ORIGINAL PAPER

Inulin and oligofructose as fat and sugar substitutesin quick breads (scones): a mixture design approach

Christian Roßle • Anastasia Ktenioudaki •

Eimear Gallagher

Received: 23 March 2011 / Revised: 23 May 2011 / Accepted: 24 May 2011 / Published online: 7 June 2011

� Springer-Verlag 2011

Abstract Mixture (D-optimal) design was used to

investigate the effects of prebiotics such as inulin and

oligofructose as fat and sugar replacers on quality param-

eters of quick breads (scones). Crust and crumb colour

increased with the inclusion of prebiotics. Higher concen-

tration of inulin and oligofructose in quick breads also

showed a slight increase in crust and crumb hardness. Loaf

volume significantly increased with the inclusion of pre-

biotics. The optimization tool indicated that by using a

mixture of margarine (3.53%), oligofructose Orafti� L95

(10%), caster sugar (0.55%) and inulin Orafti� GR

(5.92%), a quick bread with similar baking properties and

textural attributes to the control can be achieved. The

mixture design was successfully used to reduce the original

levels of 10% fat and 10% sugar (percentages are based on

flour weight). The calculated model performance indices

accuracy factor and bias factor of the predicted quick-bread

formulations showed a high applicability of the model. The

variations between the predicted and experimental values

obtained were within the acceptable error range, as

depicted by the average mean deviation. Therefore, the

predictive performance of the established model may be

considered acceptable.

Keywords Oligofructose � Inulin � Fat � Sugar �Replacer � Substitute � Mixture design � Quick bread

Introduction

Over the last number of years, a trend is emerging towards

healthier foods, in particular in the cereal area due to

increasing consumer awareness. Quick breads such as tra-

ditional scones are a popular product in Ireland, UK, USA

and elsewhere. They are consumed by people of all ages

and are characterized by a soft texture and sweet flavour.

Typically, quick breads would be consumed within 24 h of

baking. However, due to their fat (10%) and sugar (10%)

levels, regular consumption can lead to high energy intake

and may cause dental problems, obesity, type-2 diabetes,

high blood cholesterol and coronary heart disease [1]. Fat

and sugar are important ingredients in the formulation of

scones as they contribute to essential quality attributes such

as texture, flavour and appearance. Replacing these con-

stituents without affecting the quality characteristics poses

a significant technical challenge. Carbohydrate-based fat

mimetics are under investigation by a number of

researchers as they have been reported to form a gel-like

matrix in the presence of substantial levels of water,

resulting in lubricant and flow properties similar to those of

fats [2]. Inulin and oligofructose have been reported as

potential ingredients to imitate the functional and sensorial

properties of fat and sugar, while at the same time pro-

viding high-quality baked products with considerably

fewer calories. In addition, oligofructose and inulin can be

classified as functional ingredients as they are described as

prebiotics and have been defined as ‘selectively fermented

ingredients that allow specific changes, both in the com-

position and/or activity in the gastrointestinal microbiota

that confers benefits upon host well-being’ [3]. These

beneficial effects however are indirectly caused, as prebi-

otics selectively feed one or a limited number of micro-

organisms, thus causing a selective modification of the

C. Roßle (&) � A. Ktenioudaki � E. Gallagher

Teagasc Food Research Centre, Ashtown, Dublin 15, Ireland

e-mail: christian.roessle@teagasc.ie

123

Eur Food Res Technol (2011) 233:167–181

DOI 10.1007/s00217-011-1514-9

host’s intestinal microflora [4]. Probiotic micro-organisms

such as lactobacilli and bifidobacteria are examples of the

bacteria that are being stimulated [5].

Several attempts have recently been made to use func-

tional ingredients such as prebiotics to substitute fat and

sugar, in particular in cookies [6, 7], biscuits [8, 9] and

breads [10, 11], and the results are promising. However, to

date, no work has been undertaken to substitute the fat and

sugar of widely consumed quick breads with prebiotics.

Therefore, the objective of this study was to characterize

and optimize the effects of oligofructose and inulin

as healthier alternatives to fat and sugar in quick

breads (scones) through the application of mixture design

methodology.

Materials and methods

Quick-bread preparation

Cream (weak) flour (Odlum Group Ltd, Dublin, Ireland),

baking powder (Dr. Oetker Ireland Ltd, Dublin, Ireland),

salt (Premier Foods Group Ltd, Long Sutton, UK) and

margarine (Irish Bakels Ltd, Dunshaughlin, Co. Meath,

Ireland; Table 1) were initially mixed in a Kenwood pre-

mier chef (Kenwood Ltd, Havant, UK) mixing machine

with a K-beater attachment for 1 min. A solution con-

taining caster sugar and/or oligofructose (Orafti� L95;

Beneo-Orafti, Tienen, Belgium) and/or inulin (Orafti�

GR), and milk (low fat 1.7%; purchased in a local super-

market; Table 1) were then added to the dry mixture and

mixed for a further 1 min until the dough was formed.

Orafti� L95 is a syrup containing mainly oligofructose

produced by partial enzymatic hydrolysis of chicory inulin,

while Orafti� GR is a granulated powder consisting mainly

of chicory inulin. Table 2 represents the experimental

design for the four variable components (X1 = margarine,

X2 = oligofructose, X3 = caster sugar and X4 = inulin).

The dough was allowed to rest for 20 min and rolled out to

a thickness of 1.3 cm. A round cutter (6.5 cm diameter)

was used to cut out the dough pieces which were then

placed on a silicon tray. The scones were baked for 17 min

at 2108C (Tom Chandley Ovens, Manchester, UK) and left

to cool for 2 h on a cooling tray for further analysis. The

experimental design was set up using Design expert 7.1.6

(Stat-Ease Inc., Minneapolis, MN, USA). Assessment of

error was derived from two replications of fine treatment

combinations.

Quality measurements

Quick-bread samples (based on 500 g of flour) were pre-

pared as previously described. Nine breads were produced

per formulation (1–24; Table 2). Five of these were used

for bread volume, crust/crumb colour and crust texture.

The remaining four were sliced (1.5 cm thickness; two

slices per scone) and used for crumb texture, C-Cell crumb

imaging and moisture measurements.

Table 1 Formulations used in quick-bread (scone) formulation

Material Compositiona (%)

Cream (weak) flour 100

Baking powder 4

Salt 0.25

Milk (low fat 1.7%) 60

Fat 0–10 (Table 2)

Oligofructose (Orafti� L95) 0–10 (Table 2)

Caster sugar 0–10 (Table 2)

Inulin (Orafti� GR) 0–10 (Table 2)

a % Values are based on the total flour weight (100 g)

Table 2 Experimental design of four components in quick-bread

formulation

Design

point

Margarine

(X1)

Oligofructose

L95 (X2)

Caster sugar

(X3)

Inulin GR

(X4)

1 0 6.67 6.67 6.67

2 2.5 7.5 7.5 2.5

3 3.33 3.33 3.33 10

4 7.5 2.5 7.5 2.5

5 10 0 0 10

6 10 0 0 10

7 6.67 0 6.67 6.67

8 10 3.33 3.33 3.33

9 6.67 6.67 6.67 0

10 10 10 0 0

11 5 5 5 5

12 10 0 10 0

13 3.33 3.33 10 3.33

14 0 10 0 10

15 0 0 10 10

16 10 0 10 0

17 6.67 6.67 0 6.67

18 7.5 2.5 2.5 7.5

19 0 10 10 0

20 0 10 0 10

21 0 0 10 10

22 3.33 10 3.33 3.33

23 7.5 7.5 2.5 2.5

24 0 10 10 0

Where X1 ? X2 ? X3 ? X4 = 20%

168 Eur Food Res Technol (2011) 233:167–181

123

Bread volume

Quick-bread volumes of five replicates per experiment

were measured with a TexVol volume meter (BVM-L370,

TexVol Instruments, Viken, Sweden). Analysis was carried

out using VolCalc 3.2.3.10.

Crumb and crust colour

Crust and crumb colour were measured using a HunterLab

Ultrascan XE colorimeter (Hunterlab, Reston, VA, USA).

Readings were obtained as a three-dimensional L*, a*, b*

colour solid and expressed as browning index (BI) according

to Buera and others [12], as shown in Eq. 1 below.

BI ¼ 100ðx� 0:31Þ=0:172 where

x ¼ a � þ1:75L=ð5:645L � þ a � �3:012b�Þ ð1Þ

Crumb and crust texture

Crust and crumb hardness was measured using a Texture

Analyzer TA-XT2i (TAXT2i, Stable Micro Systems, Sur-

rey, UK). For crust hardness (penetration, stainless steel

cylindrical probe; 6 mm diameter; test speed 3 mm/s), the

crust was removed from the five quick-bread samples from

each experiment. Crumb hardness (texture profile analysis

[TPA], cylindrical LAP perspex probe; 20 mm diameter;

test speed 1 mm/s, 40% strain) was measured on five slices

of 1.5 cm thickness for each experiment. Results for crust

and crumb hardness were expressed in N.

C-Cell image analysis

The C-Cell Imaging System (Calibre Control Interna-

tional LTD, Warrington, UK) was used to acquire, under

Table 3 Regression models for quality parameters of quick-breads

Independent

variable

Dependent variable

Crust

colour

(Y1)

Crumb

colour (Y2)

Crust

hardness

(Y3)

Crumb

hardness

(Y4)

Crumb

moisture

(Y5)

Bread

volume

(Y6)

Area of

cells

(Y7)

Average

cell volume

(Y8)

Texture non-

uniformity

(Y9)

Margarine (X1) 264.79 64.02 2.07 5.31 35.06 111.92 45.55 1.47 5.71

Oligof. L95

(X2)

203.50 45.83 -2.79 0.82 42.09 148.44 56.94 9.42 3.78

Sugar (X3) 254.58 53.40 -3.63 -0.37 40.95 173.29 58.75 10.20 12.2

Inulin GR (X4) 146.67 49.70 2.81 4.42 36.70 149.51 53.87 10.02 3.41

Marg/L95 (X1/

X2)

-482.82 -43.48 8.37 9.86 16.60 -139.52 -6.81 2.33 -15.23

Marg/sug

(X1/X3)

-633.28 -64.39 17.30 18.65 8.23 -206.74 -8.83 1.41 -33.19

Marg/GR

(X1/X4)

-472.50 -57.91 -0.12 9.27 18.37 -137.95 -0.93 0.94 -14.94

L95/sug

(X2/X3)

-422.17 -26.64 27.73 27.29 -0.47 -268.45 -25.05 -10.12 -27.97

L95/GR

(X2/X4)

-151.31 -14.51 14.60 18.45 6.62 -194.25 -16.19 -9.71 -12.27

Sug/GR

(X3/X4)

-419.75 -47.12 18.17 26.62 2.81 -250.68 -17.05 -8.51 -25.77

Marg/L95/sug

(X1/X2/X3)

964.57 10.81 -44.54 -16.24 -20.86 413.38 23.08 0.90 51.43

Marg/L95/GR

(X1/X2/X4)

824.86 69.50 -33.16 -65.8 -10.02 334.86 51.49 41.37 36.10

Marg/L95/GR

(X1/X3/X4)

621.31 113.13 -51.00 -67.77 -11.33 359.05 -5.40 -11.28 41.13

L95/sug/GR

(X2/X3/X4)

123.26 6.06 -18.29 -32.6 -12.09 419.18 4.27 -8.53 33.25

R2 0.88 0.88 0.87 0.82 0.94 0.87 0.87 0.80 0.85

p (model) 0.005 0.005 0.006 0.026 0.0002 0.005 0.007 0.04 0.01

p (lack of fit) 0.11 0.16 0.47 0.36 0.93 0.29 0.21 0.26 0.14

Eur Food Res Technol (2011) 233:167–181 169

123

standardized conditions, high-resolution images of quick-

bread slices (1.5 cm thickness). Crumb grain properties of

the slices were calculated using the dedicated software (C-

Cell, Version 2 Software, Calibre Control International

LTD, Warrington, UK), where quality attributes such as

area of cells (as a percentage of the total slice area),

average cell volume and texture non-uniformity were

determined.

Table 4 Mean and standard deviation of quality parameters

Experiments

No.

Bread

volume

(mL)

Crust

colour (BI)

Crumb

colour

(BI)

Crust

hardness

(N)

Crumb

hardness

(N)

Area of

cells (%)

Average cell

volume (mm3)

Texture non-

uniformity

Crumb

moisture

(%)

1 94.1 ± 2.3 101.0 ± 4.7 40.0 ± 1.0 4.9 ± 0.5 8.5 ± 0.9 50.5 ± 0.5 6.6 ± 0.6 0.4 ± 0.03 40.4 ± 0.2

2 91.6 ± 0.8 89.0 ± 2.4 39.6 ± 0.3 3.5 ± 0.7 7.9 ± 0.9 49.9 ± 0.2 6.2 ± 0.4 0.6 ± 0.17 41.4 ± 0.2

3 98.8 ± 1.4 88.7 ± 3.1 40.5 ± 0.9 3.5 ± 0.6 7.0 ± 0.6 50.6 ± 0.7 7.0 ± 0.5 0.6 ± 0.01 40.4 ± 0.1

4 94.7 ± 2.1 87.8 ± 2.9 40.7 ± 0.7 2.5 ± 0.2 6.9 ± 0.5 49.6 ± 0.6 6.3 ± 0.4 0.9 ± 0.12 40.9 ± 0.2

5 97.2 ± 2.6 85.4 ± 1.5 42.2 ± 0.4 2.5 ± 0.2 7.4 ± 0.4 49.4 ± 0.6 5.7 ± 0.6 1.0 ± 0.11 40.6 ± 0.2

6 95.4 ± 3.7 86.9 ± 1.7 42.3 ± 0.9 2.3 ± 0.4 6.9 ± 0.3 49.4 ± 0.8 6.1 ± 0.6 0.7 ± 0.14 40.3 ± 0.1

7 91.6 ± 1.7 75.9 ± 4.6 40.6 ± 0.8 2.7 ± 0.3 6.8 ± 0.4 49.5 ± 0.3 5.9 ± 0.5 0.5 ± 0.16 40.3 ± 0.4

8 91.3 ± 1.3 107.3 ± 3.2 43.0 ± 0.9 2.7 ± 0.6 6.6 ± 0.7 48.9 ± 0.5 5.5 ± 0.5 0.8 ± 0.13 40.6 ± 0.6

9 92.0 ± 2.0 112.8 ± 2 40.1 ± 0.7 2.9 ± 0.6 7.5 ± 0.9 50.4 ± 1.7 6.5 ± 0.9 0.6 ± 0.06 41.2 ± 0.3

10 95.3 ± 2.3 112.0 ± 2.3 44.3 ± 0.7 1.7 ± 0.2 5.5 ± 0.3 49.4 ± 0.4 6.0 ± 0.4 0.9 ± 0.08 42.7 ± 0.1

11 95.4 ± 1.8 90.0 ± 2.2 41.4 ± 0.4 2.1 ± 0.3 5.4 ± 0.2 49.7 ± 0.9 6.3 ± 0.3 0.7 ± 0.16 41.1 ± 0.1

12 92.1 ± 2.6 101.8 ± 1.9 42.5 ± 0.8 3.0 ± 0.1 6.7 ± 0.4 49.7 ± 1.1 6.1 ± 0.6 0.5 ± 0.16 40.4 ± 0.1

13 99.7 ± 2.1 111.1 ± 4.1 40.8 ± 0.3 2.8 ± 0.5 6.7 ± 0.5 51.4 ± 1.0 7.1 ± 1.0 1.8 ± 0.13 40.7 ± 0.4

14 99.2 ± 2.1 136.1 ± 3.2 45.0 ± 0.4 3.2 ± 0.5 7.0 ± 0.3 51.6 ± 0.9 7.4 ± 0.4 0.6 ± 0.26 40.8 ± 0.1

15 98.5 ± 2.6 99.9 ± 3.7 40.2 ± 0.9 4.0 ± 0.3 8.0 ± 1.0 52.6 ± 0.3 8.6 ± 0.6 1.4 ± 0.30 39.2 ± 0.2

16 89.3 ± 2.2 101.5 ± 0.7 42.9 ± 0.5 4.1 ± 0.5 7.5 ± 0.5 50.2 ± 0.5 6.2 ± 0.5 0.9 ± 0.07 39.7 ± 0.1

17 96.8 ± 2.5 110.5 ± 1.9 42.5 ± 0.8 2.3 ± 0.5 5.5 ± 0.2 51.2 ± 1.3 7.6 ± 0.7 0.9 ± 0.08 42.1 ± 0.3

18 94.1 ± 2.8 102.8 ± 3.6 43.2 ± 0.1 1.8 ± 0.5 6.3 ± 0.5 50.6 ± 0.7 7.5 ± 0.2 0.6 ± 0.02 41.0 ± 0.3

19 93.0 ± 2.1 121.5 ± 2.7 43.2 ± 0.7 3.7 ± 0.1 6.9 ± 0.5 51.7 ± 0.9 7.3 ± 0.5 1.0 ± 0.05 41.6 ± 0.3

20 101.5 ± 3.3 137.6 ± 1.7 43.4 ± 0.8 4.0 ± 0.9 7.4 ± 0.6 51.1 ± 0.5 7.2 ± 0.5 0.5 ± 0.13 41.3 ± 0.2

21 98.9 ± 1.8 90.2 ± 3.5 39.4 ± 1.0 4.2 ± 0.3 9.4 ± 0.9 51.5 ± 0.8 7.3 ± 0.6 1.3 ± 0.27 39.9 ± 0.3

22 98.7 ± 1.6 116.7 ± 5.1 40.9 ± 0.8 2.5 ± 0.4 6.3 ± 0.2 51.4 ± 0.7 7.3 ± 0.5 0.6 ± 0.09 41.8 ± 0.2

23 93.9 ± 2.9 109.8 ± 0.8 41.0 ± 0.8 2.0 ± 0.5 5.9 ± 0.9 50.5 ± 0.6 6.5 ± 0.8 1.2 ± 0.08 42.0 ± 0.1

24 95.2 ± 2.7 127.1 ± 1.1 42.8 ± 0.7 3.6 ± 0.7 7.0 ± 0.3 51.6 ± 1.2 7.3 ± 0.9 1.0 ± 0.01 41.2 ± 0.4

Table 5 Pearson’s correlation matrix between quick-bread properties

Bread

volume

Crust

colour

Crumb

colour

Crust

hardness

Crumb

hardness

Area of

cells

Average cell

volume

Texture non-

uniformity

Crumb

moisture

Bread volume 1

Crust colour 0.32 1

Crumb colour 0.01 0.55** 1

Crust hardness 0.08 0.11 -0.28 1

Crumb hardness 0.07 -0.18 -0.47* 0.81*** 1

Area of cells 0.54** 0.51* -0.06 0.45* 0.26 1

Average cell volume 0.56** 0.42* -0.05 0.31 0.16 0.94*** 1

Non-uniformity 0.28 0.01 -0.19 0.01 0.10 0.41* 0.31 1

Crumb moisture 0.03 0.43* 0.31 -0.53** -0.65*** -0.07 -0.07 -0.12 1

* Significant at p \ 0.05

** Significant at p \ 0.01

*** Significant at p \ 0.001

170 Eur Food Res Technol (2011) 233:167–181

123

Crumb moisture

Crumb moisture analyses were carried out in duplicates

according to the official AACC method 62.05 [13]. The

crust of quick-bread slices (1.5 cm thickness) was

removed. The remaining crumb was air-dried for 24 h at

room temperature in a secure room/environment. The dried

crumb was then milled and sieved (sieve size:

1,680 microns) to obtain a homogenous crumb. Ten grams

of ground crumb were placed in Brabender aluminium

dishes and dried at 130 �C for 2 h in a Brabender oven

(Brabender GmbH & Co. KG, Duisburg, Germany). Total

moisture was calculated from air drying and Brabender

drying steps and expressed as %.

Experimental design and data analysis

Mixture experiments were designed and analysed by using

the software Design expert 7.1.6. The effects of ingredient

proportions on baked product characteristics were studied

with a D-optimal design (Table 2) for the 4-component

mixture systems with constraints which comprised mar-

garine (X1), Oligofructose Orafti� L95 (X2), caster sugar

(X3) and Inulin Orafti� GR (X4). The constraints (lower

and upper) for each ingredient were established following

extensive preliminary trials (0–10% for each of the four

components) and according to a traditional quick bread

(scone) recipe containing 10% fat and 10% sugar, as rec-

ommended by The Baking Academy Of Ireland (Dublin,

Ireland). The mixture design was constructed to enable the

study of the effects of margarine, oligofructose, caster

sugar and inulin; all of which were restricted at the total

maximum of 20% of total flour weight. After data collec-

tion, Scheffe’s special cubic model for four components

(Eq. 2) was used to model the responses.

Y ¼ b1x1 þ b2x2 þ b3x3 þ b4x4

þ b12x1x2 þ b13x1x3 þ b14x1x4 þ b23x2x3 þ b24x2x4

þ b34x3x4 þ b123x1x2x3 þ b124x1x2x4

þ b134x1x3x4 þ b234x2x3x4 ð2Þ

where Y is the predicted dependent variable; b, the equa-

tion coefficients; X, the proportions of pseudo-components.

The dependent variables, bread volume (Y1), crust colour

(Y2), crumb colour (Y3), crust hardness (Y4), crumb hard-

ness (Y5), area of cells (Y6), average cell volume (Y7),

texture non-uniformity (Y8) and crumb moisture (Y9), were

Fig. 1 3D contour plots of bread volume expressed in mL (a margarine; b oligofructose; c sugar; d inulin)

Eur Food Res Technol (2011) 233:167–181 171

123

analysed. The models were subject to variance analysis

(ANOVA) to determine the significance (P \ 0.05),

determination coefficient (R2) and lack of fit.

Model validation

The predictive performance of the developed models,

describing the effects of the mixture components with the

dependent variables margarine (X1), oligofructose (X2),

sugar (X3) and inulin (X4) on independent variables (bread

volume, crust and crumb colour, crust and crumb hardness,

area of cells, average cell volume, texture non-uniformity

and crumb moisture) of quick breads, was validated. The

assessments were carried out by calculating the model per-

formance indices, as described by Hossain et al. [14]:

accuracy factor (AF, Eq. [3a] and bias factor (BF, Eq. [3b]).

AF ¼ 10

Plog VP=VEj j

ne ð3aÞ

BF ¼ 10

Plog VP=VEð Þ

ne ð3bÞ

The criterion used to characterize the fitting efficiency of

the data to the model was the average mean deviation (E) [15]

Eð%Þ ¼ 1

ne

Xn

i¼1

VE � VP

VE

����

����� 100 ð4Þ

where E is the average mean deviation, ne is the number of

experimental data, VE is the experimental value and VP is

the calculated value.

Results and discussion

Table 2 presents the mixture design for the 24 formulations

which underwent quality analysis. The experimental results

obtained by the special cubic model for all responses were

statistically evaluated, and the calculated regression coef-

ficients are shown in Table 3. Mean values for the nine

responses analysed (crust and crumb colour, crust and

crumb texture, crumb moisture, bread volume, area of

cells, average volume of cells and texture non-uniformity)

are listed in Table 4. For comparison purposes, experiment

12 and 16 represent the control formulation. To establish

an accurate model, it is required to replicate some data

points (usually extremes/maximum). As outlined in

Table 1, the following experiments are equivalent:

Fig. 2 3D contour plots of crust colour expressed as browning index (a margarine; b oligofructose; c sugar; d inulin)

172 Eur Food Res Technol (2011) 233:167–181

123

experiment 5 = 6; 12 = 16; 14 = 20; 15 = 21; 19 = 24.

Pearson‘s correlation coefficients which were determined

by analysis of variance (ANOVA) are shown in Table 5.

All the models were statistically significant, with satisfac-

tory coefficients of determination (R2) and the lack of fit

showing no significance throughout the model.

Bread volume

Volume of quick breads is significant criteria for consumer

acceptability. As shown in Table 4 and Fig. 1, the highest

bread volume (101.5 mL) was achieved by replacing fat

and sugar completely with inulin and oligofructose. This is

significantly higher compared with the control formula-

tions 12 and 16 which showed a combined average volume

of 90.7 mL. Similar results were obtained and reported by

Hager et al. [16] and Peressini and Sensidoni [17]. This

was also confirmed by the optimization tool which pre-

dicted an optimum volume of 100.4 mL with 10% inulin,

10% oligofructose and 0% fat and sugar if all other

responses are not taken into account.

Crust and crumb colour

The crust and crumb colour of breads is generally a key

quality attribute for consumer acceptance. Crust colour

varied significantly among the experiments, as shown in

Table 4 and Fig. 2. Higher values for browning index

indicate a darker appearance. The browning index (BI) for

crust colour varied from 75.9 to 136.1 (Table 4). A general

trend emerged that higher concentrations of inulin and

oligofructose led to a higher BI (Fig. 2). This is a well-

reported phenomenon, as the addition of oligosaccharides

accelerates caramelization and Maillard reaction and

therefore speeds up the formation of bread crust colour.

[11]. However, the mixture design optimization tool indi-

cated that a BI of 107 can be achieved by minimizing

margarine (0%), sugar (2.75%) and maximizing oligo-

fructose (7.25%) and inulin (10%) if all other dependent

variables are disregarded. If compared with control

experiments (12 and 16) which showed a browning index

of approx. 101, the slight increase in crust colour would be

considered to be acceptable in practical terms.

Fig. 3 3D contour plots of crumb colour expressed as browning index (a margarine; b oligofructose; c sugar; d inulin)

Eur Food Res Technol (2011) 233:167–181 173

123

For crumb colour, the effects of substitution of fat and

sugar with prebiotics were less significant as those for crust

colour. BI for crumb colour ranged from 39.6 to 45

(Table 4); the differences are not obvious to the human

eye. Similar to crust colour, higher concentration of oli-

gosaccharides but also sugar led to an increase in BI, as

shown in Fig. 3, again due to non-enzymatic browning

reactions. This explains the occurrence of a significant

correlation (r2 = 0.55) between crust and crumb colour.

Large changes for crumb colour were not expected as it is

usually similar to the colour of the fat/sugar replacers

(prebiotics did not change colour of liquid ingredients)

used, because the crumb does not reach as high tempera-

tures as the crust. To optimize in terms of crumb colour, to

the same requirements as for crust colour the predicted BI

of 41.9 can be achieved with margarine (0%), oligofructose

(9.35%) sugar (2.31%) and inulin (8.34%).

Crust and crumb hardness

In order to satisfy consumer’s preferences, high-quality

quick bread must have an acceptable, soft/fresh crust and

crumb texture. Crust hardness varied significantly among

the mixture experiments ranging from 1.7 to 4.9 N

(Table 4). As shown in Fig. 4, reducing sugar led to a

softening effect of the crust (control approx 3.5 N). An

increase in hardness is caused when the milk solution

containing sugar interacts with the flour protein hindering

gluten development and the quick hardening of sugar once

the baked product is left to cool after baking [18]. The

presence of the prebiotics inulin and oligofructose showed

an increase in crust hardness, but this was not as significant

as for sugar. Poinot et al. [11] found similar results for

inulin-enriched breads where the addition of inulin led to a

firmer crust. The most dominant mixture component for

crust hardness was margarine. This is not surprising as the

addition of fat in baked goods has been reported to improve

softness [19]. The function of fats is important, as it

competes during the mixing of the dough with the aqueous

phase for the flour surface. Fat coats the flour and interrupts

the formation of a continuous network caused by protein

and starch [18]. Therefore, higher levels of margarine

were accountable for keeping the crust soft. In order to

gain more information on the formation of the continu-

ous network, future work will investigate the levels of

the extent of starch formation. Taking only crust texture

Fig. 4 3D contour plots of crust hardness expressed in N (a margarine; b oligofructose; c sugar; d inulin)

174 Eur Food Res Technol (2011) 233:167–181

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Fig. 5 3D contour plots of crumb hardness expressed in N (a margarine; b oligofructose; c sugar; d inulin)

Fig. 6 Images of a quick-bread slices (experiment 24) obtained with the C-Cell Imaging software showing a raw image, b cell image, c volume

image (white lines contours), d brightness image

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into account by using the optimization tool, an accept-

able low fat/sugar product can be reached with a crust

hardness of 2.83 N with a mixture of 1.5% margarine,

10% oligofructose, 0% sugar and 8.49% inulin. This is

similar to the control which had an approximate hardness

of 3.5 N.

Crumb hardness also showed variations due to changes

in the four components and ranged from 5.4 to 9.4 N

(Table 4). Similarly to the crust hardness, higher levels of

sugar and oligosaccharides increased the crumb hardness

(Fig. 5). Again this is due to the same reasons as outlined

for crust hardness. Similar observations for bread were

made by other authors where the inclusion of prebiotics

increased crumb hardness [10]. Increasing concentrations

of fat again led to a softening of the crumb. Crumb hard-

ness correlated well with crust hardness (r2 = 0.81). The

margarine used in this study serves a function to emulsify

the liquid in the formulation [20], and therefore fats con-

tribute to the soft and tender eating properties. However, in

order to reduce fat and sugar, a crumb hardness of 6.5 N is

made possible with 1.04% margarine, 10% oligofructose,

0% sugar and 8.96% inulin, which is softer compared with

the control (7.1 N).

Crumb image analysis (area of cells, average cell

volume, texture non-uniformity)

The C-Cell Imaging system was used to assess the crumb

grain characteristics of 1.5 cm slices of quick breads from

each of the 24 formulations. The results for area of cells,

average cell volume and texture non-uniformity were

obtained from images, as shown in Fig. 6. As the fat and

sugar levels in the quick-bread formulations are changing

significantly, it is important to gain a good understanding

as to the effects on crumb grain, as both of these ingredi-

ents are highly influential.

Area of cells was determined, as it represents total area

of cells as a percentage of the slice area. Large values

indicate a more open texture. The value nevertheless pro-

vides a measurement of the relative visual appearance of

slices. The area of cells varied significantly among the 24

experiments ranging from 48.9 to 52.6% (Table 4). The

components that most affected this result were in particular

sugar and oligosaccharides. As shown in Fig. 7, an

increasing amount of sugar and oligosaccharides led to a

higher area of cells and therefore indicating a more open

texture. Sugar is well known to influence structural and

Fig. 7 3D contour plots of area of cells expressed in % (a margarine; b oligofructose; c sugar; d inulin)

176 Eur Food Res Technol (2011) 233:167–181

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textural properties of baked goods. A more open texture

with a higher sugar content and the positive correlation

(r2 = 0.54) with the bread volume was found. A correla-

tion between the area of cells and crust colour (r2 = 0.51)

and crust hardness (r2 = 0.45) was noticed. This is most

likely due to the fact that higher sugar levels will influence

the distribution of water in the product and accelerate

Maillard reaction, thus influencing both the texture and the

appearance of the crumb grain.

The average volume of cells provides a good indicator

of the coarseness of the texture. It is the volume of which

half of the total area of cells is represented by cells of

smaller volume and half is represented by cells of larger

volume. The average volume of cells showed a similar

trend to the area of cells (Fig. 8) and therefore showed a

high correlation of r2 = 0.94. As explained above, the

higher content of sugar and/or prebiotics resulted in a more

open texture of the quick breads, as an increase in sugar

content tends to fine cell formation [21].

Texture non-uniformity is a measure of the lack of

uniformity between fine and coarse texture across the slice.

As shown in Fig. 9, a more uniform texture can be

achieved by replacing fat and sugar with inulin and

oligofructose. In fact the optimization tool indicated a low

non-uniformity of 0.53 by using 10% inulin and 10% oli-

gofructose (i.e. by totally eliminating the original fat and

sugar).

Crumb moisture

Crumb moisture in quick bread is an important factor as it

closely related to the textural properties. Moisture content

of the formulations ranged between 39.2 and 42.1%.

Moisture content significantly increased in the presence of

higher amounts of fat and oligofructose, while at the same

time, the crumb became less moist with higher concen-

trations of sugar and inulin (Fig. 10). This explains why

some oligosaccharides are popular for replacing fat or

sugar in baked goods. Because of their water-binding

characteristics, the use of inulin or oligofructose can allow

for the development of low-fat foods without compromis-

ing the texture and mouthfeel [22]. As expected, crust

hardness (r2 = -0.53) and crumb hardness (r2 = -0.65)

decreased while moisture increased. As reported by other

authors, this must be linked to the structuring of the crumb

and the beginning of crust formation [11].

Fig. 8 3D contour plots of average volume of cells expressed in mm3 (a margarine; b oligofructose; c sugar; d inulin)

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Optimization of quick-bread formulation

Taking all quality characteristics into account, and using

the optimization tool where the inclusion levels of mar-

garine and sugar were minimized and inulin and oligo-

fructose maximized, and where quality parameters were

minimized (crumb and crust colour, crumb and crust

hardness and non-uniformity) or maximized (crumb

moisture, bread volume, area of cells and cell volume)

three quick-bread formulation were predicted, as shown in

Table 6. These represent a quick-bread formulation with

significant lower fat and sugar levels compared with the

control. Figure 11 shows the baked quick breads of each

formulation. Overall, three formulations/recipes of quick

breads with acceptable desirability levels for the fat and

sugar substitution were identified by the mixture design.

The three predicted quick-bread formulations underwent

statistical analysis and were used to validate the model.

Model validation

Validation is the most crucial step that reveals the valid

range of a model and the limits of its performance [23].

Therefore, a predictive model may only be reliably used in

decision making when tested and validated [24]. Table 7

shows the predicted and the measured quality parameters

for the three optimized quick-bread formulations. Table 8

shows calculated model performance indices accuracy

factor (AF), bias factor (BF) and average mean deviation

(E%) of the three quick-bread formulations to quantita-

tively determine the applicability and accuracy of the

model outlined above. Formulation 1 would be the pre-

ferred recipe with the highest replacement of fat and sugar.

This is also indicated by the highest desirability level of

0.72. Higher desirability levels (i.e. close to 1.0) indicate a

more looked-for result. The model showed a good fit for

formulation 1 as shown by the accuracy factor for most

responses close to 1.00, except for crust and crumb hard-

ness which had a percentage error of 15 and 9%, respec-

tively. Ross et al. [25] reported that predictive models

should ideally have an AF = 1.00, indicating a perfect

model fit where the predicted and actual response values

are equal. In addition, Ross et al. [25] and Carrasco et al.

[26] reported that the AF of a fitted model increases by

0.10–0.15 units for each predictive variable in the model.

A model that forecasts a response from two predictive

Fig. 9 3D contour plots of texture non-uniformity (a margarine; b oligofructose; c sugar; d inulin)

178 Eur Food Res Technol (2011) 233:167–181

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variables may be expected to have AF values ranging from

1.20 to 1.30 [25] or an equivalent percentage error range of

20–30%. In this study, four predictive variables are fore-

casted, and therefore, an error of 15 and 9% is still very

acceptable. The bias factor showed a similar trend to the

accuracy factor with most responses being close to 1.00,

except for crust and crumb hardness with 0.87 and 0.92

units, respectively. These values indicate that there was a

good agreement between predicted and observed value.

Formulation 2 that had desirability level of 0.66 performed

slightly better than formulation 1 in terms of model

applicability and accuracy. However, texture non-unifor-

mity showed percentage errors of 15%. All other response

had an error level of B 5%. Formulation 3 had the lowest

desirability level overall with 0.61, but showed the best

model performance indices of AF and BF B 6%. Overall,

variation between predicted and experimental values for

the majority of the responses obtained were within

acceptable error range, as depicted by average mean

deviation (E %; Table 8) for the three quick-bread

Fig. 10 3D contour plots of crumb moisture expressed in % (a margarine; b oligofructose; c sugar; d inulin)

Table 6 Predicted formulations

and desirability levels of quick

breads

a % values are based on the

total flour weight (100 g)

Material Formulation 1 (%)a Formulation 2 (%)a Formulation 3 (%)a

Cream (weak) flour 100 100 100

Baking powder 4.0 4.0 4.0

Salt 0.25 0.25 0.25

Milk (low fat, 1.7%) 60 60 60

Fat 3.53 3.03 3.44

Oligofructose (Orafti� L95) 10 6.97 3.44

Caster sugar 0.55 2.5 3.44

Inulin (Orafti� GR) 5.92 7.5 9.69

Desirability level 0.72 0.66 0.61

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formulations used. Consequently, based on the results

obtained from the predicted model and actual experimental

values, the predictive performance of the established model

may be considered acceptable for the substitution fat and

sugar in quick bread with functional ingredients of inulin

Orafti� GR and oligofructose Orafti� L95.

Fig. 11 Baked quick breads (scones) for the three formulations (a formulation 1; b formulation 2; c formulation 3)

Table 7 Predicted and experimental values of the three quick-bread formulations used for the for the nine responses (bread volume, crust and

crumb colour, crust and crumb hardness, area of cells, average cell volume, texture non-uniformity and crumb moisture) studied

Responses Formulation 1 Formulation 2 Formulation 3

Predicted Experimental Predicted Experimental Predicted Experimental

Bread volume (mL) 99.96 100.77 97.22 98.23 98.29 99.62

Crust colour (BI) 122.19 125.53 104.80 110.51 90.32 92.92

Crumb colour (BI) 42.35 43.53 41.26 41.36 40.96 40.43

Crust hardness (N) 2.09 2.41 3.06 3.25 3.22 3.34

Crumb hardness (N) 5.42 5.88 6.52 6.84 6.84 6.97

Area of cells (%) 51.91 51.90 50.91 51.05 50.6 50.64

Average cell volume (mm3) 7.86 7.86 7.24 7.23 7.16 7.25

Texture non-uniformity 0.81 0.78 0.57 0.66 0.56 0.52

Crumb moisture (%) 42.31 42.21 41.30 41.67 40.53 40.72

Table 8 Calculated model performance indices accuracy factor (AF), bias factor (BF) and average mean deviation (E) of the three quick-bread

formulations

Responses Formulation 1 Formulation 2 Formulation 3

AF BF E (%) AF BF E (%) AF BF E (%)

Bread volume 1.01 0.99 0.81 1.01 0.99 1.03 1.01 0.99 1.34

Crust colour 1.03 0.97 2.67 1.05 0.95 5.16 1.03 0.97 2.79

Crumb colour 1.03 0.97 2.71 1.00 1.00 0.23 1.01 1.01 1.31

Crust hardness 1.15 0.87 13.39 1.06 0.94 5.7 1.04 0.96 3.71

Crumb hardness 1.09 0.92 7.83 1.05 0.95 4.68 1.02 0.98 1.92

Area of cells 1.00 1.00 0.02 1.00 1.00 0.28 1.00 1.00 0.08

Average cell volume 1.00 1.00 0.02 1.00 1.00 0.08 1.01 0.99 1.16

Texture non-uniformity 1.04 1.04 3.54 1.15 0.87 13.38 1.06 1.06 6.28

Crumb moisture 1.00 1.00 0.23 1.01 0.99 0.88 1.00 1.00 0.48

180 Eur Food Res Technol (2011) 233:167–181

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Conclusions

Mixture surface methodology using D-optimal design was

found to be an effective technique to investigate the effects

of fat and sugar substitution by inulin and oligofructose on

bread volume, crust and crumb colour, crust and crumb

texture, crumb grain properties and crumb moisture of

quick breads. Overall, it was found that quick breads

(scones) containing inulin and oligofructose can show

similar quality characteristics to a control which contains

10% fat and 10% sugar. Some quality parameters such as

bread volume even showed an improvement due to inclu-

sion of prebiotics. The validation of the model has shown

that it can be successfully used to produce low fat and

sugar quick breads with the potential health benefits of

prebiotics. All three predicted formulations used to deter-

mine the accuracy of the model showed a good model fit

and had acceptable desirability levels. The successful fat

and sugar reduction and the introduction of healthy alter-

natives may be especially appealing to the elderly, as they

are one of the main consumers of these products. Future

work will focus on the nutritional and sensory character-

ization of the optimized quick bread product.

Acknowledgments This research was funded by the Irish Depart-

ment of Agriculture Fisheries and Food under the Food Institutional

Research Measure (FIRM). The authors would like to thank Beneo-

Orafti (Tienen, Belgium) for providing the samples and the Baking

Academy of Ireland (Dublin, Ireland).

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