner across all attributes. The scales are anchored on either end and can be 15-cm line scales or, more commonly, 0 to 15 numerical scales with tenth subdivisions between, yielding 150 points. The panel leader takes an active role in panel training and along with panel members identifies the sensory language for the product, their order of appearance, and definitions for each term. The data is readily analyzed by statistical techniques and results are normally graphically presented using histograms.

Relating Sensory Perception to Consumers

Effective consumer tests are used to provide information on consumer liking as descriptive sensory analysis is used to identify and quantify information on the sensory aspects of products. Quantitative consumer liking and/or preference information is obtained from acceptability and preference tests. For this purpose screeners and questionnaires are used to gather demographic data, frequency of usage, and purchase history about a particular product or group of products and these questionnaires are often included with and are recommended with acceptance testing to aid in data interpretation. Identifying drivers of liking or disliking within consumer market segments is also critical for industry to identify which products and product attributes are preferred and by which consumer market segment. In order to relate consumer liking and descriptive sensory properties, several statistical methods are used.

Relating Sensory Perception to Chemical Components

It is difficult to relate sensory language and chemical volatile compounds because of many reasons:

• The relative amount of a compound in a product is not necessarily a measure of its sensory impact, due to different thresholds and the effects of the food matrix.

• The sensitivity of the extraction technique used must also be taken into consideration.


• There are only a small percentage of the volatile components in a food that are odor-active.

While relating the gas chromatographic data and chemical compounds to sensory impact, 1st step in the solution is gas-chromatography olfactometry (GC/O). The individual compounds in the GC effluent are sniffed and described by a trained panelist and this technique can also be quantitative if the relative intensity of the odorant may also be recorded. Generally three techniques are applied with GC/O:

1. Aroma extract dilution analysis (AEDA) 2. CharmAnalysisTM 3. OSME

AEDA and Charm Analysis operate on dilution of samples until an odor is no longer detected and the highest dilution at which the odor is detected can be converted to a flavor dilution value (FD value used with AEDA) or a Charm value. These techniques are time consuming because of large number of sample dilutions by at least 2 panelists. Moreover these techniques also assume that the response to a stimulus as well as response to all compounds is linear. On the other hand, Osme, does not involve dilutions, but instead requires panelists (3 or more) to evaluate the time-intensity of aromas and aroma character.

Recently a new olfactometry technique i.e. nasal impact frequency (NIF) has also been developed which is based on frequency of detection of a compound in the GC effluent by sensory panelists. This technique can be applied with time intensity to yield surface of nasal impact frequency (SNIF) data. Both Osme and NIF/SNIF require fewer GC runs than AEDA and CharmAnalysis, because only undiluted flavor extract is evaluated by the sensory panelist but a larger number of panelists (3 or more, compared to 2 for AEDA and CharmAnalysis) is recommended for these techniques. All of these techniques are useful for determining odor activity of volatile compounds and the information obtained is based on individual components and does not include matrix effects or the time release factor involved when a sample is chewed in the mouth.

Conclusions:

The importance of sensory analysis has grown and requires more scientific attention. Flavor lexicons are an important tool for accurately documenting the description of food flavor. More research is expected and needed to demonstrate the effectiveness of the methodology and its practical application.

References:

Bom Frost M, Dijksterhuis GB, Martens, M. 2001. Sensory perception of fat in milk. Food Qual Pref 12:327-36.


Chapman KW, Lawless HT, Boor KJ. 2001. Quantitative descriptive analysis and principal component analysis for sensory characterization of ultrapasteurized milk. J Dairy Sci 84:12-20.

Lawless HT, Claassen MR. 1993. Validity of descriptive and defect-oriented terminology systems for sensory analysis of fluid milk. J Food Sci 58:108-12,119.

Meilgaard MM, Civille GV, Carr, T. 1999. Sensory Evaluation Techniques. 3rd Ed. New York, NY: CRC Press. 387 p.

Muir DD, Hunter, EA, Watson M. 1995a. Aroma of cheese. 1. Sensory characterization. Milch 50:499-503.

Lawless HT, Heymann H. 1998. Qualitative consumer research methods. In: Sensory Evaluation of Food. New York NY: Chapman and Hall. p 519-47.

Pangborn RM. 1989. The evolution of sensory science and its interaction with IFT. Food Technol 43:248-56, 307.

Phillips LG, Barbano DM. 1997. The influence of fat substitutes based on protein and titanium dioxide on the sensory properties of low fat milks. J Dairy Sci 80:2726-31.

Piggot JR, Mowat RG. 1991. Sensory aspects of maturation of Cheddar cheese by descriptive analysis. J Sens Stud 6:49-62.

Prindiville EA, Marshall RT, Heymann H. 1999. Effect of milk fat on the sensory properties of chocolate ice cream. J Dairy Sci 82:1425-32.

Prindiville EA, Marshall RT, Heymann H. 2000. Effect of milk fat, cocoa butter, and whey protein fat replacers on the sensory properties of low fat and nonfat chocolate ice cream. J Dairy Sci 83:2216-23.

Roberts AK, Vickers ZM. 1994. Cheddar cheese aging: changes in sensory attributes and consumer acceptance. J Food Sci 59:328-34.

Watson MP, McEwan JA. 1995. Sensory changes in liquid milk during storage and the effect on consumer acceptance. J Soc Dairy Technol 48:1-8.


Dr. R.R.B. Singh Senior Scientist

Dairy Technology Division NDRI, Karnal

1. Introduction

Critically quality assessment of all classes of concentrated milk challenges both the dairy products judge and the manufacturer of these products. A thorough understanding of the sensory attributes of concentrated milk and their routine examination is imperative, not only to assure improvement of the product, but also for ensuring that the product reaches the consumer in good condition.

2. Evaporated Milk

When judging or grading evaporated milk, the judge must keep in mind the desirable qualities and standards for the product. It must be noted that, in addition to meeting the legal chemical requirements for the product high quality evaporated milk must be white to creamy in colour, have a relatively viscous body, uniformly smooth in texture and possess a mild, pleasant flavour (Bodyfelt et al., 1988).

A complete examination of evaporated milk includes test and observations on colour, container, fat separation, fill of container, film formation (protein break), flavour, gelation, sedimentation, serum separation, viscosity and whipping ability.

Some of the subjective tests, based on organoleptic examination, make use of the hedonic scale or variations of it. For example, the flavour of evaporated milk may be given a hedonic rating on a 9-point scale discussesd earlier under “Sensory tests”. A narrow band hedonic scale say, a 5-point one , may be used in rating organoleptic quality factors other than flavour.

2.1 Procedure for examination

A routine in examining cans of evaporated milk facilitates judging of the samples. The following steps have been found to be of material aid in going over a lot of samples:

Precaution: Avoid undue agitation when transporting the cans to the laboratory.

a) Examine the cans for appearance, notice the upper end of the can for polish; observe the neatness of the label.

SESNORY ATTRIBUTES OF CONCENTRATED MILK AND THEIR EVALUATION


b) Open the can in such a way that both the can and contents can be examined.

c) Notice the colour of the milk which should be uniformly white to cream colour. Intensity of darkening may be noted for its degree e.g. non, slight, distinct and pronounced.

d) Study the body and texture. Smooth, relatively viscous evaporated milk pours like a thin cream without marked splashing. Allow the can to drain well. Look for any deposit which may be present in the bottom of the can. Should the milk lack uniformity try to determine whether the chief factor is fat, protein, salts or foreign material. In case the fat is responsible, the defect will appear at the top of the can as a cream layer or as buttery particles. Defect due to protein will appear as various size curds distributed throughout or as different intensities of gelation.

e) Observe the condition of the container looking for splangling, blackening of the seam and rusting of the container. Splanging appears as clean, bright, dark, overlapping blotches on the surface as though the tin were attacked by acid.

f) Determine the colour reaction in coffee. It should be a rich, golden brown colour. Off flavour may be associated with rust formation in the container.

g) Note the miscibility with coffee. Feathering in hot coffee appears as finely divided, serrated curds shortly after the evaporated milk has been added slowly to the hot coffee.

2.2 Defects in evaporated milk 2.2.1 Flavour

The flavour defects, which may occur in evaporated milk are usually unlike those commonly occurring in fresh beverage milk. Probably the most common flavour defect in evaporated milk is that which seems to be associated with progressive age darkening or browning of the product. Terms such as slightly acid, stale coffee, old, sour and strong suggest the nature of the defect. The caramel flavour connotes a pleasant, appetizing taste sensation that is definitely lacking in the defect associated with age-darkening of evaporated milk. This flavour defect is easily detected.

The off-flavour is accompanied by only a slight odour suggesting staleness. The underlying taste reaction of the age-darkened evaporated milk is acid.

2.2.2 Body and Texture

Fresh evaporated milk is remarkably free of body and texture defects. However, when evaporated milk is held for a long period of time or under adverse conditions, the following body and texture defects may be encountered:

2.2.2.1 Fat separation: This defect appears as a layer (up to 1more thick) of fat the top of the can. Among the causes of this defect are inadequate from isolation,


high storage temperature, long storage period and impetrated handling while in store.

2.2.2.2 Curdy: Curdy evaporated milk may be noted by the presence of many coagulated particles inter spread throughout the milk or by a continuous mass of coagulum. It is chiefly associated with the protein rather than the fat. It is a serious economic defect. This condition is due mainly to abnormally low heat coagulation point of the end product and could not withstand the sterilization process.

2.2.2.3 Feathering: The feathering of evaporated milk in hot coffee cannot be foretold by macroscopic examination but by actually testing the milk in hot coffee. It has been postulated that the formation of the curd when evaporated milk is added to coffee is due entirely to an excess of viscosity.

2.2.2.4 Gassy: Gassy evaporated milk is rather uncommon. The defect is manifest by bulged cans and sometimes by a hissing sound of escaping air when the can is punctured.

2.2.2.5 Grainy: A grainy evaporated milk is the one, which lacks smoothness and uniformity, throughout. Such milk seems coarse. It is often associated with an excessively heavy, viscous body. The judge must bear in mind that grainy evaporated milk does not actually contain “grains” of sediment settled in the container. Neither does such milk contain curds or lumps of butter.

2.2.2.6 Low viscosity: A low viscosity evaporated milk may be noted by its milk like consistency. This defect is discriminated against as it connotes inadequate condensation.

2.2.2.7 Sediment: The sediment resulting from settling of leukocytes, disintegrated cells, denatured protein and foreign material of more or less of a colloidal nature is usually darker in colour than the evaporated milk. Since this sediment is readily miscible it may be seen only when a can, undisturbed for sometime, is emptied slowly.

The other type of sediment noted in evaporated milk is the result of the crystallization of some of the calcium and magnesium salts as Ca3 (PO4)2 and Mg3(PO4)2. This gritty sediment formation accompanies ageing of the evaporated milk. They are found in the bottom of the container where they may be noted especially when the contents are emptied.

2.2.3 Colour

In judging evaporated milk two possible colour defects may be encountered, viz. too light in colour and too dark in colour. Too light colour is not a serious defect although it is definitely not desired. The brown discoloration in evaporated milk associated with high sterilization temperature, high storage temperature and age is a serious defect in evaporated milk.


3. Sweetened Condensed Milk

Since sweetened condensed milk contains a sufficiently high percentage of sugar for its preservation, the flavour is pronouncedly sweet. Beyond this intense sweeteness, the flavour should be clean and pleasant with a slight trace of mild caramel as an aftertaste.

3.1 Procedure for examination

A definite routine examination enables the judge to make the best use of the available time with the assurance that the examination is complete when finished (Seehafer, 1967).

a) Appearance of the container should be as bright as new tin as the can has not been subjected to the high heat treatment of sterilization.

b) The surface of the product should have the same intensity of colour as the under layer and should be uniform in consistency with no indication of lumps, free fat or skin formation.

c) Colour of the product should be uniform throughout. Observe if the milk has a greenish white creamy or a brownish colour.

d) Viscosity desired is one which is obviously not “thin” but resembling to a marked degree that of medium – heavy molasses. In grading sweetened condensed milk, the judge must bear in mind that a desirable sweetened condensed milk pours like molasses and, when poured, seeks its own level leaving no trace of the folds on the surface.

e) Flavour should be observed both for the textural and taste sensations. Register the relative smoothness of the product as a whole and fineness of the grain by pressing the sample against the palate with the tongue.

3.2 Defects of sweetened condensed milk

3.2.1 Flavour

3.2.1.1 Metallic: The metallic flavour in sweetened condensed milk is chemical rather than bacterial in nature and is usually traceable to copper contamination.

3.2.1.2 Rancid: It occurs rather infrequently and resembles butyric acid. Rancid flavour increases in intensity with age.

3.2.1.3 Strong: It is a flavour defect, which is suggestive of caramelized sugar and is usually accompanied by brown tint to the natural colour.

3.2.2 Body and texture


Condensed milk, having a high percentage of sugar has a relatively heavy body somewhat like normal molasses. Also, it usually has a smooth, uniform texture. However, the product may have certain body and texture defects such as buttons or lumpiness, fat separation, gassiness, sandiness, sediment, thickening etc (Gupta and Patel, 1978).

3.2.2.1 Buttons/lumpy: It is a body defect which is characterized by the presence of round and firm lumps, with stale odour, at the surface of the product. Buttons result from enzymic action following mould growth.

3.2.2.2 Sandy, rough, grainy, granular: These terms are used to describe sweetened condensed milk, which contains oversized lactose crystals. The solid particles are of such size that the product lacks smoothness and grittiness is noticeable, as the sample is being tasted.

3.2.2.3 Settled: It is used to describe the condensed milk in which a definite settling of sugar crystals has occurred.

3.2.2.4 Thickened: This defect is manifest by a gel formation, which gives the product the appearance of a solid rather than a liquid. The defect varies markedly in its intensity from a slightly jelly to a firm custard consistency.

References

BIS. (1981) Method for Sensory Evaluation of Sweetened Condensed Milk. IS: 10029-1981, Bureau of Indian Standards, New Delhi.

Bodyfelt, M.S.; Tobias, J. and Trout, G.M. (1988) The Sensory Evaluation of Dairy Products, Publ. pp. 416-472.

Gupta, S. K. and Patel, A.A. (1978). Some aspects of judging condensed milk. Indian Dairyman, 30: 713-715.

Seehafer, M.E. (1967) The Development and Manufacture of Sterilized Milk Concentrate. FAO Agricultural Studies Bulletin. No. 72, pp. 1-52.


Table A. Score card for organoleptic evaluation of UHT milk.

Name of Judge_____________ Date ___________________

Sample Number Attributes Perfect Score

Criticism 1 2 3 4 5 6

Flavour 45 Score Asstringent/chalky Bitter Cabbage Coconut Cooked Flat Oxidised/cardboardy Paper like Phenolic Rancid Sour Stale Consistency 20 Score Thin Heavy/viscous Gel/ Custard like Colour & Appearance

15 Score

Dull Browning Fat separation Sedimentation 15 Score Package 5 Score Distorted/ Bulged Dented Leaky Soiled Total Score 100

General guide for grading UHT milk or the basis of total score:

Excellent 95 and above Good 90-94 Fair 85-89 Poor 75-84 Bad Below 74


Table B. Suggested score for evaluation of different intensities of defects.

Defect Slight Definite Pronounced Flavour Astringent/chalky Bitter Cabbage Coconut Cooked Flat Oxidized/ Cardboardy Paper-like Phenolic Rancid Sour Stale

37 32 37 32 42 38 32 34 32 32 28 33

32 27 34 27 40 33 27 29 27 27 23 28

27 22 31 22 38 28 22 24 22 22 18 23

Consistency Gel (Custard like) Heavy/Viscous Thin

12 18 18

8

16 16

4

13 15

Colour and Appearance Browning Dull Fat Separation

11

13.5 11

10 13 10

8

12.5 8

Sedimentation 12 10 7

Package Dented Distorted or Bulged Leaky Soiled

4

4.4 3.0 3.5

3.6 4.0 2.0 3.1

3.2 3.6 1.0 2.7

Table C. General guide for grading UHT milk on the basis of various sensory attributes.

Characteristics Grade Range of Score Specific description of criticism

Flavour Excellent 41-45 Slightly cooked.

Good 36-40 Slightly astringent / chalky, cabbagy, flat; definite to pronounced cooked.


Fair 31-35 Slightly bitter, coconut, oxidized, paper like, phenolic, rancid, stale; definite to pronounced cabbagy flat.

Poor 26-30 Slightly sour; definite bitter, coconut, oxidized, paper like, phenolic, rancid, stale; pronounced chalky. Contd..

Bad Below 25 Definite sour; pronounced bitter, coconut, oxidized, paper like, phenolic, rancid, stale.

Consistency Excellent 19-20 No criticism.

Good 17-18 Slightly thin, viscous/heavy.

Fair 15-16 Definite to pronounced thin, definite heavy.

Poor 13-14 Pronounced viscous.

Bad Below 12 Gel, Custard like.

Colour and Appearance

Excellent 13.6-15.0 No criticism

Good 12.1-13.5 Dull colour.

Fair 10.6-12.0 Slightly brown, slightly fat-separation.

Poor 9.1-10.5 Definite brown, fat – separation.

Bad Below 9 Pronounced brown, fat – separation.

Sedimentation Excellent 13.6 – 15.0 No criticism.

Good 12.1-13.5 No visible sediments but chalky perception in mouth suggesting presence of sediments in soluble particles form.

Package Excellent 4.6 - 5.0 No criticism.

Good 4.1 – 4.5 Slightly distorted or bulged.

Fair 3.6 –4.0 Dented package; definitely distorted or bulged.

Poor 3.1 –3.5 Soiled package.

Bad Below 3 Leaky package.


Dr. Latha Sabikhi Senior Scientist


1. Introduction

Cultured milk products, which include dahi, yoghurt, lassi, buttermilk, shrikhand, sour cream and kefir, play an important role in the dairy industry. Their low pH and extended shelf life make cultured milk products particularly relevant to commercial production in tropical countries.

While sensory attributes are very important determinants of the acceptability of cultured milk products, their sensory evaluation has not progressed to the same extent as the art and science of sensory discrimination for milk and many other manufactured milk products. Generally, sensory evaluation of commercial fermented milk and cream products has frequently involved more of a comparison of the products of current manufacture with those made previously. This procedure, however may tend to result in a progressively lower quality product.

2. Common Attributes of Cultured Milk Products

2.1 Flavour

Cultured milk products should impart a pleasing bouquet flavour, which results from the overall blend of a delicate, diacetyl odour and a distinctly clean, acid taste. Once the aroma and taste characteristics of good-cultured products are fixed in the mind of the evaluator they are not easily forgotten. Sometime there is possibility of occurrence of one or more of several off flavours, such as bitter, cheesey, lack of desired aroma, lack or flavour and high acid.

2.2 Body and Texture

Before being shaken the body of a good, properly cultured product should appear firm or solid and generally be uniform in appearance. It should only show a few beads of whey exuded from the surface. The mix sample should appear smooth, somewhat resembling rich cream, no curd particles or lumps should appear when in is spread in a thin layer in a glass surface or diluted with water. Some of the more common body defects of cultured milk products are described in the following paragraphs.

SENSORY ATTRIBUTES OF FERMENTED MILK

PRODUCTS


2.2.1 Curdy: A curdy body tends to lack uniformity, smoothness or homogeneity. The curd particles may be sufficiently large to be readily observed upon pouring or so small in size that close examination is necessary to see the feathens curds.

2.2.2 Lumpy: A “lumpy” body is often an aggravated case of curdy consistency; the particle size is larger in the lumpy defect.

2.2.3 Gassy: A “gassy” product is denoted by excessive gas bubbles (CO), or by streaks in the coagulum due to rise of gas bubbles to the surface. If accompanied by whey separation, a gassy sample will whey off at the bottom or at the centre of the container.

2.2.4 Ropy: A ropy product tends to stretch or string – out when poured. Sometimes the defect is so pronounced that the product strings out like a thin syrup or mucous substance.

2.2.5 Wheying-off: This defect is manifest by a shrunken curd or coagulum and the presence of liberated or “free whey” in areas around the side and on the surface of the container.

3. Yoghurt

Yoghurt is a quickly curdled milk based product with little or no alcohol content. It results from the associative growth of Lactobacillus bulgaricus and Streptococcus thermophiolus in warm milk (29-45oC). Typical yoghurt is characterized by a smooth, viscous gel, with a taste of sharp acid and a green or green apple flavour some yoghurt exhibit a heavy consistency that closely resembles custard or milk pudding by contrast, other yoghurt are purposely soft –bodied and essentially drinkable. Different type of yoghurt sold in the USA and their characteristics are given in Table 1.

3.1 Desirable attributes of yoghurt

Yoghurt should be smooth, viscous gel, with a characteristic taste of sharp acid and a green or green apple flavour. The typical acetaldehyde flavour of plain yoghurt is achieved through a symbiotic bacterial relationship is flenced by such factors as (1) temperature of incubation, (2) amount of inoculum (3) period of incubation, (4) source of culture, (5) heat treatment of yoghurt base and (6) pH of finished products. The flavour of plain yoghurt is somewhat unique and unlike that


encountered in any other type of fermented milk. The flavour components of plain yoghurt flavour include acetaldehyde acetic acid, diacetyl and several volatile falty acids.

Table 1 Characteristics of the various styles of flavoured yoghurts in U.S.A.

Yoghurt style (Type) Characteristics

1 Swiss –Style (French –

prestirred, or

Preblended

Precultured yogurt base and fruit or berry flavoring

(15-25%) blended prior to packaging

2 Sundae-style

(Fruit-on-bottom)

a. Eastern-type

b. Western-type

c. Fruit-on-top

Flavouring (15-25%) added to the container, yogurt

base added to top of flavouring.

No colouring agent, flavouring, or sweetener added to

yogurt base (milk base is white).

Colouring agent, flavour extract, or concentrate

and/or sweetener added to yogurt base.

Yogurt cups filled in a manner so that flavouring

material is on top portion of container.

3 Extract flavored

(or concentrates)

Flavour extracts and or concentrates are sole source

of flavour plus sweetener(s) (i.e., coffee, chocolate,

lemon etc.)

4 Frozen Product Form

a. Soft serve b. Hard frozen c. Novelties d. Yogurt pies

Served as cones, dish or sundaes.

Pint and quart size

On-a-stick, coated bars, “push-ups”.

In “pie” crusts.

5 Miscellaneous Types A variant of the sundae-style Western type: A firm,

flavored yogurt with additional flavoring cascading

over its exterior when emptied up-side down.


3.2 Defects in yoghurt

3.2.1 Plain yoghurt

Colour and appearance consideration for plain yoghurt are rather simple and straightforward, compared to the complexities of flavoured yoghurt. Generally, the appearance of plain yoghurt should convey a smooth, homogenous, moderately firm gel or custard-like body and texture and a uniform off-white color. The more common color an appearance defects of plain yoghurt are reviewed here.

3.2.1.1 Free whey: Wheyed-off (Syneresis) This defect is manifest by a shrunken curd’ or coagulum and the presence of liberated or “free whey” in areas around the side and on the surface of the container.

3.2.1.2 Gel-like: This condition may be considered as both an appearance and a body and texture defect. The term “gel-like” is used to describe the appearance of excessive product firmness, or a severe gelatin (liver like) consistency.

3.2.1.3 Shrunken: Occasionally in yoghurt, the gel or coagulum tends to shrink in size within the container (or pull away from the carton side wall); this leaves the impression of reduced or “shrunken” contents. Quite often, free whey will fill the void that results from this “shrinking” of the coagulum.

3.2.1.4 Surface Growth: Probably the most serious defect of yoghurt appearance. This defect consists of visible colonies of yeast and/or mold growth on the top surface of the yoghurt.

3.2.2 Body and texture defects

3.2.2.1 Grainy: In the instance of “graininess,” the product lacks the desired smoothness and uniformity of appearance. Small particles of a grit or grain size may actually be visible; graininess is quite often detectable by mouthfeel.

3.2.2.2 Ropy: A ropy product tends to stretch or “string-out” when poured. Sometimes the defect is so pronounced that the product “strings-out” like a thin syrup or mucous substance.

3.2.2.3 Too Firm: When the body of plain yoghurt is considered “too firm”, it conveys the impression of being too rigid or resistant to mastication when placed in the mouth. Also, a too firm body is often apparent by visually examining a side profile of a spoonful of product. Firm or rigid edges can be noted, rather than a more preferred “soft rounding” impression of a spoonful of product.

3.2.2.4 Weak: A weak body defect is the exact opposite of too firm; the product consistency conveys the distinct impression that it would probably be easier to consume the product as a beverage than to “spoon” it. Viewed from side profile, the product may appear practically level in the spoon, or it may spill over the lip of the spoon. For drinkable style yoghurt, a weak body is a prerequisite.


3.3 Procedure for yoghurt evaluation

A scorecard for swiss-style flavoured yoghurt (fig.1) developed and adopted by the Committee on Evaluation of Dairy products of the American Dairy Science Association carries maximum scores for different attributes (Table 2) and can be used as per the guidelines given in Table 3. This scorecard was cooperatively designed through the suggestions and efforts of ingredient suppliers and commercial yoghurt manufacturers.

Table 2. Maximum scores for the sensory attributes of yoghurt

Attributes Maximum Score Normal range

Flavour 10 1-10

Body and texture 5 1-5

Appearance 5 1-5

Product acidity 2 -

Container and closure 3 -

Table 3: Scoring guide for the sensory defects of Swiss-style yoghurt

Intensity of Defect Criticisms

Slight Definite

Pronounced

Flavour

Acetaldehyde (green) 9 7 5

Acid (too high) 9 7 5

Acid (too low) 9 8 6

Bitter 9 7 5

Cooked 9 8 6

Foreign 5 3 0

Lacks fine flavour 9 8 7


Lacks flavouring 9 8 7

Lack freshness 8 7 6

Lacks sweetness 9 8 7

Old ingredient 7 5 3

Oxidized/metallic 6 4 1

Rancid 4 2 0

Too high flavouring 9 8 7

Too sweet 9 8 7

Unclean 6 4 1

Unnatural flavouring 8 6 4

Body and texture

Gel-like 4 3 2

Grainy/gritty 4 3 2

Ropy 3 2 1

Too firm 4 3 2

Weak/too thin 4 3 2

Appearance

Atypical colour 4 3 2

Colour leaching 4 3 2

Excess fruit 4 3 2

Lacks fruit 4 3 2

Lumpy 4 3 2

Shrunken 4 3 2

Surface growth 2 1 0

Wheyed-off (syneresis) 4 3 2


4. Lassi

Lassi, popular Indian soft drink is a product resulting from the growth of a selected culture usually lactic streptococci in heat treated whole or partially skimmed milk. At the desired ripeness 0.75-0.85% lactic acid, the coagulum is broken, admixed with sugar (or sugar syrup), and flavour and packaged in glass bottles or polyethylene bags. It is stored under refrigerated conditions and invariably served cold.

4.1 Desirable characteristics of lassi

The color of lassi should be pleasing, attractive and uniform. Normally, it varies from light yellow to whitish. In general, the good, clean, pleasant diacetyl flavour of a culture is desired in lassi. The natural flavour may be enhanced or enriched by the presence of milk fat.

The demands of trade vary as to the body of lassi. Some consumers prefer a heavy viscous body while others like a rather thin body. Consequently, no uniform standard can be fixed with regard to the body of lassi. However, a medium-bodied lassi pouring similar to thin gravy seems to be most appropriate. The texture should be homogenous showing no signs of wheying off or grains or curd particles.

4.2 Score card for lassi

A scorecard based on 100-point scale is shown in Fig 1 and the guidelines given in Table 3.

Attribute Perfect Score Sample Score

1 2 3 4 5

Flavour 45 -

Body & texture 30 -

Acidity 10 -

Colour & appearance 10 -

Container & closure 5 -

Total 100 -


Table 3. Suggested deductions from maximum score for different sensory attributes of lassi

Intensity of defect Sensory Defect

Slight Definite Pronounced

Flavour (45) High acid/green cheesy,

bitter,

metallic

7

10

9

13

11

16

Body and Texture

(30)

Curdy grainy, thin/thick

body Ropy,

wheying off

1

3

3

6

5

9

Acidity (10) High acidity, Low

acidity 1 3 5

Color &

Appearance (10)

Uneven/unnatural colour 1 3 5

Container and

closure (5)

Dirty/improperly

covered 1 2 3

5. Shrikhand

Shrikhand is an acid coagulated and sweetened milk product, which is a popular delicacy in states of Gujarat, Maharashtra and partly Karnataka. This indigenous dairy product is prepared by lactic coagulation of milk and expulsion of whey from the curd followed by blending of sugar, flavour and spiced. The product has about 5% fat, 42% sugar and 60% TS. The shelf life of the product is about 40 days at 8+1oC. A 100-point score card similar to the one shown in figure 2 carries a maximum score of 55, 30, 10 and 5 for flavour, body and texture, appearance and color respectively. The sensory guide is given in Table 3 and 4.

6. References

Conolly, E.J., White, C.H. Custer, E.W. and veda muthu, E.R. (1984) Cultured Dairy

Food Quantity Improvement Manual, American Cultured Dairy Products Institute

Washington D.C.


Duthie, A.H; Nilson, K.M. Atherton, H.V. and Garrett, L.D. (1977) Proposed score

card for yoghurt. Cultured Dairy Product J., 12 (3) 100

Kemp, N. (1984) Kefir, the champagne of cultured dairy products. Cultured Dairy

Product. J. 19(3): 29

Ryan, J. M., White, C.H. Goush, R.H. and Burns, A.C. (1984) Methodology for

evaluation of yoghurt. J.Dairy Sci, 67: 1369

Dharam Pal and Gupta, S. K. (1985) Sensory evaluation of Indian Milk Products.

Indian Dairyman 37: 465

Patel, R.S. (1982) Process Alterations in shrikhand technology, Ph.D. Thesis,

Kurukshetra University, Kurukshetra


Dr. S.K. Kanawjia, Sanjeev Kumar*, Hitesh Gahane** and Vikash Gupta Principal Scientist

Cheese and Fermented Foods Lab, D.T. Division, NDRI, Karnal

* PhD Scholar, ** M.Tech (DT) Scholar, *** Research Associate E-mail: [email protected]

Introduction

New product development requires the integration of sensory attributes including product taste, texture, and appearance with consumer attitudes and health biases. Both sensory and attitudinal variables determine food preferences, product purchase and food consumption. This paper describes application of the e-tongue to eliminate panelist bias for taste evaluation of food products. The evaluation of dairy and food products for their organoleptic properties is one of the essential requirements for development of newer items as well as its perfection at the stage of production or marketing. Taste is the most important sensory attribute of any food product, which determines its acceptability. The senses of taste have always been used in monitoring and judging the quality of foods. The application of human taster to distinguish different tastes is as older as the human civilization. Unfortunately, there are several problems associated with human taster which include sensory fatigue, varied perception of similar taste to different people, health risk as associated with tasting of certain chemicals and its dependability on human mood and adaptation. In the era of sensor technology, evolution of E-Tongue has initiated renaissance in sensory assessment of foods.

E -Tongue mimics the biological tongue, which is actually a group of sensor chips capable of remitting real time data to control the quality of a liquid process. When a liquid flows over this "tongue," its exact chemical makeup can be ascertained and controlled by computer. This innovation is expected to save several lacks of rupees in industrial quality control. The tongue can "taste" liquids to detect impurities or anomalies, offering possibilities for improving water purification, blood and urine tests, even the fermentation of champagne. Using chemical sensors, University of Texas at Austin, researchers have designed an E-tongue that can taste like its natural counterpart. It has the potential someday to distinguish between a dazzling array of subtle flavour using a combination of the four elements of taste viz. sweet, sour, salt and bitter. In some ways it has outdone Mother Nature: it has the capacity to analyze

APPLICATION OF E-TONGUE IN MONITORING SENSORY QUALITY OF FOODS


the chemical composition of a substance as well. The device, which has the potential to incorporate hundreds of chemical micro sensors on a silicon wafer, has a multitude of potential uses. The food and beverage industry wants to develop it for rapid testing of new food and drink products for comparison with a computer library of tastes proven popular with consumers. E-Tongue is the most advanced device of its type worldwide and has no analogues. The know-how of the system is not disclosed in scientific papers, the patent aspects are under study.

Historical Background

E-Nose only measures volatile components only, which constitutes a sample’s odour. Human sensory perception encompasses more than just odour and aroma and includes taste, colour, texture, mouth-feel, and even sound. As E-Nose is used more routinely, instrument suppliers have continued to provide improved solutions

The tongue research, reported in the Journal of the American Chemical Society, began in 1996 when electrical and computer engineering professor Dean Neikirk and chemists John McDevitt and Eric Anslyn began casual discussion of the idea. Neikirk and McDevitt designed a nose to sniff out iodine, but soon realized that many chemicals don't evaporate. The new collaboration incorporated the work of Anslyn, a chemist and tongue researcher at the University of Texas at Austin, who used polymer micro beads to synthesize DNA and its proteins. The team attached four well-known chemical sensors to Anslyn's minute beads and placed the beads in Neikirk's micro-machined wells on a silicon wafer. Like a human tongue, the wells mimicked the tongue's many cavities that hold chemical receptors known as taste buds. Each bead, like a tongue's receptor, had a sensor that responded to a specific chemical by changing colour. One turned yellow in response to high acidity, purple under basic conditions. Then the researchers read the sensor's results through an attached camera-on-a-chip connected to a computer. The sensors responded to different combinations of the four artificial taste elements with unique combinations of red, green and blue, enabling the device to analyze for several different chemical components simultaneously. Alpha M.O.S., Toulouse, France, has now launched an E-Tongue for the analysis of taste and non-volatile chemicals that are typically found in liquids.

Development of E-Tongue

One of the overall goals of NASA's Space Life Sciences Division of Advanced Human Technology Program (AHST) research project is to understand the principles, concepts, and science which will enable the development of an integrated, rugged, reliable, low mass/power, electro analytical device which can identify and quantitatively determine a variety of water quality parameters including, inorganics, organics, gases along with physical properties like pH, oxidation reduction potential, and conductivity. The mission of its Advanced Environmental Monitoring and Control Program (AEMC) is to "provide spacecraft with advanced, microminiaturized


networks of integrated sensors" to monitor and control the environment. One of the main components of the AEMC program is the development of advanced technologies for monitoring the chemical and physical status of life support systems i.e. the water supply.

To accomplish these goals a group of scientists in collaboration with the NASA's Jet Propulsion Laboratory and Thermo Orion Research, undertook the research necessary to lead to an electrochemically-based integrated array of chemical sensors based on several novel transduction and fabrication concepts. Even though this type of sensor array might be thought of as an "Electronic Tongue", it is exceedingly more capable. Working in conjunction with a neural network, it will provide both qualitative and quantitative information for a much broader range of components, such as cations, anions, inorganic and organic than a human tongue ever could. The micro fabrication, integration, and multiplexing of such a large number of sensors on a single substrate has not been previously attempted and presents a formidable scientific and technical challenge. Their work has lead to the discovery of a unique electro-immobilization technique, which imparts special selectivity properties to each sensor. Unlike previous devices though, this electrochemically-based sensor will provide both identification and reliable quantitative data. The technology resulting from this research project has been proposed to be used in a taste of future: the E-Tongue. E-Nose developed by collaboration between the Jet Propulsion Laboratory and the California Institute of Technology analyzes gases in a similar way and was the precursor to E-tongue research at University of Texas. From the silicon tongue, the team hoped to create a process to make artificial tongues more cheaply and quickly, placing them on a roll of tape, for example, to be used once and thrown away.

E-Tongue Capability

The researchers designed E-Tongue to be structurally similar to the human tongue, which has four different kinds of receptors that respond to distinct tastes. The human tongue creates a pattern in the brain to store and recall the taste of a particular food. E-Tongue is an analytical instrument comprising an array of chemical sensors with partial specificity (cross-sensitivity) to different components in solution, and an appropriate method of pattern recognition and/or multivariate calibration for data processing. It is a new generation analytical instrument based on an array of non-selective chemical sensors (electrodes) and pattern recognition methods. Chemical sensors incorporated into the array exhibit high cross-sensitivity to different components of analyzed liquids inorganic and organic, ionic and non-ionic. Utilization of the sensors with high cross-sensitivity in conjunction with modern data processing methods permits to carry out a multi-component quantitative analysis of liquids (determination of composition and components), and also recognition (identification, classification, distinguishing) of complex liquids.


The instrument is a multisensor system for liquid media analysis only. It consists of a multisensor system (sensor unit) incorporating 15 to 40 different sensors, an electronic interface device for measuring and conversion of the sensor signals and a PC for data acquisition and processing. Not only is E-Tongue a technological breakthrough; it is also a myth-buster about the character of academic research in the era of electronics. E-Tongue may be called a micro machined sensor array that has been developed for the rapid characterization of multi-component mixtures in aqueous media. The sensor functions in a manner analogous to that of the mammalian tongue. Likewise, the sensor creates specific patterns for different mixtures of analytes. These "taste buds" are deposited into an array of micro machine-etched wells localized on silicon wafers. The hybrid micro machined structure has been interfaced directly to a charged-coupled-device (CCD), which is used for the simultaneous acquisition of the colorimetric data from the individually addressable "taste bud" elements.

With the miniature sensor array, acquisition of data streams composed of red, green, and blue (RGB) colour patterns distinctive for the analytes in the solution are rapidly acquired. E-Tongue contains tiny beads analogous to taste buds. Each "bud" is designed to latch onto specific flavour molecules and change colours when it finds one, be it sweet, sour, bitter or salty. The buds are housed in pits on the surface of the tongue itself, which is made of silicone. Each one of these pits looks like a little pyramid, and it's just the right size that we can take one of these taste buds and nestle it down inside. A little silicon chip that has micro beads arrayed on it, in a similar fashion to the taste buds on your tongue has been made at the university of Texas. Each of the beads responds to different analytes like the tongue responds to sweet, sour, salty, and bitter. There is a potential to make taste buds for almost any analytes. To build E-Tongue, the scientists’ positioned 10 to 100 polymer micro beads on silicon chip about one centimeter square. The beads are arranged in tiny pits to represent taste buds and marked each pit with dye to create a red, green, and blue (RGB) colour bar.

The colours change when the chemicals are introduced to E-Tongue. A camera on a chip connected to a computer then examines the colourrs and performs a simple RGB analysis that in turn determines what tastes are present. Yellow, for example, would be a response to high acidity, or a sour taste. The E-Tongue now uses simple markers to detect different types of taste: calcium and metal ions for salty, pH levels for sour, and sugars for sweet.

E-Tongue features an auto-samples and integrated software (Giese, 2001). It is designed to replicate human taster and consists of an array of chemical sensors, each with partial specificity to a wide range of non-volatile taste molecules, coupled with a suitable pattern recognition system. For instance, The Alpha M.O.S. E-Tongue, called the Astree, is composed of a 16-position auto sampler, an array of liquid sensors, and an advanced chemo metric software package (Alpha M.O.S, 2001 a, b, c). The instrument also has an option of sample temperature control to ensure analytical producibility of measurements.


Features of E-Tongue

One of the unique features of the system is the possibility to correlate the output of E-Tongue with human perception of taste, odor and flavour, e.g. with food evaluations made by a trained taster. A typical sensitivity limit of most such sensors for the majority of components is about several micrograms per liter. Of primary importance are stability of sensor behaviour and enhanced cross-sensitivity, which is understood as reproducible response of a sensor to as many species as possible. If properly configured and trained (calibrated), E-Tongue is capable of recognizing the quantitative and qualitative composition of multi-component solutions of different natures, e.g. beverages and foodstuffs. E-Tongue is not affected by CO2 concentration in the product .It responses to a number of organic and inorganic nonvolatile compounds in the ppm level in liquid environment .The response can be highly reproducible. In any E-Tongue application, results will be as good as the samples used in the calibration and teaching the sets.

Principle of E-Tongue

Humans have long been thought to detect four basic taste types viz. sweet, salty, sour and bitter. Very recently, a fifth candidate basic taste was identified: umami, the taste of monosodium glutamate (MSG), characteristic of protein-rich foods. Taste buds are believed to contain receptor molecules that trigger nerve signals when they encounter flavour-imparting molecules. The details of this system are still not understood. Each taste sensation may correspond to a fingerprint signal induced by the differential activation of the various taste receptors. E-Tongue works on this principle. It works by measuring dissolved compounds and taste substances in liquid samples (Giese, 2001).

It contains four different chemical sensors. The sensors comprise very thin films of three polymers and a small molecule containing ruthenium ions. These materials are deposited onto gold electrodes hooked up to an electrical circuit. In a solution of flavorsome substances, such as sugar, salt quinine (bitter) and hydrochloric acid (sour), the thin sensing films absorb the dissolved substances. This alters the electrical behavior (the capacitance) of the electrodes in a measurable way. Each sensor responds differently to different tastes. A composite sensor that incorporates all four therefore produces an electronic fingerprint of the taste. The researchers combine these responses into a single data point on a graph. The position on the graph reflects the type of taste: sweet lies towards the top left, for example, sour towards the top right (Riul et al., 2002). Different beverages have a characteristic location on the graph. Coffee is low down around the middle, for instance. Some tastes that might be expected to differ only slightly, such as distilled and mineral water lie far apart on the graph and so can be clearly distinguished.

E-Tongue –The Present


Researchers at the University of Texas have developed an E-Tongue that they hope they will someday be able to taste the differences in a variety of liquids, from orange juice to blood. But can an E-Tongue mimic the sophisticated palates of wine tasters? Eventually, its developers say, it may come close. With wine, for example, the tongue changes colour depending on how sweet or sour the vintage is. They also plan to go beyond the four tastes of the human tongue and use the device to analyze such substances as blood or urine, or to test for poisons in water. Someday, says chemist Eric Anslyn, the tongue might speed up blood analysis by testing everything from cholesterol to medications in a person's bloodstream, all at the same time. But the developers have a way to go before achieving their vision. So far, the tongue can only tell the difference between white wine and white vinegar. Alpha M.O.S. exhibited the recently introduced Astree E-Tongue at the IFT annual Meeting and Food Expo in New Orleans, La. The Astree is designed for liquid product analysis and taste control.

Advantages of E-Tongue

The typical analysis time using the E-Tongue is about 3 min from when the sensors are introduced into the beaker containing the sample. It takes only 5 minutes for analysis and sensor cleaning. It has been proved that the instrument is so sensitive that it can response to as 10-2 molar of sucrose, caffeine, salt (NaCl), sour (HCl) and Umami (MSG) (Tan et al., 2001).

Application of E-Tongue would be advantageous to analyze the taste of those toxic substances which human dare to taste due to toxicity. Successful application of E-Tongue may offer online monitor of taste and documentation, thereby permits better product process maintains. Therefore, E-Tongue may help the food processor to reduce wastage from poorly controlled processes and increased productivity. Since, E-Tongue readily tends itself to automation and computerization, monitoring taste quality can be incorporated into the manufacturing process. Another advantage is its versatility. E-Tongue being developed range from small, inexpensive, handhold devices e.g. those for periodic taste analysis goods for household purposed to sophisticated devices for contitinuous, on-line monitoring of taste quality. Eventually, E-Tongue will be inexpensive disposable units, placed on a roll of tape to be used quickly and easily. Application of E-Tongue permits many sorts of diverse sample to be examined. Once a protocol has been established, the instrument does not require highly skilled operators.

Detectors in E-Tongue

The detector consists of an array of seven different liquid cross-sensitive sensors. These detectors are selected on the basis of application, since sensitivity and selectivity are important for obtaining instrumental correlation. Upto 25 different sensors are commercially available. The sensors are made of silicon transistors with an organic coating that governs sensitivity and selectivity of each individual sensor.


The proprietary coating is used to ascertain good repeatability, sensitivity and selectivity. The response (R) of E-Tongue mathematically represented as follows: R = f (SL

, P), where SL corresponds to the liquid sensor sensitivity and selectivity and P corresponds to the liquid sample. E-Tongue seeks to measure the attributes, such as salty, sweet, bitter, sour, and savorless. The measurement consists of a potentiometric difference between each individual coated sensor and the Ag/AgCl reference electrode. The main part of E-Tongue is a set of potentiometric chemical sensors, applicable for liquid analysis Sensor arrays include different types of sensors: conventional ones, specially designed non-specific sensors with enhanced cross-sensitivities or classical electrochemical electrodes are used depending on the task, sensor stability and/or cross sensitivity.

Data Processing

The second essential part of an E-Tongue is the data processing. Since the number of sensors in the array of an E-Tongue can reach 40, each of them producing a complex response in the multicomponent environment, a relevant multidimensional data processing must be performed. This is done by different pattern recognition methods such as Artificial Neural Networks (ANNs) or multivariate calibration tools. Each method has its own advantages as well as drawbacks, which must be carefully considered to get reliable analytical results in food analysis.

Pattern-Recognition System in E-Tongue

The chemo metric package comprises various pattern-recognition analysis modules for evaluating the data recorded from the array of liquid sensors. The modules include principal component analysis (PCA), discrimination function analysis (DFA), Soft Independent Model Clam Analogy (SIMCA), and Partial Least Square (PLS) (Alpha M.OS, 2001b). The various pattern-recognition modules are utilized to achieve instrumental correlation to sensory tests that are conducted. On the basis of the objectives of analysis, different modules are used. For instance, PLS is utilized for quantitative analysis, where the objectives are to quantify a particular molecule of attribute. For qualitative analysis, SIMCA can be used for comparison to ensure good similarity of a new product to a gold standard. The modules of the E-Tongue are the same and/or very similar to those used for the E-Nose.

Training of E-Tongue

E-Tongue, like a human being, needs to be trained with a correctly selected sample set to ensure good recognition and reproducibility. E-Tongue, in fact, seems to be black box; it knows nothing until it is taught Alpha M.O.S. (2000) suggested the procedure for training, model building, and validation for the instrument.


Operation of an E-Tongue

The auto-sampler allows 15 samples to be evaluated automatically, once the sample has been prepared. Preparation of samples typically involves filling the 100 ml beakers to three-fourths full. No other sample preparation is required. One beaker position is reversed for cleaning the sensor array following analysis of each individual sample. The auto-sampler also includes fluidic pumps for cleaning out the beaker for sensor rising when needed. Cooling to 2 to 4°C also ensures that there is limited sample change during analysis cycle. Analysis of sample is followed by a wash cycle to ensure that there is no carryover of sample to the next analysis and also to ensure good reproducibility. Typically, upto five replicate measurements are made for each sample.

Correlation between E-Tongue Output and Human Perception

A good agreement was observed for coffee, wine and soft drinks. That is why "artificial tasting" of beverages and foodstuffs based on sensor arrays and multivariate data processing seems to be a highly interesting emerging field. The performance of E-Tongue is presented in Table-1.

Table-1

Attributes Qualitative Analysis Quantitative Performance

Typical sensor array size

20 - 40 sensors 10-30 sensors

Typical number of measuring sessions

4 - 8 12 - 50

Number of measurements within each measuring session

3 3 - 10

Examples Discrimination of different types of beverages.

Discrimination of different coffees by name,

Discrimination of orange juices by their quality.

Classification of the different coffees depending on acidity, Glycerol rate in wine samples, Determination of components in model blood plasma.

Applications of E-Tongue


E-Tongue has a multitude potential application, which includes its uses in quality control laboratory in space station and in medicine/body functions. A new hand-held E-Tongue promises to give accurate and reliable taste measurements for companies currently relying on human tasters for their quality control of wine, tea, coffee, mineral water and other foods. E-Tongue can sense low levels of impurities in water. It can discriminate between Cabernet Sauvignons of the same year from two different wineries, and between those from the same winery but different years. It can also spot molecules such as sugar and salt at concentrations too low for human detection. The electronic fingerprint allows the scientists to predict what a particular solution will taste like. Martin Taylor of the University of Wales at Bangor has anticipated that the device will probably be able to discriminate the umami taste too, giving it a refined palate for sushi. The food and beverage industries may want to use E-Tongue to develop a digital library of tastes proven to be popular with consumers, or to monitor the flavours of existing products.

The E-Tongue has been designed to replace human tasters. E-tongue can also "taste" cholesterol levels in blood, cocaine in urine, or toxins in water. An "E--Tongue" for monitoring water quality on spacecraft and planetary habitats. Researchers hope E-Tongue can be used by industry to ensure that beverages coming off assembly lines are uniform in flavour. Quality control for beverages is one way the E-Tongue can be used. This first-generation E-Tongue has the ability to assay solution content for Ca2+, Ce3+, H+, and fructose using colorimetric indicators that are covalently linked to polyethylene glycol-polystyrene resin beads.

E-Tongue can be applied for quantitative analysis and recognition (identification, classification) of a very wide range of liquids on aqueous and water-organic basis. The most promising are the perspectives of E- Tongue application for quality control and identification of the conformity to standards for different food stuffs - juices, coffee, beer, wine, spirits, etc. Also the system can be successfully utilized in complicated tasks of industrial and environmental analysis, such as determination of the composition of groundwater in the abandoned uranium mines.

There are several laboratory prototypes of E- Tongue, which have been constantly used for several years in the Laboratory of Chemical Sensors of St Petersburg University. Mobile versions of the system for special applications are being developed. Surprisingly this technology has created interest in vastly different areas. Besides the food industry, environmental and tourist industries want to incorporate it into hand-held monitors for feedback about local air and water. And there are huge markets in biomedical applications. E-Tongue can play a significant role in product development and quality assurance/quality control. E-Tongue measurement of taste and non-volatile components of food and beverages can be carried out easily to complement the headspace aroma/odor analyses, thereby adding a new dimension to the instrumental correlation of human perception. Like E-Nose, E-Tongue can be used for several purposes, including sample analysis, quality control,


and product matching (Madsen and Grypa, 2000). Regarding food industry and related processes, E-Tongue has already been successful in the following fields.

In the brewing industry, E-Tongue can be used to monitor batch-to-batch variation of the beers following the brewing process. E-Tongue allows product conformity testing, taste default detection, origin identification (Giese, 2001). The objective of the instrument is to complement the E-Nose and more important, allow the food and beverage industry to cover a large proportion of the sensory perception of consumers-in essence, covering both aroma/odour and taste (Tan et al., 2001). For orange juice and apple juice, E-Tongue will more typically measure the non-volatile components, including chemical molecules responsible for sweetness, bitterness, saltiness, and sourness (Tan et al., 2001). This instrument has also been used to detect off-flavour in beer, as in a pale ale lager containing too high a concentration of dimethyl sulfide (DMS), formed from a malt-derived precursor during wort production or by contaminant bacteria during fermentation (Tan et al., 2001). An extremely important taste attribute of beer is its bitterness. A range of beers has also been analyzed using E-Tongue. Result shows the good linearity of quantification of BU (Bitterness Unit) using PLS.

E-Tongue has also been used for the analysis of quality of high-fructose corn syrup to detect some taint compounds responsible for the off-flavours, such as fish taste/flavour formed by microbiological oxidation of protein residues and other taste/odour descriptors including fruity, astringent, SO2, salty, corn-caramel, and moldy. Bleibaum et al. (2001) tested a series of nine 100 % apple juices, including a three-apple blend, vitamin-C fortified apple/pear juice, and an apple cider using E- Tongue.

There are numerous fields in the food industry where E-Tongue may prove beneficial in food processing, with in principle and practice. Quality management is of utmost importance in the food industry, especially since the in guess of the good quality assurance Programme. Application of E-Tongue would allow the taste quality of a food to be monitored continuously from the raw material stage right through to final product. In recent years, E-Tongue finds food and beverage industry as the challenging environment for its routinely application in taste control and analysis.

E-Tongue-The Tomorrow

The technology is projected to save millions of rupees, as it becomes an integral part of industrial process quality control systems. Once established in the pharmaceutical industry, Scientists have planned to expand and apply the instrument to the clinical diagnostic market, developing equipment that will help physicians diagnose patients at the bedside. By saving time at the point of care, it will save lives. That's the whole point of commercializing this technology. In the epoch of miniaturization, scientists lead their concentration to develop E-Tongue of tomorrow based on a ‘single chip’. Scientists are thinking that medical diagnosis and food


quality amassment will be the most challenging application field for E-Tongue. It may be applied to solve some environmental problems such as analyzing hazardous wastes of factory and tape as well as ground water quality. The application of E-Tongue would include the inspection of quality of fish, meat and fermented products during household storage or commercial storage period.

Conclusion

For many years, assessment of sensory quality of food has been based upon the traditional method i.e. application of human senses. As new food processing lines are developed, computer control will became an increasingly important part of factory operation. Sensor technology has offered the food industry a new, rapid type of monitoring and measuring device for taste analysis of foods i.e., E-Tongue, whose speed, sensitivity stability and ease of use exceed the efficiency of human taster. The successful miniaturization of sensors would advance the capability of E-Tongue to monitor and analyze several taste analytes using ‘single chip’ instrument. In the food and beverage industry in the western countries, E-Tongue has evolved with a great deal of fanfare, which may change the scenario of the present food industry where the evaluation of the product is till relied upon human senses, such as smell, taste etc.

While the application of E-Tongue will no qualm present a radical revolution in quality control of foods providing the food industry with a great opportunity to exploit this novel technology, it will face a dual challenge involved in identifying and progressing the technology to capitalize on these. Some day is coming when you wouldn’t need to wait at the door of your panel member with a tray containing a cube of cheese for sensory evaluation, an E-Tongue fitted online would automatically analyse and document the product quality batch by batch or someday household refrigerator would automatically alert your brisk housewives that your Quarg cheese gets soured, which you put since last Deepawali.

References

Alpha MOS. (2001a). Astree electronic tongue user manual. Toulouse, France.

Alpha MOS. ( 2001b). Astree sensor technical note. Toulouse, France.

Alpha MOS. ( 2001c). Special newsletter “Basell Interview”. Toulouse, France.

Alpha MOS. (2000). FOX2000/3000/4000 Electronic Nose advanced manual. Toulouse, France.

Bleibaum, R.N., Stone, H., Isz, S, Labreche, S., Saint Martin, E., and Tan, T.T. (2001). Comparison of sensory and consumer results with Electronic Nose and Tongue sensors for apple juices. Submitted for publication (http//www.google.com/).

Giese. J. (2001). Electronic Tongues, Noses and much more. Food Technology, 55(5): 74-81


Kanawjia, S. K. and Makhal, S. (2007) E-Tongue: a device for sensory evaluation of foods. CAS Lecture Compendium: Computer applications in food and dairy processing, DT Div., NDRI, Karnal.

Madsen M. and Grypa, R. (2000). Spices flavour systems and the Electronic Nose. Food Technology, 54 (3): 44-46.

Riul, A. et al. (2002). Artificial taste sensor: efficient combination of sensors made from Langmuir-Blodgett films of conducting polymers and a ruthenium complex and self-assembled films of an azobenzene-containing polymer. Langmuir, 18: 239 – 245.

Tan, T.; Lucas, Q.; Moy, L; Gardner, J.W. and Bartlett, P.N. (1995). The Electronic Nose – A new instrument for sensing vapours. LC-GC 1NT, 8(4): 218-225.


Dr. G.K. Goyal

Principal Scientist Dairy Technology Division

NDRI, Karnal

1.0. Introduction

In modern times packaging has become an integral part of processing in the dairy industry. Package is the gateway to know a product. Packaging is also brand ambassador of a product. Packaging is technology of protecting products from the adverse effects of the environment. It is a medium for safe delivery of the products from the centre of production to the point of consumption.. A product is often identified by the package in which it is served (Goyal and Tanweer Alam, 2004). The package must ensure the same high quality of the product to the consumer. Packaging of products materially contributes to trade promotion and conserves valuable manpower and raw materials. The packaging industry is growing at a much higher rate in developing countries. Projected growth rate of demand and consumption for packaging in India is 10% to 12 % (Anon, 2005a).

2.0. Functions of Package

The packages mainly perform three functions viz. to contain, to protect and to inform / sell the product. It is essential to know the nature and composition of the product, its desired shelf-life under specified conditions of storage in terms of light, temperature, humidity, presence of oxygen, the types and causes of deterioration including mechanical stress, the product may undergo during handling and storage. The selected packaging materials for dairy products should have following properties:

• It should not impart its own odour to the product. • It should be inert to food and must be non- toxic. • It must protect from moisture, oxygen, and light • Convenient • Temper proof • Printable • Machinabe • Point of sale impact • Differentiability • Economic

ROLE OF PACKAGING MATERIALS IN ENHANCING

SENSORY QUALITY OF DAIRY PRODUCTS


The package not only protects the product but also gives information about the contents, storage conditions, methods of use, date of manufacture expiry date, price and nutritional consideratios. There are many more peculiarities, which could be identified under the following headings for, determining the packaging of dairy products.

(i) Product range (ii) Market (iii) Consumer needs

(iii) Operating margins

3.0. Traditional Milk Products:

It is estimated that nearly half of the total milk production in India is utilized for the manufacture of a range of traditional milk products viz., fat rich (ghee), heat desiccated (Khoa and Khoa based sweets, Rabri, Basundi, etc.), acid coagulated (Paneer, Chhana and Chhana based sweets), fermented (Dahi, Mishti dahi, Shrikhand), cereal based (Kheer, Payasam etc.) and frozen (Kulfi) products. Most of these products, except 10 –15% of total ghee production, are produced by unorganised sector (Halwais) using labour and energy intensive batch processes, resulting into large variations in their qualities. The shelf life of traditional dairy products is generally low and does not commensurate with the principles involved in their manufacture. One of the reasons for poor shelf life is either no packaging or inadequate packaging of traditional dairy products mainly post manufacturing, due to unhygienic conditions in production, packaging and storage areas. A number of surveys conducted on the market quality of indigenous milk products have revealed alarmingly high incidence of microbial contamination, besides large variations in chemical composition, flavour and texture. Most of the indigenous milk products have high water activity leading to rapid deterioration at ambient temperatures. Further, food products exposed to different environmental conditions without packaging get contaminated easily with moulds and bacteria. Improperly packaged foods undergo many flavour and textural changes during transportation and marketing. Lack of knowledge about the nature of food products and their compatibility with the packaging material may forfeit the purpose and lead to escalation of cost (Goyal and Gupta, 1989; Goyal and Rajorhia, 1991).

3.1. Milk Sweets:

It is common practice to keep the milk-based sweets in open metal trays. On demand, the items are weighed and placed in ordinary paper bags or kept on dhak leaves and given to the consumers. At the most, some halwais or shopkeepers wrap sweets in glassine or grease-proof paper and sell them in duplex board boxes. Also gulabjamun, which is kept soaked in sugar syrup, has no better packaging for local consumption, though it is canned for export purposes.


3.1.1.0. Khoa based sweets: Some of the khoa based sweets namely ‘peda’, ‘Carrot halwa’, ‘Kalakand’, ‘Burfi’, ‘Gulabjamun’, etc. are very common.

Table : Packaging trend of khoa based sweets (Tanweer Alam et al., 2005)

Burfi, Peda and Kalakand:

Amongst the several khoa-based sweets, burfi and peda occupy most dominating place in terms of popularity and market demand. These are mostly packaged in paper cartons or duplex board boxes with or without butter paper lining. The traditional packages do not provide sufficient protection to milk sweets from atmospheric contamination and unhygienic handling and thus susceptible to become dry, hard and mouldy and develop off flavours. Also the product packed in these wrappers / packages are not suitable for distant transportation and outstation retail sale or sale through super markets because they lack necessary mechanical and protective properties. Tin containers can be used but their cost is prohibitive. Only recently, some of the reputed manufacturers of these sweets have started packaging burfi and peda in HDPE/polypropylene boxes and cartons of 500g and 1 kg size. The modern flexible polyfilms and laminates offer alternate choice. The chemical composition of the sweet, the transportation hazards, and the period of storage under specified conditions of temperature and humidity are the major factors, which should largely decide the type of packaging materials. The common types of spoilage in burfi, peda and kalakand can be significantly delayed or altogether prevented by using flexible packages. (Pal, 2003; Tanweer Alam et al., 2005).

i) Prevention of body and texture defect:

Burfi contains moisture content ranging from 4.3 to 15.1%, while peda contains 4.2 to 22.3% moisture, and kalakand contains 16 to 28% moisture. At these moisture levels, the sweets have unique texture and typical chewing properties. For storage of burfi, an optimum RH of 70% is recommended. High RH and low RH make the product moist, pasty and hard, respectively. The choice may be from HDPE, PP, MXXT, polycel or other suitable combinations. This will prevent the ingress of

Product Packaging material

1. Peda Paperboard carton with paper lining, paper bags, dhak leaves, plastic box

2. Carrot halwa Paperboard carton, plastic box

3. Kalakand Paperboard carton, dhak leaves, plastic box

4. Burfi Paperboard carton, paper bags, dhak leaves, plastic box


moisture into the product and prevent the products becoming pasty under humid conditions.

ii) Prevention of rancid and oxidized flavours:

Khoa based sweets are quite rich in milk fat, and hence susceptible to rancidity and oxidative changes during storage. Proper packaging can play a key role in preventing the rancidity. Among the factors, which accelerate rancidity, light is most effective. Hence, this should be prevented by using packaging materials having reflecting pigments, denser films. Packaging materials which have very good oxygen barrier properties such as MST cellulose, MXXT, metallized polyester / poly, 5-layer co- extruded films, laminates having Al – foil are recommended for preventing oxidative deterioration. Vacuum packaging of the products also enhances the shelf life to a great extent.

(iii) Prevention of discolouration and absorption of foreign odours:

Burfi, peda and kalakand often lose their original colour and appearance during storage. Light induced oxidation may lead to loss of colour intensity. Maillard type browning – a common storage defect of milk sweets, is also accelerated by exposure to light and moisture. These fat rich dairy products quickly absorb foreign odours and rapidly lose their inherent delicate flavour. It is extremely important that these products are packed in such materials which can stop the two-way traffic of odours / gases in the products in order to preserve their original colour and flavour. Packaging material should also be grease resistant in order to minimise seepage of fat.

3.1.2.0. Channa based sweets:

Channa based sweets like sandesh, rasogolla, etc. are extremely popular in the eastern and north eastern regions of the country. Sandesh is generally packaged in paperboard cartons with a paper lining, ordinary paper bags and Dhak leaves. The rosogolla is packaged in tinplate cans, or in paperboards, Dhak leaves, Kulhads(earthen pots) etc. Canning of rosogolla is expensive and the other methods of packaging are unhygienic, inconvenient and unsuitable for outstation retail sales (Goyal and Rajorhia, 1991).

3.1.3.0. Gulabjamun and Rosogolla:

These sweets need to be saved from light, oxygen, ingress or egress of moisture, yeasts and moulds. Gulabjamun is a khoa based sweet while Rosogolla is prepared from chhana. The similarity between the two is based on their shape, texture and method of storage. Both are spherical in shape, spongy, porous and kept in sugar syrup. Their shape and porosity attributes are very critical and have to be maintained till the product reaches to the consumer. On an average, they contain about 40% moisture and 50% sugar. Fat content in Gulabjamun is more than Rosogolla. Yeast and mould growth is a more common problem associated with yeasty / fruity flavour


defects during storage in both the sweets. Since the body and texture of rosogolla is very delicate and it has to be preserved in sugar syrup, it is invariably packaged in lacquered tin cans of 500g and 1kg respectively. The proportion of rosogolla and syrup is kept 40:60 and product stays in good condition for more than 6 months at ambient conditions, because hot filling (at about 90ºC) technique is adopted. Gulabjamun is largely packaged without syrup in paper cartons or plastic boxes like burfi and peda. Though lacquered tin can is the most suitable packaging material for rosogolla and gulabjamun, but it is very expensive. Hence, there is a need to pack these products in composite cans made of plastic and laminated with a PP – Al foil material. (Pal, 2003; Goyal and Gupta, 1989).

3.1.4.0. Paneer:

Paneer is commonly packaged in PE bags. Recently, some organizations have started its vacuum packaging. In order to increase its shelf life significantly by employing the modified atmosphere packaging (MAP), the research work has been done at Dairy Technology Division, NDRI, Karnal.

3.1.5.0. Dahi and Yoghurt:

Dahi and yoghurt are mostly packed in PS cups, but they cause pollution besides not being health-friendly. Hence, efforts are on to switch the packaging of these products from PS cups to earthen pots.

3.1.6.0. Ghee:

Majority of the dairies pack ghee in lacquered or unlacquered tin cans of various capacities ranging from 250gm to 15kg. Tin cans protect the product well against tampering and during transportation to far off places without significant wastage. The most common and serious deterioration in ghee is the development of rancid flavour, caused by the formation of volatile compounds, which give unpleasant odour even in micro quantities. The modern packaging plays a vital role in delaying the onset of this defect. The packaging material should also possess good water vapour barrier properties. High-density polyethylene (HDPE) and polypropylene (PP) are known to have low water vapour transmission rates (WVTR), and are easily available and cheap. If such films are laminated to other suitable basic packaging materials, one can get almost negligible value for WVTR, which would be ideal. The package to be selected should show sufficient tensile strength, elongation, tear resistance and burst strength, besides overall good mechanical strength. The packaging of ghee can also be done in polymer coated cellophane, polyester, nylon – 6, or food grade PVC and their laminates. 3.1.7.0. Dried Milk Products:

Gulabjamun mix, kheer mix and kulfi mix:


Consumer packages for these products include: sachets and flexibles (having high barrier properties like metalized polyester etc) kept in cartons.

4.0 Conclusion

Due to appearance of Mall-culture, revolutionary changes are taking place at a very fast speed in packaging of food products. It is expected that new forms of packaging material such as roll wraps, pouches, cartons, PP – trays covered with transparent coloured films of MXXT or such other films are likely to appear on the market place for packaging of dairy products. Further, with a view to enhance the sensory quality vis-à-vis shelf life, thermal processing of certain milk products right in the packages is being successfully attempted.

Although, the country has made significant advances in the field of packaging material technology, the dairy packaging machineries have not been developed. Hence, there is alarming need that Dairy Engineers develop such packaging machines, which could be commercially used by medium sized milk sweet manufactures throughout the country.

5.0 Reference

Anon (2005a). Indian packaging sector, http//www. ciionline. org/news//htp

Anon (2005b). www.packagingindustry.com

Anon (2006).. Growth of Indian Dairy sector, http//www. ciionline. org/news//htp

Goyal, G.K. and Gupta, S.K. (1989). Packaging of dairy products – a review, Beverage & Food World, 16(1): 42-46

Goyal, G.K. and Rajorhia, G.S. (1991). Role of modern packaging in marketing of Indigenous dairy products. Indian Food Industry, 10(4): 32-34.

Pal, D. (2003). Packaging of traditional Indian dairy products: Present status and future prospects, compendium of lectures of 15th CAS course on ‘Advances in packaging of dairy and food products’ organized at NDRI, Karnal from 13th Feb. to 5th Mar 2003,pp 95-101.

Tanweer Alam, Goyal, G. K. and Broadway, A.A. (2005). Packaging trends in dairy Industry. In: Indian Dairy Industry, volume I, Published by Dr. Chawla Dairy Information Centre (P). Ltd, New Delhi, pp 180-185.


Dr. Latha Sabikhi Senior Scientist


1. Introduction

Consumers are the starting and end point of marketing management in any field. Food being an unavoidable commodity, consumer acceptance of food is a vital reality in food companies. The core of Consumer Acceptance Studies (CAS) pertains to the decision-making process of consumers with respect to food choice, as well as to the factors impacting on this decision-making process. Food companies analyse their consumer’s needs and wants, which are consequently translated into product specifications, product attributes, product development, price, promotion or communication and a specific retail or distribution format. Consumer acceptance of new technologies, novel products and the impact of personal characteristics on product acceptance is a specific field of study in current product management programmes. 2. What are Consumer Acceptance Studies?

Well-structured CAS in the food arena deals with the changes in food consumption in contemporary society and the developments of food practices in everyday life. Consumers not only make buying decisions but are also citizens living as a part of the whole food system. As citizen-consumers people interact in various ways with other persons in the food system, such as food producers, manufacturers, retailers, authorities and policy-makers. From this perspective, consumers are seen as part of the food system that takes shape and develops in the context of societal changes both nationally and internationally.

Eating is a complex activity of diverse developments relating to the social and individual aspects of eating, environmental and economic pressures, global and local inequalities in economic and social resources, technological developments in food production and the increasing concern about the healthiness of modern eating habits. Thus, modern CAS are generally divided into two areas, a) views on food quality and b) food production, consumption and food habits. The first of these covers studies relating to consumer aspects of developing the quality of foods, buying food, developing the responsibility in the food chain and advancing the consumer perspective in the use of health claims in food marketing. The second area focuses on changes in food practices now and in the future, paying particular attention to the

CONSUMER ACCEPTANCE STUDIES


environmental, social and cultural sustainability of food production and consumption. It also deals with the consumers’ understanding and practices of healthy eating.

3. CAS-Related Features in Food Science

The consumers’ perspectives, ideas and expectations concerning foods, food production and practices of eating must be improved before conducting food acceptance studies. Periodic training programmes are sometimes necessary to introduce the stakeholders to the different aspects of food tasting. Sensory properties such as flavour and texture play a major role in consumer perceptions of food product quality. The mouth-feel and other textural and sensory properties of food are an essential component of its perceived quality. The significance of textural properties has further increased with the trend towards low-fat, low-calorie and low-additive content products. Food structure is particularly important in baking, dairy and processed meat products. Traditionally, food structure has been improved by using ingredients such as emulsifiers or thickeners. However, consumers tend to take a rather negative view of many of these ingredients.

The goal of consumer studies is to assess the consumer’s and food industry’s perception towards novel technologies and ingredients. They may be presented with existing food technologies such as food additives, genetic modification, irradiation, vacuum packing, pasteurization, microwave ovens and canning, as well as technologies that are conventionally non-food-related as use of magnetic waves, rays and computer-aided evaluation programmes. The survey then asks participants to indicate, in their own words, which technologies concern them and why.

4. Consumer Attitude

Consumer attitudes are of profound importance when new technologies are developed and implemented into food production. Consumers are not usually aware of details of food production. However, they may form attitudes to certain food production technologies, including use of new ingredients, when they become confronted with information about it. These attitudes may prevent them from buying products where these technologies/ ingredients have been used. The way in which these skeptical attitudes affect the intentions to buy products produced using novel technologies and ingredients, particularly the possibility of a negative attitude towards to the production method with additional sensory benefits, is presently not well-understood.

There are reports that CAS has proved to be an asset for ingredient development work. While the attitudes towards the use of several novel ingredients in food production are fairly neutral, those towards use of genetic engineering in food production and ingredients produced by use of gene technology are more negative. Studies also revealed that the acceptance of the technology is closely linked to the relevance of the functionalities of the products as well as the cost vs. benefit analyses. Consumers often changed their attitudes towards new technologies/ products/


ingredients after actually tasting the product when compared to those who did not taste the products.

A study conducted at the University of Guelph (Canada) revealed that perceived risks and benefits is another important factor that influences how consumers receive new food technologies. Consumers are willing to take a risk if they receive greater benefits such as improved health, better quality or lower price. If the benefits outweigh the perceived risks, consumers are more likely to buy into the product

5. The Process

To initiate CAS, a group is identified as a sample of the entire group of potential consumers. The sample must be as close and as representative of the entire population as possible. A questionnaire or score card is formulated in accordance with the product/ process/ ingredient on which the CAS is conducted. Each member of the study group is given the product and the schedule of questions. If any special instructions are to be given on the manner of testing the product or filling the schedule, these are also handed out. A reasonable period of time is given to the consumers to test the product and fill answer the questions on the schedule. The filled schedules are collected after the stipulated time. This is a very important step, as uncollected questionnaires result in waste if efforts besides tarnishing the image and the integrity of the firm. The data collected is tabulated and subjected to appropriate statistical analysis. The interpretation is a key to the changes/ innovations to be introduced into the practice.

6. Conclusion

Consumer acceptance studies are currently the norm and practice in western countries. In the emerging countries, such studies involving the most vulnerable consumers on the lowest incomes are still relatively under-reported. Programmes seeking to introduce new products, and those who are involved in their promotion and marketing, must acquire knowledge about consumer acceptance and sensory testing in order to ensure these programmes are more effective.

7. Some Useful Websites

ftp://ftp.cordis.europa.eu/pub/food/docs/consumer-crossenz.pdf

http://www.kuluttajatutkimuskeskus.fi/index.phtml?l=en&s=150

www.uoguelph.ca/news/2005/08/study_delves_in.html


Dr. Sumit Arora Senior Scientist

Dairy Chemistry Division NDRI, Karnal - 132001

Cheese is the generic name for a group of fermented milk based food products. More than 500 varieties of cheeses are listed by the International Dairy Federation (IDF 1982), and numerous minor and/or local varieties also exist (Fox 1987). The flavor profiles of cheeses are complex and variety- and type-specific. This was realized back in the 1950s, when Mulder (1952) and Kosikowski and Mocquot (1958) proposed the “component balance” theory. According to this theory, cheese flavor is the result of the correct balance and concentration of a wide variety of volatile flavor compounds. According to Olson (1990) “There is a cheese for every taste-preference and a taste-preference for every cheese”.

Unlike many processed food products for which stability is the key criterion, cheese is a biochemically dynamic product and undergoes significant changes during its ripening period. Freshly-made curds of various cheese varieties have bland, and largely similar, flavours and it is during the ripening period that flavour compounds are produced which are characteristic of each variety. Originally, it was thought that cheese flavour resulted from a single compound or class of compounds. While this is largely true for blue-mould varieties (whose flavour is dominated by alkan-2- ones), it is now generally accepted that the flavour of most cheeses results from the combination of a large number of sapid compounds present in the correct ratios and concentrations (Bosset and Gauch 1993; Mulder 1952; Kosikowski and Mocquot 1958). The volatile flavor compounds in cheese originate from degradation of the major milk constituents; namely lactose, citrate, milk lipids, and milk proteins (collectively called caseins) during ripening which, depending on the variety, can be a few weeks to more than 2 years long.

Biochemical reactions during manufacture and ripening of cheese

Cheese ripening is a slow process, involving a concerted series of microbiological, biochemical and chemical reactions. The characteristic flavor, aroma, texture, and appearance of individual cheese varieties develop during ripening. These changes are predetermined by the manufacturing process: (a) composition, especially moisture, pH and salt, and (b) microflora, starter, and especially nonstarter microflora and adjunct starter (that is, microorganisms added to cheese milk for purposes other than acidification) (Gilles and Lawrence 1973). The ripening of cheese

CHEMISTRY OF FLAVOUR DEVELOPMENT IN CHEESE


involves 3 primary biochemical processes (Fig 1) processes— glycolysis, lipolysis, and proteolysis—the relative importance of which depends on the variety (Fox et al. 1994).

Fig 1. Biochemical pathways leading to the formation of flavour compounds (Marilley and Casey 2004)

These primary changes are followed and overlapped by a host of secondary catabolic changes, including deamination, decarboxylation and desulfurylation of amino acids, β-oxidation of fatty acids and even some synthetic changes; that is, esterification (Fox 1993). The above-mentioned primary reactions are mainly responsible for the basic textural changes that occur in cheese curd during ripening, and are also largely responsible for the basic flavor of cheese. However, the secondary transformations are mainly responsible for the finer aspects of cheese flavor and modify cheese texture. The compounds listed in Table 1 are present in most, if not all, cheese varieties. The concentration and proportions of these compounds are characteristic of the variety and are responsible for individuality. These complex biochemical changes occur through the catalytic action of the following agents:

• Coagulant

• Indigenous milk enzymes, especially proteinase, lipase, and phosphatases


• Starter bacteria and their enzymes

• Secondary microflora and their enzymes

The biochemistry of the primary events in cheese ripening is now fairly well characterized, but the secondary events are understood only in general terms.

(Singh et al. 2003)

Lactose and citrate

During Cheddar cheese manufacture, mesophilic starter bacteria ferment lactose to (mainly L+) lactic acid. In the case of Cheddar-type cheeses, most of the lactic acid is produced in the vat before salting and molding. Even after losing ~98% of the total milk lactose in the whey as lactose or lactate, the cheese curd still contains 0.8 to 1.5% lactose at the end of manufacture (Huffman and Kristoffersen 1984). The pH decreases after salting, presumably due to the action of starter, at S/M levels < 5.0%, but at high values of S/M, starter activity decreases abruptly (Fox et al. 1990) and the pH remains high. The quality grade assigned to the cheese also decreases sharply at S/M levels > 5.0% (Lawrence and Gilles 1982). Lactose is hydrolysed by starter cultures which produce glucose and galactose (galactose-6-P for lactococci). Glucose is then oxidised to pyruvate by the Emden-Meyerhof pathway of glycolysis. Galactose is converted by galactose-positive starter bacteria and leuconostocs through the Leloir pathway to glucose- 6-P and by lactococci through the tagatose pathway to glyceraldehyde-3-P (Cogan and Hill 1993). Pyruvate is a starting material for the formation of short-chain flavour compounds such as diacetyl, acetoin, acetate, acetaldehyde and ethanol (Cogan and Hill 1993; Escamilla-Hurtado et al. 1996; Henriksen and Nilsson 2001; Syu 2001; Melchiorsen et al. 2002).

Bovine milk contains relatively low levels of citrate (~8 mM). Approximately 90% of the citrate in milk is soluble and most is lost in the whey; however, the concentration of citrate in the aqueous phase of cheese is ~3 times that in whey (Fryer et al. 1970), presumably reflecting the concentration of colloidal citrate. Cheddar

Table 1: Flavour compounds generated from the 3 principle components during ripening of cheese


cheese contains 0.2 to 0.5% (w/w) citrate which is not metabolized by Lc. lactis ssp. lactis or ssp. cremoris, but is metabolized by Lc. lactis biovar diacetylactis and Leuconostoc spp, with the production of diacetyl and CO2. Due to CO2 production, citrate metabolism is responsible for the characteristic eyes in Dutch-type cheeses. Diacetyl and acetate produced from citrate contribute to the flavor of Dutch-type and Cheddar cheeses (Aston and Dulley 1982; Manning 1979a, 1979b). Citrate is metabolised to produce acetolactate, diacetyl and acetoin (Cogan and Hill 1993; de Figueroa et al. 2000, 2001). However, thermophilic starter bacteria are usually citrate-negative (Cogan and Hill, 1993). The principal flavor compounds produced from metabolism of citrate are acetate, diacetyl, acetoin, and 2, 3-butandiol (Cogan 1995). Diacetyl is usually produced in small amounts, but acetoin is generally produced in much higher concentration (10 to 50 fold higher than diacetyl concentration). Acetate is produced from citrate in equimolar concentrations.

Milk fat

Milk fat is an essential prerequisite to flavour development (Foda et al. 1974). As in all high-fat foods, lipids present in cheese can undergo oxidative or hydrolytic degradation. Because of the negative oxidation - reduction potential of cheese, oxidation of cheese lipids is probably limited; but the extent to which it occurs and its contribution (if any) to cheese flavour development has received little attention (Fox et al. 1982). Enzymatic hydrolysis of triglycerides to fatty acids and glycerol, mono- or diglycerides (lipolysis) is, however, essential to flavour development in many cheese varieties. Milk fat contains high concentrations of short - and intermediate-chain fatty acids which, when liberated by lipolysis, contribute directly to cheese flavour. The proportions of free C6:0 to C18:3 fatty acids in Cheddar cheese appear to be similar to those in milk fat, but free butyric acid (C4:0) occurs at a greater relative concentration in cheese than in milk fat, suggesting that butyrate is either selectively liberated by lipases present in Cheddar or that it is synthesized by the cheese microflora (Bills and Day 1964).

The specificity of the lipase also influences the development of cheese flavour, since short-chain fatty acids (which have the greatest flavour impact) are generally found at the sn-3 position of triglycerides. Cheese pH also influences the flavour impact of FFA, since carboxylic acids and their salts are perceived differently. Lipolysis is particularly extensive in hard Italian varieties, surface bacterially-ripened (smear) cheeses and blue mould cheeses, and is essential to correct flavour development in these cheeses. Extensive lipolysis is considered undesirable in many internal, bacterially-ripened varieties such as Cheddar, Gouda and Swiss cheeses; high levels of fatty acids in these cheeses lead to rancidity. However, low concentrations of FFA contribute to the flavour of these cheeses, particularly when they are correctly balanced with the products of proteolysis or other reactions (Rychlik et al. 1997; Bosset and Gauch 1993). Lipolysis of milk triglycerides releases high concentrations


of short- and intermediate-chain fatty acids (Bills and Day 1964). Short-chain fatty acids have a considerable flavour impact, but intensive lipolysis is undesirable in most cheese varieties because of the development of rancidity. Free fatty acids must be counter-balanced with other flavour compounds to develop an appreciated aroma (Bosset and Gauch 1993; Fox et al. 1995). Free fatty acids are substrates of enzymatic reactions yielding flavours. Oxidation and decarboxylation yield methyl ketones and secondary alcohols, and esterification of hydroxyl fatty acids produce lactones. Fatty acids react with alcohol groups to form esters, such as ethyl butanoate, ethyl hexanoate, ethyl acetate, ethyl octanoate, ethyl decanoate, and methyl hexanoate (McSweeney et al. 1997). Butyric acid concentrations found in cheeses are in part due to the hydrolytic activities of lipases (Dumont and Adda 1979; Fox et al. 1995).

γ and δ- lactones have been identified in cheeses, particularly in Cheddar, where they have been considered as important for flavor (Wong et al. 1973). Lactones are cyclic esters resulting from the intramolecular esterification of hydroxy acids through the loss of water to form a ring structure. They possess a strong aroma which, although not specifically cheese-like, may be important in the overall cheese flavor impact. The accepted mechanism of formation of lactones in cheese presumes the release of hydroxy fatty acids, which are normal constituents of milk fat, followed by lactonization.

Milk Protein

For the development of an acceptable cheese flavor, a well-balanced breakdown of the curd protein (that is, casein) into small peptides and amino acids is necessary (Thomas and Pritchard 1987; Visser 1993). These products of proteolysis themselves are known to contribute to flavor (Cliffe et al. 1993; Engels and Visser 1994) or act as precursors of flavor components during the actual formation of cheese flavor. During the manufacture and ripening of Cheddar cheese, a gradual decomposition of caseins occurs due to the combined action of various proteolytic enzymes. These generally include enzymes from the coagulant, milk, starter and nonstarter lactic acid bacteria, and secondary starter. Proteolysis directly contributes to cheese flavours by releasing peptides and amino acids. The correct pattern of proteolysis is generally considered to be a prerequisite for the development of the correct flavor of Cheddar cheese. Products of proteolysis per se (that is, peptides and free amino acids) probably are significant in cheese taste, at least to “background” flavor and some off-flavors, for example, bitterness, but are unlikely to contribute much to aroma. Compounds arising from the catabolism of free amino acids contribute directly to cheese taste and aroma. The total amount and composition of the amino acid mixture in cheese has long been used as an index of cheese ripening (Fox et al. 1995b). Amino acids are substrates for transamination, dehydrogenation, decarboxylation and reduction, producing a wide variety of flavour compounds such as phenylacetic acid, phenethanol, p-cresol, methane thiol, dimethyl disulphide, 3-


methyl butyrate, 3-methyl butanal, 3- methyl butanol, 3-methyl-2-butanone, 2-methyl propionate, 2-methyl-1-propanal, 2-methyl butyrate and 2-methyl butanal.

In lactococci, the 1st step in the degradation of amino acids is transamination (Gao and others 1997), leading to formation of α-keto acids (α -KA). Aromatic aminotransferase enzymes have been previously characterized from Lactococcus lactis subsp cremoris (Rijnen et al. 1999a) and Lactococcus lactis subsp lactis (Gao and Steele 1998). These enzymes initiated the degradation of Val, Leu, Ile, Phe, Tyr, Trp, and Met, all of which are known precursors of cheese flavor compounds. Inactivation of aminotransferase enzymes involved in the breakdown of amino acids by lactococci has been shown to reduce aroma formation during cheese ripening (Rijnen et al. 1999b).

Ney (1981) reported α -keto acids corresponding to almost every amino acid in Cheddar cheese. α -keto-3-methyl butyric acid and α -keto-3-methyl valeric acid (Ney and Wirotma 1978) were shown to have an intense cheese-like odor. The volatile fraction of cheese has several sulfur-containing compounds such as methanethiol, methional, dimethyl sulfide, dimethyldisulfide, dimethyltrisulfide, dimethyltetrasulfide, carbonyl sulfide, and hydrogen sulfide (Urbach 1995; Weimer et al. 1999), and they contribute to the aroma of cheese (Milo and Reineccius 1997). Methanethiol has been associated with desirable Cheddar-type sulfur notes in good quality Cheddar cheese (Manning and More 1979; Price and Manning 1983). However, alone or in excess, methanethiol does not produce typical Cheddar cheese flavor (Weimer et al. 1999). The discussion shows that amino acid degradation plays a vital role in flavor development in Cheddar cheese. The final products of proteolysis are FAA, the concentrations of which depend on the cheese variety, and which have been used as indices of ripening (McSweeney and Fox 1997; Puchades 1989). The concentration of free amino acids (FAA) in cheese at any stage of ripening is the net result of the liberation of amino acids from casein and their transformation to catabolic products. The principal amino acids in Cheddar cheese are Glu, Leu, Arg, Lys, Phe and Ser (Wijesundera et al. 1998). Concentrations of amino acids generally increase during ripening, with the exception of Arg, the concentration of which is reported to decrease later in ripening (Puchades et al. 1989). The level of peptides and FAA soluble in cheese in 5% phosphotungstic acid (PTA) has been considered to be a reliable indicator of the rate of flavour development (Aston and Douglas 1983) and the composition of the amino acid fraction and the 309 relative proportions of individual amino acids are thought to be important for the development of the characteristic flavour (Broome 1990). However, the relative proportion of individual amino acids appears to be similar in many varieties, and increasing the concentration of FAA in cheese does not accelerate ripening or flavour intensity. Medium and small peptides and FAA contribute to the background flavour of most cheese varieties (Urbach 1995) and some individual peptides have ‘brothy’, ‘bitter’, ‘nutty’ and ‘sweet’ tastes. Fox and Wallace (1997) have suggested that flavour and the concentration of FAA could not be correlated, since different cheeses (e.g., Cheddar,


Gouda and Edam) have very different flavours, although the concentration and relative proportions of FAA are generally similar.

Bitterness and other off-flavours

Bitterness in cheese is due mainly to hydrophobic peptides and is generally regarded as a defect, although bitter notes may contribute to the desirable flavour of mature cheese. Certain sequences in the caseins are particularly hydrophobic and, when excised by proteinases, can lead to bitterness. Low-fat cheeses have been reported to develop bitterness (Banks et al. 1992), although in full-fat cheese, a certain proportion of bitter peptides, being hydrophobic, are less likely to be perceived as being bitter, perhaps due to their partition into the fat phase. In addition to peptides, a number of other compounds can contribute to bitterness in cheese, including amino acids, amines, amides, substituted amides, long-chain ketones and some monoglycerides (Adda et al. 1982). The origin of ‘unclean’ and related flavours in Cheddar has been attributed to a number of Strecker-type compounds (Dunn and Lindsay 1985) including phenylacetaldehyde, phenylethanol, 3-methylbutanol, 2-methylpropanol, phenol, and p-cresol. Off-flavours (rancidity) can be due to excessive or unbalanced lipolysis caused by lipases/esterases from starter or non-starter lactic acid bacteria, enzymes from psychrotrophs in the cheese milk, or indigenous milk lipoprotein lipase.

References

Adda, J., Gripon, J.C. and Vassal, L. 1982. The chemistry of flavour and texture generation in cheese, Food Chem. 9:115–129.

Aston, J.W. and Dulley, J.R. 1982. Cheddar cheese flavor. Aust J Dairy Technol 37:59-64.

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Dr. Rajesh Kumar, Dr. R.B. Sangwan and Dr. Bimlesh Mann Dairy Chemistry Division

NDRI, Karnal

Introduction:

It is not uncommon for volatile and semi volatile organic molecules in ppb (parts per billion) or ppt (parts per trillion) concentration to cause off-flavours (OF). Today, sophisticated and sensitive analytical tests are capable of detecting, identifying and quantitating the specific chemical agents responsible for off-flavours. Once specific causes for off-flavours have been identified, dairy scientists can usually delineate their mechanism of formation (e.g., microbial spoilage, overheating, oxidation, photodegradation, sanitizer contamination, etc.) and take steps to reduce off-flavours. Furthermore, new analytical techniques are so powerful that they can often accomplish this with speed, accuracy and reliability which is not possible using sensory analysis alone. Combining the benefits of modern analytical testing, particularly gas chromatography with mass spectrometry detection (GC-MS), with sensory analysis results in a powerful tool for off- flavours elucidation.

Instrumental analysis

GC is a form of partition chromatography in which the separation takes place between the stationary phase (a film coated on a solid support) and the mobile phase (a carrier gas) flowing over the surface of the film in a controlled fashion. Because of their superior separation efficiency and versatility, GC methods are the most commonly used analytical techniques in flavor research. GC has tremendous separating power, sometimes in excess of 200,000 theoretical plates per column. This attribute is essential for the separation of complex flavour isolates. Using mass spectrometry as the detector for GC analysis, allows for identification of chromatographic peaks that elute from the column.

Mass spectrometry (MS) is a form of spectroscopy in which the molecule is exposed to high-energy electrons and through a sequence of steps is broken down into unique charged molecular fragments. The uniqueness of this process allows the method to be used for identification/confirmation of an unknown compound with a sensitivity of 10-100pg. MS is generally used in the flavour area either to determine the identity of an unknown or to act as a mass selective GC detector. GC-MS is an analytical technique used to identify/ confirm the identity of compounds as they elute from the GC column and has proven to be one of the most useful analytical techniques for studying volatile and semi volatile odour active chemicals in dairy products. The volatile and semi volatile compounds, in the headspace, are of interest

ANALYTICAL TECHNIQUES FOR CHARACTERIZATION OF

FLAVOURING COMPOUNDS IN DAIRY PRODUCTS


because they can travel to the nose during eating and stimulate the olfactometry receptors in the nasal cavity.

Mass spectrophotometers may be classed as low resolution (LR) or high resolution (HR) instruments. The LR instruments provide mass measurements to the closest whole mass unit, but do not provide elemental composition. High resolution instruments provide sufficiently accurate mass measurements to permit determination of elemental composition.

In addition to MS detectors, flavor chemists sometimes employ extremely sensitive detectors for specific classes of compounds. One example is the pulsed-flame photometric detector (PFPD) for sub-ppb measurement of organic sulfur compounds, chemicals that often have extremely low odour threshold detection levels.

The determination of the chemical(s) responsible for an off-flavour in a sample usually involves three steps: preparing the sample for analysis, injecting the sample (or usually an extract of the sample) into the GC-MS and data processing. In addition, many analytical systems are now used by flavor chemists to incorporate an olfactometry detector. With this method, the effluent that elutes from the end of the analytical GC column is split, with a portion of the flow going to the MS detector and a portion going to an olfactometry detector (OD), which is often referred to as a sniff port. While some of the sensitivity of the MS detector is lost, an important advantage is gained: The analyst can sniff each peak as it elutes from the column and determine its odour characteristics. By using GC-MS-OD, the flavor chemist is able to determine the identity, concentration, odour characteristics and odour intensity of each chromatographic peak.

Sample Preparation: A Key Step in Chemical Analysis of Dairy Foods

It is usually not possible to directly inject a food sample into a GC without performing some sample preparation. Proteins, fats, complex carbohydrates and other nonvolatile chemicals will degrade in the heated GC injector, resulting in the formation of numerous artifact peaks that can degrade column performance and obscure peaks of interest. Separating volatile compounds from matrix interferences and concentrating volatiles (which can be present in concentrations as low as 10-8 to 10-14%), so that they can be detected, usually requires sample preparation involving volatile isolation and concentration steps. Unfortunately, there is no single perfect sample preparation technique for flavor research. The aroma volatiles in food samples can be heterogeneous, covering a wide range of polarities, solubilities, functional groups, vapor pressures, concentrations and volatilities. Other complications include instability of aroma volatiles to certain conditions (oxygen, light, heat, pH, etc.) and the possibility that aroma volatiles may interact with chemicals in the food matrix. It is important that the extraction technique does not introduce or create volatiles that are not in the dairy product being tested. For example, sample preparation techniques that involve heating the sample to high temperatures (e.g., steam distillation) can generate artifact peaks in sample chromatograms, and these odoriferous artifacts may


be misinterpreted as the cause of the OF problem in the product. In some cases, more than one procedure may be required for optimum recovery of flavor compounds. Dairy chemists now have a wide variety of sample preparation techniques that they can use for isolating and concentrating odour-active chemicals prior to GC analysis. Frequently used sample preparation methods for flavor analysis include vacuum distillation, simultaneous steam distillation/extraction (also referred to as the Liken and Nickerson extraction procedure), static headspace, dynamic headspace and solid-phase microextraction (SPME). Some of the more popular sample preparation techniques for flavor analysis are discussed below.

(a) Solvent extraction and distillation:

Solvent extraction commonly involves the use of pentane, dichloromethane, diethyl ether or some other volatile organic solvent. This limits the method to the isolation of fat-free foods unless an additional procedure is employed to separate the extracted fat.

W. Engel et al. (1999) developed a new distillation unit, called solvent assisted flavor evaporation (SAFE), for the extraction of flavor volatiles from complex aqueous matrices, such as beer, fruit juices, milk and cheese. The distillation vessel and “transfer tubes” are thermostated at low temperatures (20°-30°C) to avoid condensation of compounds with high boiling points, and the sample is added by dropping aliquots from the funnel into the vessel to reduce time of extraction. This new method allows for the use of solvents other than diethyl ether and dichloromethane, and it could be used for extracts containing large concentrations of fat. Another advantage of the SAFE technique is that recovery of really authentic flavor—i.e., a flavor sample with organoleptic properties as close as possible to the natural product—is possible. Solvent extraction methods have disadvantages. Large volumes of solvent must be evaporated while retaining the volatile flavor components. Another problem is that sample preparation is time consuming; only one or two samples can be extracted per day.

(b) Headspace techniques:

Static headspace:

If a complex material, such as milk, yoghurt or cheese, is placed in a sealed vessel, some of the more volatile compounds in the sample matrix will leave the sample and pass into the headspace around it. If the concentration of the volatile compound reaches about 1 ppm in the headspace, it may be assayed by a simple injection of an aliquot in the vessel. How much compound enters the headspace depends on several factors, including its concentration in the original sample, the volatility of the chemical, the solubility of the chemical in the sample matrix, the temperature of the vessel and how long the sample has been inside the vessel. In practice, the food sample is placed into a headspace vial, sealed and warmed to enhance vaporization of the volatiles and incubated for a period of time to establish equilibrium at the incubation temperature. Once the volatiles have equilibrated, an


aliquot of the headspace gases is withdrawn with a syringe and injected into the GC. As an alternative, the equilibrated headspace may also be allowed to pass through a sample loop of known volume, which is subsequently flushed into the injection port. Static headspace methods eliminate the large solvent peak, which may obscure important odour-active analytes. Static headspace is a relatively rapid technique that is easily automated, making it attractive for sample screening applications. The combination of careful monitoring of temperature and equilibrium time, pressure control of the sample loop and automatic injection provides increased reproducibility over manual attempts at headspace analysis and reduces labour costs. Additional advantages include low cost per analysis, simple sample preparation and the elimination of reagents. Relatively poor sensitivity compared to other types of sample preparation techniques is a disadvantage of static headspace method. The maximum temperature for most food products is less than the boiling point of water. Analysis at this fairly low temperature limits the usefulness of the technique for analytes with boiling points over approximately 130°C. Many materials that may be extracted with solvents may elute well at higher GC column temperatures but will be poorly represented in a static headspace chromatogram. Also, reproducibility depends on analyzing a sample after it has reached equilibration, and the time required to achieve this point may, especially for less volatile compounds, be a drawback for some analyses.

Dynamic headspace:

With dynamic headspace techniques, the food sample, which is normally heated to 40° - 60°C, is purged with helium gas. Instead of allowing the sample volatiles to come to equilibrium, the atmosphere around the sample material is constantly swept away by a flow of carrier gas, taking the volatile analytes with it. The volatiles that are swept away are directed to a trap (commonly Tenax), where they are collected and stored until the end of the purging cycle is reached and the trap is ready to be desorbed onto the GC column. By removing the volatiles in a continuous fashion, more molecules of the volatiles in the sample are collected for analysis, greatly improving the sensitivity of the test. (Note: In general, the term “purge-and-trap” is used to refer to liquid samples analyzed by bubbling the carrier gas through the liquid, while “dynamic headspace” is used when the sample material is a solid.)

Dynamic headspace is significantly more sensitive than static headspace. Compared to solvent extraction techniques, it offers the advantages of no solvent to evaporate, no interfering solvent peaks in chromatograms and relatively simple automated sample preparation. The disadvantages include more complicated instrumentation. Instrumentation must monitor several steps, valving, heated zones, etc. Instrumentation is more expensive than static headspace instrumentation. Because of complex functioning of the instrument, there are many opportunities for malfunction, including heater damage, valve leaking, contamination and cold spots. Compared to static headspace, dynamic headspace techniques require a little more


time per sample (for purging, trap drying and trap transfer, all of which typically require approximately 15 min). However, the technique is much faster than most solvent extraction techniques.

(c) Solid-phase microextraction (SPME):

SPME uses a short, thin, solid rod of fused silica (typically 1 cm in length with an outer diameter of 0.11 mm) coated with an absorbent/adsorbent polymer. The coated fused silica (the SPME fiber) is attached to a metal rod, and both are protected by a metal sheath that covers the fiber when it is not in use. The assembly is placed in a fiber holder. The system is a modified syringe. Two sampling methods can be used with SPME depending on the placement of the fiber relative to the sample—immersion or headspace sampling. For dairy products, which contain high levels of fat, carbohydrate and protein, the headspace technique is preferred. In SPME headspace analysis, a fiber is placed in the headspace above the sample. For example, when analyzing volatiles in a milk sample, 3 mL of milk can be placed in a 9 mL glass GC vial containing a small stirring bar and sealed with a septum closure. The sample is then heated (e.g., to 50°C). The fiber is then exposed to the headspace gases for 10 - 30 min, depending on the sample matrix and the analytes of interest. After sample exposure time has elapsed, the fiber is retracted into the needle assembly and removed. The extracted volatiles are thermally desorbed from the fiber in the heated GC injector and transferred to the GC column for separation and analysis. Several types of fibers with varying affinities for specific classes of compounds are available.

SPME is particularly well-suited to the analysis of dairy products. The technique is capable of extracting a broader range of analytes than is possible with other headspace techniques. For example, SPME is capable of ppb detection levels for both low molecular weight, highly volatile compounds like acetaldehyde, dimethyl sulfide, acetone and 1,3-pentadiene, as well as high molecular weight, high-boiling-point compounds like vanillin, lactones and dodecyl aldehyde. Furthermore, it can be used for quantitating free fatty acids (C4 through C14) in dairy products. This important class of flavor compounds can be particularly challenging and time consuming to extract by other techniques.

Incorporating the nose in chemical analysis:

The application of new and improved volatile extraction techniques prior to GC-MS in conjunction with modern, sensitive bench top GC-MS instruments often results in dairy sample chromatograms with 100 or more peaks. Unfortunately, the relevance of each peak to a sample’s flavour or OF is not easy to evaluate. One of the major problems in aroma research is to select those compounds that significantly contribute to the aroma of a food. In general, the aroma of a food consists of many volatile compounds, only a few of which are relevant to odour and flavour. A first essential step in aroma analysis is the distinction of the more potent odorants from volatiles having low or no aroma activity. GC in combination with olfactometric techniques (GC-O) is a valuable method for the selection of aroma-active components


from a complex mixture. GC-O is a way for flavor chemists to incorporate the sense of smell into their chemical analysis.

GC-O is now accepted as one of the most powerful ways to give sensory meaning to the long lists of volatiles appearing in sample chromatograms. GC-O consists of experiments based on human subjects sniffing GC effluents. Experience shows that many key aroma compounds occur at very low concentrations; their sensory relevance is due to low odor thresholds. Thus, the peak profile obtained by GC does not necessarily reflect the aroma profile of the food—that is, sometimes the largest chromatographic peaks in a food extract have the least amount of aroma impact on the food, while the smallest peaks may have the most significant impact. In general, it is very difficult to judge the sensory relevance of volatiles from a single GC-O run. Several techniques are in use to help with this problem. This is based on successive dilutions and GC-injection of a flavor extract, until the assessor no longer detects the odour at the sniffing port. For each GC-elution, the assessor presses a button during the perception of odours to generate individual olfactograms (or aromagrams) made of a series of square signals. After data treatment, a computer-generated global olfactogram assigns greater importance to odour peaks that are smelled in the highest dilution of the extract.

Conclusion:

The advent of new, sensitive and rapid analytical methods in conjunction with olfactometry techniques and traditional sensory taste paneling approaches have greatly improved the understanding of flavour-impact chemicals in dairy products. By working together, sensory scientists and analytical flavour chemists can help the dairy industry to determine and correct the causes of off-flavours in dairy foods. This will assist in reducing waste and customer complaints and help processors develop ways to increase shelf-life of dairy products.

References:

Chaintreau, A. Quantitative Use of Gas Chromatography- Olfactometry: The GC-“SNIF” Method. In Flavor, Fragrance and Odor Analysis, Marsili, R.T. (ed.), New York: Marcel Dekker. pp. 333-348 (2002).

Engel, W., Bahr, W. and Schieberle, P. Solvent-assisted flavor evaporation—a new and versatile technique for the careful and direct isolation of aroma compounds from complex food matrices. Eur. Res. Technol. 1999; 209:237-241.

Marsili, R.T. Comparison of solid-phase microextraction and dynamic headspace methods for GC-MS analysis of light-induced lipid oxidation products in milk, J. of Chrom. Sci. 1999; 37:17-23.

Marsili, R.T. Flavours and off-flavours in dairy foods. In Encyclopedia of Dairy Sciences, Roginski, H., Fuquay, J.W. and Fox, P.F. (eds.),London: Academic Press. pp. 1069-1081 (2003).


Werkhoff, P., Brennecke, S., Bretschneider, W. and Bertram, H.J. Modern methods for isolating and quantifying volatile flavor and fragrance compounds. In Flavor, Fragrance and Odor Analysis, Marsili, R.T. (ed.), New York: Marcel Dekker. pp. 139-204 (2002).


SENSORY ATTRIBUTES OF MILK PROTEINS

Dr. Vijay Kumar Gupta Principal Scientist

Dairy Technology Division N.D.R.I., Karnal-132 001

1. INTRODUCTION Edible casein, caseinates as salts of sodium, calcium, potassium, magnesium etc., whey protein concentrates (30-80% protein), coprecipitates and protein hydrolysates are the major milk protein products. They are used in bakery products, meat products, confectionery items, beverages and in a wide variety of formulated foods and animal feed products. They are also increasingly being used in dietary preparations and pharmaceutical and medical applications. Most of the applications of milk protein products require them to be neutral or bland in taste and smell, colourless and free from extraneous matter. However, a lot more attention has been given on the flavour aspects of these products in the literature than on appearance, as flavour is considered the most important sensory quality. 2. STANDARDS FOR SENSORY ATTRIBUTES OF MILK

PROTEIN PRODUCTS Among the different protein products, only edible casein has been assigned national and international standards, particularly with respect to sensory qualities. As per BIS standards (IS:1167-1965), casein shall be nearly white or pale cream in colour and shall have no undesirable odour or any foreign matter; it shall be free from any added colour. The size of the particles shall be such that 100% by weight of casein shall pass through 500-micron IS sieve. As per international standards (FIL - IDF 45:1969), flavour and odour of acid precipitated edible casein must be neutral, free from offensive flavours, taste and odours such as sour, cheese or metallic off-flavours. Colour of the product should be white to pale cream. If ground, it should be free from lumps that do not break up under slight pressure. The maximum sediment (scorched particles) allowed is 22.5 mg in 25 g spray dried and 32.5 mg in 10 g roller dried product. The casein should not contain any foreign matter such as particles of wood, metal, hairs or fragments of insects. European Community standards (No. L237/29) are more or less similar to international standards in respect of sensory attributes. 3. FLAVOUR OF MILK PROTEIN PRODUCTS Freedom from flavour defects is very important in many of the applications of milk proteins as food ingredients. In industrial practice, fresh casein, caseinates, coprecipitates and whey proteins are usually bland in flavour. On storage, milk proteins tend to develop unpleasant flavours variously described as gluey, stale, burnt-feather or musty. Good progress has been made in research into the origins of


the flavours. Ramshaw & Dunstone (1969a, b) found a large range of volatile components in the steam distillate of gluey casein. Their findings suggested that the flavour resulted from a mixture of compounds with some synergistic effect from o-aminoacetophenone, a compound of low volatility possibly arising from breakdown of tryptophan. The type of compounds which seemed significant in the flavour spectrum and their experiments on manufacture of casein and coprecipitates indicated that non-enzymatic browning reactions were involved in the off flavour development. It appeared that reducing substances produced by this reaction subsequently degraded to flavour components. Ramshaw & Dunstone (1970) reported trials in which dispersions of milk proteins were heated to encourage this degradation so that the volatile flavour components could be removed during spray drying. By using browning inhibitors (1970b), they also obtained improved flavour stability of low-Calcium-precipitate, where the longer heating time for the milk can initiate browning reactions. Industry has sought to obtain the best flavoured product by such techniques as reducing the lactose content by thorough washing and avoiding excessive heating at any stage of manufacture so as to minimise browning reactions. The manufacture of caseinate from fresh wet curd and minimizing its storage time before use also helps in obtaining the best flavour. However, On the basis of comparing ferricyanide reducing values with flavour of low lactose casein, Walker (1970) concluded that the browning reaction did not contribute significantly to development of musty off-flavour. Sharma & Hansen (1970) linked development of gluey flavour on heating casein with breaking of ester phosphate bonds. Ramshaw & Leary (1970) found that UV treatment of casein gave unpleasant odours as well as gluey flavour. The treatment appeared to accelerate degradation of tryptophan but not the browning reaction. Table 1 gives the threshold concentration of gluey flavour in treated and untreated casein. Table 1. Threshold concentration of gluey flavour in treated and untreated casein ___________________________________________________________________ Materials and/or treatment Threshold Remarks (%) concentration ___________________________________________________________________ Gluey casein 0.3 Control Gluey sodium caseinate 0.3 Control Fresh freeze-dried casein or sodium caseinate >3.0 Control Vacuum-treated casein or caseinate 0.3 Flavour not removed Steam-distilled sodium caseinate >1.0 Distillate gluey Freeze-dried sodium caseinate (1, 5, 10%) 0.3 Flavour not removed Reprecipitated casein removed 1.0 Flavour partially Filtrate from reprecipitation 1.0 Filtrate gluey Washed casein r emoved >1.0 Flavour partially Wash water 1.0 Wash water gluey Activated carbon-treated casein >3.0 Flavour removed Sephadex G 25-treated casein >3.0 Flavour removed _______________________________________________________________________________

*Concentration at which gluey flavour was first detectable


The flavour of coprecipitates tends to follow a similar pattern to that of the caseins, high calcium coprecipitates being more stable than low calcium (acid) coprecipitates and fresh-curd soluble coprecipitates being better than those reconstituted from dry, granular, insoluble coprecipitates (Southward and Goldman, 1978) The coprecipitates, as a class, may also tend to exhibit 'cooked flavour' overtones as a result of the high heat treatment given to the milk during their manufacture (Southward, 1985). Particle size of granular caseins and coprecipitates also appears to affect their flavour; finely ground product tend to exhibit stronger off-flavours than coarser fractions. Whey protein concentrates develop a typically stale off-flavour during storage due to a set of complex, inter-related chemical reactions which include lipid oxidation and Maillard browning. There is no information in the literature on the volatile organic compounds responsible for off-flavour in whey protein concentrates (Morr and Ha, 1991). Important possible off-flavours in milk protein products are listed in Fig. 1.

4. METHODOLOGY FOR THE EVALUATION OF THE FLAVOUR OF

MILK PROTEIN PRODUCTS A method was developed at New Zealand Dairy Research Institute that has been widely used to assess the flavour characteristics of protein products. Sodium or calcium caseinate is dissolved in water at 60°C, using mechanical stirring, to produce 10% (w/v) caseinate solutions. Acid casein is treated similarly except that sodium hydroxide solution is carefully added to dissolve the casein to produce solutions of sodium caseinate at pH 6.7. Rennet casein is dissolved with sodium tripolyphosphate (5% w/w of casein) to produce solutions of pH 7-8. Because the viscosity of a rennet casein solution is greater than that of a sodium caseinate solution, the concentration of the rennet casein is usually reduced to 8% (2/v) but, for calcium caseinate, which is less viscous than sodium caseinate, the concentration is not increased correspondingly. The coded samples to be tasted (all of one type, such as acid casein, or sodium caseinates, etc.) are presented in random order to each taster at a temperature of about 40°C. Marked and coded 'good' and 'bad' control samples are included. Water and dry bread are used between each sample to remove dry lingering impression from the mouth. A typical flavour evaluation score sheet, as used for all casein products, is shown in Fig. 1. To assist the taster in describing off-flavours, the score sheet includes a list of suggested serius and non-serius off flavour descriptions. The taster is asked to give an overall score (scale 0-8, where 8 = excellent, 6 = good, 3 = poor, 0 = extremely objectionable) to each sample based on the type and intensity of off-flavour. A guide for relating the type and intensity of off-flavour to the overall score is also given ; serious off-flavours absent, 8; threshold, 7; slight, 5 etc. Mean values and


FLAVOUR EVALUATION OF MILK PROTEIN PRODUCTS

Evaluator_______________ Date_________________ Product__________________ Desirable Flavour : Bland Flavour Suggested off-flavours : Serious Non-serious

Astringent (Ast) Acidic (Ac) Bitter (Bit) Caramel (Car) Puckery (Puc) Cereal (Cer) Burnt (cooked) (But) Milky (Mlk) Card board (Cbd) Nutty (Nut) Fishy (Fsh) Sweet (Swt) Metallic (Met) Mouldy (Mol) Guide for overall Score (8-0) Gluey (Glu)

Putrid (Put) Off Flavour intensity

Serious off- flavour

Non-serious off-flavour

Rancid (Ran) Absent (Abs) 8 8 Salty (Sal) Threshold (Thr) 7 7 Soapy (Spy) Slight (Sl) 5 6 Stale (St) Moderate (Mod) 3 5 Storage (Sto) Strong (Str) 1 3 Whey (Wh.)

Note:- Type of off flavour may be described by using abbreviated terms

Sample No.

Serious off-flavour Non-serious off-flavour Over all Score

Off Flavour Intensity Off Flavour Intensity

1

2

3

4

5

Time of Evaluation_____________AM/PM Signature of the Evaluator

Fig. 1. Score-card for flavour evaluation of milk protein products


standard deviations for overall score and intensity of off-flavour are computed in the usual way. For the purpose of summarizing the information, off flavour intensity is converted into a score: 1 = absent, 2 = threshold, 3 = slight, 4 = moderate, 5 = strong. The use of a large panel of trained tasters and the inclusion of reference points for both ends of the scoring range (control samples) provide a reasonably reliable estimation of the flavour quality of any casein sample. Mean panel scores at the bottom (0-3) and top (6-8) of the range are normally more reliable than those in the middle (4-5) where standard deviations of greater that 1 are not uncommon. In general, however, the method has been a valuable tool for placing casein products into various flavour categories prior to selecting them for use in foods. 5. APPEARANCE OF MILK-PROTEIN PRODUCTS Granular casein should be of uniform particle size prescribed in standards, or of commercially desired mesh sizes like 30, 60 and 90. Desirable colour of casein is white to pale cream; however, buffalo milk casein has natural greenish tinge. Browning and other discolourations are the colour defects. Caseinates, whey protein concentrates and coprecipitate powders should possess almost similar colour as the casein. Whey protein concentrates, coprecipitates and sodium, potassium and ammonium caseinates make translucent, viscous, straw-coloured solutions, while calcium caseinate forms micelles in water, producing an intensely white, opaque, 'milky' solution of relatively low viscosity. 6. PROTEIN HYDROLYSATES The production of protein hydrolysates provides an opportunity for the dietary management of persons suffering from digestive disorders as a result of pancreatic malfunction, pre-and post operative abdominal surgical patients, patient on geriatric and convalescent feeding, and others who for various reasons are not able to ingest a normal diet. However, enzymatic hydrolysis of protein has frequently been shown to give bitter taste to digests due to liberation of bitter tasting peptides or amino acids. In aqueous solution, hydrophilic or polar groups of casein are on the outer surface and hydrophobic groups are packed inside the molecule. Enzymatic digestion exposes the peptide moieties which contain large amount of hydrophobic amino acids which on contact with the taste buds give a sensation of bitterness. Khanna (1991) used a 9-point Hedonic scale for comparison of sensory quality of casein hydrolysates adjusted to 10% T.S. concentration. For sensory evaluation of bitterness of casein hydrolysates, Khanna (1991) used 4- point scale, where 1 = extremely bitter, 2 = distinctly bitter, 3 = slightly bitter, and 4 = not bitter. Saline water (2%) was provided to the judges for rinsing their mouth before tasting each sample. The judges noticed the following sensory characteristics in different samples of casein hydrolysates: i) Flavour : Stale, foul, acidic, salty, sour and fruity.


ii) Colour : Dull white, yellowish, sparkling clear, yellowish brown and red. iii) Sediment : No sediment. REFERENCES

Kelly, P.M. (1986) Dried milk protein products. J. Soc. Dairy Technol., 39 (3): 81-85.

Khanna, R.H. (1991) Process optimization for enzymatic production of casein hydrolysate. M.Sc. Thesis, NDRI deemed University, Karnal.

Muller, L.L. (1971) Manufacture and uses of casein and coprecipitates, Dairy Sci. Abstr., 33: 659.

Morr, C.V. and Ha, E.Y.W. (1991) Off flavours of whey protein concentrates : A Literature Review. Int. Dairy J., 1: 1-11.

Ramshaw, E.H. and Dunstone, E.A. (1969a) The flavour of milk protein. J. Dairy Res., 36, 203-213.

Ramshaw, E.H. And Dunstone, E.A. (1969b) Volatile compounds associated with the off-flavour in stored casein. J. Dairy Res., 36: 215-223.

Ramshaw, E.H. and Dunstone, E.A. (1970a) Ferricyanide reducing substances and the flavour of milk protein heated in solution. XVIII Int. Dairy Cong., IE: 424.

Ramshaw, E.H. and Dunstone, E.A. (1970b) Inhibition of browning during milk protein manufacture and storage. XVIII Int. Dairy Cong., IE: 425.

Ramshaw, E.H. and Leary, J. (1970) Volatile components in casein after exposure to UV light. XVIII Int. Dairy Congr., IE: 64.

Sharma, K.K. and Hansen, P.M.T. (1970) Heat-induced dephosphorization of dehydrated caseins. XVIII Int. Dairy Cong., IE: 58.

Roeper, J., Southward, C.R. and Humphries (1978) A method for the evaluation of the flavour of casein products. N.Z. J. Dairy Sci. Technol., 13: 124-126.

Southward, C.R. (1985) Manufacture and applications of edible casein products. 1. Manufacture and properties. N.Z. J. Dairy Sci. Technol., 20: 70-101.

Southward, C.R. and Goldman, A. (1978) Coprecipitates and their application in food products. II. Some properties and applications. N.Z. J. Dairy Sci. Technol., 13: 97-105.

Walker, N.J. and Manning D.J. (1976) Components of the Musty off-flavour of stored dried lactic casein. N.Z.J. Dairy Sci. Technol. 11, 1.

Walker, N.J. (1970) Chemical changes involved in the development of off-flavour in stored casein. XVIII Int. Dairy Cong., IE: 426.

Walker N.J. (1973) Flavour defects in edible casein and skim milk powder, II The role of aliphatic monocarbonyl compounds. J. Dairy



Principal Scientist Dairy Technology Division N.D.R.I., Karnal - 132 001

1. INTRODUCTION

Milk powders possess various organoleptic, physico-chemical and reconstitutional properties, which are important to both industrial and consumer use. These properties are the basic elements of quality specifications for milk powders. During drying process, care is taken to conserve as much as possible the natural properties of the original raw milk. Quality of dried products should be such that when reconstituted with water, give little or no evidence of detrimental change compared to the original liquid products. Evaluation of milk powder, whole or skimmed, on the basis of its sensory characteristics plays an important role towards its consumer acceptance. 2. DRY WHOLE MILK

In judging whole milk powder (WMP) for flavour one first classifies the product for flavour as good, fair or poor. 2.1 Off Flavour

Milk powders are expected to demonstrate a slightly sweet, clean and pleasant flavour, though other dried milk products may be expected to confirm to certain other specific requirements. Often, dry milk gradually loses its sweet, fine, appetizing flavour upon aging, thus becoming more or less off flavoured. The more frequently occuring flavour defects of dry whole milk are discussed below: 2.1.1 Oxidized/tallowy: Dry whole milk and other dry high-fat milk products undergo oxidative deterioration (also called tallowy). Whole milk powder with low to medium preheat treatments (equivalent to a WPNI of about 3-5) has a greater tendency to undergo lipid oxidation, with distinctive tallowy and musty flavours, than powders made with higher heat treatments. Chemical changes result with the addition of oxygen to the double bonds of unsaturated glycerides, giving at first peroxides and later aldehydes, ketones etc., which impart the unpleasant flavour. Copper and iron act as catalysts. Higher storage temperature, higher acidity, sunlight and ultra violet irradiation promote faster development of oxidative deterioration. 2.1.2 Rancid: Rancidity is due to hydrolysis of fat through lipase enzyme leading to

SENSORY EVALUATION OF DRIED MILK AND MILK PRODUCTS


production of free fatty acids, like butyric acid. Rancid dry whole milk has a bitter, soapy, unclean taste which is persistent after the sample has been expectorated.

TABLE- I Evaluation Card for Milk Powder

Name........................................................... Dated........................ Code No................... Time......................... A. Score the sample for different characteristics. Indicate the degree of defects, if any,

encircling the applicable one and deduct accordingly from the attribute score.

Characteristic

(1)

Max Score

(2)

Minimum for each attribute

(3)

Sample Score

(4) i) Package Appearance 5 3

ii) Appearance of Dry Product 15 9

iii) Appearance of reconstituted milk 15 9

iv) Body and texture of reconstituted milk 20 17

v) Flavour of reconstituted milk 45 27

Note : If the sample score is less than the minimum for any characteristic, it is to be

rejected. B. Degree of Defects

CHARACTERISTICS

(1)

DEFECT

(2)

DEGREE OF DEFECT Suspicion Definite Pronounced

(3) (4) (5) i) Appearance of package

Soiled surface unsealed 1 2 3

ii) Appearance of dry product

Caked/ brown particles 2 5 10

iii) Appearance of reconstituted milk

Lumpy brown 1 2 5


iv) Flavour

Oxidized/stale/ rancid Chalky/acid/neutralizer/ salty Metalic/cooked/scorched Weedy/bitter/ foreign

2 2 1 5

3 5 2

10

10

10 5

15

Source: Method for Sensory Evaluation of Milk Powder. Indian Standard. IS : 10030-1981 2.1.3 Stale, storage, old: Stale flavours, due to carbonyl compounds, can be detected in milk powders almost as soon as they are made. The mechanism of formation of these compounds may be through the Maillard reaction, but many compounds contribute to a stale, cardboard flavour, including oxidation by products. The defect is accelerated by high moisture content and high temperature of storage. When the defect is intense it may be accompanied by a darkening of the product. 2.1.4 Cooked flavour Milk powders often have cooked flavour, which results from components formed during preheating and possibly during evaporation. During drying, conditions are mostly not such that off-flavours are induced. On the contrary, a considerable part of the volatile sulphydryl compounds (especially H2S) is removed. A cooked flavour in milk powder mainly results from methyl ketones and lactones formed by heating of the fat (they thus are almost absent in skim milk powder) and form Maillard products. . 2.2 Physical characteristics of WMP Two defects pertaining to the body and texture of dry whole milk are lumpy and caked. 2.2.1 Lumpy: A lumpy powder definitely lacks homogeneity. Hard lumps ranging in size from a grain of wheat upwards may be interspersed throughout. This defect is found more frequently in the spray process product. Lumps result from insufficient drying, drippage from spray nozzles or exposure to moisture laden air. 2.2.2 Caked: Usually this defect is not encountered in dry whole milk. When it does occur, the product loses its powdery consistency and becomes a rock like solid. When the solid mass is broken up, it remains in chunks, thus failing to return to the original powder state. This defect is serious since such milk solids have lost their sales value for human consumption.


2.3 Discolouration Milk powder should be uniform in colour, free from foreign specks and burnt particles. It should exhibit greenish white or creamish white colour, respectively in buffalo and cow milk powders. Milk powder tends to darken during storage, turning to brown due to maillard reaction, which refers to the reaction between free amino group of protein and lactose. This is associated with old or stale off-flavour. High moisture content and high storage temperature enhance browning discolouration. Spray dried milk powder is more susceptible to age darkening and to greater intensity than roller process powders. 2.3.1 Browned or darkened: The defect is usually associated with an old, stale flavour. The normal creamy colour is replaced by a distinct brown. 2.3.2 Scorched: Discolouration due to burning of the milk solids is usually associated with the roller process. The powder may vary from light to dark brown. 2.3.3 Lack of uniformity: This defect may be due to either partial discolouration (browning) after packaging or to partial scorching during the manufacturing process. 3. SKIM MILK POWDER (SMP) 3.1 Flavour Due to its low fat content, SMP does not possess the rich flavour of high fat milk powder. The flavour of high quality non fat dry milk should be clean, sweet and pleasant, when reconstituted, similar to that of fresh skim milk. The flavour may have a slightly cooked or heated note. The chief flavour defects of non fat dry milk are as follows: 3.1.1 Stale, storage, old: This flavour defect is the chief one of non fat dry milk. In this product the off-flavour is even more "quick" and distinct than in dry whole milk. Usually the flavour defect is accompanied by a darkening of the powder. The old, stale flavour develops usually more intensely in spray process than in roller process powder. 3.1.2 Cooked: As in dry whole milk, this flavour is produced in products which have been subjected to abnormally high heat during processing. 3.1.3 Oxidized, tallowy: Non fat dry milk contains a small percentage of fat which oxidizes under some conditions yielding the oxidized or tallowy flavour. A tallowy


product has a pronounced odour, whereas stale powder does not have a very intense odour. 3.2 Physical characteristics Non fat dry milk prepared by spray process is very fine in particle size and uniform throughout. Instead of being flour like in texture, instant SMP is more or less granular. The product pours readily somewhat like that of corn meal. The highly hygroscopic, light, almost air-borne dust of normal spray process is lacking in SMP. 3.3 Discolouration Non fat dry milk should be uniform in colour throughout showing the absence of foreign specks and burnt solids. The product should have a creamy white or light yellow colour which varies slightly in intensity with the season of the year. Upon ageing under certain conditions SMP tends to darken. When this defect occurs the light yellow colour has given way to a definite brown. Spray process powder appear to be more susceptible to age darkening and to a greater intensity than roller-process powder. 4. METHOD OF RECONSTITUTING DRY MILK FOR FLAVOUR

EXAMINATION Generally for examining dry milk for odour and taste, the product is reconstituted on the basis of the original concentration. The American Dry Milk Institute (ADMI) recommends examination of dry milk odour immediately after the containers are opened and again for flavour approximately one hour after the sample has been reconstituted. Judges must be mindful of the fact that freshly prepared fluid milk made from water and dry whole milk often possesses a slightly chalky, watery or slightly cooked taste. Hence permitting a short storage period for blending of flavours after reconstituting the product should aid the judge in determining more accurately the true flavour. 5. MALTED MILK 5.1 Flavour Malted milk, being composed in large part of maltose and dextrose, has a definitely sweet taste. It should have a distinct flavour of malt. The product should be judged for its lack of malt flavour and for oxidized flavour defect. 5.2 Body and texture


Malted milk has a coarse and grainy texture unlike the fine texture of spray dried milk. While judging, product must be examined for possible stickiness and formation of cakes because of its affinity for water. 6. REFERENCES

Bodyfelt, M.S., Tobias, J. and Trout, G.M. (1988). The Sensory Evaluation of Dairy Products. AVI Publishing Co., NY, pp. 384 - 415.

Indian Standard. Method for Sensory Evaluation of Milk Powder. IS: 10030 - 1981.

Prentice, J.H. (1972). Rheology and Texture of Dairy Products. J. of Texture Studies. 3, 415 - 458.


APPLICATION OF RHEOLOGY IN QUALITY ASSURANCE IN FOOD PROCESSING

Dr. Dalbir Singh Sogi Reader & Head, Dept. of Food Science & Technology

Guru Nanak Dev University, Amritsar

Quality assurance generally deals with the defining of quality standards of food products quantitatively and then controlling the entire manufacturing process so that finished product conforms to the established standards. Sensory characterustics of a food product are the most important for the consumer acceptability. Sensory parameters refer to appearance (including colour), flavour (taste, odour and Feel) and texture (Solids)/consistency (Liquids).

Senses used for evaluation of food qualiry are as follows

• Smell : Olfactory – mucous memberane of the nose; olfactory; epithelium • Taste : Gustatory-mucous membranne of toungue, palate and throat • Sight : Visual - eyes • Hearing : Auditory; Aural-ears • Touch : Haptic-tactile nerve in general • Cut/crush : Teeth

In this presentation only consistency has been discussed. It is a very important quality attribute in liquid or semisolid foods. Apart from colour and flavour, the consistency of food dictates overall acceptability of the products. The consistency has been defiend in number of terms in sensory evaluation for varous food products. The mouth can be considered as an intricate mechanical system and chemical reactor that can crush, wet, enzymetically degrade, pressurize, heat or cool, pump and sense force and temperature. In addition this “eating machine” has a sophisticated feed back control system. The mouthfeel terms of beverages have been classified in Table 1.

Category

Typical words

Beverages that have this property

Beverages that do not have this property

Thin

Water, Iced tea, hot tea

Apricot nectar, milk shake,

buttermilk Viscosity-

related terms Thick Milk shake, eggnog, tomato

juice Club soda, champagne, drink

made from dry mix Feel on soft Smooth Milk, liqueur, hot chocolate -


Pulpy

Orange juice, lemonade,

pineapple juice Water, milk, champagne

tissue surfaces

Creamy Hot chocolate, eggnog, ice-cream soda

Water, lemonade, cranberry juice

Heavy

Milk shake, eggnog, liqueur

Water, lemonade, ginger ale

Watery Bouillon, iced tea, hot tea, drink made from dry mix

Milk, apricot nectar

Body-related terms

Light Water, iced tea, canned fruit drink

Buttermilk, hot chocolate, juice

Mouth Coating

Milk, eggnog, hot chocolate

Water, apple cider, whiskey

Coating of oral cavity

Clinging Milk, milk shake, ice cream soda, liqueur

Water, ginger ale, bouillon

Slimy

Prune juice, milk, light cream Water, ginger ale, champagne Resistance to tongue

movement Syrupy

Liqueur, apricot nectar, root beer

Water, milk, club soda

Clean Water, iced tea, wine Buttermilk, beer, canned

Drying

Hot chocolate, cranberry juice

fruit drink Water

Lingering Hot chocolate, light cream, milk

Water, iced tea, club soda

Afterfeel-mouth

Cleansing Water, hot tea Milk, pineapple juice, juice

Source: Szczesniak (1979) in Food Texture and Viscosity by Malcolm C.Bourne

Perception of food and definition of the rheological terms

Food Terms Firmness (compression) Hardness (Bite)

Hardness Force to attain a given deformation

Cohesive Chewy, Fracturable (crispy/crunchy), Viscosity

Cohesiveness Degree to which sample deforms (rather than rupture)

Sticky (tooth/palate), Tooth pack

Adhesiveness Force required to remove sample from the surface

Dense/heavy, Airy/puffy/light

Denseness Compactness of cross-section

Springy/rubbery Springiness Rate of return to original shape after deformation


It is evident from the above table that the sensory response of liquid food for consistency can be measured in term of rheological parameters. Various equipments have been designed to measure the consistency or viscosity or visco-elastic behaviour. In the early time the equipments were relatively simple and produce limited information but modern equipment are complex, accurate and more informative.

CAPILLARY VISCOMETERS

These are also known as glass capillary viscometers or Ostwald viscometers. Apparatus consists of an U shaped glass tube and a controlled temperature bath. One arm of viscometer consits a precise narrow bore or capillary inbetween two bulbs. The liquid is drawn into the upper bulb by suction, then allowed to flow down through the capillary into the lower bulb. The time taken for the liquid to pass the capillary is proportional to the kinematic viscosity. The voscometers are calibtrated using standard solutions of known viscosity and a conversion factor is calculated. The time taken by the test liquid to flow through a capillary of a known diameter is multiplied with the conversion factor of the viscometer to get the kinematic viscosity. Temperature is maintained be keeping the viscometers in a water bath. The capillary viscometer has been further classified depending of the specific end uses:

Zeitfuchs Cross-Arm Viscometers

Newtonian liquids - 0.3 to 1,00,000 centistokes range.

Suitability - Transparent or Opaque liquids

Minimum sample volume - 15 mL

Liquid bath depth - 292 mm

Cannon-Fenske Routine Viscometers

Newtonian liquids





Cannon-Fenske Opaque Viscometers

Newtonian liquids - 30 to 6000 centistokes range.

Suitability – very dark coloured liquids



Ubbelohde Viscometers

Newtonian liquids - 0.5 to 100,000 centistokes range.




Cannon-Ubbelohde Semi-Micro Viscometers



Minimum sample volume – 1.0 mL

Liquid bath depth – 240 mm

Cannon-Ubbelohde Dilution Viscometersg Information



Minimum sample volume - 8.0 mL


FALLING SPHERE VISCOMETERS

The falling sphere viscometer is based on the Stokes' law, in which the fluid is stationary in a vertical glass tube while a sphere of known size and density is allowed to fall through the liquid. If correctly selected, it reaches terminal velocity, which can


be measured by the time it takes to pass two marks on the tube. Electronic sensing can be used for opaque fluids. Knowing the terminal velocity, the size and density of the sphere, and the density of the liquid, Stokes' law can be used to calculate the viscosity of the fluid. A series of steel ball bearings of different diameter is normally used in the classic experiment to improve the accuracy of the calculation.

Falling ball viscometer

BOSTWICK CONSISTOMETER It consists of a rectangular channel with spring-operated gate on one side that allows a constant flow of the sample. There are two levelling screws for the fine adjustment of incination. There are 48 engraved graduations of 5 mm devisions of the floor of channel. The gate is closed and sample is loaded. The gate is opened and fluid is allowed to flow throgh the channel for one minutes. The distance travelled by the fluid is indicative of consistency.

Suitability - Liquid or paste-like materials Minimum sample volume - 75ml Dimensions - 76 x 140 x 356 mm


ROTATIONAL VISCOMETERS

Rotational viscometers use the idea that the torque required to turn an object in a fluid, can indicate the viscosity of that fluid. The common viscometer determines the required torque for rotating a disk or bob in a fluid at known speed. 'Cup and bob' viscometers work by defining the exact volume of sample which is to be sheared within a test cell and the torque required to achieve a certain rotational speed is measured. There are two classical geometries in "cup and bob" viscometers, known as either the "Couette" or "Searle" systems - distinguished by whether the cup or bob rotates. The rotating cup is preferred in some cases. 'Cone and Plate' viscometers use a cone of very shallow angle in bare contact with a flat plate. With this system the shear rate beneath the plate is constant to a modest degree of precision and deconvolution of a flow curve; a graph of shear stress (torque) against shear rate (angular velocity) yields the viscosity in a straightforward manner. STABINGER VISCOMETER It is a modified Couette rotational viscometer where an accuracy comparable to that of kinematic viscosity determination is achieved. The internal cylinder in the Stabinger Viscometer is hollow and specifically lighter than the sample, thus floats freely in the sample, centered by centrifugal forces. The formerly inevitable bearing friction is thus fully avoided.

Stabinger viscometer The speed and torque measurement is implemented without direct contact, by a rotating magnetic field and an eddy current brake. This allows for a previously unprecedented torque resolution of 50 pN·m and an exceedingly large measuring range from 0.2 to 20,000 mPa·s with a single measuring system.


STORMER VISCOMETER

The Stormer viscometer is a rotation instrument used to determine the viscosity of paints, commonly used in paint industries. It consists of a paddle-type rotor that is spun by an internal motor, submerged into a cylinder of viscous substance. The rotor speed can be adjusted by changing the amount of load supplied onto the rotor. For example, in one brand of viscometers, pushing the level upwards decreases the load and speed, downwards increases the load and speed. The viscosity can be found by adjusting the load until the rotation velocity is 200 rotations per minute. By examining the load applied and comparing tables found on ASTM D 562, one can find the viscosity in Krebs units (KU), unique only to the Stormer type viscometer. This method is intended for paints applied by brush or roller. DYNAMIC RHEOMETER The two common approaches used in rotational rheometers are controlled rate and controlled stress. In the controlled rate approach, the material being studied is placed between two plates. One of the plates is rotated at a fixed speed and the torsional force produced at the other plate is measured. Hence, speed (strain rate) is the independent variable and torque (stress) is the dependent variable. In the controlled stress approach, the situation is reversed. A torque (stress) is applied to one plate and the displacement or rotational speed (strain rate) of that same plate is measured.

Controlled rate rheometer with Searle operation mode.


Controlled stress rheometer with Searle operation mode

VIBRATIONAL VISCOMETER

Vibrational viscometer operates by measuring the damping of an oscillating electromechanical resonator immersed in a fluid whose viscosity is to be determined. The resonator generally oscillates in torsion or transversely. The damping imposed on the resonator is directly realted to viscosity.

The resonator's damping may be measured by one of several methods: • Measuring the power input necessary to keep the oscillator vibrating at a

constant amplitude. The higher the viscosity, the more power is needed to maintain the amplitude of oscillation.

• Measuring the decay time of the oscillation once the excitation is switched off. The higher the viscosity, the faster the signal decays.

• Measuring the frequency of the resonator as a function of phase angle between excitation and response waveforms. The higher the viscosity, the larger the frequency changes for a given phase change.

Vibrating viscometers are rugged industrial systems used to measure viscosity in the process condition. The active part of the sensor is a vibrating rod. The vibration amplitude varies according to the viscosity of the fluid in which the rod is immersed.


These viscosity meters are suitable for measuring clogging fluid and high-viscosity fluids even with fibers even under extreme pH conditions.

SENSORY EVALUATION AND INSTRUMENTAL MEASUREMENT

The relationship between the sensory evaluation and instrumental measurement attributes has been studied by many workers like Hayakawa et al. 1995 (model emulsions), Hill et al. 1995 (lemon pie filling), Mela et al. 1994 (fat emulsions), Munoz & Sherman, 1990 (commercial salad dressings) and Peressini et al, 1998, Stern et al, 2001 (traditional and light mayonnaises). The relationship among rheological and sensory provided a good prediction of peak shear stress and peak time but gave only a crude prediction of stress decay. The study was found useful in modelling the human perception of fluid mechanics in the mouth.

CONCLUSIONS

Rheological properties and mouth feel are determined by measuring force and deformation as a function of time. The rheological methods are useful if they correlate with the sensory properties of interest. The selection of tests depends on the type of food, the application, and the availability of suitable instrumentation, for testing the particular attributes of food material. However, the sensory response can not be completely duplicated by an instrumental procedure but it can provide a good prediction and modelling of fluid mechanics in the human masticatory system.

References

Campanella, O.H. and Peleg, M. (1987). Analysis of the transient flow of mayonnaise in a coaxial viscometer. J. Rheol., 31, 439-452.

Dickie, A.M. and Kokini, J.L. (1983). An improved model for food thickness from non-Newtonian fluid mechanics in the mouth. J. Food Sci., 48, 57-65.

Figoni, P.I. and Shomaker, C.F. (1983). Characterization of time dependent flow properties of mayonnaise under steady shear. J. Texture Studies, 14, 431-442.

Hayakawa, F., Tanisawa Y., Hatae, K. and Shimada, A. (1995). Relationship between the sensory evaluation for oiliness and physical properties in model emulsions. J. Home Econ. Jpn., 46, 765-774.


Hill, M.A., Mitchell, J.R. and Sherman, P.A. (1995). The relationship between the rheological and sensory properties of a lemon pie filling. J. Texture Studies, 26, 457-470.

Kiosseoglou, V.D. and Sherman, P. (1983). Influence of egg yolk lipoproteins on the rheology and stability of O/W emulsions and mayonnaise. J. Texture Studies, 14, 397-417.

M.B. Sousa, W. Canet, M.D. Alvarez, C. Fernández (2007) Effect of processing on the texture and sensory attributes of raspberry (cv. Heritage) and blackberry (cv. Thornfree) Journal Food Engineering, 78, 9-21.

Mela, J.D., Langley, K.R. and Martin, A. (1994). Sensory assessment of fat content : effect of emulsion and subject characteristics. Appetite, 22, 67-81.

Munoz, J. and Sherman, P. (1990). Dynamic viscoelastic properties of some commercial salad dressings. J. Texture Studies, 21, 411-426.

N. Lassoued, J. Delarue, B. Launay, C. Michon (2008) Baked product texture: Correlations between instrumental and sensory characterization using Flash Profile Journal of Cereal Science, 48,133-143.

Peressini, D. Sensidoni, A. and de Cindio, B. (1998). Rheological characterization of traditional and light mayonnaises. J. Food Eng., 35, 409-417.

Richardson, R.K., Morris, E.R., Ross-Murphy, S.B., Taylor, L.J. and Dea, I.C.M. (1989). Characterization of the perceived texture of thickened systems by dynamic viscosity measurements. Food Hydrocoll., 3, 175-191.

S. Di Marzo, R. Di Monaco, S. Cavella, R. Romano, I. Borriello, P. Masi (2006) Correlation between sensory and instrumental properties of Canestrato Pugliese slices packed in biodegradable films. Trends in Food Science & Technology, 17, 4, 69-176.

Stern, P., Valentova, H. and Pokorny, J. (2001). Rheological properties and sensory texture of mayonnaise. Eur. J. Lipid Sci. Technol., 103, 23-28.

Wendin, K., Aaby, K., Edris, A., Ellekjaer, M.R., Albin, R., Bergenstahl, B., Johansson, L., Willers, E.P. and Solheim, R. (1997). Low-fat mayonnaise : influences of fat content, aroma compounds and thickness. Food Hydrocoll., 11, 87-99.


S. N. Jha

Senior Scientist, Central Institute of Postharvest Engineering and Technology (CIPHET),

Ludhiana – 141 004

Quality conscious consumers nowadays want to get assured about various quality attributes of food items before they purchase. Fruits, vegetables and milk are increasing in popularity in the daily diets in both developed and developing countries. Products’ quality and its measurement techniques are thus naturally extremely important. The decisions concerning the constituents, level of freshness, ripeness, and many other quality parameters are based mostly on subjective and visual inspection of the foods’ external appearance. Several nondestructive techniques for quality evaluation have been developed based on the detection of various physical properties that correlate well with certain factors of a product. The quality of foods including milk and milk products is mostly based on constituents, purity; i.e., levels of adulterants; color, gloss, flavor, firmness, texture, taste and freedom from external as well as internal defects. Numerous techniques for evaluating these parameters are now available commercially, but most of them are destructive in nature. Internal quality factors of fruits such as maturity, sugar content, acidity, oil content, and internal defects, however, are difficult to evaluate. Methods are needed to better predict the internal quality of fruits, vegetables, constituents of foods and level of adulterants, if any, without destroying the sample. Recently, there has been as increasing interest in nondestructive methods of quality evaluation, and a considerable amount of effort has been made in that direction. But the real problem is how these methods are to be exploited practically and what the difficulties are in implementing them. The objective of the present paper is thus to give exposure of recent nondestructive methods such as nuclear magnetic resonance, x-ray computed tomography, near-infrared spectroscopy and some other important methods to the stakeholders of food industry in India and to evaluate their pros and cons for suitability in commercial application.

Nuclear magnetic resonance (NMR) techniques

The nuclear magnetic resonance technique, often referred as magnetic resonance imaging (MRI), involves resonant magnetic energy absorption by nuclei placed in an alternating magnetic field. The amount of energy absorbed by the nuclei is directly proportional to the number of a particular nucleus in the sample such as the protons in water oil. The theory of NMR is presented in detail elsewhere (Farrar & Becker, 1971). The basic concepts, types of pulsed experiments and the type of information that can be extracted from these experiments are described. Information

NONDESTRUCTIVE METHODS FOR QUALITY

EVALUATION OF DAIRY AND FOOD PRODUCTS


on experimentation, assembling hardware, conducting laboratory tests and interpreting the results is also available from Fukushima and Roeder (1981). These authors also provided detailed theory for better understanding of what a scientist should seek and what he might expect to find out by using NMR.

There are many applications of NMR in agriculture (Rollwitz, 1984). The simplest among them is the determination of moisture and oil content (Mousseri et al., 1974, Leung et al., 1976; Miller et al., 1980; Brosio et al., 1978; Rollwitz and Persyn, 1971). But the NMR response many times is not clear and poses problems especially when constituents other than water are present in the material (Steinberg & Richardson, 1996). Besides the established relationship between the moisture and output of NMR experiments, various other facts helpful in determining the quality of food materials without destroying them are available in the literature: Selections of chocolate confectionary products can be made non-invasively by three-dimensional magnetic resonance imaging (Miquel et al., 1998); using a spin echo pulse sequence, 128x64x64 data sets were acquired with either a 5-or 20-ms echo time, 500-ms repetition time and signal averages, in total 2-h scan time. Such images localize and distinguish between the constituents, and visualize both the internal and external structure of matter.

Most perishable food products are now marketed in packaged form. To increase the marketability longer shelf life is needed and this is achieved by freezing and secondary processing of the food. During freezing it is natural that ice will form within the food that may change its characteristics. Ice formation during food freezing can be examined using the NMRI method as the formation of ice has been seen to reduce the spatially located NMR signal. The characteristics of a food can be better controlled as MRI can serve to assess freezing times and the food structure during the freezing process (Kerr et al., 1998). The secondary processing changes almost all characteristics of a food, such as physical and aerodynamic (Jha & Kachru, 1998), thermal and hygroscopic properties (Jha & Prasad, 1993; Jha 1999), which in turn, change its key acceptability factors, i.e. sensory texture and taste. The sensory texture of cooked food such as potatoes has been predicted using the NMRI technique (Thybu et al., 2000). In addition, NMR image intensity, the ratio of the oil and water resonance peaks of the one-dimensional NMR spectrum, and both the spin-lattice relaxation time and spin-spin relaxation time of water in the fruit are correlated with maturity of a fruit like avocado before harvesting (Chen et al., 1993). This important finding has desirable features for high speed sorting using a surface-coil NMR probe that determine the oil/water resonance peak ratio of the signal from one region in an intact fruit.

An on-line nuclear magnetic resonance quality evaluation sensor has recently been designed, constructed and tested (Kim et al., 1999). The device consists of a super-conducting magnet with a 20mm diameter surface coil and a 150 mm diameter imaging coil coupled to a conveyor system. These spectra were used to measure the oil/water ration in avocados and this ratio correlated to percent dry weight. One


dimensional magnetic resonance images of cherries were later used to detect the presence of pits inside.

X-ray and computerized tomography (CT)

X-ray imaging is an established technique to detect strongly attenuating materials and has been applied to a number of inspection applications within the agricultural and food industries. In particular, there are many applications within the biological sciences where we wish to detect weakly attenuating materials against similar background material.

X-ray computed tomography (CT) has been used to image interior regions of apples with varying moisture and, to a limited extent, density states (Tollner et al., 1992). The images were actually maps of x-ray absorption of fruit cross sections. X-ray absorption properties were evaluated using normal apples alternatively canned and sequentially freeze-dried, fruit affected by water core disorder, and normal apples freeze-dried to varying levels. The results suggested that internal differences in x-ray absorption within scans of fruit cross-sections are largely associated with differences in volumetric water content. Similarly, the physiological constituents have been monitored in peaches by CT methods in which x-ray absorbed by the peaches is expressed in CT number and used as an index for measuring the changes in internal quality of the fruit (Barcelon et al., 1999). Relationships between the CT number and the physiological contents were determined and it was concluded that x-ray CT imaging could be an effective tool in the evaluation of peach internal quality. In another study, the potential for Compton scattered x-rays in food inspection was evaluated by imaging the density variation across a food material by measuring the Compton scatter profile across a food material by measuring the Compton scatter profile across polystyenespheres with internal voids (MacFarlane et al., 2000). In this study particular attention was paid to simulate the obscuring influence of multiple scatter. The simulated result was found to be in close agreement with the experimental observation. Some experimental test sample of a Perspex block with various embedded soft materials showed that care should be taken to ensure that the transmission image is taken with x-ray within an appropriate energy range (Zwiggelaar et al., 1997). For low Z materials the contrasts between the materials became more pronounced at lower x-ray energies. If more than one soft material has to be distinguished from the surrounding area it may be advantageous to image over a range of x-ray energies.

Visual spectroscopy and colour measurements

Colour measurement is now little bit old technique to check the quality of any items in terms of appearance. It has also been tested for assessing the ripeness of fruits and measurement of aesthetic appearances of dairy products. Recently many works have been reported to correlate the internal quality such as total soluble solids contents, maturity of fruits in tree and sweetness of intact fruits using Hunter colour values and reflectance spectra in visual range of wavelengths (Jha et al, 2005 and


2006). This in fact is possible through rigorous analysis of data and modeling for a huge number of samples of varied nature.

Near-infrared spectroscopy The use of near-infrared spectroscopy as rapid and often nondestructive technique for measuring the composition of biological materials has been demonstrated for many commodities. This method is no longer new; as it started in early 1970 in Japan (Kawano, 1998), Just after some reports from America. Even an official method to determine the protein content of wheat is available (AACC, 1983). The National Food Research Institute (NFRI), Tsukuba has since become a leading institute in NIR research in Japan and has played a pivotal role in expanding near-infrared spectroscopy technology all over the country (Iwamoto et al., 1995). In Japan, NIR as a nondestructive method for quality evaluation was started for the determination of sugar content in intact peaches, Satsuma orange and similar other soluble solids (Kawano, 1994).

To determine the solid content of cantaloupe Dull et al. (1989) used NIR light at 884 nm and 913 nm. Initially the correlation of their findings was poor mainly due to light losses. Later, Dull and Birth (1989) modified the earlier method and applied it to honey-dew melons; the improved methods showed better correlation. Similarly, a nondestructive optical method for determining the internal quality of intact peaches and nectarines was investigated (Slaughter, 1995). Based upon visible and near-infrared spectrophotometer techniques, the method was capable of simultaneously predicting the soluble solid content, sucrose content, sorbitol content, etc. of intact peaches and nectarines was investigated (Slaughter, 1995). Based upon visible and near-infrared spectrophotometer techniques, the method was capable of simultaneously predicting the soluble solid content, sucrose content, sorbitol content, etc. of intact peaches and nectarines, and required no sample preparation.

Now various NIR spectrometers are available and are being used commercially. Some modifications in these available spectrometers, especially for holding the intact samples, are reported (Kawano et al., 1992; 1993). In the same sample holding a test tube for holding liquid food such as milk was also used to determine fat content (Chen et al., 1999). Recently a low cost NIR spectrometer has been used to estimate the soluble solids and dry matter content of kiwifrui (Osborne & Kunnemeyer, 1999). Errors are within the permissible limit and the time requires for obtaining data has been reduced. The influence of sample temperature on the NIR calibration equation was also evaluated and a compensation curve for the sample temperature was developed (Kawano et al., 1995) to rectify the result.

Now detection of almost all adulterants in milk in single stroke (Jha and matsuoka, 2004) and composition of milk and effect of somatic cell count on determination of milk constituents are very accurately determined ( Tsenkova et al


2001). Similarly taste of tomato juice in terms of acid-brix ratio can be determined with high accuracy (Jha and Matsuoka 2004). NIR spectroscopy in fact is the most suited technique for nondestructive analysis of dairy products.

Miscellaneous techniques Quality attributes such as invisible surface bruises, color, gloss, firmness, density, volume expansion of processed food etc are also important (Jha & Prasad, 1996). Often consumers select food materials, particularly fruits and vegetables by judging these parameters visually. Multiple efforts have been made to determine these parameters visually. A fluorescence technique was used to detect invisible surface bruises on Satsuma mandarins (Uozumi et al., 1987). The authors have also tested this method successfully to know the freshness of cucumbers and eggs and found it very useful for detecting the freshness of agricultural produce.

Matsuoka et al., (1995) measured the gloss of eggplant by a spectral radiometer system and found or to be a viable parameter for determining freshness. They observed remarkable change in relative spectral reflectance values after 48 h. Later, they compares their evaluation by eye in a sorting house with the integrated results of relative spectral reflectance in the visible range and found that the gloss on the surface differs with light and is caused by round and adhesives substances on the epidermal cells (Matsuoka et al., 1996). A unique gloss meter for measuring the gloss of curved surfaces was used in parallel with a conventional, flat surface gloss meter to measure peel gloss of ripening banana (Ward & Nussinovitch, 1996). Usually banana ripeness is judged by the color of the peel. The new gloss meter is able to measure the peel correctly which helps in predicting the correct time and level of ripening. This is also able to measure the gloss of other fruits and vegetables such as green bell pepper, orange, tomato, eggplant and onion (Nussinovitch et al., 1996).

Glossiness and color, in fact, are the only visual attributes for measuring the quality of fruits and vegetables. Another property that helps a consumer in deciding the quality is firmness. Takao (1998) developed a fruit hardness tester that can measure the firmness of kiwifruit nondestructively. The tester is called a ‘HIT counter’ after the three words, hardness, immaturity and texture. By just setting the sample in the tester, the amount of change in shape is measured and a digital reading within a few seconds indicates about the freshness. Based on the same principal another on-line prototype HIT counter, fruit hardness sorting machine has also been developed (Takao & Omori, 1991). The relationship between density and internal quality of watermelon can also be determined. An optimum range of density was first determined and then a new automatic density sorting system was develops and then a new automatic density sorting system was developed to measure the hollowness of a watermelons with cavities or deteriorated porous flesh to be removed and permits estimation of the soluble solid content of this fruits. Using gloss and other physical parameters such as stiffness and density, Jha and Matsuoka, 2002 have also determined the freshness of eggplants and have correlated it very easily with the day to day price in vegetable mandis.


Neural networks have lately gained in popularity as an alternative to regression models to regression models to characterize the biological process. Their decision-making capabilities can best to used in image analysis of biological products where the shape and size classification is not governed by any mathematical function. Many neural network classifiers have been used and evaluated for classifying agricultural products, but multi-layer neural network classifiers perform such tasks best (Jayas et al., 2000). Recently one scientist used a gamma- absorption techniques combined with a scanning device for continuous non-destructive crop mass and growth measurement in the field (Gutezeit, 200). Most important in this study was the accuracy of the measurement, which was found to be in agreement with the direct weighting system. This method has made it possible to assess the reaction of plants and their dependence on environmental factors by growth analysis.

Conclusions Determination of quality of any food material including milk and milk products is actually a complex problem that requires a variety of specific sensor, more than an accumulation of simple sensor. Various techniques are being tried. IMR, x-ray CT and NIR techniques may be useful for a large volume of work in agriculture, especially for evaluation of qualities such as maturity, internal quality of fruit and conditions of food materials after processing, level of adulterants and useful constituents. These techniques, although give a correct picture and precise measurement of parameters, are not convenient for small business except NIR and visual spectroscopy. Their high cost restricts application to large entrepreneurs and developed countries only.

Two examples of the use of x-ray imaging relevant to the agricultural and food industries have been given, notably in the inspection of vegetables and food materials using low energy x-ray imaging and in the inspection and control of dynamic processes. The x-ray imaging results have been compared with the full three-dimensional information obtained by computer tomogrphy. The CT results show more detail in the test sample than the single transmission image and detail in the inspection of materials of variable shape usually encountered in the agricultural and food industry. The imaging techniques MRI and x-ray CT are able to show only the internal structure of the material, not the compositional of nutritional details, whereas NIR and visual spectroscopy techniques are very successfully being used to determine the compositional quality of a food and can be used even at farm. However, it is not yet possible to produce an image of the internal physical quality of fruits and vegetables. All techniques are costly because most of the expertise is imported. Central Institute of Post-harvest Engineering and Technology (CIPHET), Ludhiana has taken the lead by initiating R & D works in the country about four years ago. Dairy and food processing industries and other research organizations should also work together to develop such type of instrumentation indigenously.

References


American Association of Cereal Chemists. (1983). Approved methods of the Am. Assoc. of Cereal Chemists, 8th Ed. (March. St. Paul, Minn.

Barcelon, E.G., Tojo, S. and watanable, K. (1999). X-ray computed tomography for internal quality evaluation of peaches. J. Agric. Eng. Res., 73, 323-330.

Brosia , E.F., Conti, C.L. and Sykara, S. (1978). Moisture determination in starch-rich food products by pulsed nuclear magnetic resonance. J. Food Technol., 13, 107-116.

Chen, J.Y., Iyo, C. and Kawano, S. (1999). Development of calibration with sample cell compensation for determining the fat content of unhomogenised raw milk by a simple near infrared transmittance method. J. Near Infrared Spectosc., 7, 265-273.

Chen, P., McCarthy, M.J., Kauten E., Sarig, Y. and Han, S. (1993). Maturity evaluation of avocados by NMR methods. J. Agric. Eng. Res., 55, 177-187.

Dull, G.G. and Birth, G.S. (1989). Nondestructive evaluation of fruit quality: Use of near infrared spectrophotometry to measure solube solids in intact honeydew melons. Hortscience, 24, 754.

Dull, G.G. and Birth, G.S. Smittle, D.A and Leffler, R.G. (1989). Near infrared analysis of soluble solids in intact cantaloupe. J. Food Sci., 54, 393-395.

Farrar, T.C. and Becker, E.D. (1971). Pulse and Fourier Transform NMR: Introduction to Theory and Methods. Academic Press, New York.

Fukushima, E. and Roeder, S.B.W. (1981). Experimental Pulse NMR. Addison-Wasley Publishing Company, Reading, M.A.

Gutezeit, B. (2000). Non-destructive measurement of fresh plant mass by the gamma-scanning technique applied to broccoli. J. Agric. Eng. Res., 75, 251-255.

Iwamoto, M., Kawano, S. and Yukihiro, O. (1995). An overview of research and development of near infrared spectroscopy in Japan. J. Near Infrared Spectrosc., 3, 179-189.

Jayas, D.S., Paliwal, J and Visen, N.S. (2000). Multi-layer neural networks for image analysis of agricultural products. J. Agric. Eng. Res. (in press).

Jha, S. N. and Matsuoka T. (2002). Development of freshness index of eggplant. Applied Engineering in Agriculture, ASAE, 18 (5): 57-60.

Jha, S. N. and Matsuoka, T. (2004). Detection of adulterants in milk using near infrared spectroscopy. Journal of Food Science and Technology, 41(3), 313 – 316.

Jha, S. N. and Matsuoka, T. (2004). Nondestructive determination of acid brix ratio (ABR) of tomato juice using near infrared (NIR) spectroscopy. International Journal of Food Science and Technology, 39(4): 425 - 430.

Jha, S. N.; Chopra, S. and Kingsly, ARP (2005). Determination of sweetness of intact mango using spectral analysis. Biosystems Engineering, 91(2), 157 – 161.

Jha, S.N, Chopra S., and Kingsly, ARP (2006). Modeling of color values for nondestructive evaluation of maturity of mango. Journal of Food Engineering. – in press.

Jha, S.N. (1999). Physical and hygroscopic properties of makhana. J. Agric. Eng. Res., 72 , 145-150.


Jha, S.N. and Prasad, S. (1993). Physical and thermal properties of gorgon nut. J. Food Process Eng., 16, 237-245.

Jha, S.N. and Prasad, S. (1996). Determination of processing conditions of gorgon nut (Euryale ferox). J. Agric. Engg. Res., 63, 103-112.

Jha, S.N. Matsuoka T. (2000). Review: Nondestructive Techniques for quality evaluation of intact fruits and vegetables. Food Science and Technology Research, 6(4), 248 – 251.

Jha. S. N. and Kachru, R.P. (1998). Physical and aerodynamic properties of makhana. J.Food Process.Eng., 79, 301-316.

Kato, K. (1997). Electrical density sorting and estimation of soluble solids contents of watermelon. J. Agric. Engg. Res., 67, 161-170.

Kawano, S. (1994). Nondestructive near infrared quality evaluation of fruits and vegetables in Japan. NIR News, 5, 10-12.

Kawano, S. (1998). New application of nondestructive methods for quality evaluation of fruits and vegetables in Japan. J. Jpn. Soc. Hort. Sci., 67, 1176-1179.

Kawano, S., Abe, H. and Iwamoto, M. (1995). Development of a calibration equation with temperature compensation for determining the brix value in intact peaches. J. Near infrared Spectrosc., 3, 211-218.

Kawano, S., Fujiwara, T., and Iwamoto, M.C. (1993). Nondestructive determination of sugar content in satsuma maddarin using NIR transmittance. J. Jpn. Soc. Hort. Sci., 62, 465-470.

Kawano, S., Watanabe, H. and Iwamoto, M. (1992). Determination of sugar content in intact peaches by near infrared spectroscopy with fibre optics in interactance mode. J. Jpn. Soc. Hort. Sci., 61, 445-451.

Kerr, W.L., Kauten, R.J., McCarthy, M.J. and Reid, D.S. (1998). Monitoring the formation of ice during food freezing by magnetic resonance sensors. J. Agric. Eng. Res., 74, 293-301.

Kim, S.M., Chen, P., McCarthy, M.J. and Zoin, B., (1999). Fruit internal quality evaluation using on-line nuclear magnetic resonance sensors. J. Agric. Eng. Res., 74, 293-301.

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Matsuoka, T., Miyauchi, K. and Yano, T. (1996). Basic studies on the quality evaluation of agricultural products (Part 2)- The evaluation of colour and gloss decrease on the surface of eggplants. J. Jpn. Sci. Agric. Mach., 58, 69-77.


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Osborne, S.D. and Kunnemeyer, R. (1999). A low cost system for the grading of kiwifruit. J. Near Infrared Spectocs., 7, 9-15.

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Tsenkova, R; Atanassova, S.; Ozaki, Y.; Toyoda, K.; and Itoh, K. (2001). Near-Infrared spectroscopy for biomonitoring; influence of somatic cell count on cow’s milk composition analysis. International Dairy Journal 11 (2001) 779-783.

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Dr. Rajan Sharma Senior Scientist, Dairy Chemistry Division, National Dairy Research Institute, Karnal.

(E-mail. [email protected])

The economic and social implications of new technologies are closely linked with newer chemicals. Chemicals, be they industrial chemicals, pharmaceuticals, veterinary drugs, pesticides, cosmetic products, food products, feed additives, etc., are required to be evaluated to determine their potential hazards. A number of countries require manufacturers of these chemicals to establish through data that use of these products do not pose any hazards to human health and the environment. Non-hazardous nature of these substances needs to be established through studies and data, which will be examined by the regulatory authorities of the concerned countries. Good Laboratory Practice (GLP) is a system, which has been evolved by Organization for Economic Co-operation and Development (OECD) used for achieving the above goals. GLP generally refers to a system of management controls for laboratories and research organizations to ensure the consistency and reliability of results as outlined in the OECD Principles of GLP and national regulations. GLP applies to non-clinical studies conducted for the assessment of the safety of chemicals to man, animals and the environment. The internationally accepted definition is as follows:

GLP is a quality system and the manner in which non-clinical safety studies are: planned, performed, monitored, recorded, reported and archived. These studies are undertaken to generate data by which the hazards and risks to users, consumers and third parties, including the environment, can be assessed for pharmaceuticals, agrochemicals, cosmetics, food and feed additives and contaminants, novel foods and biocides. GLP helps assure regulatory authorities that the data submitted are a true reflection of the results obtained during the study and can therefore be relied upon when making risk/safety assessments.

History of GLP

The formal concept of ‘GLP’ first evolved in the USA in the 1970s because of concerns about the validity of preclinical safety data submitted to the Food and Drug Administration (FDA) in the context of new drug applications. The inspection of studies and test facilities had yielded indications for, and instances of, inadequate planning and incompetent execution of studies, insufficient documentation of methods and results, and even fraud. For example, replacing animals which died during a study by new ones (which had not been treated appropriately with the test compound)

GOOD LABORATORY PRACTICES – GENESIS & CONCEPT


without documenting this fact; taking haematology data for control animals from control groups not connected with the study; deleting necropsy observations because the histopathologist received no specimens of lesions; or re-correcting discrepancies in raw data and final report tables by juggling around the raw data in order to ‘fit the results tables’ to the final report. These deficiencies were made public in the so-called Kennedy Hearings of the US Congress, and the outcome of these subsequently led to the publication, by the FDA, of Proposed Regulations on GLP in 1976, with the respective Final Rule coming into effect in June 1979.These regulations were intended to provide the regulatory basis for assurance that reports on studies submitted to the FDA would reflect faithfully and completely the experimental work carried out. In the chemical and pesticide field, the US Environmental Protection Agency (EPA) had encountered similar problems with study quality and issued its own draft GLP regulations in 1979 and 1980, publishing the Final Rules in two separate volumes in 1983.

OECD and GLP

On the international level, the OECD, in order to avoid non-tariff barriers to trade in chemicals, to promote mutual acceptance of non-clinical safety test data, and to eliminate unnecessary duplication of experiments, followed suit by assembling an expert group who formulated the first OECD Principles of GLP. Their proposals were subsequently adopted by the OECD Council in 1981 through its “Decision Concerning the Mutual Acceptance of Data in the Assessment of Chemicals” [C(81)30(Final)], in which they were included as Annex II. In this document, the Council decided that data generated in the testing of chemicals in an OECD member country in accordance with the applicable OECD Test Guidelines and with the OECD Principles of GLP shall be accepted in other member countries for purposes of assessment and other uses relating to the protection of man and the environment. It was soon recognized that these Principles needed explanation and interpretation, as well as further development, and a number of consensus workshops dealt with various issues in subsequent years. The outcome of these workshops was then published by OECD in the form of consensus documents. After some 15 years of successful application, the OECD Principles were revised by an international group of experts and were adopted by the OECD Council on 26th November 1997 [C(97)186/Final] by a formal amendment of Annex II of the 1981 Council Decision. A number of OECD member countries have adopted these Principles in their national legislation, notably the amendment of the European Union in Commission Directive 1999/11/EC of 8 March, 1999 to the Council Directive 87/18/EEC of 18 December, 1986, where GLP had first been introduced formally into the European legislation. Internationally, the observance of GLP has thus been defined as a prerequisite for the mutual acceptance of data, which means that different countries or regulatory authorities accept laboratory studies from other countries as long as they follow the internationally accepted GLP Principles. This mutual acceptance of safety test data will also prevent the unnecessary repetition of studies carried out in order to comply with any single


country’s regulations. In order to facilitate further the mutual acceptance of data and to extend this possibility to outside countries, the OECD Council adopted, on 26 November 1997, the ‘Council Decision concerning the Adherence of Non-member Countries to the Council Acts related to the Mutual Acceptance of Data in the Assessment of Chemicals’ [C(81)30(Final) and C(89)87(Final)] [C(97)114/Final], wherein interested non-member countries are given the possibility of voluntarily adhering to the standards set by the different OECD Council Acts and thus, after satisfactory implementation, to join the corresponding part of the OECD Chemicals Programme. Mutual acceptance of conformity of test facilities and studies with respect to their adherence to GLP, on the other hand, necessitated the establishment of national procedures for monitoring compliance. According to the OECD Council ‘Decision-Recommendation on Compliance with Principles of GLP’ of 2 October 1989 [C(89)87(Final)], these procedures should be based on nationally performed laboratory inspections and study audits. The respective national compliance monitoring authorities should exchange not only information on the compliance of test facilities inspected, but should also provide relevant information concerning the countries’ procedures for monitoring compliance. Although devoid of such officially recognized national compliance monitoring authorities, some developing countries do have an important pharmaceutical industry, where preclinical safety data are already developed under GLP. In these cases, individual studies are – whenever necessary – audited by foreign GLP inspectors (e.g. of FDA, the Netherlands or Germany).

Principles of Good Laboratory Practice

The purpose of these Principles of GLP is to promote the development of quality test data. Comparable quality of test data forms the basis for the mutual acceptance of data among countries. If individual countries can confidently rely on test data developed in other countries, duplicative testing can be avoided, thereby saving time and resources. The Principles of GLP have been developed to promote the quality and validity of test data used for determining the safety of chemicals and chemical products. It is a managerial concept covering the organizational process and the conditions under which laboratory studies are planned, performed, monitored, recorded and reported. Its principles are required to be followed by test facilities carrying out studies to be submitted to national authorities for the purposes of assessment of chemicals and other uses relating to the protection of man and the environment. The application of these Principles will help to avoid the creation of technical barriers to trade, and further improve the protection of human health and the environment. There are ten principles of GLP that have been framed by OECD and complete text regarding these principles can be accessed from http://www.oecd.org/document/63/0,2340,en_2649_ 34381_23461751_1_1_1,00.html. These principles in brief are as follows:


1. Test Facility Organization and Personnel

Each test facility management should ensure that these Principles of GLP are complied with in its test facility. It should ensure that a statement exists which identifies the individual(s) within a test facility who fulfill the responsibilities of management as defined by these Principles of GLP; ensure that a sufficient number of qualified personnel, appropriate facilities, equipment, and materials are available for the timely and proper conduct of the study; ensure the maintenance of a record of the qualifications, training, experience and job description for each professional and technical individual. For each study, a study director should be appointed. The Study Director is the single point of study control and has the responsibility for the overall conduct of the study and for its final report.

2. Quality Assurance Programme

The test facility should have a documented Quality Assurance Programme to assure that studies performed are in compliance with these Principles of GLP. The Quality Assurance Programme should be carried out by an individual or by individuals designated by and directly responsible to management and who are familiar with the test procedures. This individual(s) should not be involved in the conduct of the study being assured.

3. Facilities

The test facility should be of suitable size, construction and location to meet the requirements of the study and to minimize disturbance that would interfere with the validity of the study. The design of the test facility should provide an adequate degree of separation of the different activities to assure the proper conduct of each study. The test facility should have facilities for handling test and reference item, archive facilities, waste disposal etc.

4. Apparatus, Material, and Reagents

Apparatus, including validated computerized systems, used for the generation, storage and retrieval of data, and for controlling environmental factors relevant to the study should be suitably located and of appropriate design and adequate capacity. Apparatus used in a study should be periodically inspected, cleaned, maintained, and calibrated according to Standard Operating Procedures. Records of these activities should be maintained. Calibration should, where appropriate, be traceable to national or international standards of measurement. Apparatus and materials used in a study should not interfere adversely with the test systems. Chemicals, reagents, and solutions should be labeled to indicate identity (with concentration if appropriate), expiry date and specific storage instructions. Information concerning source, preparation date and stability should be available. The expiry date may be extended on the basis of documented evaluation or analysis.

5. Test Systems


Apparatus used for the generation of physical/chemical data should be suitably located and of appropriate design and adequate capacity. The integrity of the physical/chemical test systems should be ensured. Proper conditions should be established and maintained for the storage, housing, handling and care of biological test systems, in order to ensure the quality of the data.

6. Test and Reference Items

Records including test item and reference item characterization, date of receipt, expiry date, quantities received and used in studies should be maintained. Handling, sampling, and storage procedures should be identified in order that the homogeneity and stability are assured to the degree possible and contamination or mix-up are precluded. Storage container(s) should carry identification information, expiry date, and specific storage instructions. Each test and reference item should be appropriately identified (e.g., code, Chemical Abstracts Service Registry Number [CAS number], name, biological parameters).

7. Standard Operating Procedures

A test facility should have written Standard Operating Procedures approved by test facility management that are intended to ensure the quality and integrity of the data generated by that test facility. Revisions to Standard Operating Procedures should be approved by test facility management. Each separate test facility unit or area should have immediately available current Standard Operating Procedures relevant to the activities being performed therein. Published text books, analytical methods, articles and manuals may be used as supplements to these Standard Operating Procedures. Deviations from Standard Operating Procedures related to the study should be documented and should be acknowledged by the Study Director and the Principal Investigator(s), as applicable.

8. Performance of the Study

For each study, a written plan should exist prior to the initiation of the study. The study plan should be approved by dated signature of the Study Director and verified for GLP compliance by Quality Assurance personnel. The study plan should also be approved by the test facility management and the sponsor, if required by national regulation or legislation in the country where the study is being performed.

9. Reporting of Study Results

A final report should be prepared for each study. In the case of short term studies, a standardized final report accompanied by a study specific extension may be prepared. Reports of Principal Investigators or scientists involved in the study should be signed and dated by them. The final report should be signed and dated by the Study Director to indicate acceptance of responsibility for the validity of the data. The extent of compliance with these Principles of GLP should be indicated.


10. Storage and Retention of Records and Materials

The following should be retained in the archives for the period specified by the appropriate authorities: a) the study plan, raw data, samples of test and reference items, specimens, and the final report of each study; b) Records of all inspections performed by the Quality Assurance Programme, as well as master schedules; c) records of qualifications, training, experience and job descriptions of personnel; d) records and reports of the maintenance and calibration of apparatus; e) validation documentation for computerized systems; f) the historical file of all Standard Operating Procedures; g) environmental monitoring records.

Material retained in the archives should be indexed so as to facilitate orderly storage and retrieval. Only personnel authorized by management should have access to the archives. Movement of material in and out of the archives should be properly recorded. If a test facility or an archive contracting facility goes out of business and has no legal successor, the archive should be transferred to the archives of the sponsor(s) of the study(s).

GLP in India

The Indian test facilities, involved in safety studies have been appraising concerned Government Departments(s) for the need of having a system of GLP certification whereby they can demonstrate their capabilities, to a third-party as per the international norms. Few of the Indian laboratories have even obtained GLP-compliance certification based on OECD Principles of GLP from OECD member countries to meet their pressing needs. However, it partly served their purpose as such as a GLP certification is not acceptable in remaining OECD member countries because it lacks the Indian commitment for mutual acceptance.

National GLP Compliance Monitoring Authority was established by the Department of Science & Technology, Government of India, with the approval of the Union Cabinet on April 24, 2002. Presently, India enjoys the status of a provisional member of the OECD for GLP. India is an Observer to the OECD’s Working Group on GLP and also a member of the OECD Test Guidelines Programme. The aim is be to get the status of full membership in the near future so that the Indian industries do not have to get their test facility (products) certified from safety angle by other GLP monitoring authorities and do not lose on the trade front.

The National GLP Programme functions through an Apex Body, which has Secretaries of concerned Ministries/Departments, Director-General, CSIR and the Drugs Controller General of India as its members with Secretary-DST as its Chairman. This Apex Body is responsible to ensure that the National GLP Programme functions as per OECD norms and principles. The Apex Body is supported by Technical Committee on GLP, National Coordination Committee for OECD Test Guidelines Programme and Legislation Committee to enact a national


legislation on GLP. The Authority has trained 33 experts in the country as GLP inspectors.

GLP-compliance certification is voluntary in nature. Industries/test/ facilities/laboratories dealing with chemicals and looking for approval from regulatory authorities before marketing them, may apply to the National GLP Compliance Monitoring Authority for obtaining GLP Certification for one or more of the following areas of expertise:

• physical-chemical testing • toxicity studies • mutagenicity studies • environmental toxicity studies on aquatic and terrestrial organisms • studies on behaviour in water, soil and air • bio-accumulation, residue studies • studies on effects on mesocosms and natural ecosystems • analytical and clinical chemistry testing • Others

The test facilities/laboratories have to apply in the prescribed application form. After the application for GLP certification is received, a pre-inspection of the laboratory is carried out by the GLP inspectors, followed by a final inspection. The report, prepared by the inspection team, is put to the Technical Committee for recommendation to Chairman, National GLP-Compliance Monitoring Authority. GLP-compliance Certification is valid for a period of three years and the GLP Secretariat organizes annual surveillance and a re-assessment during third year for maintaining the certification.

Application of GLP concept in food and dairy laboratories

The essential quality elements in the food industry are stability, safety, wholesomeness and purity of the products. The requirements generally applied to food/dairy production originate from legislative governmental standards in different countries, specifications in international markets, and consumers’ and customers’ needs. The food laboratories are generally involved in two distinct types of operations, each with defined responsibilities.

The responsibility of the laboratory situated in a factory is to control the quality of the production and the products. The functions of the laboratory are an essential part of the manufacturing process, and also an important sector of good manufacturing practices. The success in good manufacturing practices and critical control point surveillance depends on the reliability, adequate and accuracy of the analytical work of the laboratory. Prerequisite for high quality manufacture is the certainty that the laboratory has the capacity and is able to use proper and appropriate


analysing techniques, to ensure that the results are correct and fulfill the requirement of accuracy, reproducibility and repeatability. This can be achieved by following the guidelines of ISO 17025 followed by the accreditation of the laboratory which provides formal recognition to competent laboratories.

However, for a research and development (R & D) laboratory, working under GLP environment by following the principles of GLP is a good proposition. R & D laboratory are generally entrusted with the job of product development, identification of health benefits of a particular food, isolation of some health promoting factors/constituents from the food, identification of new technological parameters for alternative processing of raw material etc. GLP studies must be fully documented (methods, procedures, deviations), which means that they can be accurately repeated at any time in the future. The full documentation of the studies, from planning activities right through to the production of reports, means that all the activities of the study are traceable and therefore the study may be audited by third parties. Since GLP is an internationally accepted standard for the organization of studies, performing such experiments with compliance to GLP promotes their acceptance world-wide. In case of any dispute, the GLP documentation may be of great help for the validation of any claim made by R & D laboratory.

Conclusion

The purpose of these Principles of GLP is to promote the development of quality test data and to provide a managerial tool to ensure a sound approach to the management, including conduct, reporting and archiving, of laboratory studies. The Principles may be considered as a set of criteria to be satisfied as a basis for ensuring the quality, reliability and integrity of studies, the reporting of verifiable conclusions, and the traceability of data. Consequently the Principles require institutions to allocate roles and responsibilities in order to improve the operational management of each study, and to focus on those aspects of study execution (planning, monitoring, recording, reporting, archiving) which are of special importance for the reconstructability of the whole study. Since all these aspects are of equal importance for compliance with the Principles of GLP, there cannot be any possibility of using only a choice of requirements and still claiming GLP compliance. No test facility may thus rightfully claim GLP compliance if it has not implemented, and if it does not comply with, the full array of GLP rules.

Reference

Handbook of Good laboratory practices. Quality practices for regulated non-clinical research and development. UNDP/World Bank/ WHO – Special program for research and training in tropical disease, Geneva, Switzerland.


Requirement for test facilities. OECD principals of good laboratory practice for GLP certification. National GLP compliance monitoring authority. Department of Science Technology, India.

Green, M.J. (1996). A practical guide to analytical method validation. Analytical Chemistry, 68: 305A-309A.

Sivela, S (1988). Good laboratory practices in the dairy industry. Bulletin of the International Dairy Federation; 229: 24-26.

Wood R, Nilsson A and Walin, H (1998). Quality in the food analysis laboratory.

RSC Food Analysis Monograph. The Royal Society of Chemistry, Cambridge.


Dr. (Mrs.) Bimlesh Mann

Dairy Chemistry Division NDRI, Karnal-132001

The main milk processing treatments involve separation, homogenization, heating, membrane processing and fermentation. The manufacture of virtually all milk and dairy products involves heat treatment. Such treatment is mainly aimed at:

1. Warranting the safety of consumer.

2. Increase the keeping quality.

3. Establishing specific product properties

Milk is a heterogeneous system and each component has its own role in heat processing treatment. The main variable is, of course, heating intensity (i.e., temperature and duration of heating). Many combination of time and temperature may thus be of the same intensity (i.e., cause the same extent of reaction), but the combinations are usually different for different reaction. A proper understanding of these factors and a balance between them is essential for producing products with desirable properties

Influence of heat treatment on the constituents of milk:

Changes in milk caused by increase in temperature may be reversible or irreversible. Reversible changes include the mutarotation equilibrium of lactose and ionic equilibria, including pH. Numerous irreversible changes caused by heat treatment are as follows:

• Gases, including CO2, are removed (if they escape from the heating equipment). Loss of O2 is important for the rate of oxidation reaction during heating and for the growth rate of some bacteria afterward. The loss of gases is reversible, but uptake of air may take a long time.

• The cream plug phenomenon is evident at 74°C. Various theories have been discussed, but it appears that liberated free fat cements the fat globules when they collide. Homogenisation is recommended to avoid cream plug formation. Fink and Kessler (1985) have shown that free fat leaks out of the globules in cream with 30% fat, unhomogenised as well as homogenised, when it is heated to temperatures between 105 and 135°C. This is believed to be caused by destabilisation of the globule membranes resulting in increased

CHEMISTRY OF QUALITY ATTRIBUTES IN HEAT PROCESSED DAIRY PRODUCTS


permeability, as a result of which the extractable free fat acts as cement between colliding fat globules and produces stable clusters. Above 135°C the proteins deposited on the fat globule membrane form a network which makes the membrane denser and less permeable. Homogenisation downstream of the steriliser is therefore recommended in UHT treatment of products with a high fat content. The composition of the surface layers of the fat globules formed during homogenization or recombination is affected by the intensity of heating before homogenization, mainly because of denaturation serum proteins. This affects some properties of the products. For example, the tendency to form homogenization clusters. Glycerides are hydrolysed and interesterified. Lactones and methyl ketones are formed from the fat.

Fig 1: During denaturation κ-casein attach to β-lacto globulin • The major protein, casein, is not considered denaturable by heat within normal

ranges of pH, salt and protein content. Whey proteins, on the other hand, particularly β-lactoglobulin which makes up about 50% of the whey proteins, are fairly heat sensitive. Denaturation begins at 65°C and is almost completed when whey proteins are heated to 90°C for 5 minutes. Whey protein heat denaturation is an irreversible reaction. The randomly coiled proteins "open up", and β-lactoglobulin in particular is bound to the κ-casein fraction by sulphur bridges. The strongly generalised transformation is shown in figure 1. Blockage of a large proportion of the κ-casein interferes with the renneting ability of the milk, because the rennet used in cheese making assists in splitting the casein micelles at the κ-casein locations. The higher the pasteurisation temperature at constant holding time, the softer the coagulum; this is an undesirable phenomenon in production of semi-hard and hard types of cheese. Milk intended for cheese making should therefore not be pasteurised, or at any rate not at higher temperatures than 72°C for 15 – 20 seconds. In milk intended for cultured milk products (yoghurt, etc.), the whey protein denaturation and interaction with casein obtained at 90 – 95°C for 3 – 5 minutes will contribute to improve quality in the form of reduced syneresis


and improved viscosity. Milk heated at 75°C for 20 – 60 seconds will start to smell and taste “cooked”. This is due to release of sulphurous compounds from β-lacto globulin and other sulphur-containing proteins. Most of the serum proteins are denatured and thereby are rendered insoluble.

• Enzymes can be inactivated by heating. The temperature of inactivation varies according to the type of enzyme. There are some bacteria, Pseudomonas spp, (spp = species) nowadays very often cited among the spoilage flora of both raw cold-stored milk and heat treated milk products, that have extremely heat-resistant proteolytic and lipolytic enzymes. Only a fraction of their activity is inhibited by pasteurisation or UHT treatment of the milk.

• Lactose undergoes changes more readily in milk than in the dry state. At temperatures above 100 °C a reaction takes place between lactose and protein, resulting in a brownish colour. The series of reactions, occurring between amino groups of amino acid residues and aldehyde groups from milk carbohydrates, is called the Maillard reaction or browning reaction. It results in a browning of the product and a change of flavour as well as loss in nutritional value, particularly loss of lysine, one of the essential amino acids. It appears that pasteurised; UHT and sterilised milks can be differentiated by their lactulose content. Lactulose is an epimer of lactose formed in heated milks (Adachi, 1958). Martinez Castro & Olano, 1982, and Geier & Klostermeyer, 1983, showed that pasteurised, UHT and sterilised milks contain different levels of lactulose. The lactulose content thus increases with increased intensity of the heat treatment. Lactose isomerizes and partly degrades to yield, for instance, lactulose and organic acids.

• Vitamin C is the vitamin most sensitive to heat, especially in the presence of air and certain metals. Pasteurisation in a plate heat exchanger can however, be accomplished with virtually no loss of vitamin C. The other vitamins in milk suffer little or no harm from moderate heating.

• Of the minerals in milk only the important calcium hydroxyphosphate in the casein micelles is affected by heating. When heated above 75°C the substance loses water and forms insoluble calcium orthophosphate, which impairs the cheese making properties of the milk. The degree of heat treatment must be carefully chosen. The amount of colloidal phosphate increases and the Ca2+ decreases. These changes are reversible, though slowly. Phosphoric acid esters, those of casein in particular are hydrolysed. Phospholipids and some dissolved esters are also split. Consequently the amount of inorganic phosphate increases.

• Heating milk at first makes it a little whiter, maybe via the amount of colloidal phosphate increases and the Ca2+ decreases. At increasing heating intensity the color becomes brown, due to reactions between lactose and protein occur, Maillard reactions in particular.

• Viscosity may increase slightly because most of the serum proteins are denatured and there by are rendered insoluble, and much more due to Casein


micelles become aggregated. Aggregation may eventually lead to coagulation. The latter change especially occurs when concentrated milk is sterilized. Tendency for age thickening and for heat coagulation of concentrated milk may be decreased.

• Several bacteria can grow faster in heat treated milk because bacterial inhibitors like lactoperoxidase-H2O2-CNS and immunoglobulin are inactivated. Furthermore, heat treatment may lead to formation of stimulants for some bacteria or inhibitors for still other bacteria. All these changes greatly depend in heating intensity.

• The proneness to auto oxidation is affected in several ways mainly due to formation of free sulfhydryl groups which causes, for instance a drop of the redox potential (Eh), inactivation of enzymes and several changes occur in the fat globule membrane, e.g., in its Cu content. Heating of milk above 70ºC causes a noticeable decrease in the Eh due to liberation of –SH groups from whey protein and loss of O2. Compounds formed by the Maillard reaction between lactose and proteins can also influence the Eh of heated milk, particularly dried milk products.

• Pasteurization causes some changes in pH due to the loss of CO2 and precipitation of calcium phosphate. Higher heat treatment (above 100 ºC) results in a decrease in pH due to the degradation of lactose to various Organic acids.

• Pasteurization of milk has little effect on its surface tension although heating milk to sterilization temperatures causes a slight increase in surface tension, resulting from denaturation and coagulation of protein which are then less effective as surfactants. Heat treatment of buffalo milk causes reduction of curd tension.

Heat processed Dairy products and chemical quality attributes:

The deteriorative processes that occur in foods after harvesting and during storage and distribution are unavoidable. If food is untreated, microbial deterioration becomes the dominant process affecting safety and quality. Even if the foods are treated to reduce or eliminate microbial contamination, chemical and physical deterioration become the dominant processes in determining storage life time and in altering product quality. Accordingly, if technological strategies are to be devised to retard such deterioration and to minimize the consequent loss of quality, it is crucial to understand the nature of physico-chemical changes (instabilities) in the constituents and the factors that control component degradation. Physico-chemical changes takes place in milk during processing directly related to the colour, flavour and texture of the final dairy products. Ultimately delivering quality dairy foods desired by consumers depends on being able either to modify the instability of major constituents or choose processing and storage conditions that minimize the chemical or physical deterioration. Some of such major changes which act as quality indices are being mentioned below.


Nonenzymatic browning

One of the most common reactions in milk products is the non enzymatic browning reaction, the Maillard reaction. It can involve reducing sugars and amino groups on the protein, consequent changes in colour and texture of the proteins can occur under abusive storage conditions, because it’s Q-10 (the increase in the rate constant as temperature is raised by 10°C) in 3-4.

The thermal processing involved in condensing and drying initiates a Maillard reaction between the reducing sugar and the amino containing molecules of foods. The Maillard reaction, other reactions following from it, continue during storage and eventually result in the development of brown colour, changes in solubility of powdered foods, loss of nutritional value (chiefly lysine availability) and stale flavours. These reactions may limit the shelf life of sterile concentrates and powders. Much of 5 hydroxy- methyl furfural produced by these reactions in sterile-concentrated skim milk occur during thermal processing but that the brown pigment is produced during storage and is protein bound. Commercial non fat powder and infant formulae stored at 30°C to 40°C for one month at aw 0.8 become dark brown and lost 29 and 45% of their available lysine respectively. At aw 0.2 or less the loss was greatly reduced. Available lysine was reduced by 12, 23 and 49% for non-fat dry milk stored at 4, 20 and 37°C at aw 0.75. At water activity 0.44 the loss of available lysine and browning increases. The greater deterioration at aw 0.44 was attributed to the crystallization of lactose and release of moisture that occurred at this aw (La Grange and Hammond, 1995). Sterilization of paneer cubes leads to browning accompanied with cooked flavour affected the organoleptic quality of paneer. Paneer cubes fried prior to sterilization spoiled earlier due to the development of oxidised flavour.

Many of the sulphur-containing flavours that are produced during heating capable of further reaction, particularly with carbonyls, and this can cause abatement of the heated flavour during storage and some time the generation of new flavours. Sulphur compounds dominate the flavour produced by milk heat treatment. Reps et al., (1987) showed that autoclaving milk gave rise to glyoxal and methyl glyoxal, presumably through carboxyl amine reaction. These dicarbonyl have shown to react with methionine and cysteine to produce many of the sulphur compounds identified in milk. Reaction of these dicarbonyls with phenylalanine account for many of the aromatic compounds identified in heated milk. Methyl glyoxal can dimerise to 2, 5 dimethyl-4hydroxy3 (2H) furanone which may partly account for the caramelised flavours that are observed. Lactones and methyl ketones released from milk fat by heat treatment also may play a role in heated flavour (Scanlan et al., 1968).

The stale flavour resulted primarily from reaction of free amino acids with carbonyl generated in the browning quality of protein foods due to Maillard reaction can occur during storage with minimal change in the nutritive value, it can be avoided


by a proper selection of ingredients, time and temperature during thermal processing and storage conditions like aw and temperature. Conversely the nutritional value of the proteins should not be a significant problem as long as the foods are acceptable to the consumer from sensory point of view.

Proteolysis

Proteolysis means proteolytic changes occurring in foods that have low but discernible enzymatic activity for example protein changes associated with storage in some dairy products (Barnett and Kim, 1998). Cheese manufacture begins with the use of proteolytic enzymes such as rennet to cause milk to coagulate to produce cheese curd. During aging, continued proteolysis contributes to flavour and texture development. For some cheese types residual protease activity can have an adverse effect on quality. For example, the tensile strength of Mozzarella cheese decreases logarithmically with storage time due to protease activity.

Paneer is highly susceptible to chemical changes. Proteolysis and lipolysis are two major changes which affect the quality of paneer during storage. UHT processed milk eventually gel upon storage and develop off flavours even through there is no microbial growth. One of the proteases responsible for is plasmin, which probably enters the milk from blood in the forms of its precursor, plasminogen (Walstra and Jenness, 1984). In fresh milk, most of the enzyme is present as the precursor. Increased plasmin activity is observed after UHT treatment and plasminogens decrease with plasmin activity increases upon storage (Manji, 1987).

Lipolysis

The majority of natural lipids consist of fatty acids attached to glycerol through carboxylic ester bonds. Hydrolysis of the ester bonds catalyzed by acid, alkali, heat, moisture or lipolytic enzymes result in the liberation of per fatty acid. Enzyme may be present naturally in food or in constituents mixed with food; some could be associated with microbial contamination. Temperature, moisture and pH are among the factors control lipase activity.

Off favours resulting from hydrolytic rancidity are more likely to occur in fat containing relatively short chain fatty acid i.e. C 4-10). The potential for lipolysis in milk however is minimized due to the structure of the milk emulsion, which limits physical contact between triacyl glycerol substance residing in fat glycerols and the lipase enzyme in the skim milk. Agitation during processing the native milk structure and promote enzyme substrate interaction. Although heat inactivates the lipolytic micro organisms, the lipases produced by them can survive normal pasteurization temperatures. Many typical flavours are produced where short chain fatty acids are hydrolysed by natural milk or microbial lipolytic enzymes (Nawar, 1998).

Khoa has a low keeping quality at room temperature and develops rancid flavour due to hydrolysis of fat by lipase action. Due to the vigorous agitation of milk at high temperature, the fat globules are appreciably sub-divided. Considerable free fat is also


produced due to the rupturing of the fat globule membrane by the vigorous scraping action of the stirrer. The vigorous agitation of hot milk has an appreciable homogenising action so that when the stage of coagulation is reached all the fat globules are entrained in the coagulum. Almost half of the globular fat is released as free fat. The extent of which depend upon fat contents of milk and manufacturing process.

Oxidation

Lipid oxidation is of paramount important to food quality. It may lead to the development of rancid off flavours, cause change in colour texture, reduce shelf life and/or impair nutritional quality. However a limited degree of lipid oxidation is sometimes desirable, as in the formulation of typical flavours and aromas that are associated with cheese and fried food.

Lipid oxidation proceeds via a typical self propagating free radical mechanism where oxygen attaches occurs mainly at positions adjacent to the double bounds. The breakdown of hydro peroxides leading to the formation of volatile and non volatile product may also be catalysed by enzyme (i.e. hydroperoxidase). Obviously due to the greater specificity in enzyme-catalysed formation and decomposition of fatty acid; hydro peroxide and other specific oxidation and products are encountered. The various factors which influence the lipid oxidation are free fatty acids, the fatty acids positions in triacylglycerols, oxygen concentration, temperature, water content, physical conditions, prooxidants and antioxidants.

Although milk fat contains relatively low concentrations of poly unsaturated fatty acid (about 3%). These play the primary role in the development of oxidized flavours-vinyl ketones such as 1-octen-3 one or octa-1 cis 5 diene- 3 one play a dominant role in the flavour of oxidized milk. The vinyl ketones themselves give milk a metallic flavour but when blended with an aldehyde give a typical oxidized flavour. Oxidation in dairy products also can be initiated by exposure to light (Korycka and Richardson, 1978; 1979 and 1980). But the irradiated flavours produced in this way are significantly different from those produced by metal-catalyzed oxidation. Riboflavin is the primary pigment involved in irradiated flavour. Light activated riboflavin is reduced by molecules such as methionine, producing sulphur compounds typical of irradiated flavour. The reduced riboflavin can react with oxygen can be quite rapid and lead to noticeable flavour in a few minutes to a few hours of exposure, depending on the intensity and wave length of the light.

Typically ghee possesses a pleasant buttery, light caramelized sweet acidic aroma. Flavour development and its profile in ghee is quite complex.According to Bindal and Jain (1973), the flavour of ghee developed through the possible interaction during heating process among a protein (probably casein degradation product), a reducing sugar (lactose) and minerals. At the temperature of clarification employed during the process of manufacture, the carbonyl content increases. On prolong storage of ghee the off flavour has been found in ghee samples with three fold increase in


total carbonyls. The level of lactones increased with increase in clarifying temperature. The deterioration due to oxidative rancidity of ghee depends on degree of unsaturation in fat, availability of oxygen, heat, light, moisture content, free fatty acids, oxidation catalyst and antioxidants. Heating milk and cream to 75°c - 85°c develops a cooked flavour due to release of –SH group which delays oxidation significantly. Addition of heat dried skim milk act as an antioxidant in butter or cream.

Butter, cream, whole milk powder and even milk may develop a fishy flavour due to certain amines that possesses fishy flavour and odours. Lecithin is the only source of organic nitrogen in the amine form because of the presence of choline. Choline is decomposed to trimethylamine. Production of fishy flavour is related to conditions favourable for hydrolysis and oxidation of lecithin.

Irradiated flavour often is the most common defect in market milk because of the wide spread sale of milk is translucent polyethylene in well lightened place. In powdered milk especially powdered whole milk, flavour deterioration can occur through fat oxidation. This can be affected by the amount of free fat on the particle surface, the water content of the powder, the sort of packaging used, storage temperature, exposure to light and the addition of antioxidants. The oxidation of cholesterol in spray dried powders may be a health concern (Hall and Linguert, 1984; and Clevelard and Harris, 1987).

A number precautionary measures based mainly on the various aspects considered above can be recommended for prolonged shelf-life that is limited by oxidation and for minimizing undesirable changes in the quality of edible oil and fat containing foods:

• Select high quality raw material (e.g. seeds with minimum damage, oils with low FFA content and high resistance to oxidation.

• Use high quality food ingredient (e.g. milk, nuts, spices) • Use techniques that reduce substrate catalyst interaction (i.e. avoid cell

disruption, contact with enzyme). • Minimize contact with oxygen, light and/or trace metals. • Minimize exposure to elevated temperature. • Use packaging that provides a reasonable gas barrier during storage

and distribution. • Minimize surface area in contact with air. • Design and maintain proper storage tanks and pipe line (e.g. stainless

steel, if possible; glass lining, free of copper and copper alloys, frequent cleaning, minimal head space, lowest practical temperatures, protection for contamination with micro organisms and regular inspection)


• Use appropriate antioxidants. Beside non-enzymatic browning and lipid oxidation there are other physico-

chemical changes take places during storage of food products which affect the texture, colour, flavour and overall acceptability of foods.

Viscosity, gelation and sedimentation

The factors limiting the shelf life and acceptability of liquid products are change in viscosity, precipitation and gelation. These reactions are seen typically in concentrated, sterile products. The traditional process for in-can sterilization of milks, supplemented with, judicious addition of phosphates, citrates and gums, typically produces products that are reasonably stable for over a year. However holding the concentrated product in refrigerated storage before canning, results in more rapid gelation (Halwalkar et al., 1983). When ultrahigh temperature processing and aseptic packaging is used, precipitation and gelation are a more common problem. The cause of those changes is not definitely established but may cause by incomplete destruction of the milk protease plasmin which in fairly heat stable. In any event the storage of UHT-sterilized milk is often accompanied by proteolysis (Harwalker and Vreeman, 1978); Mckenna and Singh 1991), but leads to the joining of casein micelles by thin, hair like linkages and gelation (Zadow and Hardham, 1981; Koning et al., 1994). Gelation often in proceeded by precipitation and an increase in viscosity (Newstead et al., 1978; Snoren et al., 1984). These changes are affected by extent of concentration, season and lactation of milk production, extent of heat treatment, temperature of storage, pH and addition of polyphosphate and other ions.

Sedimentation of protein in UHT milk occurred if the pH was less than 6.55 (Zadow and Hardham, 1981). Sequestering calcium reduced sedimentation. Harwalker and Vreeman 1978 found that the viscosity of UHT treated skim milk was much increased in 9 weeks, while samples with added phosphate lasted 12 weeks. Both of these samples gelled by 18 weeks. Samples with added polyphosphate showed no increase in viscosity on gelation at 18 weeks. Mckenna and Singh (1991) reported that UHT processed reconstituted concentrated milk containing 0.075% Hexametaphosphate did not gel or become viscous for 6 months at 22°C. To achieve this shelf life at 30°C 0.075 to 0.15% Hexametaphosphate was needed.

Crystallization of lactose

The major detriment to the shelf life of dry milk products in moisture, to much moisture in processed dry milk and or moisture from the atmosphere getting into the product during storage. The dry lactose in milk powder is very hydroscopic and readily picks up moisture from the atmosphere. Amorphous lactose is formed when a solution (e.g. milk) is dried rapidly as in a spray drier or frozen. If the water content of amorphous lactose is low, say 3% crystallization may be postponed almost indefinitely; nucleation rate is negligible because of the


extremely high viscosity of the solution. The product is however very hydroscopic, and when moisture content arises to about 8%, lactose hydrate starts to crystallize. But when crystallization of lactose caused by moisture uptake occurs in milk or whey powder, the result in caking, powder particles and concentrated together by crystalline lactose, forming large and strong lumps. (Walstra and Jenness, 1984). Controlling the moisture of milk/powder between 3.5 and 3.9% and maintaining this moisture level within package will assume a shelf life of at least one year from the date of processing and packaging (Laarange and Haurmond 1993).

Lactose crystals rupture fat globule membrane and thus free fat amount is considerably increased and it leads to an oily layer formation, greasiness etc. after reconstitution and faster oxidation. Thus crystallisation of lactose affects the dispersibility, baking and sensory quality of the products. In roller dried powder about 90% of fat exists as free fat whereas in spray dried milk powder, most of the fat exists as globule with membrane intact. In spray dried milk powder, the action is similar to homogenization, fat globules subdivide, otherwise remain distributed throughout the particles in globular form. During spray, air is incorporated so fat tends to surround the air particles. The free fat is milk powders leads to oxidation. High moisture, excessive absorption of moisture by lactose resulting in lactose crystallisation leads to lumping/ caking of powder. High moisture powder is more prone to insolubility due to denaturation of protein. More moisture also favours browning reaction, auto oxidant and hydrolytic rancidity.

During storage and distribution, dairy foods are exposed to a wide range of environmental conditions. Environmental factors such as temperature humidity, oxygen and light can trigger several reaction mechanisms that may lead to food degradation. As a consequence, food may be altered to such an extent that they are either rejected by consumer, or they may become harmful to the person consuming them. It is, therefore, imperative that a good understanding of different physico-chemical reactions that cause deterioration is gained prior to developing specific methods for the evaluation, monitoring and predicting the quality of these dairy products.

References: Barnett R.E. and Kim. H.J. (1998) Protein instability. In Food storage stability, CRC Press, chapter 3, pp 75-87.

Bindal,MP and Jain,MK(1973) Indian J.Ani. Sci.43(10),918-24

Cleveland H.Z. and Harris, N.D. (1987) J. Food Protection 50: 867-871.

Fink A. and H.G. Kessler (1985) Milchwissenschaft 40, 6-7.

Hall G. and Lingnert H. (1984) J. Food Quality 7:131-151.


Harwalkar, V.R.; Backett. D.C. ; McKeller, R.C.. Emmons, D.B. and Doyle G.E. (1983) Age thickening and gelation of sterilized evaporated milk. J. Dairy Sci., 66 : 735-742.

Harwalkar, V.R. and Vreeman H.D. (1978) Neth Milk Dairy J. 32 204-216.

Harwalkar, V.R. and Vreeman, H.D. (1978) Neth. Milk Dairy J. 32: 204-216.

Korycka, Dahl. M. and Richardson, J. (1978) J.Dairy Sci., 61: 400-407.

Korycka, Dahl. M. and Richardson, J. (1979J. D. Sci. 62 : 182-183.

Korycka Dahl M and Richsrdson, J. (1980J. Dairy Sci., 63 : 1181-1198.

La Grange, W.S. and Hammond, E.G. (1995) The Shelf life of Dairy Products. In Shelf life Studies of Foods and Beverages, Elsevier Science publishers Chapter 1, pp 1-20.

Mauji, B.S. (1987) Ph.D Dissertation University for Guelph Ont, cited from Food Storage Stability, Taub and Paul Singh, Eds. CRC Press, Chapter 3, pp 75-87.

Mckenna, A.B. and Singh H. (1991) Int. J. Food Sci. and Technol., 26: 27-28.

Newstead, D.F., Baldwin, A.j. and Hughes, I.R. (1978) New Zealand Dairy Sci. and Technol., 13: 65-70.

Nawar, W.W. (1998) Biochemical Processes: Lipid instability, In Food storage stability, Taub and Paul Singh Ed, CRC Press, Chapter 4, pp 89-103.

Repg, A., Hammond, E.G., Glatz, B.A. (1987) J. Dairy Sci. 70: 559-562.

Scaulan, R.A., Lindsay, R.C., Libbey, L.M. and Day E.A. (1968) J. Dairy Sci., 51: 1001-1005.

Szczesniak, A.S. (1998) Effect of storage on texture. In Food storage stability, Taub and Paul Singh Eds., CRC Press, Chapter 4, pp 89-103.

Walstra, P. and Jennes, R. (1984) Enzymes. In Dairy Chemistry and Physics, Wiley, New York, Chapter 7, pp-133.

Zadow, J.G. and Hardham, J.F. (1981) Aust. J. Dairy Techol., 36: 30-33.


R. R. B. Singh Division of Dairy Technology

National Dairy Research Institute, Karnal Introduction Water is an integral part of all food systems. It determines behavior of food products during many processing operations and significantly affects the quality of food. An understanding of the state of water in foods that is characterized by water activity aw is therefore essential to control and optimize various physical, chemical and microbial changes in food systems. Determination of sorption isotherms thus, has several applications in food science. In mixing operations and development of a new product formulation, sorption isotherm data of each component will help to predict transfer of moisture from one product to another, which is essential for controlling the deterioration of the final product. Determination of enthalpy of sorption and desorption of water at two different temperatures gives an indication of binding strength of water molecules to the solid and has definite bearing on the energy balance during drying and freezing operations. Sorption isotherm is also important in packaging operations as the knowledge of initial and maximum allowable moisture content and aw along with the surface and permeability of the packaging material will help in determining shelf life of the packaged foods under varying conditions of storage.

Methods for determination of sorption isotherms

Methods that have been developed for determining sorption isotherms can be broadly classified under two heads: A. Gravimetric methods; B. Manometric and Hygrometric methods. Although several innovations have been tried in both the group of methods of measurement to improve the rapidity and accuracy of measurement, the following gravimetric method has been recommended by the cost projects 90 and 90-bis on physical properties of foodstuffs. (COST = Co-operation in the field of Scientific and Technical Research in Europe) and remains by far the most widely used and reliable method of determining sorption isotherms. Principle of measurement

The principle underlying the method of measurement is that food product is exposed to a controlled environment of relative humidity at defined temperature

DETERMINATION OF SORPTION ISOTHERMS AND GENERATION OF SORPTION DATA


condition. The weight of the sample is monitored at definite intervals till a time there is no change in weight as the food attains equilibrium with the environment. Such determinations at several relative humidity (RH) conditions will yield a sorption isotherm.

2.2 Design of the sorption apparatus The equipment which has been recommended consists of a simple arrangement comprising of glass jars as sorbostat with vapour tight lids. Sorbed source in sufficient quantity is placed in the jar to maintain large sorbate to sample ratio. The substance is placed in small weighing bottles standing on trivets directly above the sorbate source. The jars are then placed in thermostatically controlled incubators or water baths maintained at predetermined temperatures.

Fig. 1. Sorption apparatus

1. Sorption container; 2. Weighing bottle with ground in stopper; 3. Petridish on trivets; 4. Saturated salt solution

2.3 Sorbate sources for creating constant ERH

2.3.1 Sulphuric acid solutions of varying concentrations: Depending on the

concentration, sulphuric acid solutions will have varying water vapour pressure and the ERH in the headspace will accordingly change. The major limitations of using H2SO4, however, remains change in the concentration due to loss or gain of moisture thereby altering the ERH conditions. The following Table gives aw of sulfuric acid solutions at different temperature.


Table 1. Water activity of sulfuric acid solutions at different concentrations

and temperatures

Source: Rao and Rizvi, 1986

2.3.2 Glycerol solutions: Glycerol solutions of varying concentrations (adjusted with water) can also be used for creating constant ERH conditions. The difficulty in using glycerol solutions, however, arises from the fact that glycerol can volatilise and absorb into the foods thereby causing error. Un like H2SO4, it is non-corrosive but gets diluted or concentrated during sorption due to loss or gain of moisture from the sample. Table 2 below gives aw of glycerol solutions.

Temperature (oC) H2SO4

(%)

Density at 25oC (g/cm3) 5 10 20 25 30 40 50

5 1.0300 0.9803 0.9804 0.9806 0.9807 0.9808 0.9811 0.9814

10 1.0640 0.9554 0.9555 0.9558 0.9560 0.9562 0.9565 0.9570

15 1.0994 0.9227 0.9230 0.9237 0.9241 0.9245 0.9253 0.9261

20 1.1365 0.8771 0.8779 0.8796 0.8805 0.8814 0.8831 0.8848

25 1.1750 0.8165 0.8183 0.8218 0.8235 0.8252 0.8285 0.8317

30 1.2150 0.7396 0.7429 0.7491 0.7521 0.7549 0.7604 0.7655

35 1.2563 0.6464 0.6514 0.6607 0.6651 0.6693 0.6773 0.6846

40 1.2991 0.5417 0.5480 0.5599 0.5656 0.5711 0.5816 0.5914

45 1.3437 0.4319 0.4389 0.4524 0.4589 0.4653 0.4775 0.4891

50 1.3911 0.3238 0.3307 0.3442 0.3509 0.3574 0.3702 0.3827

55 1.4412 0.2255 0.2317 0.2440 0.2502 0.2563 0.2685 0.2807

60 1.4940 0.1420 0.1471 0.1573 0.1625 0.1677 0.1781 0.1887

65 1.5490 0.0785 0.0821 0.0895 0.0933 0.0972 0.1052 0.1135

70 1.6059 0.0355 0.0377 0.0422 0.0445 0.0470 0.0521 0.0575

75 1.6644 0.0131 0.0142 0.0165 0.0177 0.0190 0.0218 0.0249

80 1.7221 0.0035 0.0039 0.0048 0.0053 0.0059 0.0071 0.0085


Table 2. Water activity of glycerol solutions at 20oC

Concentration (kg/L) Refractive index Water activity

- 1.3463 0.98 - 1.3560 0.96

0.2315 1.3602 0.95 0.3789 1.3773 0.90 0.4973 1.3905 0.85 0.5923 1.4015 0.80 0.6751 1.4109 0.75 0.7474 1.4191 0.70 0.8139 1.4264 0.65 0.8739 1.4329 0.60 0.9285 1.4387 0.55 0.9760 1.4440 0.50

- 1.4529 0.40 Source: Rao and Rizvi, 1986

2.3.3 Salt slurries: Saturated slurries of various inorganic and organic salts produce constant ERH in the headspace of sorption container. The ERH decreases with increasing temperature due to increased solubility of salts with increasing temperatures. The Table 3 below gives aw of different salt slurries at varying temperatures.

Table 3. Water activities of different salt slurries at various temperatures

Temperatures (oC) Salt 5 10 20 25 30 40 50

Lithium chloride 0.113 0.113 0.113 0.113 0.113 0.112 0.111 Potassium acetate - 0.234 0.231 0.225 0.216 - -

Magnesium chloride 0.336 0.335 0.331 0.328 0.324 0.316 0.305 Potassium carbonate 0.431 0.431 0.432 0.432 0.432 - - Magnesium nitrate 0.589 0.574 0.544 0.529 0.514 0.484 0.454 Potassium iodide 0.733 0.721 0.699 0.689 0.679 0.661 0.645 Sodium chloride 0.757 0.757 0.755 0.753 0.751 0.747 0.744

Ammonium sulfate 0.824 0.821 0.813 0.810 0.806 0.799 0.792 Potassium chloride 0.877 0.868 0.851 0.843 0.836 0.823 0.812 Potassium nitrate 0.963 0.960 0.946 0.936 0.923 0.891 0.848 Potassium sulfate 0.985 0.982 0.976 0.970 0.970 0.964 0.958

Source: Rao and Rizvi, 1986


Preparation of salt slurries: The Table 4 gives the proportion of different salts to water for preparing saturated slurries.

Table 4. Preparation of recommended saturated salt Solutions at 25oC

Salt RH (%) Salt (g) Water (ml)

LiCl 11.15 150 85

CH3COOK 22.60 200 65

MgCl2 32.73 200 25

K2CO3 43.80 200 90

Mg(NO3)2 52.86 200 30

NaBr 57.70 200 80

SrCl2 70.83 200 50

NaCl 75.32 200 60

KCl 84.32 200 80

BaCl2 90.26 250 70 Source: Spiess and Wolf, 1987 The following equations (Table) can be used for predicting aw of known salt slurries at any temperature.

Table 5. Regression equations of water activity of selected salt solutions at different temperatures

Salt Equation R2

LiCl Ln aw=(500.95/T)-3.85 0.976

KC2H3O2 Ln aw=(861.39/T)-4.33 0.965

MgCl2 Ln aw=(303.35/T)-2.13 0.995

K2CO3 Ln aw=(145.0/T)-1.3 0.967

MgNO3 Ln aw=(356.6/T)-1.82 0.987

NaNO2 Ln aw=(435.96/T)-1.88 0.974

NaCl Ln aw=(228.92/T)-1.04 0.961

KCl Ln aw=(367.58/T)-1.39 0.967

Temperature ‘T’ in Kelvin Source: Rao and Rizvi, 1986


While preparing salt slurries, the following care must be taken to improve precision of measurement:

• Only AR grade salts should be used. • Salt crystals in excess should be present at the bottom. • Before the samples are placed, the containers with slurries should be

maintained at required temperatures for 3-4 days for allowing equilibration. • The ratio of slurry surface to sample surface should preferably be >10:1. • The ratio of air volume to sample volume should be 20:1 • The salt slurries should be occasionally stirred to prevent change in

concentrations of top liquid layer due to loss or gain of moisture from the sample. Precautions:

• Some salts are caustic: potassium dichromate, potassium chloride • Some salts are highly toxic: lithium chloride, sodium nitrite • Alkaline solutions such as K2CO3 absorb large amounts of CO2 with time

thereby decreasing aw significantly Standardization of sorption apparatus with reference materials The recommended material for this purpose is microcrystalline cellulose (MCC). This material is very stable against changes in the sorption behaviour and can be used even after 2 to 3 repeated adsorption and desoption cycles. It does not exhibit hysteresis between adsorption and desorption and require very short periods for reaching equilibrium. Preparation of samples: The test substrate should be prepared in a way that ensures homogeneity so that sample drawn for sorption studies is representative of the bulk. Sample size should normally be 1 gm and at least three replications should be used for minimizing error in the study. For adsorption isotherms, samples should be vacuum dried preferably at 30°C for 30-40 hrs. followed by freeze drying and desiccant drying to reduce the moisture level to a level lower than the corresponding lowest water activity of the saturated salt being used. Once the samples have been weighed accurately and placed in the sorption jar, weighing should follow at regular intervals till the sample reaches equilibrium and the change in weight in three subsequent weighing does not change by more than 2 mg per gm of sample.

Types of moisture sorption curves The sorption isotherms are obtained by drawing a plot of moisture (g/100 g of sample, db) vs water activity. The isotherms thus obtained could be classified according to the following five general types.


Type II

(sigmoid)

Type IIIType IV

Type V

Type I (Langmuir)

Moisture

a

Fig. 2. The five types of van der Walls adsorption isotherms Isotherm models Over the years, a large number of isotherm models have been prepared and tested for food materials. These can be categorized as two, three or four parameter models. Some of the most commonly used models are presented hereunder:

Two parameter models

1. Oswin b

w

w

aa

aW⎭⎬⎫

⎩⎨⎧

−=

)1(

2. Caurie w

w

aa

WC

WCW−

+=1

ln2.1ln1ln

00

3. Halsey ⎥⎦

⎤⎢⎣

⎡ −

=bWa

w ea

4.BET Equation ])1(1)[1(

.0

awBawawBW

W−+−

=


Three parameter models

1. GAB [ ]www

w

GkakaakGka

WW+−−

=1)1(0

2. Modified Mizrahi 1

).(−

++=

w

ww

abacaa

W

Where,

W = Equilibrium moisture content, g/100 g solids W0 = Moisture content equivalent to the monolayer aw = water activity a, b = Constants B = Constant C = Density of sorbed water G = Guggenheim constant k = Correction factor for properties of multiplayer molecules

with respect to the bulk liquid Of these, GAB model has been found to be most appropriate for describing sorption behaviour of food systems over a wide range of water activity. Both BET (application range aw=0.05-0.45) and GAB equations can be used for obtaining monolayer moisture that is critical for quality and shelf life of foods

Effect of temperature on water activity Knowledge of the temperature dependence of sorption phenomena provides valuable information about the changes related to the energetic of the system. The shift in water activity as a function of change in temperature at constant moisture constant is due to the change in water binding, dissociation of water or increase in solubility of solute in water. At constant water activity, most of the foods hold less water at higher temperature. The constant in moisture sorption isotherm equations, which represents either temperature or a function of temperature, is used to calculate the temperature dependence of water activity. The clausius-clapeyron equation is often used to predict aw at any temperature if the isosteric heat and aw

values at one temperature are known. The equation for water vapour in terms of isosteric heat (Qst) is given by:

⎥⎦

⎤⎢⎣

⎡−=

212

1 11TTR

Qaa

In st

w

w

Where Qst is net isosteric heat of sorption or excess binding energy for the removal of water also called excess heat of sorption.


600

1100

1600

2100

2600

3100

3600

4100

4600

10 20 30 40 50 60 70 80 90 100

Moist ur e cont ent ( % db)

Figure 3. Typical diagram showing net isosteric heat of sorption

Hysteresis in adsorption–desorption isotherms

When adsorption and desorption isotherms for the same food material are plotted on the same graph, usually the desorption isotherm lies above the adsorption isotherm and sorption hysteresis loop is formed. Moisture sorption hysteresis has both theoretical and practical implications. The theoretical implications include considerations of the irreversibility of the sorption process and also the question of validity of thermodynamic functions derived therefrom. The practical implications refer to the response of the effects of hysteresis on chemical and microbiological deterioration in processed foods intended for prolonged storage. The hysteresis property of foods is generally affected by the composition, temperature, storage time, drying temperature, and number of successive adsorption and desorption cycles. Several theories have been proposed to explain hysteresis phenomenon in foods. A typical diagram showing hysteresis in sorption isotherm is given below:

Figure 4. Typical diagram showing hysteresis phenomenon

References:

Kapsalis, J.G. (1981) Moisture sorption hysteresis. In : water activity: influences on food quality (eds. L. B. Rockland and G. F. Stewart), Academic Press. Inc., New York, USA, pp. 143.

Water activity

Moistur

Adsorption

Desorption


Rizvi, S.S. H. (1986) Thermodynamic properties of foods in dehydration. In : Engineering properties of foods (eds. M. A. Rao and S. S. H. Rizvi), Marcel Dekker, Inc., New York, USA, pp. 133

Spiess, W.E.L. and Wolf, W. (1978). Critical evaluation of methods to determine moisture sorption isotherms . In: Water activity: Theory and applications to foods (eds. L.B. Rockland and

L.R. Beuchat.), Marcel Dekker, Inc., New York, USA, pp. 215.

Wolf, W., Spiess, W.E.L. and Jung, G. (1985) Standardization of isotherm measurement (COST- Project 90 and 90-bis). In: Properties of water in foods (eds. D. Simatos and J. L. Multon), Martin Nijhoff Publishers, Dordrecht, The Netherlands, pp. 661.


Rajan Sharma Sr. Scientist, Dairy Chemistry Division,

NDRI, Karnal

A biosensor is a device incorporating a biologically derived sensing element (e.g. enzyme, antibodies, microorganisms or deoxyribose nucleic acid –DNA) either integrated with or in intimate contact with a physicochemical transducer (e.g. electrochemical, optical, thermoelectric or piezoelectric). The usual aim is to produce a continuous or semi-continuous digital electronic signal which is proportional to a specific chemical or group of chemicals. Devices may be configured as fixed or portable instruments giving qualitative or quantitative information. The modern concept of a biosensor owes much to the ideas of Clark and Lyons (1962). They proposed that enzyme could be immobilized at electrochemical detectors to form ‘enzyme electrode’ which would expand the analyte range of the base sensor. The first biosensor described in literature (Clark and Lyons, 1962; Updike and Hicks, 1967) was based on the combination of glucose oxidase with electrochemical determination of O2 and H2O2. Since then, this principal has been extended to the development of sensors for other analytes using the electroactivity of not only H2O2 and O2 but also NADH and other compounds, so called mediators, for electron transfer from enzyme to the electrode.

The key part of a biosensor is the transducer which makes use of a physical change accompanying the reaction. This may be

1. the heat output (or absorbed) by the reaction (calorimetric biosensors), 2. changes in the distribution of charges causing an electrical potential to be

produced (potentiometric biosensors), 3. movement of electrons produced in a redox reaction (amperometric

biosensors), 4. light output during the reaction or a light absorbance difference between the

reactants and products (optical biosensors), or 5. effects due to the mass of the reactants or products (piezo-electric biosensors).

A comprehensive list of types of transducers, their characterization and applications is given in Table 1.

There are three so-called 'generations' of biosensors; First generation biosensors where the normal product of the reaction diffuses to the transducer and causes the

BIOSENSOR IN CHEMICAL QUALITY ASSESSMENT OF DAIRY AND FOOD

PRODUCTS


electrical response, second generation biosensors which involve specific 'mediators' between the reaction and the transducer in order to generate improved response, and third generation biosensors where the reaction itself causes the response and no product or mediator diffusion is directly involved.

The electrical signal from the transducer is often low and superimposed upon a relatively high and noisy (i.e. containing a high frequency signal component of an apparently random nature, due to electrical interference or generated within the electronic components of the transducer) baseline. The signal processing normally involves subtracting a 'reference' baseline signal, derived from a similar transducer without any biocatalytic membrane, from the sample signal, amplifying the resultant signal difference and electronically filtering (smoothing) out the unwanted signal noise. The relatively slow nature of the biosensor response considerably eases the problem of electrical noise filtration. The analogue signal produced at this stage may be output directly but is usually converted to a digital signal and passed to a microprocessor stage where the data is processed, converted to concentration units and output to a display device or data store.

Table 1. Types of transducers, their characteristics and application

Transducer

Advantages Disadvantages Application

Ion-selective electrode(ISE)

Simple, reliable, easy to transport.

Sluggish response, requires a stable reference electrode, susceptible to electronic noise.

Amino acids, carbohydrates, alcohols and inorganic ions

Amperometric Simple, extensive variety of redox reaction for construction of the biosensors, facility for miniaturization

Low sensitivity, multiple membranes or enzyme can be necessary for selectivity and adequate sensitivity.

Glucose, galactose, lactate, sucrose, aspartame, acetic acid , glycerides, biological oxygen demand, cadaverine, histamine, etc

FET Low cost, mass production, stable output, requires very small amount of biological material, monitors several analytes simultaneously.

Temperature sensitive, fabrication of different layer on the gate has not been perfected.

Carbohydrates, carboxylic acids, alcohols and herbicide.

Optical Remote sensing, low cost, miniaturizable, multiple modes: absorbance, reflectance, fluorescence, extensive electromagnetic range can be used

Interference from ambient light, requires high-energy sources, only applicable to a narrow concentration range, miniaturization can affect the magnitude of the signal.

Carbohydrates, alcohols, pesticide, monitoring process, bacteria and other


Thermal Versatility, free from optical interference such as color and turbidity.

No selectivity with the exception of when used in arrangement.

Carbohydrates, sucrose, alcohols, lipids, amines.

Piezoelectric Fast response, simple, stable output, low cost of readout device, no special sample handling, good for gas analysis, possible to arrays sensors.

Low sensitivity in liquid, interference due to non specific binding.

Carbohydrates, vitamins, pathogenic microorganisms (e.g. E. coli, Salmonella, Listeria, Enterobacter), contaminants (e.g antibiotics, fungicides, pesticides), toxic recognition as bacterial toxins.

Biosensor technology and food analysis

The control of food quality and freshness is of growing interest for both consumer and food industry. In the food industry, the quality of a product is evaluated through periodic chemical and microbiological analysis. These procedures conventionally use techniques such as chromatography, spectrophotometry, elctrophoresis, titration and others. These methods do not allow an easily continuous monitoring, because they are expensive, slow, need well trained operators and in some cases require steps of extraction or sample pre-treatment thus increasing the time of analysis. The food and drink industries need rapid and affordable methods to determine compounds that have not previously been monitored and to replace existing ones. An alternative to ease the analysis in routine industrial products is the biosensor development. These devices represent a promising tool for food analysis due to the possibility to fulfill some demand that the classic methods of analysis do not attain. Original characteristic turns the biosensor technology into a possible methodology to be applied in real samples. Some advantages as high selectivity and specificity, relative low cost of construction and storage, potential for miniaturization, facility of automation. simple and portable equipment construction for a fast analysis and monitoring in platforms of raw material reception, quality control laboratories or some stage during the food processing.

The development of biosensors is described in several fields; the majority restricted to other areas of application, as clinical, environmental, agriculture and biotechnology. Developments involving the use of this type of sensor could be employed in foods, especially applied to the determination of the composition, degree of contamination of raw materials and processed foods, and for the on line control of the fermentation process. The food industry has a very set of constrains compared with, for example, the pharmaceutical industry or medical diagnostics. It is important to consider both the limitations and benefits when selecting the target analyte and medium


Some of the drawbacks include

- The range of potential applications is enormous. Each product has its own analytical requirements. However, the market for an individual sensor is small compared to, for example, a glucose sensor for use by diabetics.

- Most biosensor research has been aimed at the medical diagnostics field, where the extreme specificity of a biosensor targeting one selected analyte is extremely advantageous. This is not always applicable to food industry requirement where, for example, a particular taste or smell may be a control parameter.

- Multisensors would be preferable in many instances due to the complexity of food process control requirement.

- Most existing processes have been fine-tuned over many years. Often, little process control is required due to the experience of operating the process over such a long period. The biosensor must provide an increase in productivity or quality of the product to be viable. This is not always possible.

- The technology available must be low cost, simple to use and above all, reliable. Long term stability, drift and calibration are problems which often prevent the introduction of biosensors to industrial processes.

There are also some factors which make the introduction of biosensors in the food industry more feasible:

- Experiment miniaturization is not usually required. This has implications on the fabrication process and on the size of the signal.

- Destructive or by-pass sampling techniques are usually tolerable. This may facilitate the use of techniques such as flow-injection analysis with biosensor detection.

- A great deal of work has been carried out in the medical diagnostics industry. Some of the lessons learned in this field may be transferable to food industry requirements.

- It may not be necessary to provide an instant or continuous measurement. An improvement from days to hours or minutes may be considered sufficiently beneficial.

- The influence of consumers and regulatory bodies may encourage the use of advanced technolologies such as biosensors.

Table 2 presents some of the important food biosensors, described during last 10 years in the literature. Most of the biosensors described in the literature for food analysis are electrochemical type based on amperometry. The table starts with glucose and other carbohydrates and end with complex parameters such as contaminants and additives compounds. As most works cited are prototypes, they are not fully optimized for a defined application in real samples. Some applications are synthetic


samples and can be applied in food samples. Some biosensors listed in the table are used to determine more than one analyte. These are either suitable for determining more than one substrate or are used in combination for simultaneous measurements.

Table 2. Applications of biosensors in food analysis

Analyte Application Biocomponent Transducer Detection

range

Refer-ence

Glucose Soft drinks, juices and milk

Glucose oxidase

Amperometric 50-500 mM 8, 36

Glucose Juices & Honey

Glucose oxidase

Amperometric 0.5-10 mM 11

Glucose and lactose

Milk Glucose oxidase, β-galactosidase & mutarotase

Amperometric 4.44 g/10 g (lactose)

18

Glucose and galactose

Yoghurt and milk

Glucose oxidase, galactose oxidase & peroxidase

Amperometric 250-4000 mg/L

19

Glucose, fructose, ethanol, L-lactate, L-malate and sulfite (simultaneous)

Wine Glucose oxidase, D-fructose dehydrogenase, alcohol dehydrogenase, L-lactate dehydrogenase, L-malate dehydrogenase, sulfite oxidase & diaphorase

Amperometric 0.03-15 mm (glucose)

0.01-10 mM (fructose)

0.014-4 mM (ethanol)

0.011-1.5 mM (L-lactate)

0.015-1.5 mM (L-malate)

0.01-0.1 mM (sulfite)

22

Glucose Beverages Glucose oxidase

Optical 0.06-30 mmol/L

38

Glucose Fruit juice and cola drinks

Glucose oxidase

Thermal 0.2-30 mM 27



range

Refer-ence

Fructose Honey D-Fructose dehydrogenase

Amperometric <1.0 mM 4

Lactose Milk β-Galactosidase & D-fructose dehydrogenase

Amperometric 1-30 µm 31

Glucose & lactose

Milk β-Galactosidase & glucose oxidase

Manometric 0-5 mM 16

Starch Wheat flour α-Amylase, amyloglucosidase & glucose oxidase

Amperometric 5x10-6-5x10-4 mol/L

20

Ethanol Beer Alcohol oxidase

Amperometric 0.12-2.0 mM 4

Ethanol Alcoholic beverages

Alcoholic dehydroge-nase & NADH oxidase

Amperometric 3x10-7-2x10-4 M

17

4Acetaldehyde

Alcoholic beverages

Alcoholic dehydrogenase,

Amperometric 0.5-330 µM 26

Polyphenols Olive oils Tyrosinase Amperometric 1-37 µM (hexane)

10-350 µM (Chloroform)

7

Ascorbic acid

Juices Ascorbate oxidase

Amperometric 5x10-5-1.2x10-

3 M 1

Biotin and folate

Infant formula & milk

Anti-biotin antibody and anti-folic acid antibody

Surface plasmon resonance

2-70 ng/ml 13

Urea Milk Urease Amperometric 35

Urea Milk Urease Manometric 2-7 mM 15

L-Lactate Milk Lactate mono-oxygenase

Amperometric 50-800 mg/100 ml

33



range

Refer-ence

L-lactate Milk Lactate mono-oxygenase

Amperometric > 8.6 µm 9

L-amino acids

Milk and fruit juices

D-amino acid oxidase


L-glutamate Soy sauce L-Gultamate oxidase

Amperometric <1.6mM 14

Amines Fish Diamine oxidase

Amperometric <6 mM 5

Nitrate Synthetic samples

Nitrate reductase

Amperometric < 100 µM 24

oxalate Spinach Oxalate oxidase Amperometric 0.12 – 100 µM

23

Sulfite Wine Sulfite oxidase Amperometric 0.002-0.3 mM 32

Aspartame Foods Alcohol oxidase, α-chymotrysin & catalase

Amperometric - 6

Antibiotics Milk Antibodies Surface plasmon resonance

- 3

Antibiotics Foods Antibodies Surface plasmon resonance

20-35 ng/ml 12

Pesticide Synthetic samples

Acetylcholinesterase

Fiber optic 5x10-8-5x10-7 M(carbofuran)

5x10-7-5x10-6 M(Paroxon)

2

Organophosphates

Model system Organophosphate hydrolase


Paraoxon Model system Choline oxidase Amperometric 0.5 ppm 28


The table shows the detection range of the biosensors and most researchers define detection range as the linear part of the calibration curve of the particular equipment that was used during the experiments. Normally the response of the biosensor extends beyond both the upper and lower ends of the linear range.

Commercial food biosensors

In spite of the great number of publications on biosensors applied in food analysis, only a few systems are commercially available. Drawbacks that have to be overcome are the limited lifetime of the biological components, mass production as well as practicability in handling. However, this problem will be managed in the near future, since biosensors offer unique solutions to food analysis in terms of specificity and time savings.

Very few biosensors are presently used in the food industry for on-line analysis (Mello, and Kubota, 2002) although in principle they can be combined with flow-injection analysis for on-line monitoring of raw materials, product quality and possibly the manufacturing process. Commercial biosensors are available in several forms, such as autoanalysers, manual laboratory instruments and portable (hand held) devices. Commercially devices for the food industry are listed in table 3. They are based on similar technology, either an oxygen electrode or a hydrogen-peroxide electrode in connection to an immobilized oxidase as Apec Glucose Analyser, ESAT Glucose Detector, ISI Analysers and Oriental Freshness Meter.

Table 3. Commercial biosensors for food industry

Companies(country) Biosensors Target compounds

Danvers(USA) Apec glucose analyser Glucose

Biometra Biomedizinische Analytik GmbH (Germany)

Biometra Biosensors for HPLC

Glucose, ethanol and methanol

Eppendorf(Germany) ESAT 6660 Glucose Analyzer

Glucose

Solea-Tacussel(France) Glucoprocesseur Glucose and lactate

Universal Sensors(USA) Amperometric Biosensor Detector

Glucose,galactose,L-amino acids,ascorbate and ethanol

Yellow Springs Instruments(USA)

ISI Analysers Glucose,lactose,L-lactate, ethanol,methanol,glutamate and choline

Toyo Jozo Models:PM-1000 DC(on Glucose, lactate, L-amino acids,


Biosensors(Japan) line),M-100,AS-200 and PM-1000 DC

cholesterol, tryglicerides, glycerin, ascorbic acid, alcohol

Oriental Electric(Japan) Oriental Freshness Meter Fish freshness

Swedish BIACORE AB(Sweden)

BIACORE Bacteria

Malthus Instruments(UK) Malthus 2000 Bacteria

Biosensor SpA(Italy) Midas Pro Bacteria

Biotrace(UK) Unilite Bacteria

Conclusion

Despite the enormous diversity of research involving biosensors for the food industry, its application in this area, for any analyte is still restricted. On the other hand, tests of prototype in real samples have critical stages such as immobilization of the bio-component during the construction of the device and sample preparation for analysis. The biosensors need mild conditions of temperature and pH to maintain active the biological element, therefore, in some cases, a pretreatment of the sample is recommended to remove interfering species such as ascorbic acid, tyrosine and others. It is important to emphasize that, to be successful, a biosensor needs to offer either an improvement to an existing technology, an existing measurement in a novel environment or an entirely new measurement. Generally, the choice of whether or not to turn to the biosensor approach is governed by financial constraints. Many biosensors have not progressed beyond laboratory demonstration due to the cost of developing a product which is suitable for use in the desired environment. Nevertheless, there is a strong motivation to reduce the time lag between production and quality assurance, at least for certain parameters. Potential candidates for examination are found in all stages of production cycle: from farming (pesticide residues, fertilizers and ripeness), raw material (food adulteration, freshness), process control (organoleptic considerations, microbiological safety) and distribution. The enormous breadth of products, test sites and the wide variety of matrices, within which an analysis needs to be performed, make the development of biosensors for use in the food industry particularly challenging.

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A. K. Sharma† National Dairy Research Institute, (Deemed University),

Karnal 132 001 (Haryana) India. Introduction

The study of human intelligence has a vast history spread over three millenniums. In the 20th century, the advent of electronic computers paved the way for building and studying systems that exhibit features or behaviour traditionally attributed to intelligence, but these machines are not natural in the sense that they are man made. This emerging discipline of Computing Science is usually known as Artificial Intelligence (AI). Typically, AI is strongly oriented towards symbolic representations and manipulation (or reasoning) in a top-down manner, i.e., the structure of a given problem is analysed beforehand and the construction of an intelligent system is based upon this structure. Of late, it has been emphasised that there are several alternative approaches possible to realise such intelligent features or behaviour in computing machines. Although these approaches differ from each other, they possess a common property of being non-symbolic; and operating in a bottom-up fashion, where structure transpires from an unordered beginning, rather than being imposed from above. Computational intelligence (CI) is a successor of AI. The new name ‘Computational Intelligence’ is chosen so as to indicate the link to and the difference with AI.

Computational Intelligence is an emerging field of Computer Science that involves fundamental and applied research exploiting a number of advanced information processing technologies. Defining computational intelligence is not an easy task. In a nutshell, which becomes quite apparent in light of the current research pursuits, the area is heterogeneous with a combination of such technologies as neural networks, fuzzy systems, rough set, evolutionary computation, swarm intelligence, probabilistic reasoning, multi-agent systems, etc. The recent trend is to integrate different components to take advantage of complementary features and to develop a synergistic system. The areas covered by the term computational intelligence are also known under the name Soft Computing (SC). According to scientific folklore, this name was chosen to distinguish SC from Operations Research (OR), which is also known as hard computing. The two areas are connected by the problem domains they are applied in, but while OR algorithms usually come with crisp and strict conditions on the scope of applicability and proven guarantees for a solution (or even an optimal

† Information Officer, BTIS Project & Senior Scientist (Computer Applications), Dairy Economics, Statistics and Management Division. Phone: +91 184 2259015 (Office), +91 9896391267 (Mobile). E-mail: [email protected].

SOFT COMPUTING MODELS WITH APPLICATIONS TO DAIRYING


solution), SC puts no conditions on the problem but also provides no guarantees for success, a deficiency which is compensated by the robustness of the methods.

While some techniques within CI are often counted as AI techniques (i.e., Genetic Algorithms, Neural Networks, Swarm Intelligence, Fuzzy Systems, Machine Learning, Adaptive Intelligent Systems, Intelligent Software Agents, etc.), there is a clear difference between these techniques and traditional, logic based AI techniques. CI combines elements of learning, adaptation, evolution and fuzzy logic (or rough sets) to create programs that are, in some sense, intelligent. Computational intelligence research does not reject statistical methods, but often gives a complementary view. Unlike traditional computing, CI is tolerant of imprecise information, partial truth and uncertainty. Neural and fuzzy computing systems are key elements of CI and their use has been found to be promising in Bioinformatics applications.

Neural networks

The field of ‘neural networks’ (also known as connectionism, parallel distributed processing, natural intelligent systems, machine learning algorithms, neurocomputing, artificial neural networks, neural systems and computational neural networks) is a branch of cognitive science and has originated from diverse sources, ranging from the fascination of mankind with understanding and emulating the human brain, to broader issues of copying human abilities such as speech and the use of language, to the practical commercial, scientific, and engineering disciplines of pattern recognition, modelling, and prediction.

Artificial neural networks

Artificial neural networks (called neural networks now onwards) consist of the following three principal elements:

i) Topology – the way a neural network is organised into layers and the manner in which these layers are interconnected;

ii) Learning – the technique by which information is stored in the network; and iii) Recall – how the stored information is retrieved from the network.

The basic structure of a neural network consists of artificial neurons (Fig.1). The neurons are also sometimes referred to as processing elements (PEs), nodes, neurodes, units, etc., and are analogous to biological neurons in the human brain, which are grouped into layers (also called slabs). The most common neural network structure consists of an input layer, one or more hidden layers and an output layer. The neural networks considered for empirical investigation in this study are also one hidden layer networks with a single output.


Fig.1: Schematic representation of general neuron model.

Let the input dimension be ( )+∈Znn ∗ and let the number of hidden neurons

be ( )+∈Zmm . The training pairs are represented by ( ) ( ){ }pp t,D x= ∗∗, where Pp ,...,2,1= ; ,+∈ZP is the number of training exemplars; and the index p is

always assumed to be present implicitly. The matrix w denotes the input to the hidden neurons connection strength, wij is the (i, j)th element of the matrix w representing the connection strength between the jth input and the ith hidden layer neuron. With this nomenclature, the net input to the ith hidden layer neuron is given by

( ) ( ) ( )1...1

11∑=

+⋅=+=n

jiiijiji xwnet θθ xw

where ( )1iθ is the bias of the ith hidden layer neuron. The output from the ith hidden

layer neuron is given by

( ) ( )( ) ( )2...1ii netfh =x

where ( ) ( )⋅1f is a nonlinear activation function.

The activation function determines the output from a summation of the weighted inputs of a neuron. The activation functions for neurons in the hidden layer are often nonlinear and they provide the nonlinearities for the network. The choice of transfer functions may strongly influence complexity and performance of neural networks. Although sigmoidal transfer functions are most commonly used, there is no a priori reason why models based on such functions should always provide optimal decision borders. A number of alternative transfer functions have been surveyed by some researchers. The use of new activation functions in neural networks is an open research activity that can produce interesting results in various applications.

The net input to the output neuron may be defined similarly as Eq. (1) as follows

( ) ( ) ( )3...2

1

2 θθ +⋅=+= ∑=

m

iiihvnet hv

∗ +Z is the set of positive integers.

∗∗ ( )px and

( )pt denote input and corresponding target patterns.


where iv represents the connection strength between the ith hidden layer neuron and

the output neuron, while ( )2θ is the bias of the output neuron.

Adding a bias neuron 0x with input value as +1, Eq. (1) can be rewritten as

( )4...0

xW ⋅==∑=

i

n

jjiji xwnet

where ( )100 iii Ww θ≡= and iW is the weight vector iw (associated with the ith hidden

neuron) augmented by the 0th column corresponding to the bias. Similarly, introducing an auxiliary hidden neuron (i = 0) such that 10 +=h , allows us to redefine Eq. (3) as

( )5...0

hV ⋅==∑=

m

iiihvnet

where ( )20 θ≡v .

The equation for the network output neuron is given by

( ) ( ) ( )6...2 netnetfneto ==

as we have considered ( ) ( )⋅2f as a linear function in this study.

The notations are diagrammatically exemplified in Fig.2. This figure represents an n-input, m-hidden neuron and one-output feedforward neural network. Such a neural network is trained to fit a dataset D by minimising an error function (or performance function) as

( ) ( ) ( )( ) ( )7...111

2

1

2 ∑∑==

−===P

p

ppo

P

pD tnet

PPEF εW

This function is minimised using standard optimisation method.

Fig.2: Schematic of feed forward neural network models.


Fuzzy logic

Fuzzy logic (FL) is a departure from classical Boolean logic as it implements soft linguistic variables on a continuous range of truth values, which allows intermediate values to be defined between conventional binary. It can often be considered a superset of Boolean or ‘crisp logic’ in the way fuzzy set theory is a superset of conventional set theory. Since FL can handle approximate information in a systematic way, it is ideal for controlling nonlinear systems and for modelling complex systems (such as biological processes) where an inexact model exists or systems where ambiguity or vagueness is common. Due to the growing complexity of current and future problems, the ability to find relatively simple and less expensive solutions has fuelled the research into fuzzy technology.

It was designed to mathematically represent vagueness and develop tools for dealing with imprecision inherent in several problems. Normally, in digital computers one uses the ‘binary’ or Boolean logic where the digital signal has two discrete levels, viz., Low (i.e., binary ‘0’) or High (i.e., binary ‘1’); nothing in-between. This phenomenon can be expressed mathematically with the help of classical set theory. Consider that U denotes a given universe of disclosure (i.e., the universe of all the possible elements from which set can be formed) and u be an element, i.e., Uu∈ . Then a crisp (discrete) set A can be described by the characteristic function Aµ as follows:

( ) ( )8...,1,0

⎩⎨⎧

∉∈

=AuifonlyandifAuifonlyandif

uAµ

Fuzzy systems use soft linguistic variables (e.g., hot, tall, slow, light, heavy, dry, small, positive, etc.) and a range of their weightage (or truth) values, called membership functions, in the interval [0, 1], enabling the new computers to make human-like decisions. This phenomenon can be expressed mathematically using fuzzy set theory. A fuzzy set A in the universe of disclosure U (i.e., the range of all possible values for an input to a fuzzy system) can be defined as a set of ordered pairs:

( )( ){ } ( )9..., AuuuA A ∈= µ

[ ] ( )10...1,0: →Uwhere Aµ

Thus, FL is basically a multi-valued logic that allows intermediate values to be defined between conventional evaluations like yes/no, true/false, black/white, etc. Notions like rather warm or pretty cold can be formulated mathematically and processed by computers. In this way an attempt is made to apply a more human-like way of thinking in the programming of computers.


In essence, FL deals with events and situations with subjectively defined attributes:

A proposition in FL does not have either ‘True’ or ‘False’ An event (or situation) can be, for example, ‘a bit true’, ‘fairly true’, ‘almost

true’, ‘very true’, or ‘not true’ depending on the event (or situation) attributes. Fuzzy logic application to problem solving involves three steps: converting

crisp (numerical) values to a set of fuzzy values, an inference system (based on fuzzy if-then rules) and defuzzification (Fig.3). In the first step, live inputs such as temperature, pressure, etc.; generate a real action in the form of a pulse width measurement driving a motor, or a voltage driving a motor or a relay. This is realized with the help of special functions, viz., membership functions, which are the equivalents of adverbs and adjectives in speech such as very, slightly, extremely, somewhat and so on. Next, the membership functions are put in the form of ‘if-then’ statements and a set of rules is defined for the problem under investigation. These rules are of the form:

IF temperature is cold AND humidity is high THEN fan_speed is high

where temperature, humidity are inputs variables (i.e., known data) and fan_speed is an output variable (i.e., data value to be computed). The adjectives ‘low’ in relation to ‘temperature’, ‘high’ in relation to ‘humidity’ and ‘high’ in relation to ‘fan_speed’ are the membership functions in the present case.

Fig.3: The structure of a fuzzy logic control system

Knowledge

Fuzzification Interface

Fuzzy Inference Unit

Defuzzification Interface

Process


The IF is an antecedent; usually a sensor reading while THEN is a consequent, a command, in control applications. Unlike binary logic, each antecedent can lead to several premises and several consequences. Hence, in fuzzy logic more than one rule may operate at the same time but with varied degrees. This set of rules (with different weigthages) leads to a crisp control action through the process of defuzzification.

Applications

Today, neural and fuzzy computing techniques are employed in a variety of Dairy applications including prediction of total milk, fat and protein production for individual cows; diagnosing clinical mastitis in dairy cattle; dairy sire prediction capability; lameness detection in dairy cows; oestrus detection in dairy cattle; prediction of health of dairy cattle from breath samples; online feedback treatment strategy for parturient paresis of cows; dairy food safety and quality analyses, modelling and predictive control of milk pasteurisation plant; prediction of milk ultrafiltration performance; predicting milk shelf-life; prediction of bulls’ slaughter value from growth data; prediction of acidification step in cheese production; embryo culture media-formulations; etc.


Dr. Sudhir Tomar Sr. Scientist & In-Charge

Electron Microscopy Centre, Dairy Microbiology Division, NDRI, Karnal.

1.0 Introduction

Dairy products constitute a large group of foods either consisting mainly of

proteins (dried milk, yoghurt, cheese) or of fat (cream, ice cream, dairy spreads,

butter). The electron microscopy was first time used by Nitschmann (1949) who

studied casein micelles in skim milk. Since then, the microstructure of dairy products

has been extensively studied by a number of research workers prompted by the

recognition of correlation of physical properties with microstructure of the product.

Besides, these food systems though being apparently homogenous along with

insoluble milk components and various additives display specific fine structures on

the microscopic and submicroscopic scale. Further, when milk is subjected to an array

of technological/processing treatments (high heat, pH, presence of enzymes etc.) or

certain additives of both dairy and non dairy origin are incorporated, its

microparticulate moieties/constituents i.e. fat globules, the colloidal casein micelles

and molecular dispersion of whey proteins undergo physical alterations as a result of

mutual interactions. These modifications manifest into characteristic macroscopic

structure and physical attributes (viscosity, firmness, elasticity, vulnerability to

syneresis) of the product. Therefore, electron microscopy of dairy products is

extremely useful in elucidating the relationship between macroscopic properties of the

product and its submicroscopic structure. The last three decades has witnessed great

development in the area of study of microstructure of dairy products, and different

techniques have been developed to discern the structural attributes of these products.

The information so gathered is being scientifically used in quality appraisal, in food

product development and in trouble-shooting during manufacturing.

MICROSTRUCTURE OF CULTURED DAIRY PRODUCTS: AN UPDATE


There exists a continuum of scientific information about low fat dairy products

ranging from the microstructural level and product composition, through human

sensing of taste, aroma and texture, to consumers’ perception and appreciation.

Studies with different types of low fat dairy products have ehibited that the

relationship is system-dependent. In diluted (acidified milk drinks) and highly

concentrated (cream cheese) systems a close and straightforward relationship between

texture properties (viscosity, hardness, melt down) and creaminess was demonstrated.

In weak gels (plain yoghurt), the relationship between structure and creaminess was

more complex and also encompassed taste and flavour properties.

2.0 Electron Microscopic Techniques

2.1 Scanning Electron Microscopy

Understanding the food microstructure is of great importance for process and

product development having great effects on the sensory characteristics. The

specimens examined by SEM are either conventional (dry) or frozen (cold-stage

SEM). Investigation of water-containing dairy products by SEM requires adequate

reinforcement of fragile structures and also careful selection of drying procedures.

Structural stabilization can be achieved by fixation with glutaraldehyde and

dehydration is performed mainly by critical-point drying. The SEM exhibits only the

details of surfaces, internal structure may be studied by fracturing the sample and

examining the surface thus formed. The conventional method of sample preparation

for SEM includes chemical fixation (Glutaraldehyde, Osmic Acid), dehydration with

a graded series of ethanol or acetone and subsequently drying by air drying, freeze-

drying or critical point drying. The specimen is mounted on an aluminum stub and

coated with heavy metal to make it electrically conductive. It has been demonstrated

that simple air-drying of the specimen yields collapsed micelles even after proper

fixation due to the strong interfacial forces created as a result of passage of receding

water surfaces over the particles. Better results have been obtained with freeze-drying

and critical point drying.

Since biological and food materials contain high amounts of water, they can

not be observed using a Scanning Electron Microscope (SEM) without removing the

water. However, cryo-SEM can be used to study the microstructure of hydrated

samples. The goal of the low temperature cryo-SEM is to vitrify the liquid phase with


all the constituents, i.e. macromolecules, thus preserving them in their natural and

original state. Conventional SEM is used to study chemically fixed and subsequently

dried samples at ambient temperature. Samples fixed by rapid freezing are examined

in the frozen-hydrated state at temperature below –100 C using Cryo-SEM.

2.2 Transmission Electron Microscopy

The TEM is patterned essentially after Optical microscopy and yields information

on the size, shape and arrangement of particles which make up the specimen as well

as their relationship to each other on the scale of atomic diameters. The

electromagnetic lenses (first & second) determine the spot size of the electron beam

generated by electron gun and also alters the spot to a pinpoint beam. Further,

condenser lens restricts the beam by knocking out high angles electrons and beam

strikes the specimen and parts of it are transmitted. The transmitted portion is focused

by the objective lens into an image which is passed down the column through the

intermediate and projector lenses, being enlarged all the way.

Transmission electron microscopy can be performed using various techniques

such as ultrathin sectioning, negative staining, metal shadowing and Freeze-

fracturing and freeze-etching.

3.0 Microstructure

There has been a significant development in study of microstructure of dairy

products using both conventional and cryo electron microscopy in last couple of

decades. An attempt is being made here to discuss the advances made in study of

microstructure of cultured dairy products in the following sections:

3.1 Cheese

Cheese microstructure is the spatial arrangement of the casein particles that

join together into clusters and chains to form a protein matrix throughout which are

dispersed water, fat globules and minerals. Microstructure is one of the major

controlling factors of texture and functional properties of cheese. Cheese

microstructure analysis plays an important role in the quality control of the dairy

products. SEM imagery allows to study cheese microstructure by qualitative visual


evaluation as well as quantitative analysis. Image analysis and quantification of

relevant features is the basis of modern food microscopy.

3.1.1 Cheddar Cheese

During curd formation the proteases break down kappa casein present on the

surfaces of casein micelles in milk. Deprived of its protective action, casein micelles

coagulate and form a gel. When examined by electron microscopy, the coagulum

consists of casein micelle clusters and short chains. They encapsulate fat globules -

the natural large corpuscular particles present in milk. Void spaces in the casein

matrix are filled with the liquid milk serum called whey which is a solution of lactose,

minerals, and vitamins, and a suspension of whey proteins. Further, cutting of

coagulum exposes fat globules and causes them to be washed away from the surface

formed by cutting. During cooking, whey gets expelled from the porous protein

matrix causing its compaction. Matting of curd granules leads to junction formation

in the region where low fat surface layers fuse together. On the basis of junction

patterns, distinction can be made from the curd that had been cut with a set of wire

knives from the curd cut by stirrer. In cheddar cheese, milling and pressing of

cheddared curd leads to the development of another set of junction patterns called

milled curd junctions.

Shredded Cheese

There is great consumer interest worldwide in shredded cheese. Many natural

cheeses can be grated but storage costs before a given variety reaches shredability are

significant. These costs include refrigerated storage space and inventory keeping

which require a large capital investment. The study of microstructure has been

successfully employed to evaluate the effect of moderately high hydrostatic pressure

processing to reduce production costs of shredded cheese (Serrano et al., 2005). Short

and moderate hydrostatic pressure (MHP) treatments were found to accelerate the

shredability of Cheddar cheese. The findings led to the conclusion that MHP would

allow processors to shred milled curd Cheddar cheese immediately after block cooling

with expected refrigerated storage savings of more than US $30 /1000 kg cheese and

could simplify the handling of cheese for shredding.


3.1.2 White Fresh Cheese

Iranian White cheese is a close textured brined cheese made from cow’s milk,

sheep’s milk or mixtures of them. The main flavor characteristics are acidity and

saltiness. Madadlou et al., (2006) studied the effect of starter concentration on

compositon, microstructure, opacity and fracture stress of Iranian White cheese. Three

treatments of cheese were made using 1-fold (IWC1S), 2-fold (IWC2S), and 4-fold

(IWC4S) concentrations of a direct-to-vat mesophilic mixed culture containing

Lactococcus lactis ssp. cremoris and Lactococcus lactis ssp. lactis as starter. As

ripening progressed, moisture and protein contents of the treatments continuously

decreased, whereas their total ash, salt, and salt in moisture contents increased. Fat

content and pH of cheeses remained stable during ripening. As the concentration of

starter inoculated to milk increased, the value of fracture stress at a given ripening

time significantly decreased, leading to a less resistant body against applied stress. A

similar trend was also observed for fracture strain during cheese ripening. The

micrographs taken by SEM provided a meaningful explanation for decrease in the

value of fracture stress.

Rahimi et al., (2007) studied the texture of Low-Fat Iranian White Cheese as

influenced by gum Tragacanth as a fat replacer. The effect of different concentrations

of gum tragacanth on the textural characteristics of low-fat Iranian White cheese was

studied during ripening. Cheese samples were analyzed with respect to chemical,

color, and sensory characteristics, rheological parameters ( , and microstructure.

Reducing the fat content had an adverse effect on cheese yield, sensory

characteristics, and the texture of Iranian White cheese, and it increased the

instrumental hardness parameters (i.e., fracture stress, elastic modulus, storage

modulus, and complex modulus). However, increasing the gum tragacanth

concentration reduced the values of instrumental hardness parameters and increased

the whiteness of cheese.

3.1.3 Domiati Cheese

The addition of -galactosidase to cheese milk before renneting (El-

Zayat,2006) has been found to enhance the fusion of protein and the formation of

more homogenous structure of sub micelles compared to control, as well as produced

a good quality cheese of 1.5-2.5 fold free amino acids and free fatty acids as much as


those of control. The differences were particularly great in respect of serine,

phenylalanine, aspartic acid, glutamic acid, myristic acid, palmatic acid and oleic

acid.

3.1.4 Brine salted Cheese(Ragusano cheese)

The study of microstructure of brine salted cheese can offer insight into

relation of porosity of within the casein matrix of the cheese with rate of salt

diffusion. The goal of this study (Melilli et al.,2005) was to characterize the changes

in chemical composition, porosity, and structure that occur at the surface of a block of

brine-salted cheese and their relationship to the rate at which salt is taken up from the

brine. To create a difference in composition, salt uptake, and barrier layer properties,

identical blocks of Ragusano cheese were placed in saturated and 18% salt brine at

18°C for 12 d. The overall moisture content and porosity decreased, whereas salt and

salt in moisture content increased near the surface of blocks of brine-salted Ragusano

cheese for all treatments. The general appearance of the microstructure of the surface

of the blocks of brine-salted cheese was much more compact than the microstructure 1

mm inside the block at both brine concentrations. However, no large differences in

the size of the macro channels in the cheese structure due to the difference in brine

concentration were observed by scanning electron microscopy.

3.2 Yoghurt

Yogurt has traditionally been made from milk that had been partially

condensed by evaporation while it had been heated almost to boiling. Coagulation of

the milk proteins is induced by thermophilic bacteria, such as Lactobacillus

delbrueckii subsp. bulgaricus and Streptococcus thermophilus , i.e., bacteria which

propagate well at an elevated temperature of 40° to 45°C. The milk is coagulated by a

slowly increasing concentration of lactic acid as the bacteria metabolize lactose. The

proteins do not precipitate (as would happen following an addition of a large amount

of lactic acid) but form a gel. Its ability to retain all the water present in the milk is the

result of a peculiar microstructure of the protein network. It consists of short branched

chains of casein micelles and resembles a sponge with very small pores.

Here recent findings on effect of various processing parameters on microstructure of

yoghurt are discussed.


3.2.1 Fortification

Inulin

Guven et al., (2005) studied the influence of different levels of inulin on the

quality of fat-free yogurt production was investigated. The addition of inulin at more

than 1% increased whey separation and consistency while acetaldehyde, pH and

titratable acidity remained unaffected. Tyrosine and volatile fatty acidity levels were

negatively affected by inulin addition. With respect to the organoleptic quality of

yogurt, inulin addition caused a decrease in organoleptic scores: the control yogurt

had the highest score, and the lowest score was obtained in yogurt samples containing

3% of inulin. Overall, the yogurt containing 1% of inulin was similar in quality

characteristics to control yogurt made with whole milk.

Sweetener

Addition of sweeteners can affect the microstructure of fermented milks.

Studies show that type of sweetener impacts state of association of casein micelles

and thus effects microstructure. Haque and Kayanush (2002) determined the effect of

a peptide sweetener, Aspartame, compared to a carbohydrate sweetener, sucrose, on

the microstructure of yogurt. Microstructure was determined by transmission and

scanning electron microscopy. Without the sweeteners, casein micelles that make up

the yogurt matrix were observed in single longitudinal polymers. When Aspartame

was used, casein micelles formed double longitudinal polymers. In comparison sugar

caused casein micelles to form clusters.

Stabilizer

The effect of trisodium citrate (TSC) on the rheological and physical

properties and microstructure of yogurt was investigated by Ozcan-Yilsay et al.,

(2007). Addition of TSC was found to reduce casein-bound Ca and increased the

solubilization of CCP. At low TSC levels, the removal of CCP crosslinks may have

facilitated greater rearrangement and molecular mobility of the micelle structure,

which may have helped to increase G' and LT values of gels by increasing the

formation of crosslinks between strands. At high TSC concentrations the micelles

were completely disrupted and CCP crosslinks were dissolved, both of which resulted

in very weak yogurt gels with large pores obvious in confocal micrographs.

Whey Protein


The investigation carried out by Bhullary et al., (2004) studied the effects of

incorporating whey powder, whey protein concentrate and skim milk powder on

textural characteristics and microstructure of yoghurt. Yoghurt supplemented with 2%

whey protein concentrate was firmest while the control yoghurt was least firm. An

average increase in viscosity of 1.2 to 5 times was observed on addition of whey

powder, skim milk powder and whey protein concentrate.The microstructure analysis

showed that yoghurts containing whey protein concentrate had a more regular and

dense protein network as compared to those containing whey powder and skim milk

powder. Yoghurts made with whey powder and skim milk powder had more voids

and interstitial spaces as compared to those containing whey protein concentrate.

3.2.2 Addition of Cultures

Probiotic culture

Sodini et al., (2005) studied the physical properties and the microstructure of

yoghurts containing probiotic bacteria, and supplemented with milk protein

hydrolysates. Microstructural observations showed a more open and less branched

structure in yoghurts when milk protein hydrolysates were incorporated. The

difference in fermentation time between milks with different levels of added

hydrolysates could partially explain the differences in microstructure and physical

properties of the final yoghurts.

EPS Cultures

Microbial exopolysaccharides (EPSs) synthesized by lactic acid bacteria play

a major role in the manufacturing of fermented dairy products. EPS production is

characterized by a large variety in terms of quantity, chemical composition, molecular

size, charge, type of side chains and rigidity of the molecules. Monosaccharide unit's

composition, linkages, charge and size determine the EPS' intrinsic properties and

their interactions with other milk constituents. The EPSs contribute to texture,

mouthfeel, taste perception and stability of the final product. Furthermore, it was

reported that EPS from food grade organisms, particularly LAB, have potential as

food additives and as functional food ingredients with both health and economic

benefits. A better understanding of structure-function relationships of EPS in a dairy


food matrix and of EPS biosynthesis remain two major challenges for further

applications of EPS and the engineering of functional polysaccharides.

Hasan et al., (2003) studied the microstructure and rheology of yogurt made

with cultures differing only in their ability to produce exopolysaccharide. Yogurt was

made using an exopolysaccharide-producing strain of Streptococcus thermophilus and

its genetic variant that only differed from the mother strain in its inability to produce

exopolysaccharide. Yogurt made with the exopolysaccharide-producing culture

exhibited increased consistency coefficients, but lower flow behavior index, yield

stress, viscoelastic moduli and phase angle values than did yogurt made with the

culture unable to produce exopolysaccharide. The exopolysaccharides, when present,

were found in pores in the gel network separate from the aggregated protein. These

effects could be explained by the incompatibility of the exopolysaccharides with the

protein aggregates in the milk. Stirring affected the yogurt made with

exopolysaccharide differently from yogurt without exopolysaccharide, as it did not

exhibit immediate syneresis, although the structural breakdown was increased. The

shear-induced microstructure in a yogurt made with exopolysaccharide-producing

culture was shown to consist of compartmentalized protein aggregates between

channels containing exopolysaccharide, hindering syneresis as well as the buildup of

structure after stirring.

3.2.3 Post Incubation Treatment

Stirring

Yogurt is unique from both the structural as well as compositional viewpoints,

because it is solid and has the highest water content of all solid milk products. Yogurt

that has been stored for a long period of time may show some syneresis as the

separation of a liquid phase from a gel is called. This is only a minor cosmetic defect

and the liquid soaks back into the body of the yogurt as soon as the yogurt is stirred.

Skriver (1995) studied the characterization of stirred yogurt by rheology, microscopy

and sensory analysis. The ability of three different electron microscopy techniques to

visualize the microstructure of stirred yoghurt was examined. The protein network, fat

globules and bacteria were detectable with all three methods. The SEM of critical


point dried specimens as well as TEM of freeze-etched preparations resulted in

differentiation between yoghurt fermented with and without ropy strains. Though

TEM of thin-sectioned yoghurt specimens did not result in visualization of these EPSs

yet it was the most suitable for image analysis. The optimum heat treatment of the

yoghurt milk compared to the high heated milk resulted in yoghurt with a higher

surface content, smaller values of star volume (a less coarse structure of the casein

network) and a covariance function with a faster decay.

HHP Technology

High hydrostatic pressure (HHP) processing technology has recently received

considerable attention among food researchers. High pressure technology (100–1000

MPa) is of increasing interest for use in biological and food systems, primarily

because it permits microbial inactivation at low or moderate temperature. Penna et al.,

(2007) studied the effect of milk processing on the microstructure of probiotic low-fat

yogurt. The microstructure of heat-treated milk yogurt had fewer interconnected

chains of irregularly shaped casein micelles, forming a network that enclosed the void

spaces. On the other hand, microstructure of HHP yogurt had more interconnected

clusters of densely aggregated protein of reduced particle size, with an appearance

more spherical in shape, exhibiting a smoother more regular surface and presenting

more uniform size distribution. The combined HHP and heat milk treatments led to

compact yogurt gels with increasingly larger casein micelle clusters interspaced by

void spaces, and exhibited a high degree of cross-linking. The rounded micelles

tended to fuse and form small irregular aggregates in association with clumps of

dense amorphous material, which resulted in improved gel texture and viscosity.

4.0 Conclusion

Further improvements on the quality of existing foods and the creation of new

products to satisfy expanding consumer’s demands during this century will be based

largely on interventions at the microscopic level. This is so because the majority of

elements that critically participate in transport properties, physical and rheological

behavior, textural and sensorial traits of foods are below the 100 µm range. Another

reason that favors the change in scale of intervention is that we now have the tools

and basic knowledge of materials science, biology, genomics and computer science.

Introduction of nanotechnology in food research means scaling up from one


nanometer to the micron range, such as those found in self-assembly colloidal

structures and interfaces. Textural perception occurs mostly in the mouth which is a

very biased sensor. For instance, the presence of particles (or graininess) may be

desirable in the case of a bean paste or undesirable in most foods including sweetened

condensed milk, ice cream and chocolate where the threshold of size detection is

between 10 and 50 µm but decreases with increasing degree of circularity. Under

certain formulations small particles may contribute to the perception of creaminess in

several food systems. Besides, in view of the recent trend of incorporating milk

constituents generally after physical, enzymatic or chemical modification into other

food, electron microscopy is poised to play increasingly a significant role in

elucidation of the properties of such new products. In this respect newly developed

cryotechniques should prove to be quite fruitful.

Reference:

Bhullary, S.; Uddin, M. A.; Shah N. P. 2002. Effects of ingredients supplementation on textural characteristics and microstructure of yoghurt Milchwissenschaft, 57:329-332.

El-Zayat, A.I.2006. Microstructure, free amino acids and free fatty acids in Domiati cheese treated with ß-galactosidase. Food / Nahrung, 31: 27 – 37.

Guven, M.; Yasar, K.; Karaca, O. B.; Hayaloglu, A. A.2005.The effect of inulin as a fat replacer on the quality of set-type low-fat yogurt manufacture Internat. J of Dairy Technol., 58:180-184.

Haque, Z.Z., and Kayanush, J. A.2002. Effect of Sweeteners on the Microstructure of Yogurt, FSTR, 8, 21-23.

Hassan, A.N.; Ipsen R.; Janzen T., Qvist, K. B.2003. Microstructure and rheology of yogurt made with cultures differing only in their ability to produce exopolysaccharide. J Dairy Sci. 86:1632-1638.

Laure, J.; Sébastien, J. F.; Philippe Duboc, V and Jean-Richard Neeser.2002. Exploiting exopolysaccharides from lactic acid bacteria, Antonie van Leeuwenhoek, 82: 367-374.

Madadlou, A.; Khosroshahi, A.; Mousavi S. M. and Djome, Z.E. 2006. Microstructure and Rheological Properties of Iranian White Cheese Coagulated at Various Temperatures. J. Dairy Sci. 89:2359-2364.

Melilli, C.; Carcò,; D, D. M. Barbano2, G. Tumino1, S. Carpino1 and G. Licitra. 2005. Composition, Microstructure, and Surface Barrier Layer Development During Brine Salting J. Dairy Sci., 88:2329-2340.


Ozcan-Yilsay, T.; Lee, W.J.; Horne, D.; Lucey, J.A.2007.Ozcan-Yilsay Effect of trisodium citrate on rheological and physical properties and microstructure of yogurt. J Dairy Sci., 90: 1644-52.

Penna, A.L.B., Subbarao-Gurram and G.V. Barbosa-Cánovas, G.V.2007.High hydrostatic pressure processing on microstructure of probiotic low-fat yogurt Food Research International, 40:510-519.

Rahimi, J; Khosrowshahi, A.; Madadlou, A and Aziznia, S. 2007. Texture of Low-Fat Iranian White Cheese as influenced by gum Tragacanth as a fat replacer. J. Dairy Sci.90:4058-4070

Serrano, J.; Velazquez, G; Lopetcharat, K.; Ramirez, J. A.;Torres J. A.2005. Moderately high hydrostatic pressure processing to reduce production costs of shredded cheese: Microstructure, texture, and sensory properties of shredded milled curd cheddar. J. food sci.; 70: S286-S293.

Skriver, A .1995. Characterization of stirred yogurt by rheology, microscopy and sensory analysis. Ph. D Thesis. University of Copenhagen, Denmark

Sodini,I.; Montella, J and Phillip S, T.2005. Physical properties of yogurt fortified with various commercial whey protein concentrates J. Sci Food Agric 85:853–859


NUTRITIONAL AND THERAPEUTIC ASSESSMENT FOR FUNCTIONAL DAIRY PRODUCTS

Dr. Suman Kapila Senior Scientist

Animal Biochemistry Division, NDRI, Karnal-132001

Functional foods are foods, and in fact they are foods, that go beyond simple nutrition and have specific targeted actions. Various strategies have been adopted to develop functional foods. Some of the multiple ways to approach the development of functional foods follow.

1. First approach is to use probiotics, which are specific live microorganisms that have a beneficial effect on the host. Specific probiotics are used for specific functions.

2. The second approach is based on prebiotics. Prebiotics are ingredients or

compounds that have a beneficial effect on the microflora in the host itself.

3. A third possibility is a mixture of probiotics and prebiotics, called synbiotics.

4. The last approach to developing functional foods is based on the addition of ingredients that are very specific and have a much targeted action. Examples are conjugated linoleic acids or polyunsaturated fatty acids, and many others.

Based on this strategy, the domain of action of functional foods could be divided into two parts.

First, there are functional foods that are used to enhance a certain physiological function, and second, there are functional foods that are used to reduce the risk of disease. These two strategies have been applied in the development of functional foods.

Nutrition is a relatively new science. For most of the last 100 years, nutritionists

were concerned primarily with preventing deficiencies. In the past 20 years the emphasis moved to preventing excess. As nutrition moves into the new millennium, the emphasis changes to enhancing the quality of life. There has been a tremendous improvement in the knowledge of diet and genetics. However, consumers frequently receive conflicting messages; and it is often difficult for them to separate good science from exaggerated claims. There are 52 nutrient RDIs (Recommended Daily Intakes), which are set, not at an optimal level, but at a level that will prevent deficiency. There are nutrient nutraceuticals, for example the antioxidants, vitamin C, vitamin E, and also beta-carotene that play roles in reducing the risk of cancer and


cardiovascular diseases. There are also thousands of other chemicals coming from various food entities for which no formal nutritional requirement has been established. However, these nutraceutical or functional ingredients show benefits for risk reduction and enhancement of “structure or function” of the body. One example is lycopene, a strong antioxidant, which shows benefits for prostate cancer.

The first step in the development of a functional food is the identification, as well

as some understanding, of the interaction mechanism between the food component and a body function. On such a basis, a functional effect can then be defined and demonstrated in relevant models including human nutrition studies, which are different from clinical studies. These studies should be hypothesis-driven, and should look for changes invalidated and relevant biological markers. The demonstration of these effects must also include safety assessment. Following human nutrition studies, claims can be defined. Claims are any representation relating to nutritional properties. They may be a marker of a target biologic response, and have no relationship to disease. For example, an improvement in calcium absorption is a modification of the function without any relation to disease. There may also be an effect on an intermediate marker, such as a reduction in blood pressure, which would lead to the reduction of the risk of the cardiovascular disease. The ultimate goal of the scientific community and food industry should be to develop functional foods that improve the quality of life. To do this, they must also educate consumers to make some changes in their eating habits and their lifestyles. Academia must also strongly urge the food industry to stay on the scientific side. The food industry needs to be reminded to keep the quest for functional foods a scientific challenge, and not just a marketing challenge.

Functional dairy foods and the risk for various diseases Hypertension

An inverse relationship between intake of dairy products and blood pressure levels was first suggested by several epidemiological surveys in the early 1980’s. Subsequent laboratory and clinical investigations provided further evidence of the association between calcium and blood pressure, but the results of these studies were often inconsistent due to variations in study design and methods, study participants, and calcium sources. In the management of high blood pressure, lifestyle is important. Overweight is the biggest predictor of hypertension; exercise improves blood pressure, and moderate consumption of alcohol is acceptable. The overall adequacy of the diet is very important. Unfortunately, many of the currently recommended solutions to hypertension do not rely on science.T he recently published results of the large and carefully executed “Diet and Blood Pressure in America” study, demonstrated a dramatic blood-pressure lowering effect of diets rich in dairy products, fruits, and vegetables. The study revealed that individuals with low calcium intake ate less cheese, yogurt and ice cream. Results of the NHANES (National


Health and Nutrition Examination Study) study showed that to prevent high blood pressure, the most important food group that needs to be added to the diet is dairy products. Recent reviews of a very large database, comparing all randomized trials, suggested that for African Americans, the elderly, and those who are salt sensitive, increasing mineral intake, specifically calcium, will be important for the regulation of blood pressure. Those with low calcium intake responded positively to calcium supplements. This effect is present in both normal and hypertensive subjects, and is more pronounced for high risk individuals. In another study, the DASH diet (Dietary Approaches to Stop Hypertension), subjects ate 3 servings per day of low-fat dairy products, and 8-10 servings per day of fresh fruits and vegetables. High, normal and mild hypertensives were studied. The study was published in the New England Journal of Medicine in 1997. The normal group had a 3 mm drop in systolic pressure on the fruit and vegetable diet, and a 6 mm drop on the combination diet (dairy plus fruit/vegetable). This compares to a 0.6 mm drop achieved by lowering salt intake in another study.

Also in the DASH diet, individuals with mild hypertension achieved an 11.6 mm reduction in blood pressure. The test was heavily weighted to African Americans. No individual stopped the trial because of lactose intolerance, probably due to the fact that yogurt was emphasized. The authors concluded that blood pressure reduction was rapid; and I twas independent of sodium and weight change. Public health implications of this diet as a preventive measure against hypertension could lead to a 27% reduction in stroke and a15% reduction in coronary heart disease. The Vanguard studies, a multi-center dietary intervention study carried out at various universities, compared a comprehensive diet containing 100 to 115% of the US recommendation for all macro- and micronutrients, to the Step 1 and Step 2 American Heart Association diets. This test included four trials involving 1300 subjects. In a high-risk group for diabetes, dislipidemia, hypertension and obesity, results showed that a comprehensive diet intervention has a significant effect on blood pressure, weight, lipid profiles, homocysteine, hemoglobin A1c, and insulin. The overall conclusion was that when nutritional adequacy improved, the quality of life improved. The DASH II diet findings were recently reported at the American Society of Hypertension. In this 12-week study, 3 meals per day were provided. The control diet, which was low in fruits, vegetables, and dairy products, was compared to the DASH diet, at three sodium levels. This was a randomized cross-over test, with 30-day intervention. Subjects were 57% black, 57% women, and 41% hypertensive. The average BMI approached 30, and the group was heavily weighted for sodium sensitivity. Subjects on the DASH diet showed a greater reduction of systolic blood pressure than the control group. DASH II implications are summarized below:

• Low-fat dairy foods plus fruits and vegetables are far more effective than sodium restriction in changing blood pressure.


• Low-fat dairy foods plus fruits and vegetables virtually eliminate sodium’s effects on blood pressure.

• All population segments benefit from the improved diet.

• A national nutrition policy to prevent and manage hypertension must focus on the DASH diet, not on salt.

One lesson from these studies is that single-nutrient interventions do not exist.

Foods are the issue, not individual nutrients. Dairy products are critical in achieving results. In addition, especially in the US, lowering weight and getting adequate exercise are both paramount. It is important to prevent any evidence of deficiency, but sodium restriction is a mute point. The greatest cardiovascular benefits are achieved by weight loss. Dietary patterns characterized by fruits, vegetables, whole grains, low-fat dairy products, and lean meats, are associated with a lower risk of mortality. Cardiovascular diseases

In the 1970’s and 1980’s the simple lipid hypothesis dominated the scientific community. Emphasis was on the total fat and saturated fatty acids of the diet, and the ratios of LDL (low density lipoproteins) to HDL (high density lipoproteins) cholesterol. This focus led to an increased consumption of skimmed milk, and reduced consumption of cheeses, creams and other higher fat dairy products. This resulted in a lower total fat and saturated fatty acids (SFA) intake and a higher intake of polyunsaturated fatty acids (PUFA) from margarines and spreads. Little emphasis was placed on the beneficial effects of other fatty acids in the diet, particularly the monounsaturated fatty acids (MUFA), the omega-3fatty acids, and conjugated linoleic acids. Current advances in cardiovascular nutrition indicate that high MUFA diets are more cardioprotective than low-fat diets. This is particularly true for those subjects who show a decline in HDL in response to a low-fat diet. The challenge then is to find effective simple strategies to substitute MUFA in the diet.

After feeding a diet rich in SFA versus a diet high in MUFA, scientists were able to show a significant reduction in expression of adhesion molecules on leukocytes with subjects on the MUFA diet. This reduction is also beneficial in reducing tendency to inflammation. Studies also show that when you give fat acutely to individuals, it leads to activation of factor 7, a predictor of increased clot formation. Individuals on a high MUFA diet show less activation of factor 7 than individuals on a high SFA diet. Thus cholesterol reduction is important, but is not the only factor in determining cardiovascular risk. High MUFA diets are shown to reduce inflammatory and coagulation tendencies. Currently 87% of dietary PUFA intake is n-6 or omega-6 class, and consumption of omega-3 PUFA is down. The population eats only 1.4 grams of alpha-linolenic acid andless then 0.2 g per day of docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), which are very powerful long chain PUFA helpful in preventing cardiovascular disease.Omega-3 PUFA are able to reduce triglyceride blood levels, inhibit platelet aggregation, reduce blood clotting, are anti-


arrhythmic, reduce blood pressure, and are antiinflammatory. Purportedly, about 1 gram per day of DHA and EPA can reduce heart disease risk. In subjects who ate 2-3 servings of oily fish per week, there was a significant improvement in survival versus a control diet group which ate a healthy diet. Osteoporosis

Osteoporosis is an important public health problem. In the US, 28 million people suffer from osteoporosis; and the majority of these are women. The annual cost of osteoporosis is estimated to be USD 14 billion per year. The disease is a global issue, and it is estimated that in 2020, half of all osteoporosis in the world will be in China. The role of dairy products in the prevention of this disease is clear to all. In the US, dairy foods are the major contributor of calcium in the diet. Estimates are that 75% of dietary calcium in the US is supplied by dairy foods. Still, the population does not consume enough calcium. For males, median intake is 800 mg per day, significantly less than the RDA of 1000 mg; for females, median intakes are around 600 mg per day. Significant marketing and nutrition education are needed to encourage use of dairy products and other foods to increase the level of calcium in the diet.

There are three main organ systems involved in the metabolism of calcium and phosphorus: the skeleton, the kidney and the intestine. Besides the level of dietary calcium, other body processes that can be manipulated through functional foods are urinary loss of calcium, efficiency of calcium absorption, and bone formation or bone resorption. These could promote optimal bone mineral density, optimal bone structure, and reduce the risk of bone fracture. Two specific approaches to fortify dairy products with functional foods have been suggested. First, dietary phytoestrogens could be used to reduce bone loss; and second, dietary oligosaccharides could be used to enhance calcium absorption. There are many sources of dietary estrogens in the diet, as well as various synthetic compounds that have shown estrogenic effects in vivo.

There are also many naturally occurring phytoestrogens, coming from plant foods, animal foods and fungi. Current emphasis on phytoestrogens centers on isoflavonoids, particularly isoflavones, and lignins. Two of these isoflavones, daizein and genistein, are found in soy products. However, the type of soy can determine the level of these compounds. For example, tofu and soybeans are not equivalent as vehicles for delivering bioactive compounds. The three main isoflavones found in soybeans, daizein, genistein, and glycitein, have similar structures. These compounds have the potential to interact with the estrogen receptor. Evidence from studies over the course of the last 20 years with synthetic isoflavones, such as ipriflavone, indicates that isoflavones may be beneficial in the area of osteoporosis.

Current research has explored the role of ipriflavone in preventing bone loss. One test utilized ovariectomized (OVX) rats. Ovarectomy creates an estrogen deficiency state over the course of the experiment, which is one month. Studies have compared a soy protein diet to casein, and measured bone mineral density. Actual


study results revealed that protective effects on bone come with the isoflavones, and not with the soy protein. There is very little data from human studies. In one study, on three groups of women, as isoflavone dosage increased, a positive effect on bone mineral density was shown in older women. In summary, evidence suggests that synthetic and dietary isoflavones can influence bone metabolism in rats and humans. Additional studies are needed on humans in particular, to define the optimal and safe dose, to identify the form that would have the most positive effect on bone, and to demonstrate the long-term efficacy on bone mineral density and fracture incidence.

Cancer

The search for cancer prevention involves lofty goals that are not easy to achieve. Science is looking for a way to slow the onset of carcinogenesis, and to prevent tumors from progressing. Some food substances such as garlic are beginning to show promise in this area, and there is hope that simple dietary substances may be very relevant in cancer prevention. In human nutrition, interactions between nutrients are important. Very high or low intakes of certain nutrients may create imbalances with other nutrients. A good example of this is the relationship between zinc and vitamin E. In an animal model, a decrease in dietary zinc will inhibit the attainment of maximum levels of vitamin E. This has also been confirmed in human studies. Recent studies to compare risks for breast cancer have compared the Western diet to a control diet. Results showed that subjects on the Western diet, which is high in fat, high in phosphate, low in calcium and low in vitamin D, showed a higher rate of proliferation in the small ducts where breast cancer occurs as compared to the control diet. Researchers concluded not only that the western diet has an adverse effect on risk factors for cancer, but also that the combination of nutrients is important. Both overnutrition and undernutrition may be causative factors in cancer. Overweight relates to a higher incidence of cancers of the breast, colon, prostate, and probably the uterus; while underweight increases the risk of cancers of the stomach, esophagus, and liver. Consumption of fruits, vegetables and grains is protective. Currently researchers are seeking to identify the specific micronutrients involved in risk reduction. There has been a great deal of interest in selenium, omega-3 fatty acids, folic acid, and antioxidant vitamins. Role of dairy foods

There has been some concern about dairy products and increased risk of prostate cancer, but dairy foods may be protective against breast and colon cancer. Current focus is on finding the mechanisms that account for changes. One possibility is that vitamin D in its active form, 1,25 D, serves to slow down cancer development. Increased calcium may suppress the concentration of 1,25 D. Therefore, it is important to have adequate amounts of vitamin D in the dairy products, or to take additional vitamin D. Purportedly, calcium in milk may have anti-proliferative effects, and inhibit tumor promotion. It is thought that fermentation and production of probiotics may reduce the progression of preneoplastic lesions. There are many


mechanisms through which dairy products might work. Probiotics may have a role in cancer prevention by influencing microbial flora. This may be due not only to the presence of the probiotic bacteria, but also to the removal of other bacteria by competition in the gut. Probiotics may also improve nutrient bioavailability. In addition, they may have immunologic effects, may stimulate IgA response, and may effect production of cytokines.

Immunity and cancer

The GI tract is the body’s first barrier, and the immune system plays a critical role in cancer prevention. Primary immune deficiencies are associated with increased risk for gastric cancer. Immune factors are critical in determining cancer development, and immunodeficiency may be one of the ways in which cancer develops. Again, there is a risk for both overnutrition and undernutrition. Individuals with bone marrow transplants are at an increased risk of cancer. Weight loss has a severe prognosis.How might probiotic bacteria be helpful specifically in colon cancer prevention? They may enhance post-immune response. They may crowd out organisms that are involved in producing carcinogens, and they may neutralize carcinogens. They may alter the metabolism of intestinal flora, and may produce antitumor factors. In the human colon, there have been studies on several lactic acid bacteria, especially Bifidobacterium longum, that show that this organism may reduce tumor ornithine decarboxylase activity, ras p21 expression, and have strong antitumor activity. The biomarkers reflect this. Some safety issues such as the possibility of uncontrolled growth and proinflammatory immune response may warrant further study. To summarize, nutrition is enormously linked to cancer prevention. This is true for nutrition in general, as well as for specific nutrients and secondary plant products. Further study is needed into the mechanisms of action and validation of these agents. Probiotics may be a vital key to cancer prevention. Two specific fatty acids outlined below show promise in the reduction of cancer risk. One is of dairy origin, and the other of marine origin, but might be a candidate for inclusion in dairy foods.

Conjugated linoleic acid

Conjugated linoleic acid, CLA, occurs naturally in red meat and dairy products, as a by-product of ruminal hydrogenation. CLA has potent anti-carcinogenic effect, reduces fat deposition, and has anti-atherogenic properties. To date, most of the studies using CLA have been done in small experimental animals, and have been using high-dose levels. A recent paper showed that levels of CLA in human adipose tissue strongly correlated with intake of milk fat. Milk fat is an important dietary source of CLA. The data for CLA and human health benefits is weak, and in animal studies the data is still equivocal. Two studies have shown reductions in cholesterol, but did not show a dose response. Anti-carcinogenic effects were shown in animals at levels that would be the equivalent of ingesting 3 grams per day of CLA. Anti-atherogenic effects have been found at very high levels, equivalent to human consumption of 400 grams per day, which would not be feasible.


Docosahexaenoic acid

Docosahexaenoic acid (DHA), is a major polyunsaturated fatty acid (PUFA) most often derived from marine sources. DHA can be synthesized from alpha linoleic acid, but this biosynthesis is not very efficient, and a deficiency may lead to impairment of functions such as visual acuity and learning ability. DHA is highly concentrated in the brain, retina and spermatozoa. Despite some knowledge of the biological functions of DHA, relatively little is known about its metabolic fate. In a recent study, elderly people were given a daily intake of 150 mg DHA, plus 30 mg EPA (eicosapentaenoic acid), for six weeks. Using 13C-labeled DHA, researchers were able to show that DHA esterified in lysophosphatidylcholine is the main provider of DHA to erythrocytes and the brain. A daily intake of only 100 mg DHA in triglycerides by elderly people, a population in which an oxidative stress may be evidenced, appears to be able to reverse this oxidative stress. It is concluded that a low intake of DHA might be useful, both for adequate supply to target tissues, especially the brain, and to prevent lipid peroxidation. DHA thus finds a role as an anticarcinogen, and may also have beneficial effects against atherogenesis and arrhythmia.

Dairy foods are rich sources of protein, calcium and a variety of vitamins, minerals and bioactive compounds. They provide an ideal food medium for delivering probiotics and other functional ingredients. The message was strongly relayed by many of the experts in various fields, that functional foods should be foods and not pills. Elderly populations often experience PEM (protein energy malnutrition). For these individuals who may be lacking protein, calcium and vitamin D, yogurt provides an excellent source of these and other nutrients. It provides an added advantage for all populations who may have lactase deficiency, because the probiotic bacteria in yogurt produce lactase, which is the enzyme which breaks down lactose. It has been demonstrated innumerous tests that yogurt is well tolerated by individuals with decreased lactase levels. Probiotics can be delivered through yogurt, fermented milks, cottage cheese and similar dairy products, and also through fortified juice, and infant formulas. Two other vehicles for delivery are pills and nutritional supplements. There is no guarantee that pills will contain the advertised bacteria, or that the probiotics will be viable when they reach the body.

Fermented milk products may have a short shelf-life. One challenge is to incorporate prebiotics into a wider range of food. A novel application was a biscuit formulation. In a recent double-blind, randomized, placebo controlled test with 31 volunteers, a prebiotic was served in a biscuit formulation for a 21-day treatment period. Using fluorescent in situ hybridization, researchers confirmed that there was a significant increase in Bifidobacterium levels in the active group, thereby confirming that the prebiotic nature of the FOS did hold up in this real food product. Development of prebiotics should look at enhanced application in food system. These food ingredients should exhibit good storage, varying sweetness for different applications, as well as pathogen binding ability.


Specific methods to enhance functionality of functional dairy products

specifically probiotics ans prebiotics may include the following.

• Targeted activities for the distal colon: as most large gut disorders are of left-sided origin, the persistence of prebiotics and probiotics towards this area is desirable. Most release occurs in proximal regions, where carbohydrates enter through the ileocecal valve. As food moves through the large intestine towards the distal area, there is more proteolysis, resulting in more phenolic compounds and carcinogens. More sacrolytic balanceis needed to alleviate ulcerative colitis and bowel cancer.

• Encapsulation: to more fully protect probiotics in the gastrointestinal tract, lyophilized cultures have been encapsulated. This has often included materials such as gelatin, shellac and amylose. However, encapsulation with a prebiotic may offer both a protective capacity, as well as increased levels of growth substrate.

• Synbiotics: the combination of probiotics and prebiotics, called synbiotics, may offer the dual advantages of each as well as provide a selective substrate for the livemicroorganisms in the gut. A good example would be a mixture of bifidobacteria and FOS.Moreover, the use of reverse enzyme technology may allow the probiotics to generate theirown substrate. Current research focuses on a mixture of prebiotics that are of varying molecular weights.

Anti-adhesive properties: receptor sites for a variety of gut pathogens involve oligosaccharide sequences. If these could be incorporated into existing prebiotics they mayact as “decoy” molecules. That is, the pathogen would bind to the prebiotic at an appropriatesite, instead of the gut wall. Thus, the first line of pathogenesis would be compromised.

• Attenuative properties: the oligomer cellobiose is able to repress virulence in Listeria monocytogenes. When exposed to rotting vegetation, in a natural environment, Listeria is not a pathogen. In foods, where there is no cellobiose, the virulence is allowed to be expressed. This down regulatory process may be a further facet to consider in prebiotic research.

• Species-level changes: most existing prebiotics tend to act at the genus level. However, finer control of microflora modulation may be directed towards distinct species. For example, Bifidobacterium infantis is a more powerful inhibitor of gut pathogens, such as E. coli H0157, than other bifidobacteria. Certain galactooligosaccharides can confer species level changes in bifidobacteria.

• Activity at low dosage and with no side-effects: the minimum active dosage varies according to prebiotic type and excessive dosages may result in excessive gas production. As this arises from non-specific metabolism, prebiotics targeted more closely at bifidobacteria and lactobacilli would be highly desirable. In human trials,


doses of up to 40 grams of FOS per day, has been reported with little or no adverse side effects. Minimal operative dose of lactulose is about 10 grams per day, for FOS it is 8 grams per day, and in vitro data indicate it may be as low as 4 grams per day. For probiotics, there seems to be no upper dosage limit. Whenever human trials are done on probiotics, the starting level of bifidobacteria may determine the impact of the prebiotic on probiotic growth. At lower initial microbial levels, the increase in growth may be more significant.

Altering dairy foods

Much of the research on functional foods is focused on probiotics and prebiotics. However, other approaches to developing functional dairy foods might include adding garlic, bovine colostrum, isoflavones, or a variety of other functional ingredients. Another approach is to manipulate the fatty acid content of dairy foods to produce a more favorable fatty acid profile. Altering the composition of feed to dairy cattle has shown success. Animals fed canola seed oil, show increased MUFA content, increased PUFA content, and reduced LDL and total cholesterol content.

In terms of public health, a modest increase in PUFA is warranted in the population as a whole. There is still debate about optimal levels. One problem is that people do not like the flavor of fish oils. Another problem is that these fats are unstable when added to foods during processing. However, increasing the amounts in animal feed could increase omega-3 PUFA in the food chain. Unfortunately, these foods may be hydrogenated in the rumen and then become ineffective. Studies in the UK suggest that using fish oils in dairy cattle feed could increase the content of PUFA in milk. However, the content in enriched milk is extremely low.

There are questions about the efficacy of this measure as a potent means of increasing supplies of these fatty acids in food. There is also the issue of sustainability of sufficient fish oil production. There are alternative strategies for increasing omega-3 PUFA. One strategy is to synthesize DHA and EPA from alpha-linolenic acid. Another option is to feed canola oil to dairy cattle, and protect them from hydrogenation in the rumen. This has been tested and produces milk and a cheese that is pleasant and higher in alpha-linolenic acid content. Designing human trials

Earlier functional foods were defined from a government and food industry perspective. However, from a consumer perspective; they might be defined as “products that contain extra ingredients that manufacturers ‘claim’ to give an extra benefit”. Claims are a vital part of functional foods, and human intervention studies or “clinical trials” are considered as the ultimate scientific proof for health claims. Well-designed studies will produce outcomes or biomarkers relevant for substantiating the claim. They should not be misleading, and they should not be medical. The emphasis should be on human volunteer studies, and not clinical trials. Studies should:

• be based on scientific evidence;


• include studies on humans;

• deal with products not ingredients;

• contain reproducible data;

• be relevant for target group; and

• be consistent with nutrition guidelines.

In clinical trials, the “gold standard” is the double-blind, placebo-controlled, parallel-designed trial. For human volunteer studies, the standard will have to be modified in several ways. Everyone agrees that the tests should be “randomized”, as to which group receives the active treatment and which group receives the placebo. They should also be“ placebo-controlled”, but with nutrition research, it is often hard to determine an appropriate control. Also, with nutrition studies it is often difficult to make a “double-blind” study; for example if an individual consumes 400 grams per day of brussel sprouts, it is difficult to be blinded to this fact. A “parallel” design will usually be the standard. In nutrition research, tests may be either a parallel design or a cross-over design. In a parallel design, one group receives the A treatment, and the other group receives the B treatment. In contrast, in a cross-over design, one group receives the A treatment first, followed by a wash-out period, then the B treatment. The other group receives the B treatment first, then a wash-out, and finally the A treatment. A parallel design gives the opportunity for shorter-term experiments. The advantage of cross-over tests is that they can compare more treatments; but the disadvantage is that they take longer, which often limits the test to fewer treatments.

In designing human volunteer studies, it is important to look at safety, and strict diet control. Confirmation of mechanisms must also be considered. A smaller study often allows for stricter control. Researchers should assess voluntary intake before the start of the trial. Compliance is often difficult with a recall diet. Efficacy is often established in larger studies with mixed foods, in larger doses than would normally be consumed. The research must then correlate this to normal dosages and normal use. To keep up compliance in trials, researchers should check on what volunteers eat, keep up motivation, include markers for compliance, and maintain a good atmosphere. Each study requires careful consideration of the size and characteristics of the study group. For example in a cholesterol test, it is best to choose a slightly hyper-cholesteremic group. In a bone density test, good subject choices would be post-menopausal women or adolescents.

In a test of probiotics, a healthy group might not show improved immune function; while a group with lowered immunity might show a greater test effect. The inclusion criteria for a sample population would be an individual with apparently good health, stable, eating the standard diet, and with no history of drug use. Another necessity in volunteer studies is to have good clinical practice. This includes complying with ethical standards, designing tests that are scientifically sound and well described, and having independent audits, and traceable data and results. Such tests


would be a standard for regulatory authority, but there is a lot of work to comply with these criteria.

In tests, functional foods will have an important role in maintaining homeostasis. This may not be reflected in mean levels, repeated measurements may be more informative, and alternative statistics may be required. In conclusion, when testing functional foods, human trials are crucial. Careful consideration of trial design and markers, and study population, will determine success. Some alternatives to double-blind, placebo-controlled trials should be considered.


G. R. Patil

Joint Director (Academic) National Dairy Research Institute, Karnal

Introduction Most semi-solid and solid foods including cheese, paneer, sweetened condensed milk, ice cream and indigenous dairy products are viscoelastic in nature. The time dependency of stress-strain relationship results in a behaviour called viscoelastic, which combines liquid-like and solid like characteristics i.e. these bodies combine the properties of both viscous and elastic materials. The ratio of elastic to viscous properties depends on the time scale of the deformation. At short time scales its behaviour is mainly elastic: a test piece (almost) regains its original shape after the stress applied to it is removed. At long time scales the behaviour is mainly viscous: (most of) the deformation remains after the stress is removed. Mechanical Models Several models have been developed to describe the viscoelastic behaviour of materials. There are two basic viscoelastic models viz. Kelvin and Maxwell. Other complex viscoelastic behaviours are described by using combinations of these basic models. Kelvin model The Kelvin model employs the spring (elastic component) and dashpot (viscous component) in parallel. In this stress is the sum of two components of which one is proportional to the strain and the other is proportional to the rate of shear. Since the elements are in parallel they are forced to move together at constant rate. When a constant load is applied to Kelvin model, initially a retarded deformation is obtained followed by a final steady state deformation. When the load is removed the Kelvin model recovers completely but not instantaneously. The model is expressed mathematically as: �t = �o / E (1 - e -t/Tret) Where, �t is strain at time t �t is applied stress, E is elastic modulus and Tret is retardation time.

VISCOELASTIC BEHAVIOUR OF FOODS


Maxwell model The maxwell model employs a spring and dashpot in series. In this model the deformation is composed of two parts, one purely viscous and the other purely elastic. When a constant load is applied to Maxwell body, instantaneous elastic deformation will take place followed by continuing viscous flow, which will continue indefinitely as it is not limited by the spring component. When load is removed, the Maxwell body recover instantly but not completely. The Maxwell body shows stress relaxation but Kelvin body does not. The stress-strain-time relationship in Maxwell model can be given as: �t = �o [�d e -t/�rel + �e] where, �t is stress at time t, �o is fixed strain, �d is elastic decay modulus and �rel is relaxation time and �e is equilibrium modulus. Burger model This 4-element model is one of the best known rheological models which have been used to predict the creep behaviour in a number of materials. The model is composed of a spring and dashpot in series with another spring and dashpot in parallel. When a burger's body is subjected to constant load, there is instantaneous deformation (�o) is followed by retarded flow. When the load is removed there is instantaneous recovery followed by incomplete and slow recovery. The stress-strain time relationship can be given as: �t = �o / Eo + �o / Er (1 - e -t/T ret) + �o t/ηv In terms of compliance function Jt which is reciprocal of Young's modulus (E) the above equation can be given as: Jt = Jo + Jr (1 - e -t/T ret) + t/ηv Where, Jo is (1/Eo) initial compliance, Jr is is (1/Er) retarded compliance and t/ηv is Newtonian compliance. Generalised Maxwell model A generalised Maxwell model is composed of n Maxwell elements with a spring in parallel with nth element. The elastic modulus Ee of last spring corresponds to the equilibrium modulus in the stress relaxation test. The stress-strain time relationship is given by:


�t = �o (Ed1 + e -t/�1 + Ed2 e -t/�2 +................. Edn e -t/�n + Ee) Where, T1, T2..............Tn are relaxation times. Generalised Kelvin model Experimental data on many viscoelastic materials including biological materials have shown more than one relaxation time or retardation time. For these materials, complete behaviour cannot be represented by a single Maxwell or single Kelvin model or even 4 elements model. Each or these models have only one time constant. To represent the viscoelastic behaviour more realistically a chain of Kelvin models, each with its own time of retardation is assumed and the model is called a generalized Kelvin model. It consists of "n" Kelvin elements connected in series with an initial spring and final viscous element. The equation for generalised Kelvin model is: �t = �o [1/E0 + 1/Er1 (1 - e -t/T1) + 1/Er2 (1 - e -t/T2) + ………1/Ern (1 - e -t/Tn) + t/ηv]

Where, T1, T2..............Tn are relaxation times. Plasto-viscoelastic or Bingham model A more common type of body is the plasto-viscoelastic or Bingham body. When the stress is applied which is below the yield stress the Bingham body reacts as an elastic body. At stress values beyond the yield stress there are two components. One is constant and is represented by the friction element and the other is proportional to the shear rate and represents the viscous flow element. In a creep experiment with stress not exceeding yield value, the creep curve would be similar to the one for an elastic body. When the shear stress is greater than the yield stress, the strain increases with time similar to the behaviour of a Maxwell body. Upon removal of stress at time the strain decreases instantaneously and remains constant thereafter. The decrease represents the elastic components and the plastic deformation is permanent. Viscoelastic Characterisation Of Materials There are a number of tests that may be used to study viscoelastic materials and determine the relation among stress-strain-time for a given type of deformation and a given type of loading pattern. The most important tests include stress relaxation, creep and dynamic tests. Stress relaxation In stress relaxation test the specimen is suddenly brought to a given


deformation (strain), and the stress required to hold the deformation constant is measured as a function of time. The results are expressed in terms of time dependent modulus Et in tension or compression, Gt in shear or Kt in bulk compression. The rheological models representing stress relaxation are Maxwell model and generalised Maxwell model. One of the most important viscoelastic parameters which can be obtained from stress relaxation test is the relaxation time. It is the time at which the stress in the body resembling a maxwell model decays to 1/e of initial stress. It is the measure of the rate at which a material dissipates stress after receiving a sudden force. There are a number of methods for treating experimental data on stress relaxation and finding the relaxation time. The method of successive residuals involves as the first step in analysis of stress relaxation data plotting the logarithm of stress vs. time. If the plot were linear, the behaviour of the material is Maxwellian and the time of relaxation can be determined from the slope of the straight line. In most cases the plot of logarithm of stress vs. time is non-linear; indicating that the rheological behaviour cannot be represented by a single Maxwell element but an array of Maxwell elements connected in parallel is required. This is the most commonly used method of calculating relaxation times. Creep measurement In this test the stress is suddenly applied and held constant, and strain is measured as a function of time. The results are expressed in terms of time-dependant parameter, Et (�t/�o) or its compliance (1/Et) in tension or compression creep, in terms of Gt or Jt in shear creep or in terms of Kt or Bt in bulk creep. For a viscoelastic material the slope (dγ / dt = γׂ) gives an apparent viscoelasticy. The deformation γo is a measure of the elastic part. The rheological model to represent the creep behaviour is the Kelvin model and 4 elements Burger's model. Creep measurements are very useful for studying stand up properties of foods. Dynamic Measurements Despite the simplicity of creep and stress relaxation experiments, there are two disadvantage in these tests. The first disadvantage is that in order to obtain complete information about viscoelastic behaviour of the material, it is necessary to make measurements over many decades of time scales. This in addition to prolonging the experiment may cause chemical and physiological changes in the specimen which will affect the physical behaviour of the material. The second disadvantage is the impossibility of having a truly instantaneous application of load or deformation at the beginning of the experiment. These disadvantages can be overcome by dynamic tests in which the specimen in deformed by stress which varies sinusoidally with time. The time scale of the


measurement can be varied by changing the frequency (�) of the oscillation. Dynamic measurements are a powerful method for research on viscoelastic systems. One can determine both the elastic and viscous components in the reaction of a material on an applied stress or strain over a wide range of time scales. In a dynamic experiment in which the shear strain γ is varying sinusoidally, the latter is given by γ(t) = γo sin (�t) where, γo is the maximum shear strain. The strain is associated with a sinusoidally varying shear stress (γ) as follows: �(t) = �o sin ( �t + � ) where γo is the maximum stress and �is the phase angle between the deformation and stress. This phase differnece originates from the viscous properties of the material. For ideally elastic solid, �is in phase with γ. For ideally viscous fluid �is �/2 radians out of phase, then � equals �/2. For a viscoelastic material like a milk gel�has a value between and �/2. Within the linear region �o is by definition proportional to γo. The elastic part of the stress, which is the part of the stress in phase with strain, corresponds to the storage modulus G´, which is defined as: G´ ( �) (�o/ γo) cos �. It is the measure of the energy stored and subsequently related per cycle of deformation. The viscous part of the stress which is part of the stress out of phase with the strain, corresponds to the loss modulus G", which is defined as: G" ( �) (�o/ γo) sin �. It is the measure of the energy dissipated as heat per cycle of deformation. The ratio of G" to G' is tan �: tan �( �)= G" (�) / G' (� A higher tan � means that the material behaves in a relatively more viscous and less elastic manner. Conclusion Viscoelasticity is a combined solid-like, liquid like behaviour of materials. Most semi-solid and solid foods are viscoelastic in nature. Several rheological models


and test methods are now available to characterize the viscoelasticity of foods. Viscoelastic characteristic of foods are of great importance to the manufacturers, the trade and the consumers as these properties affect 'eating quality' usage properties such as ease of cutting, spreading and melting characteristic and handling and packaging characteristics.

References

De Man, J.M., Voisey, P.W., Rasper, V.F. and Stanley, D.W. (1976) Rheology and Texture in Food Quality. The AVI Pub. Com., Westport, USA.

Mohsenin, N.N. (1970) Physical properties of Plant and Animal Material. Vo. I Gordon and Breach Sci. Publ. N. Y.

Walstra, P. (1991) Rheological and fracture properties of cheese IDF Bull. N. 268.


Dr. Dalbir Singh Sogi, Reader & Head,

Dept. of Food Science & Technology, Guru Nanak Dev University, Amritsar.

RHEOLOGY

Rheology refers to study of the way any matter responds to applied forces. It is the science of flow and deformation. Eugene Bingham coined the term rheology in 1920 from Heraclitus's famous expression ‘panta rei’ which mean “everything flows". It is important in food science due to its utility in food processing operations and sensory characteristics. It gives information about the microstructure of a food. Rheological properties are manifestation of the rate and nature of the deformation that occurs when a material is stressed. These parameters can be used to predict how the fluid will behave in a process and in determining the energy requirements for transporting the fluid from one point to another in processing plant. Rheological parameters are also useful in defining the quality attribute of food products.

SIGNIFICANCE OF RHEOLOGY

Rheology is very important in the following processes:

Mixing – Two or more material are blended manually or mechanically.

Flow Control – Flowablity of material varies from very thin to highly viscous.

Dispensing - Material comes out easily or with difficulty.

Settling / Floating – Material with different specific gravity either settle or float depending on viscosity of the material.

Pumping – Liquids or semi-solids are forced through the pipe.

Coating – Spresding of one matrial as thin layer over other.

Cleaning – Soil removal from the surface of the equipments and pipeline. BASIC CONCEPT

Viscosity is a measure of resistance to flow of a fluid arising from internal friction. Viscosity is a principal parameter when any flow measurements of fluids,

FUNDAMENTALS OF RHEOLOGY


such as liquids, semi-solids, gases and even solids are made. The internal friction becomes apparent when a layer of fluid is made to move in relation to another layer. The greater the friction, the greater the amount of force required to cause this movement, which is called shear. Shearing occurs whenever the fluid is physically moved or distributed, as in pouring, spreading, spraying, mixing, etc. Highly viscous fluids, therefore, require more force to move than less viscous materials.

Viscosity Profile

Isaac Newton defined viscosity by considering two parallel planes of fluid of equal area A are separated by a distance ‘dx’ and are moving in the same direction at different velocities ‘V1’ and ‘V2’. Newton assumed that the force required to maintain this difference in speed was proportional to the difference in speed through the liquid, or the velocity gradient. The velocity gradient, ‘-dv/dx’ , is a measure of the change in speed at which the intermediate layers move with respect to each other. It describes the shearing the liquid experiences and is thus called shear rate. This will be symbolized as S in subsequent discussions. Its unit of measure is called the reciprocal second (sec-1). The term ‘F/A’ indicates the force per unit arearequired to produce the shearing action. It is referred to as shear stress and will be symbolized by T. Its unit of measurement is dynes per square centimeter (dynes/cm2). Using these simplified terms, viscosity ‘µ’ may be defined mathematically by this formula

T = µ (-dV/dx) ……(Eq 1)

UNITS OF MEASUREMENT

The fundamental unit of viscosity measurement is the poise. A material requiring a shear stress of one dyne per square centimeter to produce a shear rate of one reciprocal second has a viscosity of one poise. Conversion into other unis is as follows

1 poise = 100 centipoise = 0.1 Pascal-second 1 centipoise = 1 milli-Pascal-second = 0.1 g / cm.s = 3.60 kg/m.h = 6.72*E-4 lb /

ft.s

TYPES OF FLUIDS

The fluids can be classified into following categories depending on the response to the aplied shear force.


Newtonian Fluids

Fluid which exihibit a linear increase in the shear stress with the rate of shear (Eq 1) are called Newtonian fluids. Newtonian fluids are those which exihibit a linear relationship between the shear stress and the rate of shear. The slope ‘µ’ is constant therefore, the viscosity of a Newtonian fluid is independent of the rate of shear. The term viscosity is appropriate for use only with Newtonian fluids.

Non-Newtonian Fluids

A non-Newtonian fluid is broadly defined as one for which the relationship between shear stress and shear rate is not a constant. When the shear rate is varied, the shear stress doesn't vary in the same proportion. These fluids exhibit either shear thinning or shear thickening behaviour and some exhibit a yield stress. The two most commonly used equations for characterizing non-Newtonian fluids are the power law model (Eq 2) and the Herschel-Bulkley model for fluids (Eq 3):

T = K (γ)n .. .….(Eq 2)

T = To + K (γ)n ……(Eq 3) where T: shear stress; K: consistency index; γ: shear rate; n: flow behaviour

index; To : yield stress Power Law model can fit the shear stress-shear rate relationships of a wide

variety of foods. Thus, the experimental parameters of Viscometer model, spindle and speed all have an effect on the measured viscosity of a non-Newtonian fluid. This measured viscosity is called the apparent viscosity of the fluid and is accurate only when explicit experimental parameters are furnished and adhered to. The apparent viscosity ‘µapp’ has the same units as viscosity, but the value varies with the rate of shear.

µapp = K (γ)n …….(Eq 4)

There are several types of non-Newtonian flow behavior, characterized by the way a fluid's viscosity changes in response to variations in shear rate. The most common types of non-Newtonian fluids are:


Psuedoplastic

This type of fluid will display a decreasing viscosity with an increasing shear rate. Probably the most common of the non-Newtonian fluids, pseudo-plastics include emulsions and dispersions of many types. This type of flow behavior is sometimes called shear-thinning.

Dilatant

Increasing viscosity with an increase in shear rate characterizes the dilatant fluid. Although rarer than pseudoplasticity, dilatancy is frequently observed in fluids containing high levels of deflocculated solids, such as candy compounds, corn starch in water etc. Dilatancy is also referred to as shear-thickening flow behavior.

Plastic

This type of fluid will behave as a solid under static conditions. A certain amount of force must be applied to the fluid before any flow is induced; this force is called the yield value. Tomato ketchup is a good example of this type fluid, its yield value will often make it refuse to pour from the bottle until the bottle is shaken or struck, allowing the catsup to gush freely. Once the yield value is exceeded and flow begins, plastic fluids may display Newtonian, pseudoplastic, or dilatant flow characteristics.


Thixotropy and Rheopexy

Some fluids will display a change in viscosity with time under conditions of constant shear rate. There are two categories to consider:

Thixotropy

A thixotropic fluid undergoes a decrease in viscosity with time, while it is subjected to constant shearing.

Rheopexy

This is essentially the opposite of thixotropic behavior, in that the fluid's viscosity increases with time as it is sheared at a constant rate. Both thixotropy and rheopexy may occur in combination with any of the previously discussed flow behaviors, or only at certain shear rates. The time element is extremely variable under conditions of constant shear, some fluids will reach their final viscosity value in a few seconds, while others may take up to several days. Rheopectic fluids are rarely encountered.


Thixotropy, however, is frequently observed in materials such as greases, heavy printing inks, and paints. When subjected to varying rates of shear, a thixotropic fluid will react. A plot of shear stress versus shear rate was made as the shear rate was increased to a certain value, then immediately decreased to the starting point. Note that the up and down curves do not coincide. This hysteresis loop is caused by the decrease in the fluid's viscosity with increasing time of shearing. Such effects may or may not be reversible; some thixotropic fluids, if allowed to stand undisturbed for a while, will regain their initial viscosity, while others never will.

Dynamic Rheology [Oscillatory test] The behaviour of fluids mentioned so far is based on static force, however in

dynamic rheology force is applied on the material in oscillatory fashion. Dynamic rheological tests are used to evaluate properties of gel systems.


The storage modulus, G’, and the loss modulus G”, and tan δ = (G”/G’), the loss factor, can be obtained. G’ value is a measure of the deformation energy stored in the sample during the shear process, representing the elastic behavior of a sample. In contrary, G” value is a measure of the deformation energy used up in the sample during the shear and lost to the sample afterwards, representing the viscous behavior of a sample. If G’ is much greater than G”, the material will behave more like a solid; that is, the deformations will be essentially elastic o recoverable. However, if G” is much greater than G’, the energy used to deform the material is dissipated viscously and the materials behavior is liquid-like. On the other hand, the lost factor (or damping factor) reveals the ratio of the viscous to the elastic portion of the deformation behavior. A phase angle δ = 0 or tan δ = 0 corresponds to an elastic response and δ = 90_or tan δ = 1 is a viscous response. If the phase angle is within the limits of 0 < δ < 90, the material is called viscoelastic. Three types of dynamic tests can be conducted to obtain useful properties of gels, gelation, and melting: (1) frequency sweep studies in which G’ and G” are determined as a function of frequency (x) at fixed temperatures, (2) temperature sweep in which G’ and G” are determined as a function of temperature at fixed x, and (3) time sweep in which G’ and G” are determined as a function of time at fixed x and temperature

Stress Relaxation Test

In the stress relaxation test, an instantaneous deformation is applied to a body. This can be done while in compression, extension, or shear. A level of strain is picked to maximize sensitivity and minimize sample damage. Deformation or strain is maintained

constant throughout the test while the stress is monitored as a function of time. For viscoelastic materials, this stress will decay to an asymptotic value.


SWITCHING SWEETENERS –A SWEET APPROACH

Sumit Arora Senior Scientist

Dairy Chemistry Division, N.D.R.I., Karnal

Sugar is an important ingredient in the preparation of enumerable dairy products. It is consumed not only for its sweetness but it has many functional properties in foods that make it useful as a bulking agent, texture modifier and preservative. Sugars used in Indian sweets have many functions including bulking agent, preservative, texturizer, humectant, dispersing agent, stabilizer, fermentation substrate, flavor carrier, browning agent and decorative agent. However, the high content of sugar at times makes it an undesirable item of consumption from the health point of view, especially for diabetic and obese individuals. With increased consumer interest in reducing sugar intake, food products made with sweeteners rather than sugar have become more popular. The quality of these products depends on their body and texture, because the amount of solids is rather low. Thus, the use of stabilizers to improve texture and reduce whey separation is a common practice. When sugar is removed from food, it has to be replaced by alternative substances which maintain the sweet taste of the product and which may, among other functions, act as a bulking agent, such as polyols.

Table 1: PFA limits of artificial sweeteners in dairy based sweets

Name of artificial sweetener Article of food

Maximum limit of artificial sweetener

(ppm)

Saccharin Sodium 500 ppm

Aspartame (methyl ester)

200 ppm

Acesulfame potassium

500 ppm

Sucralose

Sweets (carbohydrates based and milk

products based):-Halwa, Mysore Pak,

Boondi ladoo, Jalebi, Khoya burfi,

Peda, Gulab Jamun, Rasogolla and

similar product based sweets sold by

any name. 750 ppm


Comparative properties of approved intense sweeteners

Property Aspartame Acesulfame-K Saccharin Sucralose

Chemical nature/Structure

Methyl ester of a dipeptide

Derivative of oxathiazin

Derivative of isothiazol

Chlorinated dissacharide

Composition

Acesulfame potassium The

amino acids aspartic 1,2-

benzisothiazol- Triclorogalacto-

sucrose

(6-methyl-1,2,3- acid and

phenylalanine 3(2H)-one-1,1-dioxide (1,6-dichloro-1,6-

dideoxyoxathiazin-

4-one- (N-L-_-aspartyl-L- _-D-fructosuranosyl-

2,2-dioxide) phenylalanine 1-methyl ester)

4-chloro-4-deoxy-_-

Relative sweetness

(w.r.t. sucrose)

200 -300

200

400 - 500

600 - 800

After taste

No Little bitter Bitter, metal

like No

Calorific value 4 kcal/g Calorie free Calorie free Calorie free

Stability

Stable to low

acidic conditions (pH 3-6), heat

unstable

Stable to heat, acid and alkali

Stable to heat, acid and alkali

Stable to heat, acid, alkali and

light

Solubility

Sparingly soluble in water

and slightly soluble in

ethanol (10 gm/l at 20°C)

Freely soluble in water, very

slightly soluble in ethanol

(270 gm/l at 20°C)

Slightly soluble in water, sparingly soluble in alcohol

(2 gm/l – acid saccharin, 100

gm/l – Na saccharin)

Freely soluble in water,

methanol and alcohol, slightly soluble in ethyl

acetate (283 g/l)

pH for max.

stability 4.5 – 6.0 6.5 – 7.5 7.0 – 8.5 5.0 – 6.0

Melting point (°C)

246

200 (decomposes

before reaching this temp.)

228.8 – 229.7 (acid saccharin),

> 300 (Na saccharin)

125

Acceptable Daily

Intake (ADI)

(mg/kg body

wt/day)

50 15 5 5


High-intensity low-calorie sweeteners provide consumers with many benefits, both psychological and physiological. Health professionals and consumers believe that low-calorie sweeteners are effective in weight maintenance, weight reduction, management of diabetes, reduction of dental cavities and reduction in the risks associated with obesity. According to a notification issued by the Ministry of Health and Family Welfare, Government of India (PFA 2004), the use of low-calorie sweeteners saccharin, acesulfame-K, aspartame and sucralose has been allowed in sweets like halwa, khoya burfi, rasgolla, gulabjamun.

Low-calorie sweeteners offer a means to enjoy good-tasting foods and beverages without as many calories. The ideal low-calorie sweetener also should be colorless, odorless, and have no aftertaste and sweetness should be experienced immediately and should taste as sweet as sugar but have fewer calories. These sweeteners should not cause cancer and should be inexpensive to produce. Availability of a variety of low-calorie sweeteners for use in foods expands the capability to develop reduced-calorie products that better meet consumer needs and desires. Blends of some low-calorie sweeteners in foods and beverages may also act synergistically to produce the desired level of sweetness with smaller amounts of each sweetener. The resulting taste often better meets consumer expectations of a sweetness profile close to that of sugar. The products may also have longer sweetness shelf lives.

The change in the food law opens up a vast untapped market of sugar-free food products including dairy products. With several sweeteners available, food manufacturers can use sweeteners in the applications for which they are best suited, and limitations of individual sweeteners can be overcome by using them in blends. Most sweeteners, including the polyols, are synergistic, so the sweetness of sweetener blends is greater than that produced by individual sweeteners. In the field of sugar replacement, the use of high potency artificial sweeteners to develop low calorie foods has been a success. However, extensive evaluation would still be essential to ensure the safety of these sweeteners when consumed in formulated dairy products.

Metabolism/ Excretion

Not metabolized excreted by the

kidneys unchanged;

Upon digestion, breaks down to aspartic acid,

phenylalanine and small amount of methanol all of

which are metabolized

normally

Not metabolized;

excreted by the kidneys

unchanged

Not metabolized

excreted in the feces and urine


Applications of sweeteners:

Acesulfame potassium

Acesulfame K is marketed under the brand name Sunett™, etc in food products and Sweet One or Swiss Sweet and as a tabletop sweetener. Acesulfame K is currently used in thousands of foods, beverages, oral hygiene and pharmaceutical products. Among these are tabletop sweeteners, chewing gums, baked goods, sauces, alcoholic beverages, canned foods, and candies. It also is used in dry beverage mixes, instant coffees, teas, gelatin, puddings, and nondairy creamer. Application has been made for its use in carbonated and noncarbonated beverages, baked goods, and soft candy, hard candy (including breath mints, cough drops and lozenges) and alcoholic beverages. Acesulfame K finds wide applications in dairy products namely ice creams, cheese, chocolate preparations, frozen desserts, flavoured milk, indigenous dairy products e.g. burfi, kalakand etc.

Aspartame

Aspartame is marketed under the brand names Nutrasweet®, Equal®, Spoonful®, and Equal-Measure®. It is used in food products and also as a tabletop sweetener. In addition to being used as a tabletop sweetener, aspartame is used in cold cereals, chewing gum, dry beverage mixes, carbonated and tea beverages, frozen stick novelties, dry beverage mixes, breakfast cereals, chewing gum, gelatins, puddings and fillings, dry mixes for dessert toppings, carbonated and noncarbonated beverages, carbonated beverage syrups, juices, refrigerated and non-refrigerated ready-to-drink beverages, frozen stick-type confections and novelties, breath mints, yogurt-type products, frozen desserts, confections, fruit spreads, toppings and syrups, frozen nondairy frostings and toppings, fruit wine beverages, hard and soft candies, cough drops, malt beverages, some pharmaceuticals such as chewable multi-vitamins and sugar-free cough drops, jams, jellies, and breakfast cereals. It also is used in selected prescription drugs and as a flavor enhancer, especially with fruits. Aspartame has been successfully used in a variety of dairy products namely diabetic and dietetic ice cream, desserts, yoghurt, ready to eat cheesecakes, frozen dairy frostings and toppings, flavoured and chocolate milk, skim milk, whey beverages and indigenous sweets e.g. burfi, kalakand, rasogolla and kulfi.

Saccharin

Saccharin is marketed under the brand name Syncal ® SDS etc. Saccharin has been available for more than 100 years and is the foundation for many low-calorie and sugar-free products around the world. It is stable under the normal range of conditions employed in food formulations. Most commonly used as a tabletop sweetener and in beverages. Saccharin is also used in cosmetics, vitamins, and drugs. It is used in tabletop sweeteners, carbonated and noncarbonated beverages, juice, chewing gum,


confections, desserts, puddings, jams and jellies. It also finds application in dairy products viz. fermented milks, unripened cheese, desserts, yoghurts, flavoured milk, burfi and kalakand.

Sucralose

Sucralose is marketed under the brand names Splenda ® by Tate and Lyle who holds exclusive patent rights for this sweetener. The greatest advantage of sucralose for food and beverage manufacturers and consumers is its exceptional stability. It retains its sweetness over a wide range of temperature and storage conditions and in solutions over time. Because of its stability, food manufacturers can use sucralose to create a number of great-tasting new foods and beverages in categories such as tabletop sweeteners, baked goods, carbonated beverages, processed fruit products such as juices, jams, pie fillings, chewing gum, canned fruit, low-calorie fruit drinks, baked goods, sauces and syrups. Sucralose also can be used as a sweetener in nutritional supplements, medical foods, and vitamin/mineral supplements. This general purpose sweetener also finds application in dairy products and dairy foods such as flavoured milk, plain and fruit yoghurt and desserts etc. Sucralose has also been successfully used in the preparation of indigenous sweets namely burfi and kalakand.

Sweetener blends:

The synergistic combination of sweeteners, known as multiple sweetener approach, has been found to increase low-calorie product choices for the consumer, performance of certain low-calorie sweeteners in certain products than in others, reduce costs and improve product taste and stability. The special features of sweetener blends are: (i) improves the quality of sweet taste, (ii) masks the shortcoming of the individual sweetener, (iii) taste profile similar to sugar, (iv) adds versatility to products, (v) lengthens sweetness shelf-life and (vi) cost effective. Blends commonly being used are acesulfame-K/sucralose, aspartame/acesulfame-K, aspartame/saccharin and acesulfame-K/ aspartame/ saccharin. Major manufacturers are using a variety of sweetener blends in many market-leading products. Several product categories in the world have also been successfully using sweeteners blends in dry mixes, desserts, carbonated and non-carbonated beverages, chewing gums, confectionaries and table top sweeteners.

Sweetener blends also find wide applications in dairy products. Blends of aspartame, acesulfame-K and saccharin have been used in ice cream. Low fat sugar free ice cream using artificial sweeteners (aspartame, acesulfame-K, saccharin and sucralose) singly and in combination has been developed. Blend of aspartame with acesulfame-K finds wide applications in the sweetening of whey based fruit beverages and indigenous dairy products namely lassi.


Role of Low-Calorie Sweeteners in a Healthful Diet

The growing availability of affordable and palatable dairy products in combination with an increasingly sedentary lifestyle underscores the important role that low-calorie sweeteners can play in achieving a healthful diet. Because they do not affect insulin levels, intense sweeteners also play an important role in the diets of people with diabetes. The use of low-calorie sweeteners results in a wide range of dairy products that can aid individuals in managing their caloric and carbohydrate intakes. Intense sweeteners can play a useful role in helping people achieve or maintain a healthy weight by providing good-tasting alternatives to foods and beverages including dairy products that are typically higher in calories. Low-calorie sweeteners offer the best method to date of reducing calories while maintaining the palatability of the diet. However, because they provide the pleasure of sweetness without adding calories or carbohydrates, low-calorie sweeteners can facilitate compliance with restricted eating plans. Hence, the sweet choices for Indian dairy industry are great.


S. N. Jha Senior Scientist (AS & PE)

AS & EC Division, CIPHET, Ludhiana

The analysis of colour is frequently an important consideration when determining the efficacy of variety of postharvest treatments. Consumers can easily be influenced by preconceived ideas of how a particular fruit or vegetable should appear, and marketers often attempt to improve upon what nature has painted. Recently colour measurements have also been used as quality parameters and indicator of some inner constituents of the material. In spite of the significance of colour in food industries, many researchers continue to analyze it inappropriately. This lecture thus tries to remove unnecessary confusions and through lights on various aspects of colour measurements, its basic units and spectra acquisition for extracting various hidden information through analysis.

Light and Color Among the properties widely used for analytical evaluation of materials, color

is unique in several aspects. While every material can be said to possess a specific property such as mass, no material is actually colored as such. Color is primarily an appearance property attributed to the spectral distribution of light and, in a way, is related to some source of radiant energy (the illuminant), to the object to which the color is ascribed, and to the eye of the observer. Without light or the illuminant, color does not exist. Therefore, several factors that influence the radiation subsequently affect the exact color that an individual perceives:

• Spectral energy distribution of light

• Conditions under which the color is viewed

• Spectral characteristics of the object with respect to absorption, reflection, and transmission

• Sensitivity of the eye

Thus, in reality, color is in the eye of the observer, rather than in the “colored" object. The property of an object that gives it a characteristic color is its light-absorptive capacity.

CONCEPT OF COLOUR MEASUREMENT AND SAMPLING TECHNIQUES FOR QUALITY EVALUATION OF FOOD


Color Specification

There are three characteristics of light by which a color may be specified: hue, saturation, and brightness. Hue is an attribute associated with the dominant wave-length in a mixture of light waves, i.e., it represents the dominant color as perceived by an observer. Saturation refers to relative purity or the amount of white light mixed with a hue. Brightness is a subjective term, which embodies the chromatic notion of intensity. Hue and saturation taken together are called chromaticity. Therefore, a color may be characterized by brightness and chromaticity.

CIE system

The Commission de Internationale de l’Ec1airage (CIE) defined a system of describing the color of an object based on three primary stimuli: red (700 nm), green (546.1 nm), and blue (435.8 nm). Because of the structure of the human eye, all colors appear as different combinations of these. The amounts of red, green, and blue needed to form any given color are called the' 'tristimulus" values, X, Y, and Z, respectively. Using the X, Y, and Z values, a color is represented by a set of chromaticity coordinates or trichromatic coefficients, x, y, and z, as defined below:

ZYXZ z

ZYXYy

ZYXX

++=

++=

++=x

It is obvious from the equations above that x + y + z = 1. The tristimulus values for any wavelength can be obtained from either standard tables or figures. A plot that represents all colors in x (red)-y (green) coordinates is known as a chromaticity diagram. For a given set of x and y, z is calculated from the above equations. Therefore, colors are generally specified in terms of Y, x, and y.

There are a number of color metrics based on the CIE system. They include CIE Lightness, CIELUV, CIELAB, etc. In the food industry, the CIELAB system has been popular. For example, objective measurements of color using the CIELAB color parameters such as L* (lightness), a* (redness), and hue angle have been used to evaluate pork quality on-line in an industrial context (5,6).

Other color models, such as the RGB, CMY, and HSI, etc., are very similar to the CIE system, and numerical representation of a color in one system can be converted into another.

Munsell system and atlas

The Munsell color-order system is a way of precisely specifying colors and


Fig. 1.Chromaticity diagram showing the ripeness

locus of oil palm and also the location of

white in illuminant “c”

showing the relationships among colors. Every color has three qualities or attributes: hue, value, and chroma. A set of numerical scales with visually uniform steps for each of these attributes has been established. The Munsell Book of Color displays a collection of colored chips arranged according to these scales. Each chip is identified numerically using these scales. The color of any surface can be identified by comparing it to the chips under proper illumination and viewing conditions. The color is then identified by its hue, value, and chroma. These attributes are given the symbols H, V, and C and are written in a form H V/C, which is called the Munsell notations. Using Munsell notations, each color has a logical relationship to all other colors. This opens up endless creative possibilities in color choices, as well as the ability to communicate those color choices precisely. The Munsell system is the color order system most widely quoted in food industry literature. Food products for which the U.S. Department of Agriculture (USDA) recommends matching Munsell discs to be used include dairy products such as milk and cheese, egg yolks, beef, several fruits, vegetables, and fruit juices.

Other color atlases and charts are available for use in the food industry, such as the Natural Color System and Atlas, Royal Horticultural Society Charts, etc. These atlases and charts are used for visual comparison of a product color with that of a standard color diagram, which is still commonly practiced in the food industry. The evaluation of potato chip color is a very good example.


Machine Reading of Color

A digital color is usually represented by red, green and blue (RGB) tristimullus values. This approximation is a consequence of the fact that human perception of color is mediated by the response of three different types of photoreceptors in the retina called cones (Wright 1964). A color standard like the CIE (Commission Internationale de1’ Eclairage) 1931 standard as mentioned above defines color based on its psycho-physical properties (Wyszecki and Stiles 1967). A useful feature of this system is that it enables the tristimullus values be transformed into lower dimension quantities. In this case they are the chromaticity coefficients expressed in terms of x and y values. An object's color can be assessed by simply plotting x and y in rectangular coordinates, producing a chromaticity diagram (Fig. 1).

To facilitate discussion, the locus is superimposed, with the reference horseshoe curve obtained from a standard monochromatic light. It can be seen from this figure that the chromaticity of unripe fruits falls near the center of the CIE diagram. The position marked "C" in this diagram represents color which is biochromatically achromatic or hueless., Oil palm actually appears reddish black when unripe and this agrees well with colorimeter since this equipment treats both pure white and black as hueless. As the fruit starts to ripen; the locus moves from the hueless zone to the reddish red zone and ends at a point bordering the reddish orange zone. It can be seen from this diagram that the difference in chromaticity between unripe and underripe is relatively small compared to the difference in chromaticity between optimally ripe and overripe, indicating that there is a small degree of change in color at the early stage of ripening. The distance between unripe and overripe is 0.202 compared to slightly over 0.03 between unripe and underripe. Hence, distinguishing unripe from underripe samples or vice versa may be difficult chromatically.

Even though the chromaticity diagrams are useful in specifying color, they do not accurately represent color closest to that of human perception. For proper quantification of tristimulus and efficient colour processing, this variable is usually


Bluish green

180o

‐50

50

‐2 1

Yellow

90o

Red purple

0o 360o

Blue 270o

Fig. 2 Representation of peel hue affected by heat treatments of grapefruits. CIE LAB a* and b* values are plotted on horizontal and vertical axes respectively

represented on cylindrical coordinates of L* a* and b* or simply the CIELab, a uniform colour space, values. These values later can be transformed to through simple trigonometric functions (Hunter, 1942) comprising psychometric lightness (L*), hue (ho) and chroma (C*) (Eqns 1 – 3). A colour wheel subtends 360o, with red-purple traditionally placed at the far right (or at an angle of 0o), yellow, bluish-green, and blue follow counterclockwise at 90o, 180o, 270o, respectively (Fig. 2).

( )( ) 3 *b *a *

2 1tan

1 **

22

0

+=

−=

=

Ca

bh

LL

Arctangent, however, assumes positive values in the first and third and negative values in the second and fourth quadrants. For a useful interpretation, ho should remain positive between 0o to 360o of the colour shed.


Fig. 3. Colorimetric plot of oil palm showing the change in

hue and chroma during ripening

Figure 3 shows the variation of CIELab values calculated from the oil palm image. It can be seen that both hue and chroma increase in curvilinear fashion with ripeness. The small hue and chroma values for unripe class (approximately 7.6° and 2.62, respectively) pushed the psychromatic point nearer to the origin or the achromatic zone of color. These values increased to approximately 48° in hue and 72.1 in chroma for overripe case. This location is equivalent to reddish orange color

on CIELab space. Hence, the hue-moves further away from the origin and in the upward direction as the oil palm ripens. These observations are consistent with human vision and match strongly with the trend of the ripeness locus shown in Fig. 1. Thus hue provides a much better discrimination compared to either RGB or CIExy values when specifying colors of food materials. Because of this reason usually hue is chosen for colour inspection by machine.

Unlike colorimeter, the calculation of hue using machine vision system is mathematically involved since it requires color conversion from RGB to HSl (Hue, Saturation and Intensity) space. One way of achieving this is by firstly I establishing a new coordinate system, YIQ. The relationship between the two coordinate systems is:

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡=

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

BGR

QIY

0.31 00.52 - 0.210.32 - 0.28- 0.600.11 0.59 30.0

- (4)


Secondly, h is the rotational angle around the Q, I plane and therefore can be written as:

Equations (4) and (5) are theoretically valid and they can be found in almost any textbook on color and image processing. For practical reasons, h was calculated according to the Munsell's color system, which is given by:

( )[ ] (6) G B if 360255

B)B)(G(RG)(RB)(RGR0.5cos360h

2

1oo ≥×⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛

−−+−

−+−−−= −

or

( )[ ] (7)G B if 360255

B)B)(G(RG)(RB)(RGR0.5cosh

2

1o <×⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛

−−+−

−+−−= −

The above equation transforms RGB information from three-dimensional space to one-dimensional h space. In order to speed-up analysis only h values may be processed. The hue values shown in this figure are normalized to 255 for the 8-bit machine vision system. A different approach is needed to solve this type of problem. The method investigated for this application was to treat hue distributions as features and apply multivariate discriminant technique to establish classification.

Example of transformation of CIE/Hunter L a b values to chroma (c) and hue (ho)

Assume a practical data given in Table 1 for analysis of grapefruit colour after three heat treatments for quarantine control

Treatment Colour characteristics

L a b c ho

1 76.6 -2.0 56.0 56.0 92.0

2 74.4 2.0 56.0 56.0 88.0

3 63.0 1.2 34.0 34.0 88

(5) 1tan ⎥⎦

⎤⎢⎣

⎡−=

QIho


Following subprogram was used to compute the ho and C for above data

Data colour

READ (*,*) L, a, b

C=SQRT((a*b)+(b*b))

THETA=(ATAN(b/a)/6.2832)*360

IF a>0 AND b>=0 THEN h = THETA

IF a<0 AND b>= THEN h=180+THETA

IF a<0 AND b<0 THEN h=180+THETA

IF a>0 AND b<0 THEN h=360+THETA

WRITE (*,*) a, b, THETA, h

STOP

END

After converting L, a, and b values to hue and chroma as shown in Table 1 can be correlated to any desirable attributes of food. But sometimes these values may give poor correlation with internal attributes such as total soluble solids, dry matter content etc. In order to search for better correlation one should investigate whole range of spectral data of available wavelengths.

Spectra of Light

Spectra are curves drawn as a continuous function of relative reflectance or absorbance data with respect to wavelengths. Since light is the basic stimulus of colors, it is important to consider the electromagnetic spectrum (Fig. 4). Several optical methods have been developed based on radiation from different regions of this spectrum.

Radiation is one of the basic physical processes by which energy is transferred from one place to another. Propagation of radiation through free space is the same for the entire electromagnetic spectrum, i.e., radiation of all wavelengths-from the shortest gamma rays to the longest radio waves-travels with the same speed in vacuum.


Visible light forms only a small part of the electromagnetic spectrum, with a spectral range from approximately 390 nm (violet) to 750 nm (red). The sensitivity of the eye varies even within this narrow visible range. Under conditions of moderate-to-strong illumination, the eye is most sensitive to yellow-green light of about 550 nm.

If the spectral distribution throughout the visible region is unequal, then the sensation of color is evoked by radiant energy reaching the eye's retina. An equal spectral distribution makes the light appear as white. The unequal distribution responsible for color sensation may be characteristic of the source itself; such as flame spectra composed of one or more monochromatic wavelengths, or may result from selective absorption by the system, which appears colored. The latter includes several systems that show selective absorption for light and exhibit color as a result of reflection or transmission of unabsorbed incident radiant energy (Fig. 5). The three basic factors required in color sensation include the radiator or illuminant, the object, and the observer. The radiant energy emitted by the radiator is characterized by its spectral quality, angular distribution, and intensity.

The following material properties and lighting of the scene as affecting the total appearance of the object:

Material properties:

Optical properties (spectral, reflectance, transmission)

Physical form (shape, size, surface texture)

Temporal aspects (movement, gesture, rhythm)

Lighting of the scene:

Illumination type (primary, secondary, tertiary)

Fig. 4. Classification of electromagnetic spectrum of light


Spectral and intensity properties; directions and distributions

Color-rendering properties

Fig. 5 Schematic representation of interaction of light with matter, θ1= angle of inci-dence, θR = angle of reflectance, θT = angle of transmittance, n1 n2 = refractive index of medium 1 and 2, respectively.

Interaction of Light with Matter Physical Laws

When light falls on an object, it may be reflected, transmitted, or absorbed (Fig. 5.) Reflected light is the part of the incident energy that is bounced off the object surface, transmitted light passes through the object, and absorbed light constitutes the part of the incident radiant energy absorbed within the material. The degree to which these phenomena take place depends on the nature of the material and on the particular wavelength of the electromagnetic spectrum being used. Commonly, optical properties of a material can be defined by the relative magnitudes of reflected, transmitted, and absorbed energy at each wavelength. Conservation of energy requires that sum of the reflected (IR), transmitted (IT), and absorbed (IA) radiation equals the total incident radiation (I). Thus,

(8)IIII A TR ++=


According to its transmittance properties, an object may be transparent, opaque, or translucent. Almost all food and biological products may be considered to be opaque, although most transmit light to some extent at certain wavelengths. The direction of a transmitted ray after meeting a plane interface between any two nonabsorbing media can be predicted based on Snell’s law:

The attenuation of the transmitted ray in a homogeneous, nondiffusing, absorbing medium is defined by Beer-Lambert’s law:

(10) abc/I)(I log T =

The ratio IT/I is known as the transmittance T and is related to absorbance A as:

(11) log(I/T)A =

From Eqs. (10) and (11), absorbance A can also be written as:

(12) abcA =

where a is called the absorptivity. [if c is expressed in mol/L and b in cm, a is replaced by the molar absorptivity, ε (L/mol.cm).]

Various constituents of food products can absorb a certain amount of this radiation. Absorption varies with the constituents, wavelength, and path length of the light. Reflection is a complex action involving several physical phenomena. Depending on how light is reflected back after striking an object, reflection may be defined as regular or specular reflection and diffused reflection (Fig. 5.). Reflection from a smooth, polished surface is called” specular” or “regular”. It mainly produces the gloss or shine of the material. The basic law of specular reflection states that the angle at which a ray is incident to a surface must equal the angle at which it is reflected off the surface. Fresnel equations define the phenomenon of specular reflection. The intensity of parallel Rpl and perpendicular Rpr components of the reflected light are:

(13) ]θsin)/n[(nθ cos)/n(n]θsin)/n[(nθ cos)/n(nR

2

1/21

22121

212

1/212

2121

212

pl ⎥⎦

⎤⎢⎣

⎡−+−−

=

(14) ]θsin)/n[(nθ cos]θsin)/n[(nθ cos

R2

1/21

22121

1/212

2121

pr ⎥⎦

⎤⎢⎣

⎡−+−−

=

(9) θsin n - θsin n 1T2


The regular reflectance R = Rpl2 + Rpr

2 and for normal incidence (θ = 0o), Rpl = Rpr,

and hence.

2

12

12

nnnn

R ⎥⎦

⎤⎢⎣

⎡+−

= (15)

where n1 and n2 are reflective index of the medium and object, respectively : and θ1 is the incident angle (Fig. 5.). If the material is absorbing, the reflective index is a complex number n (1-ik), where n is the real part of the complex number and k is an absorption constant, and the regular reflectance is written as:

⎥⎦

⎤⎢⎣

⎡+++−

= 22

212

22

212

k)(n)n(nk)(n)n(n

R (16)

When the incident light is reflected from a surface evenly at all angles, the object appears to have a flat or dull finish termed “diffuse reflection” No rigorous theory has been developed for diffuse reflectance, but several phenomenological theories have been proposed, the most popular being the Kubelka-Munk theory. The Kubelka-Munk model relates sample concentration to the way Beer-Lambert’s law relates band intensities to concentration to the intensity of the measured spectrum in a manner analogous to the way Beer-Lambert’s law relates band intensities to concentration for transmission measurements. The Kubelka-Munk function f(R ∞ ) is generally expressed as:

sk

RRRf =

−=

∞

∞∞ 2

)1()(2

(17)

where R ∞ = absolute reflectance of an infinitely thick layer, k = absorption coefficient, and

s = scattering coefficient.

Kubelka-Munk theory predicts a linear relationship between spectral data and sample concentration under conditions of constant scattering coefficient and infinite sample dilution in a nonabsorbing matrix such as KBr (potassium bromide). Hence,


the relationship can only be applied to highly diluted samples in a nonabsorbing matrix. In addition, the scattering coefficient is a function of particle size, so samples must be prepared to a uniform fine size of quantitatively valid measurements are desired.

It is not easy to quantify diffuse reflectance measurements since sample transmission, scattering, absorption, and reflection all contribute to the overall effect. By reducing particle size and dilution in appropriate matrices, surface reflection that can give strong inverted bands is reduced and the spectra more closely resemble transmission measurements. Typically, quantitative diffuse reflectance measurements are presented in log (I/R) units, analogous to absorbance log (I/T) units for transmission measurements. Bands increase logarithmically with changes in the reflectance values. By comparison, bands in spectra displayed in Kubelka-Munk units vary as a function of the square of reflectance. This difference emplasizes strong absorbance bands relative to weaker bands.

The diffuse reflectance may be measured with respect to a nonabsorbing standards and converted to produce a nearly relationship with concentration c as follows:

ac/s)log(R'(I/R)log/R)log(R' ≅+= (18)

where R' and R – reflectance of the standard and the sample ( R' > R), a = absorptivity, c = concentration, and s = scattering coefficient. For monochromatic radiation, log R' is constant and may be ignored, and Eq. (18) may be writes as (17):

/R)(s/a)log(Ikc += (19)

where k = absorption coefficient. It should be noted that s is not a constant but depends on a number of properties of the sample such as particle size (s is inversely proportional to particle size) and moisture content. In food materials, the primary factors that influences light reflection is a phenomenon known as scattering or diffusion. If the surface of incidence is rough, incident light will be scattered in all directions. Since the incident rays strike a rough surface more than once before being reflected, they would be expected to have a lower total reflectance than those reflected from a smooth surface.

In classical optics, diffuse reflection was thought to be responsible for color. It was also commonly believed that colour of natural objects, such as foods and plant


foliage, are seen by means of light reflected off their surfaces. It is also known that the light must be transmitted through pigment within the cells in order to produce a colored appearance. Since most food materials are optically nonhomogeneous, light entering such material is scattered in all directions. Only about 4-5% of the incident radiation is reflected off the surface of these materials as regular reflectance. The remaining radiation transmits through the surface and encounters small interfaces radiation from within the material is scattered back to the surface through the initial interface. This type of reflection is termed as “body reflectance”. The body reflectance is nearly always diffuse and is the most significant orom of reflectance is nearly always diffuse and is the most significant form of reflectance for foods. Some part of the transmitted light diffuse deeper in to the material and may eventually reach the surface some distance away from the incident point.

Factors Affecting Diffuse Reflectance Spectral Data

Diffuse reflectance spectroscopy offers exceptional versatility in sample analysis. This versatility results from both its sensitivity and optical characteristics. Classically, diffuse reflectance has been used to analyze powered solids in a nonabsorbing matrix of an alkali halide such as KBr. The sample is typically analysed at low concentrations, permitting quantitative presentation of the data in Kubelka-Munk units. This technique yields spectra that are qualitatively similar to those produced by conventional transmittance or pellet methods. However, they exhibit higher sensitivity for quantification and are less subject to scattering effects the cause slopping baselines in pellet measurements.

Several factors determine band shape and relative/absolute intensity in diffuse reflectance spectroscopy through their effect on the reflection/absorbance phenomena specific to the sample. These include:

Refractive index of the sample Particle size Sample homogeneity Concentration

1. Refractive index Refractive index affects the results via specular reflectance contributions to

diffuse reflectance spectra. With organic samples, the spectra display pronounced changes in band shape and relative peak intensities, resulting in nonlinearity in the relationship between band intensity and sample concentration. For some inorganic samples, strong specular reflection contributions can even result in complete band inversions. Diluting the sample in nonabsorbing matrix can minimize this overlay of diffuse reflectance and specular reflectance spectra, as well as the resulting spectral


distortions. In addition, accessory design can help reduce specular reflectance contributions.

2. Particle size

Particle size is a major consideration when performing diffuse reflectance measurements of solids. The bandwidth is decreased and relative intensities are dramatically altered as particle size decreases. These effects are even more pronounced in spectra of highly absorbing inorganic materials with high refractive indices. For these samples, specular contributions can dominate the final spectra if the particle size is too large. To acquire a true diffuse reflectance spectrum, it is necessary to uniformly grind the sample and dilute it in a fine, nonabsorbing matrix. Similar preparation must be applied to the nonabsorbing matrix material in order to provide and “ideal” diffuse reflector for background analysis and as a support matrix for the samples.

3. Sample homogeneity

The Kubelka- Munk model for diffuse reflectance is derived for a homogeneous sample of infinite thickness. However, some sample analysis methods, especially those designed for liquid sample (e.g., deposition of sample onto a powdered supporting matrix) can result in a higher concentration of sample near the analysis surface. In these circumstances, variations in relative peak intensities may be noticed. In particular, more weakly absorbing wavelengths tend to be attenuated at higher sample concentrations. To avoid these peak intensity variations it is necessary to distribute the analyte as uniformly possible within the nonabsorbing background matrix.

4. Concentration

One particularly important advantage of diffuse reflectance spectroscopy, especially in comparison to transmittance measures, is its extremely broad sample-analyzing range. While it is theoretically possible to acquire usable diffuse reflectance spectra on samples of wide-ranging concentrations, practical considerations often complicate the analysis process. With high concentration samples, especially those with a high refractive index, one can expect a dramatic increase in the specular contribution to the spectral data. As a result, some sample data may be uninterpretable without adequate sample dilution. Even when samples can be measured satisfactorily at high concentrations, it is advisable to grind the sample to a very uniform and fine particle size to minimize both specular reflectance and sample scattering effects, which adversely affect quantitative precision.

Sampling for measurement of colour and prediction of internal attributes affecting quality of materials requires considerable attention towards the factors described above.


Samples and Sample Preparation

Availability of wide range of techniques for measurement of quality parameters, necessitates to know the suitability of characteristics of samples, i.e. whether it is liquid, solid, paste, semisolid, transparent, opaque or translucent etc for a particular technique to be employed. Complete history of the source of sample and a careful attention to proper instrument operation and consistent sample handling is required particularly for colour measurement.

Preparing samples for measurement

When measuring samples it is important to select samples appropriately, use an established measurement method, and handle all samples in a consistent manner.

Selecting samples

Samples representative of the entire batch should be selected for measurement. Always one should try to collect as number of varied materials as possible from different sources whose colour is to be measured and

1. Choose samples that are truly representative of those materials collected from various sources,

2. Prepare samples in exactly the same manner each time they are measured. Follow standard method, if they exist such as ASTM, BIS etc, and

3. Present the samples to the instrument in a standard, repeatable manner. Results obtained depend on the condition of the samples and their presentation. For established procedure, make a checklist so that laboratory personnel may simply check each step. The checklist will also help in training of new workers.

The sample must also be representative of attributes that are of interest. If samples are non-representative of the batch or are spoiled, damaged, or irregular, then the sample may be biased. When choosing a sample, select in random fashion and examine the sample to avoid biased results. If sampling procedures are adequate, a different sample selected from the same batch should result in comparable measured values.

Sample handling and measurement methods

If method of measurement is established so that same procedure is used each time for specific samples or types of samples, results may be validated for comparison purposes. This also insures repeatability of results when measuring the same sample.


There are a variety of techniques that can be used in handling various forms of objects and materials so that the most valid and repeatable measurement of their appearance results. Consideration must be given to the conditions for sample preparation that are dependent upon the type of measurement to be made. For example, when measuring the colour of sample that might pillow into the viewing aperture, one should hold the surface flat by using a cover glass taped over the aperture window. Other materials being measured for colour may be chopped up and placed in a glass specimen cell or made into paste and applied to a glass plate. Sheets and films should be flattened by tension or by a vacuum, if necessary.

Directional samples

Averaging several measurements with rotation of the sample between readings can minimize directionality. Examination of the standard deviation displayed with the average function can guide in selecting the appropriate number of readings to average.

Non-opaque samples

Non-opaque samples must have a consistent backing. A white uncalibrated tile is recommended. If the sample is such that it can be folded to give multiple layers, such as fruit leather, the number of layers for each sample should be noted.

Translucent samples

Light trapped in a translucent sample can distort the colour. The thickness of the sample presented should be chosen to maximize the haze or colour difference.

Granular, powdery and liquid samples

These foods in required quantity may be taken into a petty dish of known composition and characteristics and covered by other complete transparent and flat petty dish of known properties. Thickness or depth of the sample should be so maintained that it presents an opaque mass. Colour readings may be taken keeping the flat portion of nosecone of the colourmeter on the surface of the top petty dish ensuring that light thrown by the instrument neither goes out of the nosecone nor passes through the sample. Part of the light is absorbed by the sample and remaining portion (reflected from the sample) again comes back to the nosecone of the instrument for measurement and interpretation. If samples cannot be prepared to make it opaque (in case of transparent liquid sample) instruments such as tintometer, photospectrometer etc should be used in visual range of wavelength.


REFERENCES

Birth G. S. (1978). The light scattering properties of foods. J. Food Sci 43:915.

Hutchings J. B. (1999). Food colour and Appearance. 2nd edition, Gaithersburg. MD: Aspen Publishers Inc.

Jha, S. N. (2004). Non-destructive methods for quality evaluation of foods. Indian Food Industries, 23(5): 21 –26.

Jha, S.N. (1999). Physical and hygroscopic properties of makhana. J. Agric. Eng. Res., 72 , 145-150.

Jha, S.N. and Prasad, S. (1993). Physical and thermal properties of gorgon nut. J. Food Process Eng., 16, 237-245.

Jha, S.N. and Prasad, S. (1996). Determination of processing conditions of gorgon nut (Euryale ferox). J. Agric. Engg. Res., 63, 103-112.

Jha, S.N. Matsuoka T. (2000). Review: Nondestructive Techniques for quality evaluation of intact fruits and vegetables. Food Science and Technology Research - Review, 6(4), 248 – 251.

Jha. S. N. and Kachru, R.P. (1998). Physical and aerodynamic properties of makhana. J. Food Process Eng., 79, 301-316.

Kortum G (1969). Reflectance spectroscopy: Principles, Methods, Applications. New York:Springer-Verlag.

Osborne B G, Fearn T (1986). Near-Infrared spectroscopy in Food Analysis. Longman Scientific and Technical Avon, England


SENSORY EVALUATION OF RIPENED VARIETIES OF CHEESE

Dr. Shivashraya Singh

Emeritus Scientist, Dairy Technology Division NDRI, Karnal

Introduction It has long been recognized that enjoyment of food is essential for good health.

Enjoyment would mean choice and acceptance and not always nutrition and wholesomeness. The consumer’s appreciation of food quality is, thus, all-important. For consumers, the perceivable sensory attributes, colour, appearance, feel, aroma, taste and texture are the deciding factors in food acceptance. The sensory evaluation may be defined as a scientific discipline used to evoke, measure, analyze and interpret results of those characteristics of foods and materials as they are perceived by the senses of light, smell, taste, touch and hearing. The definition makes clear that sensory evaluations encompass all the senses, and not taste testing alone. As the definition implies, sensory evaluation involves the measurement and evaluation of the sensory properties of the foods and other materials. Therefore, sensory evaluation helps in ensuring that the consumers get consistent, non-defective and enjoyable foods.

Of all the foods which mankind has created for eating pleasure, cheese is unique in many ways. No other group of foods possesses such variations in flavour, consistency, appearance or number of categories. Perhaps, no other form of food is more universally known and enjoyed around the world.

Cheese is one of the oldest foods of mankind. It seems that the cheese originated accidentally as a result of the activities of nomadic tribes. Since animal skin bags were a convenient way of storing liquids for nomadic people, these were used for storing surplus milk. Fermentation of the milk sugars in the warm climate prevailing would cause the milk to curdle in the bags. The swaying animals would have broken up the acid curd during journeys, to produce curds and whey. The whey provided a refreshing drink on hot journeys, while the curds, preserved by the acid of fermentation and a handful of salt, became a source of high protein food supplementing the meager meat supply.

Until the 18th century, cheese making was essentially a farmhouse industry, but towards the end of the century scientific findings began to provide guidelines, which were to have an impact on the process of making and ripening cheese. Thus cheese making became an ‘Art with Science’. Now the mechanization and automation


has been taken to such a high level that tones and tones of cheese can be produced without a touch of hand. Definition

The word “cheese” is derived from the Old English “cese” which in turn was

derived from the Latin “caseus” which means correct or perfect thing. A complete definition is as follows:

Cheese is the curd or substance formed by the coagulation of milk of certain mammals by rennet or similar enzymes in the presence of lactic acid produced by added or adventititious microorganisms, from which part of moisture has been removed by cutting, warming and pressing, which has been shaped in mould and then ripened (also unripened) by holding for sometime at suitable temperatures and humidities.

According to the PFA Rules (1976), cheese (hard) means the product obtained by draining after the coagulation of milk with a harmless milk-coagulating agent, under the influence of harmless bacterial cultures. It shall not contain any ingredient not found in milk, except coagulating agent, sodium chloride, calcium chloride (anhydrous salt) not exceeding 0.02% by weight, annatto or carotene colour and may contain emulsifiers and/or stabilizers, namely citric acid, sodium citrate or sodium salts of orthophosphoric acid and polyphosphoric acid not exceeding beyond 0.2% by weight; wax used for covering the outer surface should not contain any thing harmful to the health. In case wax is colored only permitted food colours may be used. Hard cheese shall contain not more than 43% moisture and not less than 42% of milk fat of the dry matter. Hard cheese may contain 0.1% of sorbic acid or its sodium, potassium or calcium salts or 0.1% of nisin. Essential Steps of Cheese Making

The conversion of milk into finished cheese can generally be divided into several distinct steps. Numerous variations and sub-routines within each of these general steps make possible the hundreds of cheese varieties. Five essential steps in cheese making are:

1. Preparing and inoculating the milk with lactic acid bacteria.

2. Curdling the milk (forming a coagulum or gel)

3. Shrinking the gel (curd) and pressing the curd into forms.

4. Salting the curd or formed cheese.

5. Ripening or curing the cheese (optional).


Cheese Properties

Moisture content and acidity are regarded as the two most important factors in the control of cheese properties (characteristics). Given a constant milk fat-to-casein ratio, the hard ness of a given cheese is a function of moisture content. Generally the firmer a cheese (due to low moisture), the slower the rate of ripening, the more selective the microflora, the milder the flavour, and the longer the product keeping quality. On the basis of moisture content, cheese may be classified as: (1) very hard; (2) hard; (3) semi hard (also known as semisoft) and (4) soft. The extent of protein hydrolysis, salt content, and the relative amounts of milk fat in cheese also help determine the extent of softness or harness. Cheese may be: (1) unripened; (2) internally ripened by the action of bacteria, molds, and /or enzymes; or (3) externally ripened as the result of surface growth of bacteria, yeasts and/or molds.

Classification

There are about 2000 names of cheeses. It is very difficult to classify the different cheese satisfactorily in groups. There are probably only about 18 types of natural cheeses. These are: Cheddar, Gouda, Edam, Swiss, Brick, Herve, Camembert, Limburger, Parmesan, Provolone, Romano, Roquefort, Sapsago, Cottage, Neufchatel, Trappist, and Cream and whey cheeses.

These can also be classified on the basis of their rheology (the science of the

deformation and flow of matter) and according to the manner of ripening as shown below. From the point of view of cheese, it may be considered as the study of how hard and how elastic a cheese may be and the reasons for these particular properties:

1. Very hard (grating) – Moisture <35% on matured ripened by bacteria, e.g.

Parmesan, Romano. 2. Hard – Moisture <40%

• Ripened by bacteria, without eyes: Cheddar • Ripened by bacteria, with eyes: Swiss

3. Semi –hard-Moisture 40-47% • Ripened principally by bacteria: Brick • Ripened by bacteria and surface microorganisms: Limburger • Ripened principally by blue mould:

a) External – Camembert b) Internal – Gorgonzola, Blue, Roquefort

4. Soft- Moisture > 47% • Unripened – Cottage • Ripened – Neufchatel (as made in France)


Cheese Grading

A Cheese judge is often called upon to evaluate one or more varieties or types of cheese. To be proficient, the evaluator should be knowledge-able in the sensory characteristics and the desirable and undesirable qualities of each cheese type under consideration.

The relative amounts of various milk components and the amount of whey retained in the curd have much to do with the flavour and body characteristics of the finished cheese. Chemical changes that result from the controlled growth of various microorganisms and associated enzymatic activity during manufacturing and ripening processes help develop desired sensory characteristics in matured cheese. Hence, a combination of factors is responsible for yielding the many kinds of cheese.

Certain soft cheese such as cream cheese or cottage cheese, which primarily derive their flavour from added lactic cultures and/or a cream dressing, are generally consumed while fresh. Hard or semi-hard cheese varieties are generally made from whole or part-skim milks coagulated by rennet or other milk-coagulating enzymes and are usually ripened or aged before they are consumed. Cheese properties such as an intense aroma or piquant taste can also be a function of the bacteriological or enzymatic treatment of cheese milk before coagulation. The addition of proteolytic and lipolytic microbial culture (usually mold) to curd before pressing can also determine cheese characteristics. Cheddar Cheese

Cheddar cheese is most common type of cheese produced in the U.S. and is sometimes referred to as “American” Cheese. It is a hard, ripened cheese made from raw, flash-heated, or pasteurized whole milk to which about 0.5-1.0% lactic starter culture has been added. The curd formed by the addition of milk-coagulating enzyme(s) is firmed by heating (cooking) and stirring to about 38oC (100oF). The curd may be pressed in several different styles of hoops or in large barrel forms.

Degree of Ripening

Much pasteurized milk Cheddar cheese is marketed shortly after manufacture (<90 days), as a mild cheese or for use in producing processed cheese. The ripening or curing of Cheddar cheese to develop characteristic Cheddar cheese flavour is a slow, complex, bacteriological, chemical and enzymatic process which requires months (sometimes years, for extra-sharp cheese flavour).

Unripened Cheddar cheese if often referred to as “fresh” or “green” cheese. Cheese at this stage is characterized as having a flat or weak flavour and a relatively tough, curdy, or corky body. Good-quality Cheddar cheese that has been properly


cured for at least three months or longer, has a moderate, slightly nutty, “Cheddar” flavour, and is generally referred to as a “young” or “mild” cheese. At six to eight months of age, more of the distinct, aromatic Cheddar flavor should be evident; such cheese is considered as “semi” or “medium-aged”. Generally, a year or longer is required to develop the fully aromatic or robust cheddar cheese flavor desired in an “aged,” “sharp,” or “matured” cheese. “Extra-sharp” Cheddar cheese is usually aged in excess of one and one-half to two years. Tempering Cheese

Before evaluation, cheese samples should be tempered at 10oC to 15.5oC (50oF to 60oF) for a sufficient length of time to ensure a uniform temperature throughout the cheese. Generally, a cheese plug taken from a warm (overtempered) cheese appears weak-bodied; by contrast, a cold plug may appear brittle or corky. Actual body and texture characteristics cannot be determined readily unless cheese samples are properly tempered before evaluation. Preparing for Evaluation

Appropriate facilities for cheese tempering, sampling, proper disposal of waste cheese and cleaning of triers should be provided for evaluators. Prior to sampling, one’s hands should be washed and dried, since they directly contact exposed cheese surfaces. As soon as the cheese samples to be evaluated are arranged in order and numbered or coded for proper identification, the sensory evaluation process may begin. SEQUENCE OF SENSORY OBSERVATIONS Appearance

Typically, the first procedure in grading Cheddar cheese is visual examination of surface finish or packaging material. The judge should note the physical appearance of the sample surface. Next, the evaluator should look more closely the coating of plastic film (or paraffin) is smooth and free form holes, tears, or wrinkles. Finally, a close examination of the surface for possible mold growth should be undertaken by the judge. Sampling

Cheese samples are usually obtained with a double-edged curved blade instrument known as a cheese (or butter) trier. For best service, the edges of a cheese trier need to be sharp. A trier that cuts a larger plug has an advantage over one of a smaller diameter since the extent of “openness” and possible color defects are easier to detect with a large plug.


The trier should be inserted into the top surface of the cheese, preferably about

half way between the center and the outer edge of the cheese sample. After insertion, the trier should be turned one-half way around to cut a sample core. The plug is withdrawn, which produces a long tapered cylinder of cheese. The upper 1inch (2.54 cm) of the cheese plug is immediately broken off and replaced, flush with the surface of the original hole. This partially protects the cheese from developing mold contamination and retards drying and cracking of the cheese surface surrounding the hole.

The evaluator should carefully examine the cheese plug and note whether the plug has a clean-cut surface (with no loose particles) or whether it is rough (with a feather-like edge) as though the cheese had been cut with a dull knife. The evaluator should make a mental note of these observations. Color

The evaluator should observe the color of the cheese and determine whether the appearance is bright and clear or dull and lifeless. It should be noted whether the colour is uniform (free from mottled or light and dark portions) or whether there are curd seams or faded areas. The cheese judge should reexamine the plug and observe whether the cheese appears to be; (1) translucent, which is desirable, or (2) opaque, wherein it is difficult for the eyes to observe beyond the surface. The evaluator should especially note whether the color is uniform throughout the sample. Normally consumers seem to prefer an intense deep-orange color for Cheddar cheese. Openness

The judge should observe the nature and extent of the mechanical openings in the cheese. Their shape or configuration should be examined closely to see whether they are regular, angular, rounded, large and/or small. It is also helpful the luster or sheen of the inner surfaces of these openings and note whether the surfaces appear dry (preferable) or wet. Body and Texture

The evaluator should take the ends of the cheese plug by the forefingers and thumbs of both hands and bend the plug slowly into a semicircle, and observe when the sample breaks, as well as the nature of the break. It should be determined whether the cheese plug: (1) shows a definite resistance toward any bending and finally breaks abruptly (short); (2) bends until the plug ends nearly touch (weak), if it breaks apart at all; (3) bends into approximately one –third to one –half of a full circle before it breaks apart (preferred elasticity)


Next, the judge should take one of the broken pieces of cheese between the thumb and the forefingers and attempt to manipulate it into a uniform mass. The relative resistance (or lack of resistance) offered by the cheese to applied pressure from the thumb and fingers should be ascertained. The “worked cheese” should remain smooth, waxy and somewhat pliable for an “ideal” Cheddar cheese. The tempered sample should exhibit a tendency to remain as a solid mass upon gentle finger manipulation.

Aroma

By the time the sample has been worked into a semi-soft ball, the temperature of the cheese mass should have increased from combined pressure and hand warmth and thus enable easier detection of any aroma. The evaluator should then place the tempered cheese sample directly under the nose and observe the aroma a second time. The judge should compare the aroma with that noted when the sample first was removed from the cheese.

It can be helpful to rinse the mouth occasionally with a lukewarm saline solution to cleanse the mouth of previous cheese flavors (or off-flavors). A Pinch of common table salt placed into the mouth and rinsed out with tepid water can be equally effective. An experienced cheese judge can often grade cheese without actually tasting, on the basis of the color and appearance, amount and nature of openness, body and texture and the perceived aroma of the worked sample mass. The experienced judge may taste an occasional sample simply to verify judgments ascertained by means of other sensory observations.

Smelling and testing the cheese samples generally completes the evaluation

process. All sensory observations should be recorded on a designated cheese scorecard or a form provided for this purpose.

Color Evaluation

The color of Cheddar cheese, regardless of the chosen intensity, should always be uniform throughout the cheese. American cheese may be uncolored, light to medium colored, or high in color. For uncolored cheese, the most desired color is a light cream shade; for medium intensity colored cheese a deep cream color or a pleasant yellow-orange hue is acceptable. The cheese surface color should be slightly translucent; that is, it should appear as if one could actually see into the cheese interior for a short distance. The “translucent” quality of Cheddar cheese is closely associated with desirable body and texture.


Body and Texture Attributes

Cheddar cheese with the most desirable body and texture displays a full, solid, close-knit plug that possesses smoothness, meatiness, waxiness and silkiness, and is entirely free from gas holes or mechanical openings. Cheddar cheese with the above-described quality attributes lends itself to uniform slicing into thin, intact pieces.

The term “body”, as applied to cheese, usually refers to various physical attributes which primarily affect the relative firmness or softness of the cheese. By contrast, the term “texture” refers particularly to the structure and arrangement of the various parts which make up the whole (the cheese unit). Thus, texture in cheese is observed visually by the quantity, size, shape, and distribution of openings and by the sense of touch (as in mealy/grainy) to uncover internal particles. Body Defects

Many duplicate terms are used in an effort to characterize undesirable body and texture defects of Cheddar cheese. The more common descriptors of cheese body defects are listed as follows: Corky (dry, hard, tough) Pasty (smeary, sticky, wet) Crumbly (friable) Short (flaky) Curdy (rubbery) Spongy Greasy Weak (soft). Texture Defects The texture defects of Cheddar cheese may be listed as follows:- Mealy/Grainy (Gritty) Sweet-curd holes (swiss holes, shot holes) Slits (fish eyes, yeast holes) Fissures Gassy (pin holes) Open (mechanical holes) Evaluating the Flavour of Cheddar Cheese

High-quality Cheddar cheese should possess the characteristic “Cheddar flavor”, which is best described as clean, moderately aromatic, nutty-like and pleasantly acidic. While the same general flavor qualities are desired in fresh, medium-cured and aged cheese, the intensity of the characteristic Cheddar flavor will primarily depends upon the extent of curing and actual curing conditions. Usually, aged cheese has a sharp, aromatic, intense flavor that is entirely lacking in young cheese.


Flavor Defects and Their Characteristics Off-flavors in Cheddar cheese show wide variation and may be listed as follows: High acid (sour) Metallic (oxidized) Bitter Moldy (musty) Fruity/fermented Rancid (lipase) Flat (lacking flavour) Sulfide (skunky) Garlic/onion (weedy) Unclean (dirty aftertaste) Heated (cooked) Whey taint (sour whey) Malty (“Grape Nuts”) Yeasty.

Gouda Cheese

Gouda is one of the most important Dutch type varieties of cheese produced in the world. It belongs to semi-hard to hard varieties of cheese with few or no eyeholes. It is a mild variety, ready to be consumed within 3 – 4 months of ripening. It is a waxy and firm body and is characterized by the presence of shiny ‘eyes’. The consistency varies from rather firm and smooth to semi-soft and changes during natural ripening to a firmer and more brittle structure. The flavor also changes from mild to strong during such long ripening times. The interior of the main cheese types shows some round eyes about the size of a pea.

Swiss Cheese

Swiss Cheese, also known as Emmental, Emmentaler, Schweizer, or Sweitzer cheese, is a type of hard cheese made from clean, fresh, whole milk. Specific processes of manufacture are use, which differ widely from those for Cheddar cheese. The utilization of thermophilic lactic bacteria and Propionibacterium shermanii for milk fermentation results in a cheese having flavor, body, texture and appearance characteristics peculiar unto itself.

High-quality Swiss cheese is characterized by: (1) a cream-yellow color; (2) a solid, compact, slightly translucent body, interspersed with large, shiny-surfaced gas holes that are evenly distributed (preferably) throughout the center, but become less numerous near the edge of the cheese; (3) a characteristic “sweet-hazelnut” flavor.


STATISTICAL TECHNIQUES FOR ANALYSIS OF SENSORY DATA

Ravinder Malhotra Senior Scientist (Agri. Statistics)

DES&M Division, NDRI, Karnal-132001 Introduction

Dairy and food processing as also other sciences consist of understanding the living world. Statistics promises carefully controlled acquisition of information for this understanding. Generally research is a complex process, but it typically consists of the following key steps which involve extensive use of statistical techniques:

◊ Formulate hypothesis and propose experiments; ◊ Identify appropriate experimental designs; ◊ Carry out the proposed experiments and collect data; ◊ Summarize the data and conduct appropriate statistical analysis; ◊ Evaluate the proposed hypothesis and draw conclusions.

Thus statistics plays an important role in food technology, biological and agricultural research.

What is statistics? In the narrow sense, statistics means an accumulation of facts and figures,

graphs and charts, that is, any kind of factual information given in numbers. However, in the broad sense, statistics is the branch of applied mathematics that deals with data-based decision making. Therefore, statistics has two essential parts:

a) Descriptive statistics: Collecting, organizing, presenting and analyzing data without drawing any conclusion or inference (e.g., drawing histograms from grouped data, computing mean, standard deviation, median and mode, etc.);

b) Inferential statistics: This is the science of decision-making in the face of uncertainty, i.e., making the best decision on the basis of incomplete information available from sample data or experimental data. Inevitably, uncertainty arises when we have only a sample taken from a population about which inferences are to be made. Thus, probability is important in the statistical decision-making.

When the dairy/food scientist designs an experiment or organizes a sampling his main aim is to use the information contained in a partial set of data to understand the phenomena or to take a useful decision. The main problem in this respect is the variability of the responses. Overcoming this difficulty requires the use of statistical techniques both in a descriptive way and in an inferential way. To bring out the information we use measure of central tendency and dispersion. It appears that such analysis lose part of the information due to links between the variables.


Experiments are fundamental to the progress of science. They are the means by which theories are developed, tested or refuted. Steel and Torrie (1980) have defined the term experiment as a planned inquiry to obtain new facts or to confirm or deny the results of previous experiments, where such inquiry will aid in an administrative decision, such as recommending a variety, procedure, or pesticide.

What is sensorial analysis and sensometrics ?

Sensory methods are used to measure quality characteristics of goods that cannot be assessed directly by physical or chemical tests. They imply the set up and training of a group of panellists and the use of various techniques to organise sensory analysis sessions. Statistical methods take an important place in this process namely in the design of sensory experiments and in the statistical modelling of sensory data. The use of statistical methods in sensory and consumer science is called Sensometrics.

Which Statistical tool should be used?

Exactly which statistical method(s) should be used? The answer to this question requires an examination of (i) whether the variables in the experiment are dependent (measurement) variables or independent (treatment) variables and (ii) whether the variables are continuous or discrete. With such examination, it is relatively straightforward to identify appropriate statistical methods to be used for analyzing the experimental data at hand. The following section summarizes the matching of statistical methods with the types of data:

a) Correlation and regression: In all areas of dairying and food research, experimenters are interested in the relationships between quantitative response variables y and quantitative stimulus variables x1, x2, x3, ----, xk in the sense that y is determined by some function of the stimulus variables. Correlation is a statistical method used to determine if a relationship exists between variables. The correlation coefficient measures the strength and direction of a linear relationship between two variables. The symbol for the sample correlation coefficient is “r” and for the entire population is ρ (Greek letter rho).

∑ ∑ −−

∑ −−=

2)YY(2)XX(

)YY)(XX(xyr

b) Spearman’s Rank Correlation Coefficient: This method is based on the ranks of the items rather than their actual values. The advantage of this method over the others is that it can be used even when the actual values of items are unknown, e.g., to determine correlation between the degrees of agreement between the scores given by two judges. The formula is:


)12n(n

2n

1iid6

1R−

∑=−=

where R = rank correlation coefficient di = difference between the ranks of two items n = the number of observations.

c) Regression is a statistical method use to describe the nature of the relationship between variables. It maybe positive, negative, linear, etc.

Linear Regression: Correlation gives us the idea of the measure of magnitude and direction between correlated variables. Regression helps us in estimating the value of one variable when the other is known. A statistical procedure called regression is concerned with causation in a relationship among variables. It assesses the contribution of one or more variable called causing variable or independent variable or one, which is being caused (dependent variable). When there is only one independent variable then the relationship is expressed by a straight line. This procedure is called simple linear regression.

The term ‘regression’ can be defined as a method that estimates the value of one variable when that of other variable is known, provided the variables are correlated. The dictionary meaning of regression is "to go backward." Sir Francis Galton used it for the first time in his research paper "Regression towards mediocrity in hereditary stature."

Lines of Regression: In scatter plot, we have seen that if the variables are highly correlated then the points (dots) lie in a narrow strip. If the strip is nearly straight, we can draw a straight line, such that all points are close to it from both sides. Such a line can be taken as an ideal representation of variation. This line is called the line of best fit if it minimizes the distances of all data points from it.

This line is called the line of regression. Now prediction is easy because now all we need to do is to extend the line and read the value. Thus to obtain a line of regression, we need to have a line of best fit. But statisticians don’t measure the distances by dropping perpendiculars from points on to the line. They measure deviations (or errors or residuals as they are called) (i) vertically and (ii) horizontally. Thus we get two lines of regressions as shown in the adjacent figures (1) and (2).


(1) Line of regression of y on x Its form is y = a + b x It is used to estimate y when x is given (2) Line of regression of x on y Its form is x = a + b y It is used to estimate x when y is given. They are obtained by i) graphically - by Scatter plot; ii) Mathematically - by the method of least squares. Some useful probability distributions

a) Binomial Distribution. The binomial distribution is useful for describing distributions of binomial events, such as the number of panel members giving the correct response in a taste test. The binomial distribution is defined as:

f(x) = [n!/(x!*(n-x)!)]*px * qn-x, for x = 0,1,2,...,n

where p is the probability that the respective event will occur q is equal to 1-p n is the maximum number of independent trials.

b) Poisson Distribution. The Poisson distribution is also sometimes referred to as the distribution of rare events. Examples of Poisson distributed variables are number of accidents in a milk plant , the number of catastrophic defects found in a production process. It is defined as:

f(x) = ,x!

ex λ−λ

for x = 0,1,2..., ∞ where λ Is the expected value of x (the mean)

e The base of the natural logarithm, sometimes called Euler's e (2.71...)

c) Normal Distribution. The normal distribution (the "bell-shaped curve" which is symmetrical about the mean) is a theoretical function commonly used in inferential statistics as an approximation to sampling distributions. The normal distribution developed by Gauss is a continuous distribution of maximum utility.

Definition: If we know a curve such that the area under the curve from x = a to x = b is equal to the probability that x will take a value between a and b and that the total area under the curve is unity, then the curve is called the probability curve.

Among all the probability curves, the normal curve is the most important one. The corresponding function is called the normal probability function and the probability distribution is called the normal distribution. The normal distribution can


be considered as the limiting form of the Binomial Distribution, however n, the number of trials, is very large and neither P nor q is very small. The normal distribution is given by

where y = ordinate, x = abscissa of a point on the curve, µ = the mean of x, σ = S.D. of x. π= a constant = 3.1416 and e = a constant = 2.7183

Testing Statistical Hypothesis

Every research is carried out to test one or more hypotheses but not all hypotheses are statistical hypotheses. To be a statistical hypothesis, an assumption needs to be made about one or more parameters of a population (i.e., assigning a value to one or more parameters of the population). For example, the following statement is NOT a statistical hypothesis: Mars is inhabited by living beings, because it makes no assumption about one or more parameters of a population. However, the statement that the average copper contents of the tomato ketchup are 15 ppm is a statistical hypothesis.

The test of statistical hypothesis consists of the following steps:

1 Clearly state the null hypothesis (the hypothesis of "no difference") (H0). An alternative hypothesis (HA) must also be specified;

2 Identify the test statistic, the value of which will be used to test whether to accept or reject H0. If H0 is rejected, we are left with the choice of accepting HA. NOTE: While we accept or reject a hypothesis, we have not proved or disproved it.


3 Specify the decision-making rule. The null hypothesis is considered to be true if the calculated probability (based on the test statistic) is greater than the desired level of significance (α ); it is considered to be false if the calculated probability is less than or equal to the significance level, and the result is termed significant. One has to make an arbitrary decision about the level of significance. It is commonly accepted that a result is considered to be significant if the calculated probability is less than 0.05.

When we make a decision to accept or reject a hypothesis based on the results

of an experiment (sample data), it is possible that we will make one of two types of wrong decisions:

True Situation

H0 is true H0 is false H0 is accepted Correct decision Type II error (β ) H0 is rejected Type I error (α ) Correct decision

Clearly, Type I error equals to the level of significance (α ). The power of

testing a statistical hypothesis is the probability that H0 is rejected given that HA is true, which is one minus the probability of Type II error = 1 - β . However, the relationship between Type I error and Type II error is such that decreasing the probability of Type I error increases the probability of Type II error; decreasing the probability of Type II error increases the probability of Type I error. (i) Increasing sample size or (ii) using more efficient experimental designs or both can solve this dilemma. Thus, the experimenter has the direct or indirect control of both errors.

Goodness of Fit Experiments often produce data which can be grouped into a number of classes

or categories and in many situations we wish to test whether the differences between the frequencies observed in these classes or categories and the frequencies predicted or expected under some hypothesis can be attributed to chance (i.e. random sampling effects) or whether these differences are so large that the hypothesis should be abandoned.

The question to be asked is whether the observed frequencies deviate significantly from the expected frequencies if the null hypothesis (that the die is fair) were true.

The following calculation, of a statistic called CHI-SQUARE, is used as a measure of how far a sample distribution deviates from a theoretical distribution.


∑=

−=χ

n

1i iE

2)iEiO(2

Here Oi represents the observed frequency in class or category i and Ei the

corresponding expected frequency, if the null hypothesis is true, and the summation is performed over all n categories.

t-test for Differences of Means The main purpose of an unpaired t-test is to decide if two independent normally

distributed populations are likely to have the same mean, based on samples taken from the population. However, the test is only really valid if the populations have equal variances (although there are ways of dealing with the case when they are not equal). Suppose we want to test two independent samples Xi (i=1,2, ---, n1) and Yj (j=1,2, ---,

n2) of sizes n1 and n2 have been drawn from two normal populations with means xµ

and yµ respectively. Under the null hypothesis (H0) that the samples have been drawn from the

normal population with means xµ and yµ and under the assumption that the population variances are equal then the statistics

)2n

11n

1(2S

_)y

_x(t

−

−=

where

]2

)YjYj

(2

)_XiX(

i[

22n1n12S

−−∑+−∑

−+=

is an unbiased estimate of population variance 2

σ , follows t-distribution with (n1+n2 -2) d .f.

Comparison of Variances (F-test)

Suppose independent samples of sizes and respectively are drawn from

two populations, X and Y of unknown variances and . Unbiased estimates of these variances are

with degrees of freedom


and with degrees of freedom. Consider the ratio

. If these samples have been drawn from populations having the same variance we would expect the numerical value of this ratio to be near the value 1. Obviously we would not expect to get exactly 1 but if a very large value or a very small value were

observed we might conclude that In fact repeated sampling from populations having the same variance will

produce values for this ratio, which form a distribution called the F - distribution. Analysis of Variance

Analysis of variance (ANOVA) is a family of methods that can be used to design and analyze the results from both simple and complex experiments. It is one of the most important statistical techniques available to biologists and provides a link between the design of experiments and the analysis of experimental data. ANOVA has its origins in biology, or at least agriculture, since the methods were specifically developed to deal with the type of variable response that is common in field trials; it now has a much wider application.

Two key considerations in designing an experiment are (i) simplicity and (ii) efficiency. By simplicity, we mean that the simplest experimental design be chosen among many possible candidates to achieve the same proposed objective(s). By efficiency, we mean that the investigation should be conducted as efficiently as possible; that is, every effort should be made to save time, money, personnel and experimental materials.. Fortunately, most simple designs are also efficient (both statistically and economically).

To achieve optimal levels of simplicity and efficiency in designing an experiment, three basic principles should always be considered: replication, randomization and local control (blocking). Replication means the repetition of treatments in an experiment. There are two reasons why we need replications:

� If a treatment appears only once in an experiment (i.e., n = 1), there is no replication of the treatment and the error associated with the estimate of the treatment effect cannot be estimated. Experimental error occurs when two or more identically treated experimental units fail to yield identical results. Thus, replication of treatments provides an estimate of experimental error;


� Replication also enables us to obtain a more precise estimate of the main

effect of any factor since the standard deviation of the mean = n/2σ ,

where 2σ represents the true experimental error and n the number of

replications. Randomization legitimatizes the statistical test of significance of observed

differences between the treatments. The process of randomization involves random allocation of treatments to the experimental units. Thus the process makes the law of chance applicable to our experimental data and ensures that the data are free from any systematic error. Randomization tends to make experimental errors independent of each other and provides an unbiased estimate of the experimental error and treatment means. Thus, it allows an objective comparison among treatment means.

Local control refers to grouping of the experimental units in such a way that

the units within a group (i.e., block) are more homogeneous than are units in different groups. The experimental materials or conditions are more alike within a group. Thus, the variation among experimental units within a group is less than the variation would have been without grouping. This leads to the comparison of treatment effects under more uniform conditions or on the more uniform materials. For example, the total variation in Randomized Complete Block Design (RCBD) is partitioned into variation due to two assignable causes, blocks and treatments, and variation due to a non-assignable cause or experimental error. This latter source of variation is reduced as the variation due to block is removed:

Experimental error = Total variation - Treatment variation - Block variation. Relationship of Three basic

Principles of Experimental design

RANDOMISATION

Some definitions:

Validity of estimated Experimental Error

Reduction of Experimental Error

Efficiency

REPLICATION

LOCAL CNTROL Blocking or Stratification


1. Experimental Design: is the complete sequence of steps involving randomization, assignment and application of treatments, collection of data, etc. 2. Experimental Unit: is the smallest unit of experimental material to which a treatment is assigned by a single act of randomization. 3. Sampling Unit: is the unit of experimental material on which the observation (s) is recorded is referred to as the sampling unit. The sampling unit may be the same as the experimental units; the entire experimental unit may be divided into sampling units; or one or a few (less than the total) sampling units may be randomly sampled experimental from the experimental unit. The sampling unit is some times also referred to as the observation unit. 4 Experimental error is a measure of the variation between experimental units treated alike. The variation is due to both the inherent variability of the experimental material and failure of the experimental units to be handled (processed, measured, analyzed, etc.) identically, in spite of our best efforts.

Experimental error may in some experiments be directly computable, while in other design only an indirect measurement will be possible. In those analyses where experimental error has been measured directly, it is commonly termed the "within" variation. The term "within" refers to the variation between experimental units" within" treatments. When no direct calculation is possible, the experimental error is estimated by computing all assignable sources of variation and assuming that any variation left over represents experimental error. For these analyses, the experimental error is frequently termed "residual" or "remainder" 5. Treatment: Treatment refers to a set of experimental conditions which will be applied (or associated with) the experimental units. One particular set may be, for example, a "control" set. The particular set used a varies in a certain way from treatment to treatment, and it is the effect of these varying conditions in which we are interested. A certain condition, when allowed to take on a number of possible levels, is called a factor. When an experiment contains more than one factor, each assigned more than one level, the treatment combinations form a factorial experiment. Factorial experiments are probably the most important sets of treatments in biological research. The Completely Randomized Design (CRD)

The CRD is the simplest of all designs. It is equivalent to a t-test when only two treatments are examined. t uses two principles of experimental designs into consideration. Field marks:

1 Replications of treatments are assigned completely at random to independent experimental subjects.

2 Adjacent subjects could potentially have the same treatment.


Sample Layout:

Different alphabets represent different treatments. There are 4 (A-D) treatments with 4 replication (1-4) each.

A1 B1 C1 A2

D1 A3 D2 C2

B2 D3 C3 B3

C4 A4 B4 D4

ANOVA Table Format

Sources of Variation Degree of Freedom#

Sum of Squares (SSQ)

Mean Square (MS)

F

Treatments (Tr) t-1 SSQTr SSQTr/(t-1) MSTr/MSE

Error (E) t*(r-1) SSQE SSQE/(t*(r-

1))

Total (Tot) t*r-1 SSQTot #where t = number of treatments and r=number of replications per treatment.

Two-way Factorial arrangement on a CRD Field marks:

1 Treatments are combinations of two factors, such as chemical compositions and methods of applying chemicals. This is an arrangement of treatments within a CRD design.

2 Replications of treatments are assigned completely at random to independent experimental subjects.

3 Adjacent subjects could potentially have the same combination of treatments.

Sample layout: Different big and small alphabets represent different combinations of treatments; each horizontal row represents a block. There are 4 replications (1-4) and 4 treatment combinations (Aa, Ba, Ab and Bb) in this example.

Aa1 Ba1 Ab1 Aa2

Bb1 Aa3 Bb2 Ab2

Ba2 Bb3 Ab3 Ba3

Ab4 Aa4 Ba4 Bb4


ANOVA Table Format Sources of Variation

Degree of Freedom#


Mean Square (MS) F

First factor (F1) f-1 SSQF1 SSQF1/(f-1) MSF1/MSE

Second factor (F2) s-1 SSQF2 SSQF2/(s-1) MSF2/MSE

Interaction First X Second

(FxS) (f-1)*(s-1) SSQFxS

SSQFxS/((f-1)*(s-1))

MSFxS/MSE

Error (E) f*s*(r-1) SSQE SSQE/(f*s*(r-1))

Total (Tot) f*s*r-1 SSQTot

#where f=number of treatments in the first factor, s=number of treatments in the second factor and r=number of replications.

Randomized Complete Block Design This is the most commonly used experimental design. In sensory comparisons

where several judges at a single sitting taste and score each of various samples of a food product, the arrangement of scores in known as a randomized complete-block design. Each score (measurement) is fixed in the design; it belongs to one of the samples (treatments) and to one of the judges (blocks). It is therefore classified according to two criteria. The analysis of such a design is similar to that fore the one-criterion case. The only modification necessary is to take account of the variations between the (means of scores of) judges. This sum of squares as well as that between samples must be subtracted from the total to obtain the error (sometimes called interaction) sum of squares. In completely randomized design in which the different treatments are assigned to the experimental units in a purely random manner.

For example consider an experiment to compare the effects of five treatments on the moisture contents of Paneer .If four observations on each treatment were required, the test area would be divided into twenty experimental plots and each treatment randomly assigned to four of these plots a typical arrangement might be

C B D B A B C E B E D E E A D D A C A C

where A, B, C, D, E are the 5 treatments.


A better design would be to divide the 20 plots into 4 sets of 5 so that the fertility is approximately the same in each set (or BLOCK), and then randomize the order of treatments within each block so that each treatment occurs once in each block.

This is called the randomized complete block design. A typical arrangement might then be

Blocks 1 A D C E D 2 B E D A C 3 C B A D E 4 D C B E A

The objective in using the randomized complete block design is to isolate and remove from the error term the variation attributable to the blocks while assuming that treatment means will be free from block effects. This increases the precision of the analysis.

ANOVA Table Format Sources of Variation

Degree of Freedom#


Mean Square (MS)

F

Replication (r) r-1 SSQr SSQr/(r-1) MSr/MSEl Treatments (Tr) t-1 SSQTr SSQTr/(t-1) MSTr/MSE

Error (E) (t-1)*(r-1) SSQE SSQE/ (t-1)*(r-1)) Total (Tot) r*t –1 SSQTot

#where, t=number of treatments and r=number of blocks or replications. References :

Steel , R.G.D. and Torrie, J.H. (1980) Principles and Procedures of Statistics- A Biometrical Approach ,McGraw-Hill International Book company. Piggott , J.R.(Ed.) (1986) Statistical Procedures in Food Research ,Elsevier Applied Science Publishers, London


DESCRIPTIVE SENSORY ANALYSIS

Dr. Ashish Kumar Singh*, Ms. Rekha Chawla**

*Senior Scientist, **Ph. D. Scholar Dairy Technology Division, NDRI, Karnal.

Introduction Quality evaluation of every foodstuff is an important parameter. Grading and judging are used extensively by every industry for quality evaluation on all products. For grading and judging, a product is evaluated based on the presence or absence of specific defects and then an overall quality score is given. These quality scores are usually based on the opinions of one individual and the quality score is subjective rather than specifically defined. These scores due to nonlinearity are unsuitable to be used in statistical analysis and also they do not describe all of the attributes and intensities of flavor attributes, texture, colour, and many more. This information would be useful in market niche identification and end product application. Sensory evaluation comprises a set of techniques for accurate measurement of human responses to foods, and has been defined as a scientific method used to evoke, measure, analyze and interpret those responses to products as perceived through the senses of smell, sight, hearing, taste and touch. Foods are submitted to sensory evaluation to provide information that can lead to product improvement, quality maintenance, development of new products, or analysis of the market. Three primary kinds of sensory tests focus on the overall differences among products (discrimination tests), specificity of attributes (descriptive analysis) and measuring consumer likes or dislikes (affective or hedonic testing). Sensory tests provide useful information about the human perception of product.

Descriptive Sensory Analysis (DSA) Descriptive sensory analysis is the most comprehensive and important tool in the arsenal of the sensory, which allows the scientist to obtain complete sensory description of products, help identifying underlying ingredient and process variables. These techniques can provide complete sensory descriptions of products, determine how ingredient or process changes affect product characteristics, and identify key sensory attributes that promote product acceptance. It is desirable where a detailed specification of the sensory attributes of a product is desired but associated with certain drawbacks like for daily to daily base analysis descriptive analysis would be costlier and also for this test, the panelist should be familiar and very much clear regarding the associated terms to avoid “Jargon”. Descriptive analysis can be further divided into various sub- components like:


Components of descriptive sensory analysis:

Sensory Characteristics- (Qualitative)

It expresses the perceived sensory parameters which define the product. It is denoted by various items as attributes, characteristics, character- notes, descriptive terms, descriptors or terminology. It defines the sensory profile or represents the picture thumb nails of the sample.

• Appearance- colour, surface texture, size, shape, interactions among particles/ pieces

• Aroma characteristics- olfactory sensations, nasal feeling factors

• Flavour characteristics- olfactory sensations, taste sensations, oral feeling factors

• Oral texture characteristics- mechanical parameters, geometrical parameters, fat/ moisture parameters

• Skinfull characteristics- mechanical, geometrical, fat/ moisture and appearance parameters

• Texture/ Touch

Training of panelists should be related the real chemical and physical properties of the product and their ability to familiarize themselves on characterization of production for various identified attributes. A sound background in the subject matter help in understanding of underlying mechanisms responsible for development of desirable attributes. Intensity- (Quantitative) expresses the degree in which each of the characteristics is present and usually expressed by assignment of some value along a measurement scale. Three types of scales are normally used:

• Category scale: Yield ordinal or interval data. Category scale 0-9 is most common.

• Line scale: Intensity can be more accurately measured, 15 cm scale. • Magnitude estimation (ME): Used where a single attribute has to be measured.

Validity and reliability of intensity measurement depends on selection of scaling techniques, thorough training of panelists and use of reference scales for intensity of different properties.


Order of Appearance

Related to detection of differences among products in the order in which certain parameters manifest themselves and complete picture of the product requires perception of all attributes, their rating for intensity. After taste or after feel is also part of order of appearance, since it affects the perception of the foods.

Overall impression- Describes the Integrated assessment of the product properties. There are 4 different ways by which we can integrate the overall impression of a product and are: total intensity of aroma, balance/ blend amplitude, overall difference, and hedonic ratings.

A range of tests are available to classify attributes quantitatively as well as qualitatively but commonly detailed descriptive assay can be done using flavour profile analysis, or texture profile analysis. Preference mapping refers to a group of statistical techniques used to understand consumer preferences in terms of sensory attributes and assists in understanding the descriptive sensory attributes that influence consumer acceptance. The procedure requires an objective characterization of product sensory attributes, achieved by descriptive analysis, which is then related to preference ratings for the product obtained from a representative sample of consumers. The two main areas of preference mapping techniques are internal preference mapping and external preference mapping. A consumer panel would be the most appropriate tool to determine when a food product reaches the end of its shelf life. Although the primary objective of most of the textural studies is to measure sensory properties, sensory evaluation is time – consuming and costly. Therefore, many researchers choose instrumental methods and relate those measurements to sensory evaluation. Flavor profile

Flavour is widely recognized as a significant factor of any food product. Extensive research has been conducted in understanding flavor in different products through analytical and sensory analysis methods. Flavour profile was first used to describe complex flavor systems measuring the effect of monosodium glutamate (MSG). The FP considers the overall flavor and the individual detectable flavor components of a food system. The profile describes the overall flavor and the flavor notes and estimates the intensity and amplitude of theses descriptors. The food samples are tested and all perceived notes are recorded for aroma, flavour, mouthfeel and aftertaste. The panel is exposed to a wide range of food products within the food category. After this exposure, the panelists review and refine then descriptors used. The vocabulary used to describe a product and the product evaluation itself is achieved


by reaching agreement among the panel members. The following table defines some of the main flavor defects with respect to cheese.

Table: List of cheese flavor defects used with judging cheese flavor.

Flavor defect Definition

High acid Excessive acid or sour taste

Bitter Bitter taste resembling caffeine or quinine

Fruity/Fermented Aroma of fermenting or overripe fruit

Flat Devoid of flavor

Garlic/Onion Flavor resembling garlic, onion, or leeks

Heated Not the clean cooked flavor of pasteurized milk but a flavor resembling the

odor of old or spoiled milk

Malty Flavor similar to grape nut cereal

Metallic A flat metal-like taste and a lingering puckery mouthfeel

Moldy Musty, reminiscent of a damp cellar

Rancid Also called lipase, caused by short-chain fatty acids, flavor described as bitter,

soapy, disagreeable

Sulfide Also called skunky. Similar to water eith high sulfer content.

Unclean Dirty aftertaste that fails to clean-up after the cheese is expectorated

Whey taint Also called sour whey. The dirty sweet acidic taste and odor characteristic of

fermented whey

Yeasty Sour, bread-dough, earthy aroma characteristic of yeast.

Adapted from Bodyfelt and others (1988)

The Table depicts some of the flavor associated with cheddar cheese and also the

various references that are used to familiarize the panelist.

Table: The basic Cheddar cheese flavor lexicon

Descriptor Definition Reference Cooked/milky Aromatics associated with cooked milk Skim milk heated to 85°C for

30 min

Whey Aromatics associated with fresh cheddar cheese

whey

Fresh Cheddar cheese whey

Diacetyl Aromatics associated with diacetyl Diacetyl 20 ppm

Milk fat/lactone Aromatics associated with milk fat Fresh coconut meat, heavy


cream, δ dodecalactone,

40ppm

Fruity Sweet aromatics associated with different fruits,

primarily pineapple

Fresh pineapple Ethyl

hexanoate, 20 ppm

Sulfur Aromatics associated with sulfurous compounds Boiled mashed egg

H2S bubbled through water

Brothy Aromatics associated with boiled meat or vegetable

soup stock

Canned potatoes

Low sodium beef broth cubes

Methional, 20 ppm

Free fatty acid Aromatics associated with short-chain free fatty

acids

Butyric acid, 20 ppm

Nutty Nutty Aromatics associated with various nuts Roasted peanut oil extract

Light toasted unsalted nuts

Catty Aromatics associated with tomcat urine 2-mercapto 2- methyl-

pentan-4-one, 20 ppm

Cowy/barny Aromatics associated with barns and stock trailers,

indicative of animal sweat and waste

A mixture of p-cresol (160

ppm) + isovaleric acid (320

ppm)

Sweet Fundamental taste sensation elicited by caffeine Sucrose (5 % in water)

Sour Fundamental taste sensation Citric acid (0.08 % in water)

Salty Fundamental taste sensation Sodium chloride (0.5% in

water)

Bitter Fundamental taste sensation Caffeine (0.08% in water)

Umami Chemical feeling factor elicited by certain peptides

and nucleotides

Mono sodium glutamate (1%

in water)

Chemical references prepared in 95% ethanol, then blotted onto filter paper into jars for sniffing. Adopted from

Drake and others (2001).

Quantitative Descriptive Analysis: Quantitative Descriptive Analysis (QDA) is a comprehensive system covering sample selection, assessor screening, vocabulary development, testing and data analysis. Variants of the original QDA procedures are probably used more than any other profiling procedure. This particular technique uses small numbers of highly trained assessors. Typically, 6 to 12 people are screened for sensory acuity and trained to perform the descriptive task of evaluating the product. The results are analyzed statistically and further data is represented in the form of “Spider web”. The Spectrum Method: This more recent method provides a tool with which to design a descriptive procedure for a given product category. The method resembles QDA in many respects: for example: the panel must be trained to fully define all product sensory attributes, to


rate the intensity of each, and includes all other relevant characterizing aspects. The spectrum method is based on an extensive use of reference points. The scale to be used is chosen with respect to available facilities and on need for sophisticated data analysis but care should be taken that while choosing a scale, it must have at least two or preferably, five reference points distributed across a range. Texture Profile Method

Based on the similar principles of flavor profile method, the texture profile was developed by the product evaluation and texture technology groups at general foods corp. to define the textural parameters of foods. Panelists are selected on the basis of ability to discriminate known textural differences in the specific product application for which panel is to be trained. In addition, panelists are introduced to the underlying textural principles involved in the structure of the products under study. Samples are evaluated by panelist independently, using one of the scaling techniques. Depending upon the type of scale used by the panel and on the way data to be treated, the panel verdicts may be derived by group consensus, as with the flavor profile method, or by statistical analysis of the data. Bourne (1982) classification on textural characteristics-

1 Critical : Texture dominant characteristics ex: meat, potato chips, celery

2 Important: Foods in which texture makes a significant but not dominant contribution in comparison with other attributes. Ex: sugar, confectionary, fruits, bread etc.

3 Minor: Foods in which texture made a negligible contribution to overall quality

Mechanical Properties: Related to reaction of food to stress • Primary parameters: Hardness, cohesiveness, viscosity, elasticity,

adhesiveness. • Secondary parameters: Brittleness, chewiness, gumminess.

Geometrical Characteristics Relating to the size, shape and orientation of particles, within the food. .Ex: powdery, gritty, lumpy, flacky, fibrous, cellular, aerated, crystalline. Other characteristics are related to perception of the moisture and fat contents of the food. Ex: dry, moist, oily, greasy wet. Sherman (1969) has given following classification of the foods based on their textural characteristics:-

1 Primary: basic properties of food, geometric characteristics such as particle size, air content, size distribution, cell size, cell shape.

2 Secondary: derived by combination of two/ more attributes in unknown proportion, rheological properties such as viscosity, adhesion.

3 Tertiary: Sub divided according to the type of the process involved. Success of descriptive analysis depends on


1 Better understanding of the technical and physiological principles of sensory attributes

2 Proper training of all panelists

3 Use of references of terminology

Descriptor Scale value Product Sweet 2.0 2 % sucrose solution

4.0 Ritz cracker

7.0 Lemonade

9.0 Coca cola classic

12.5 Bordeaux cookies

15.0 16 % sucrose solution

Sour 2.0 0.05 % citric acid- water solution

4.0 Natural apple sauce

5.0 Reconstituted frozen orange juice

8.0 Sweet pickle

10.0 Kosher dill pickle

15.0 0.20 % citric acid- water solution

Salt 2.0 0.2 % sodium chloride- water solution

5.0 Salted soda cracker

7.0 American cheese

8.0 Mayonnaise

9.5 Salted potato chips

15.0 1.5 % sodium chloride- water solution

Bitter 2.0 Bottled grapefruit juice

4.0 Chocolate bar

5.0 0.08 % caffeine – water solution

7.0 Raw endive

9.0 Celery seed


10.0 0.15 % caffeine- water solution

15.0 0.20 % caffeine- water solution

Conclusion: Opportunities for sensory evaluation continue to develop primarily as a result of significant changes in the marketplace and to a much greater extent then changes in sensory evaluation.

LIST OF PARTICIPANTS

Sr. No.

Name of the Participants

Address Email Mobile

1 Dr. Pradyuman Kumar Dept. of Food Engg. & Tech., SLIET, Longowal 148106 Distt: Sangrur (Punjab)

[email protected]

09417321580

2 Er. Sandeep .G.M. Prasad Department of Dairy Technology Allahabad Agricultural Institute (Deemed University) Allahabad 211007

[email protected],

[email protected]

09415630377

0532-2684594

3 Dr Sanjeev Kr. Garg

Assistant Professor

Department of post Harvest process and food Engineering College of technologyGBPUA&T

Pantnagar 263145

[email protected]

09997706926

4 Dr J B Singh

Lecturer

Department of Natural Recourse Management Mahatma Gandhi Chitrakoot Gramodaya Vishvavidyaly Satna (MP) 485331

[email protected]

09451676707

05198-233720

5 Dr A. A. Wani

Lecturer

Dept of Food Technology Islamic University of Science & Technology Awantipora Kashmir (J&K)

[email protected]

9858420721

6 Er.Avinash singh Department of Dairy Technology Allahabad Agricultural Institute Deemed University Allahabad -211007

[email protected]

[email protected]

9453460756

0532-2695295

7 Mr. Sanjeev Kumar

Assistant Professor

Department of Dairy Technology, Sanjay Gandhi Institute of Dairy Tech. (RAU, Bihar), Jagdeo path, BVC, Campus ,Patna (Bihar)

[email protected]

09931230858

09896965771

0612-2348482

8 Dr. M.P.S. Yadav

Associate Professor

Department of A.H & Dairying CSA University of Agri. & Tech. Kanpur -208 002 (UP)

[email protected]

09935526634

9 Dr. Rita Narayanan University Research Farm, Tamilnadu Veterinary and Animal Science University Madhavaram Milk Colony Chennai - 600051

[email protected]

09841390169

10 Dr. B. S. Jadhav

Associate Professor

Associate Professor, College of Agriculture, Kolhapur (MS)

09420352216

11 Dr. D.D. Patange

Assistant Professor

College of Agriculture, Kolhapur (MS)

[email protected]

09421800941

12 Sr. Shrikant D Kalyankar

Assistant Professor

College of Dairy Technology, Pusad, Maharashtra Animal and Fishery Sciences University, Nagpur (Maharashtra)

[email protected]

09423435757

13 Dr. S.U. Suryawanshi

Associate Professor

College of Vet. and AS, Pusad, Maharashtra Animal and Fishery Sciences University, Nagpur (Maharashtra)

[email protected]

09423154646

14 Dr. G. Sashidevi Dept of Family Resource Management

Home Science College and Research Institute,

Tamil Nadu Agricultural University

Madurai – 625 104.

[email protected]

09443821090

15 Dr. R.Vijayalakshmi Dept. of Apparel Designing and Fashion Technology

Home Science College and Research Institute,

Tamil Nadu Agricultural University

Madurai – 625 104.

[email protected]

09442879422

16. Ms. Mallika Manral

Scientist B

Defence Food Research Laboratory, Siddarthanagar, Mysore 570011

[email protected]

09986984641

17. Dr. Archana Singh Scientist B, CPT Division

Defence Food Research Laboratory, Siddarthanagar, Mysore 570011

[email protected]

09916250347

18 Mr. Shajanand Thakur Department of Dairy Technology Allahabad Agricultural Institute Deemed University Allahabad 211007

09889500703

19 Mr. Manoj Chauhan Institute of Food Technology, Bundelkhand University, Jhansi (UP)

09935663199

Documents

Sensory and Related Techniques for Evaluation of Dairy Foods-2008.pdf