Colour Matching of Dyed Wool by Vibrational Spectroscopyeprints.qut.edu.au/15867/1/Mandana_Mozaffari-Medley_Thesis.pdf · known as keratins (from the Greek word 'κερας' meaning

Colour Matching of Dyed Wool by Vibrational

Spectroscopy

Mandana Mozaffari-Medley BSc.

A thesis submitted in partial fulfilment of the requirement of the Degree of Master of Applied Science

School of Physical and Chemical Sciences Queensland University of Technology

July 2003

ii

Statement of Original Authorship

The work contained in this thesis has not been previously submitted for a

degree or diploma at any other higher education institution. To the best of my

knowledge and belief, the thesis contains no material previously published or

written by another person except where due reference is made.

iii

"w¬Ã�f Í¤‡ ä… ,w¬� ÑŸ£•"

dZ¯‹®• pz®•

“I conquered, I got published”

Forough Farokhzaad, Pioneer female poet I would like to dedicate this thesis to my family, friends, and colleagues who believed in me and encourage me to be a true scientist. Especially Dr. B. Frost, the dean of faculty of science at the University of Queensland.

iv

Acknowledgement

I would like to acknowledge my supervisor, Dr. Serge Kokot for providing me with a very interesting project. I would like to thank him for being there whenever I was facing a challenge in the course of my study. I would like to thank Professor George, my co-supervisor. For that although as the dean of the faculty he was very busy, he always found time to listen and provide guidance and encouragement. I also wish to thank Dr. Jeff Church, from CSIRO, for providing me with samples to study and for his help at the beginning of my study. Many many thanks go to Dr. Llew Rintoul for teaching me how to use the instruments and for discussing various aspects of my work and providing me with excellent ideas. Not to mention that he spent a few Saturday and Sunday afternoons reading my thesis. Thank you Llew, you are an invaluable friend. I would like to thank Associate Professors Brian Thomas and Peter Fredericks and Ms. Elizabeth Stein. Without your support this thesis would not have been possible. I would like to thank Drs. Dalius Sagatys, Bob Johnson and Graham Smith for listening to me when I was down and for inviting Greg and I to their circle of friends. All the postgraduate students are not forgotten, specially Dr. Shona Stewart, Sandra Dütt and Thanh Van den Elst, for making my life at QUT that much more pleasant. Also I would like to thank a few people at work: my boss Cathrine Neuendorf for supporting me in taking time off work to finish, Sandra for listening to all my stories and buying me lots of gifts to cheer me up and Helen Woods for making sure that I was on the track and that I had a plan to follow. I’d like to thank my parents. To whom I owe my achievements: To my father, the greatest textile engineer, whose passion for textiles inspired me to do this project. Dad, many times when I read about wool processing, I remembered you, teaching me enthusiastically about the textile science of fabrics whenever the possibility came up. Does summer holidays in Europe ring a bell!!! To my mother, a teacher by profession and in life. You are my role model. You have taught me never to give up and always think positive. Thank you for being there for me.

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I also wish to thank my brother, Maziar, who is also finishing his PhD thesis. Thank you for all your support and encouragement. I am proud to be your sister. Last but by no means least; I’d like to thank my husband, Dr. Greg Medley. For all that time that I discussed my scientific ideas with him, for teaching me how to write a thesis and for correcting all my grammatical errors and funny English!!! For many times that he worked 12-14 hours at uni so that I would keep going, for listening to my grumbles and always saying: “Sweetie, you are going to be fine, keep going”.

vi

Abstract

The matching of colours on dyed fabric is an important task in the textile industry.

The current method is based on the matching the visible reflectance spectrum to

standard spectral libraries. In this study, the amount of dye on various wool and

wool-blend fabric was measured using vibrational-spectroscopic techniques.

FT-IR PAS and FT-Raman spectroscopy was used to analyse the following set of

samples: woollen fabrics (supplied by CSIRO- Geelong, Australia), dyed with

Lanasol dyes (Red 6G, Blue 3G and Yellow 4G) and wool/polyester fabrics (supplied

by Ceiba-Geigy, Switzerland), dyed with Forosyn dyes (grey, yellow, green, brown,

orange, red). A minimum of six spectra was recorded for each sample. The spectra

recorded were consistent with those reported previously. FT-IR PA spectral data were

block normalised with Y-mean centring and examined using Principle Component

Analysis (PCA) and Partial Least Squares (PLS).

Although PCA separates the woollen fabrics dyed with a combination of two colours,

it does not do equally well for samples dyed with three colours. The dyed wool/

polyester blend samples appeared in a totally random fashion on the PCA plot.

The PLS analysis of PA spectra of various ratios of dyes on woollen fabrics as well as

wool/polyester blend was found to be a viable procedure and should be investigated

further, perhaps with a broader set of data.

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FT-Raman spectra were examined using PCA and PLS. The best pre-treatment for

FT-Raman spectral data was found to be normalising followed by Y-mean centring.

The PCA plots demonstrate that woollen samples are separated according to the dye

ratios and that the presence or absence of some of the peaks is influenced by

individual dyes. For example, the presence of the peak at 1430cm-1 is inversely related

to the presence of blue dye on the fabric. The PLS resulted in SEE and SEP values of

around 1 and 2 respectively indicating that the prediction of the dye ratios have not

been very successful and suggesting that there was some problem with the measured

values of the calibration set.

PCA plots of wool/polyester fabrics dyed with a single colour indicate that PC1

separates the samples according to how close the shades are together, while PC2 and

PC3 separate samples according to their individual colours. PC4, although explaining

only a small percentage of variance, suggests that the samples are not homogeneously

dyed. PCA plots of the samples dyed with various combinations of the three main

dyes display each cluster of samples in their right position on the colour card.

Calculated SEE and SEP values (Yellow: ~0.30, ~0.55, Brown: ~0.30, ~0.79, Red:

0.16, 0.49 and Grey: ~0.2, ~0.40, respectively) indicate that FT-Raman spectroscopy

and chemometrics may offer promising methods for measuring the ratio of various

dyes on wool/polyester fabrics.

FT-Raman spectroscopy and chemometrics were also used to investigate the change

in the ratio of dyes on UV-treated dyed woollen samples. Samples were weathered for

7 and 21 days, using accelerated weathering instrument. The substrate subtracted

viii

spectral data were normalised to 100% substrate of the first derivative (9 points and 7

degrees) followed by double centring of the matrix in the spectral region of 1500-

500cm-1. PCA effectively separated non-irradiated from the irradiated sample but did

not separate the irradiated samples further according to the number of days of

irradiation. The pre-treatment used for PLS was first derivative of substrate subtracted

spectral data normalised to 100% substrate, and then Y-mean centred. PLS failed to

predict the ratio of the irradiated dyes very well. This may be because degradation

products are not modelled by PLS or because the total amount of dye has reduced

without changing the dye ratios.

Table of Contents

1… Chapter 1 — Introduction 1… 1.1 Introduction 2… 1.2 Morphology 6… 1.3 Chemical Structure of Wool 9… 1.4 Wool Processing 11… 1.5 Finishing 23… 1.6 Colour Matching 27… 1.7 Vibrational Spectroscopy 36… 1.8 Chemometrics 38… 1.8 References 40… Chapter 2 — Experimental 40… 2.1 Materials 42… 2.2 Instrumentation 43… 2.3 Vibrational Spectroscopy Analysis 49… 2.4 Chemometrics 58… 2.5 References 59… Chapter 3 — FT-IR Spectroscopy of Dyed Wool 59… 3.1 Introduction 63… 3.2 FT-IR PA spectroscopy of undyed Wool 65… 3.3 FT-IR Studies of dyed wool 67… 3.4 Chemometrics studies of dyed wool 72… 3.5 Photo-acoustic Spectroscopy of Wool/Polyester Blends 75… 3.6 References 76… Chapter 4 — FT-Raman Spectroscopy of Dyed Wool 76… 4.1 Introduction 77… 4.2 Dyed Wool 85… 4.3 Chemometrics 95… 4.4 Wool/ Polyester Blend Dyed With Foron and Lanasyn dyes 104… 4.5 Chemometrics 120… 4. 6 References 121… Chapter 5 — FT-Raman Spectroscopy of Irradiated Dyed Wool 121… 5.1 Introduction 127… 5.2 FT-Raman Spectroscopy of Irradiated Wool 128… 5.3 Chemometrics 144… 5.4 References 145… Chapter 6 — Conclusions 145… 6.1 Conclusions 150… 6.2 Future Work 151… 6.3 References

Chapter 1 Introduction Page 1

Chapter 1 Introduction

1.1 Introduction

It is not known when wool was first used as a textile fibre but since sheep were one of

the first animals domesticated by Neolithic people; it seems likely that wool may have

been an early textile material1. Likewise, the art of dying wool predates written

history but woollen articles have been found in central Asia which are at least 2500

years old and their subtle dying shows that the dyer’s art was already well developed

by that early stage. For most of its long history, the process of dying textiles has been

a 'black art' with individual dyers jealously guarding secret recipes of plant, animal

and mineral products which they knew would produce a fast dye in desirable colours.

It was only in the mid-nineteenth century with the development of the first synthetic

dyes that the dying of wool began to be transformed from an art to a science.

Wool is a proteinaceous fibre with the majority of these proteins belonging to a class

known as keratins (from the Greek word 'κερας' meaning horn). Keratin proteins are

found in all higher vertebrates. They may be divided into two groups: hard and soft

keratins. Hard keratins are found in hair, hooves and feathers. They have relatively

high proportions of sulfur (more than 3%)2 and hence tend to be highly cross-linked

and inelastic. Soft keratins such as those found in skin contain much less sulfur.

Keratins may also be classified according to their folding structure as determined by

their x-ray diffraction patterns3. Unstretched wool contains α-keratins, which have a

Chapter 1 Introduction 2

α-helical structure. When the wool is stretched it converts to the β-keratin structure

which is predominately folded as β-sheets.

1.2 Morphology

The wool fibre is not homogenous. It has several distinct regions arranged in coaxial

layers. These layers have very different chemical and physical properties; it is,

therefore, necessary to understand the morphology of the fibre in order to study the

response of wool processes such as dying. For this reason, wool morphology has been

the subject of extensive study over the past few decades. The fibre may be divided

into a central region known as the medulla surrounded by a cellular region known as

the cortex, which is in turn surrounded by a region known as the cuticle.

Figure 1.1 The Structure of a wool fibre4

halla

This figure is not available online. Please consult the hardcopy thesis available from the QUT Library.


The Cuticle

The cuticle is the outer layer of the fibre. It is composed of large flattened cells. The

number of cuticle cells is determined by the thickness of the fibre. In coarse wool the

cuticle may be up to fifteen cells thick while in fine wool it is a single cell layer with

about 15% cell overlap5. The outermost cuticle cells protrude outward to form scales

pointing toward the distal end of the fibre6. Keratins in the cuticle contain a greater

proportion of cystine and other non-helix-forming amino acid residues7. This makes

them less extensible and so, when the fibre is stretched, the cuticle cells tend to

crack8.

The cuticle may be further subdivided into four coaxial layers: the epicuticle, the

exocuticle, the endocuticle and the cell membrane complex.

The epicuticle is a thin hydrophobic membrane, which surrounds the cuticle cells. It is

only a few molecules or 3-6nm thick and contains only about 0.1% of the mass of the

fibre5, 9 but because it is the outermost layer, it is the most exposed to chemical attack.

The exocuticle lies directly below the epicuticle. It is about 0.3µm thick and contains

about 60% of the cuticle mass10. The exocuticle has a higher degree of cross-linking

than the rest of the cuticle and hence is resistant to enzymatic digestion. It may,

however, be solublised by oxidation or reduction.

The endocuticle lies beneath the exocuticle. It is slightly smaller than the exocuticle

being about 0.2µm thick and composing about 40% of the cuticle mass. It is less


highly cross-linked than the exocuticle and hence it is more susceptible to enzymatic

digestion11. Industrial processes to remove the scales from wool employ chemical

attack on the endocuticle.

The cell membrane complex (CMC) is a thin proteinaceous membrane about 25nm

thick which lies between the cuticle and the cortex and which acts to bind the two

together12. Even though the CMC constitutes a small proportion of the mass of the

wool fibre, it plays an important role in the mechanical and chemical properties of the

fibre13.

The Cortex

The cortex lies beneath the cuticle. It is clearly distinguished from the cuticle by the

different morphology of the cells. The cortex cells have an elongated shape

approximately 100µm long and 3-6µm wide and aligned parallel to the fibre axis14.

The cortex is largely responsible for the mechanical behaviour of the fibre. Within the

cortical cells are macrofibrils embedded in a protein matrix. The macrofibrils are

composed of hundreds of microfibrils, which are in turn composed of protofibrils. A

protofibril consists of two double-stranded helices coiled into a superhelix. Each of

the helices consists of two α-helical protein chains.

The cortex may be divided into two regions, namely: the orthocortex and the

paracortex. In coarse fibres there is a third region known as the mesocortex which can

account for up to 4% of the fibre mass15.


The paracortex cells contain regions known as nuclear remnants composed of non-

keratinous material and cytoplasmic debris16. In the orthocortex cells, this material is

less easily resolved because it is distributed evenly between the microfibrials. The

nuclear remnant material dispersed through the cell make the orthocortex cell more

wettable and more accessible to surfactants and to high molecular mass dyes such as

heavy metal ions and basic dyes17. These chemicals diffuse into the cortex via the

CMC, which runs the full length of the fibre.

The paracortex cells are less susceptible to attack by heavy cations but show the same

reactivity toward acid dyes as the orthocortex18.

The Medulla

Along the cental axis of the fibre there is a region known as the medulla, which

appears as a dark canal. The cells in the medulla are surrounded by air-filled spaces

and, for this reason the medulla is believed to serve as a thermal insulator and to

reduce the weight of the fibre. The medulla increases the light-scattering ability of the

fibre and makes the fabric appear lighter.

Dye migration

In the early days, wool fibre was treated as a cylinder of uniform composition. Hence

it was assumed that a plot of dye uptake by wool would be a straight line. This

however, did not agree with the Fick’s law of diffusion, which states that a plot of dye

uptake against the square root of time must be nearly a straight line. In practice, the

dyeing curve is a concave one at the beginning of the plot, and it only becomes a

linear line after a while. This suggested that there must be a barrier to the dye uptake.


Many theories have been proposed as to what the barrier in the wool fibre is. Some of

the earlier theories assumed that epicuticle was a continuous layer around the wool

fibre. In 1937 however, R.O. Hall19 proposed that the dye molecule diffuses into the

wool fibre through the gap in between the scales. Another factor that has been

proposed as a barrier to the dye uptake is the lipids present at the junctions of the

cuticles. In 1985, Leeder and his co-workers20 were able to follow the dye molecule

migration into the fibre using Transmission Electron Microscopy (TEM). They

showed for the first time that the dye molecule does enter the fibre via cuticle cell

junctions.

When the dye molecule passes the barrier, it should still get through the whole cross

section of the fibre, if maximum colour yield and fastness is to be obtained. Leeder et

al.13 have shown that the non-keratineous part of the wool fibre is important in the

wool dyeing process. They also demonstrated that the dye diffuses throughout all the

non-keratineous part of the cell membrane complex, the endocuticle and the inter-

macrofibrillar material and then slowly migrates from the non-keratineous section into

the sulfur rich proteins of the matrix around the microfibrils within each cortical cell.

The dye also migrates from the endocuticle into the exocuticle. In this way, at the end

of the dyeing process, the non-keratineous part of the wool fibre becomes saturated

with dye.

1.3 Chemical Structure of Wool

Approximately 1% of the mass of a clean wool fibre is made up of lipid material most

of which is found in the intercellular regions21. About 40% of this lipid material is


sterols, and about 30% are polar lipids such as cholesterol sulfate. About 25% are

fatty acids including every straight chain, mono-saturated fatty acid between C7 and

C26 (22, 23) .

Amino Acid Composition of Wool

Various people have studied the relative abundance of amino acid in wool as a whole

and in different parts of the wool fibre24, 25, 26. The hydrolysis of clean wool yields 18

amino acids with the relative amount of the amino acids differing from one sample to

another, even within a single breed of sheep. The amino acids and their approximate

concentration present in a Merino wool fibre are shown in the Table 1.1.

The abundances of the various amino acids residues may be different in different wool

samples. Some of these differences are due to the genetic variations, diet, etc. One of

amino acid residues particularly affected by these factors is the cystine content of the

wool. Wool fibres usually have relatively high sulfur content. This is due to the

disulfide group of the amino acids’ cystine. Cystine may be oxidised to form cysteic

acid27.

Figure 1.2 The Chemical Structure of Cysteic Acid, Cystine and Cysteine

NH2

O

OH

SH

NH2

O

OH

SO

O

OH

NH2

O

OH

S S

NH2

O

OH

Cysteic Acid Cysteine Cystine

Table 1.1 Amino acid composition of wool Amino acid Chemical structure Abundance

(mole % ) Nature of the side-chain

Glycine CH

COOH

NH2

H

8.6 Hydrocarbon

Alanine CH

COOH

NH2

H3C

5.3 Hydrocarbon

Phenyl-alanine

CH2 CHCOOH

NH2

2.9 Hydrocarbon

Valine

CHCOOH

NH2

H3C

H3C

5.5 Hydrocarbon

Leucine H3C

H3CCH2

NH2

COOHCH

7.7 Hydrocarbon

Isoleucine

NH2

COOHCH

CHCH2

CH3

CH3

3.1 Hydrocarbon

Serine CH

COOH

NH2

CH2HO

10.3 Polar

Threonine CH

H3C NH2

COOHCH

HO

6.5 Polar

Tyrosine

CH2 CHCOOH

NH2

HO

4.0 Polar

Aspartic acid CH

COOH

NH2

CH2HOOC

6.4 Acidic

Glutamine O

O

NH2

NH2

OH

11.9 Basic


Both cystine and cysteine (the precursor of cystine in the reduced form) can usually

be found in wool. Figure 1.2 shows the chemical structure of both cystine and

cysteine. Cysteine is also referred to as ‘half-cystine’. The hydrolysis of the cystine is

facilitated under alkaline conditions. This sometimes results in the formation of

lanthionine cross linkages (-HC-CH2-S-CH2-CH-) causing the fibre to display

yellowness.

Many of the polypeptide chains in wool are in the form of a α-helix. There are two

types of cross linkage which may occur in the polypeptide chains: covalent and

non-covalent bonds. These cross linkages may appear in between the chains

(inter-chain) or between different parts of the same chain (intra-chain). The cross

linkages play an important role in the physical as well as mechanical properties of the

wool fibre. For example, when a wool fibre is stretched (by up to 30%) and then

released, it returns to its original length. This is due to the presence of inter-chain

cross linkages.

In the dyeing process water molecules penetrate into the fibre and make it swell. This

disrupts the inter-chain bonds and consequently facilitates the penetration of the dye

molecules into the wool fibre.

Various types of bonding contribute to the chemical and physical characteristics of

wool. Some of which are:

Table 1.1 Amino acid composition of wool Amino acid Chemical structure Abundance

(mole % ) Nature of the side-chain

Histidine

NHN

CH2NH2

COOHCH

0.9 Basic

Arginine

NH

O

NH

NH2

NH2

OH

6.8 Basic

Lysine

NH2

COOHCH

H3N

3.1 Basic

Methionine

NH2

COOHCH

CH2CH2

SH3C

0.5 Sulphur-containing

Cysteine CH

COOH

NH2

CH2HS

10.5 Sulphur-containing

Tryptophan

CH2NH2

COOHCH

HN

N/A Heterocyclic

Proline

NH

COOH

5.9 Heterocyclic


Non-covalent Bonds

Non-covalent bonds are secondary bonds, which may occur within a single protein

chains or in between different chains. The non-covalent bonds can be divided into

three groups: hydrogen bonds, ionic bonds and hydrophobic bonds.

A. Hydrogen Bonds

In wool hydrogen bonding occurs between –CO and –NH groups in the peptide chain

and the amino and carboxyl group in the side chains. There is a great amount of

hydrogen bonding within the α-helix in wool.

B. Hydrophobic Interactions

Hydrophobic interactions are formed between two non-polar side-chains, with the

liberation of a water molecule. Hydrophobic bonds are important in the mechanical

strength, setting and smooth drying of wool28.

C. Ionic Bonds

In wool there is approximately the same number of basic amino and acidic carboxylic

groups both of which may carry a charge depending on the pH. An ionic attraction —

also know as a ‘salt link’ may be formed between oppositely charged regions of the

protein. Ionic bonds together with hydrogen bonding are important in the physical

characteristics of the wool fibre. However, upon wetting the fibre these types of

bonds are progressively broken.

1.4 Wool Processing


Between being on the sheep's back and being made into the finished fabric, wool is

subjected to various chemical and physical treatments to improve its aesthetic

properties. The exact treatments that are used depend on the nature of the raw wool

and the intended end-use for the fabric. There are two stages in wool treatment:

processing normally followed by finishing. Wool processing involves various

treatments on the wool fibres. Some of these treatments have been briefly described

here.

Scouring

As much as 50% of the mass of newly-shorn wool is made up of wool wax and suint

— the secretions of the sebaceous and sebiferous glands respectively — as well as

dirt, vegetable matter and other detritus that the sheep has picked up in its travels. In

the scouring process the bulk of this contamination is removed by agitating the wool

in a hot aqueous solution of detergent. Lanoline may be recovered from the wool wax

as a valuable by-product of scouring.

Carbonising

The remaining vegetable matter may be removed from the wool by a technique known

as carbonising in which the wool is soaked in a dilute solution of sulphuric acid, dried

and then baked to about 125 °C for a minute. This degrades any cellulosic material to

dust, which is removed by milling. The excess acid is then removed by rinsing and

neutralising the wool.

Carbonising may be done either before or after dying though it is generally most

economical if the carbonising is done first. The process of carbonising reduces the

uptake of acid dyes because it increases the acidity of the wool environment. If the


wool is to be dyed before being carbonised, the dye must be fast to the rigorous

conditions of the carbonising process29.

Shrink proofing

The surface of wool fibres is covered with scales which all point toward the distil end

of the fibre. This results in what is known as the Differential Friction Effect (DFE)

whereby the fibre experiences greater friction when it slides in the distal direction

than when it slides in the proximal direction. When wool is agitated or put under

pressure in water the DFE causes the fibres to move together and become entangled.

This process is known as felting. In woven fabric, felting causes the fabric to shrink

and become denser. There are two main types of shrinkproofing processes:

degradative and additive 12,30.

Mothproofing

Untreated wool is subject to attack by the larvae of clothes moths and other insects.

To prevent insect damage, the wool may be treated with standard insecticides. If the

mothproofing is to last the life of the fabric the insecticide must be bound to the fibre

in the same manner as a dyestuff is bound to the fibre. Common organochlorine

insecticides may be applied in the same manner as acid dyes and, in practice, a small

amount of insecticide is often added during the dying process.

1.5 Finishing

Many finishing processes are conducted in aqueous environment; therefore sometimes

they are referred to as wet processing. Some of these processes are mentioned below:


Bleaching

Bleaching is the process by which the fabric is “whitened”. This is done for the

purpose of the production of pale or very bright shades on the fabric or yarn.

Dyeing

What is a dye?

Dyes are organic molecules in an aqueous solution, which selectively absorb light in

the visible spectrum. A dye molecule has three different chemical groups in it: the

chromophore, auxochrome and solubilising group. The chromophore lends the colour

while the auxochrome determines the intensity of the hue and provides a site for the

attachment of the dye to the fabric. The solubilising group assists the dye molecule to

become water-soluble.

Dye Types Suitable for Wool

Dyes may be classified by their chemical composition, or by the method of its

application to the fabric. Here, they have been classified according to their

application.

Several different types of dyes may be used in dyeing of wool. They are acid dyes,

mordant dyes and reactive dyes.

A. Acid dyes


Acid dyes are used for wool as well as for the other protein fibres and polyamides.

They are so called because they are applied to the fibre using an acidic or neutral dye

bath (pH ≥ 7.5). Acid dyes offer the widest range of shades in the wool dyes.

Acid dyes may be classified as:

1) Level-dyeing or equalising acid dyes

Acid levelling dyes are of small to moderate molecular size, applied in a strong

acid bath (pH 2.5-4.0).

2) Intermediate dyeing dyes

These dyes are applied from a weaker dye bath (pH 3.5-5.0). The dye bath

normally contains sodium sulfate.

3) Acid milling dyes

Acid milling dyes owe their name to their high fastness to the wool fibre and the

milling process.

These dyes generally have large molecular size and a very high affinity for the

wool fibre. Acid milling dyes are applied from a neutral or weakly acidic dye bath

(pH 4.5-6.5)

Among the acid dyes, the most popular chromophore is the azo group. Some

examples of dyes with the azo chromophore are shown in Figure 1.3.


Figure 1.3 Dyes with Azo Chromophores

N N

HOSO3Na

H3CCOHN

N N

HO SO3Na

SO3Na

B. Mordant dyes (or chrome dyes)

Chrome dyes are also referred to as Mordant dyes since in order to increase their

affinity for the fibres a mordant (such as a metallic salt) is necessary. Mordant dyes

normally contain an –OH or –COOH group which can form bonds with the metal ion

in the fibre to create a large molecule. Mordant dyes are subdivided into four groups:

• Anthraquinone

They generate the shades blues, reds and browns. The parent dye of this group

is alizarin:

O

O

OH

OH

Alizarin is a polygenetic mordant dye i.e. it produces different hues when used in

conjunction with various mordants.


• Azo

This subtype contains the biggest number of the mordant dyes. They have high

fastness to light and to wet treatment.

• Triphenyl methane

These produce mostly bright violets and blues and they have moderate fastness

to light on wool.

• Xanthene

Xanthene dyes display mostly red hues.

C. Metal complex (pre-metallised dyes)

In these dyes a metal atom, mostly chromium, is chelated to one or two molecules of a

monoazo dye containing –OH, -NH2, -COOH groups. Metal complex dyes yield

duller shades than non- metallised and reactive dyes. They are however brighter than

mordant dyes.

D. Reactive Dyes

One of the most used dye types for dyeing wool fibres is the reactive dye31. Reactive

dyes are applied to wool from a weakly acidic dye bath (pH4-6). They are different

from the other wool dye types in that they bind covalently to the fibre.

Reactive dyes were first produced in 1956 and were developed for dyeing cotton.

They have found particular utility in the dyeing of wool/ polyester blend.


The chemical structures of some of the reactive dyes that can be applied to wool are

shown in Table 1.2.

Table 1.2 Some Reactive Dyes

Structure

Reaction Group Trade Name Manufacturer

D-SO2CH=CH2

Vinyl sulfone Remazol/ Remalan

Hoechst

D-SO2 CH2 CH2 OSO3 H

β-sulfatoethyl sulfone

Remazol/ Remalan

Hoechst

D-NHCOCH2Cl

chloroacetamide Cibalan Brilliant CIBA-GEIGY

D-NHCOCH=CH2

(Metal complex)

Acryl amide procilan ICI

NH CH2

BrO

D

α-bromo-acrylamide

Lanasol CIBA-GEIGY

D SO2N

SO3H

CH3N-methyl taurine-ethyl sulfone

Hostalan Hoechst


In the wool fibre, reactive dyes react with the terminal amino groups, OH, SH and

NH3. This can happen in two ways:

Nucleophilic displacement reactions such as:

XHNN

Cl NH

Cl

D

+NN

X NH

Cl

D

X=NH, O, S

subs

trate

subs

trate

And addition reactions such as:

subs

trate

NH + DS

O

O-

subs

trate

XS

O

OD

X=NH, O

The first Lanasol dye was produced in 1966. The name Lanasol is a trademark of the

company Ciba-Geigy. It generally refers to α,ε-dibromopropinylamide dyes which are

the precursor of α-bromoacrylamido dyes.

α-bromoacrylamide dyes have the potential to undergo both displacement and

addition reactions but it has been shown32,33 using model theory, that Lanasol dyes


react with α-amino groups to produce aziridine derivatives. It is postulated that the

bromine is the leaving atom in this reaction:

subs

trate

NH2 +

CH2

Br

O

NH D

N

CH3

O

NH D

.

subs

trate

Overall, these dyes have good wet treatment and light fastness. They produce bright

colours and are highly reactive.

Binding Sites in Wool for Reactive Dyes

The three important reactive groups in the wool fibre are peptide bonds, disulphide

cross-links and the side chains of the amino acid residues. This makes the wool fibre

prone to different chemical reactions.

Numerous studies have been done to establish the sites in a wool fibre where a

reactive dye molecule can attach itself. For example, the behaviour of the reactive dye

and the fibre has been studied34 by modifying some of the amino acid residues in wool

and studying the reaction between the dye molecule and the model structure of

different amino acid residues of wool. While another approach is the analysis of acid

hydrolysates of dyed wool35 showing the extent of the reaction as well as the sites of

the reaction of the dyed wool.

In general, these studies have concluded that the location of the dye reaction in the

wool fibre depends on:


• The binding sites available

• The electrostatic charge distribution in the fibre, and

• The steric hindrance in the fibre, which restricts the diffusion of the dye to some

positions.

The variation in the reactivity of different sites in the wool fibre can be understood on

the basis of their net electrostatic charges and the functionality of their side chains.

For instance, the high sulfur proteins in wool carries overall positive charge attracting

anionic dyes readily. While the low sulfur proteins in the wool carry an overall

negative charge, which causes a barrier to the diffusion of dye into the fibre. Overall,

however, the charge effect does not stop the reaction of the dye with the fibre but

rather it retains it.

Finally MacLaren et.al.35 have concluded that, in the reaction of wool and the reactive

dyes, cysteine, lysine, histidine and N-terminal residues of wool are the main binding

sites, regardless of the type of reactive dyes.

In this project, woollen fabrics dyed with reactive dyes as well as wool/ polyester

blend fabrics dyed with a mixture of reactive dye and disperse dye were studied.


Dyeing Wool/ Polyester Blends

Blending synthetic and natural fibres enhances the aesthetic properties of the fabric

and makes it more economical. During the past fifty years blending of wool with

nylon, polyester and acrylic fibres has become very important36.

Polyester fibre is made of condensed ethylene glycol and dimethylterephthalate

(terephthalic acid), followed by polymerisation:

H3COOC COOCH3 + n HO(CH2)2OH

COH3COOC COO(CH2)2O H[ ]n + (2n-1) CH3OH

Non-ionic disperse dyes such as those in Figure 1.4, which were originally made for

cellulose are used to dye polyester.

Wool/ polyester blends are normally dyed with a dye mixture that contains disperse

and wool dye. The samples studied in this project, were dyed using metal complex

and disperse dyes. Dyeing of wool/ polyester blends however, can present problems

since the polyester fibres have to be exposed to elevated temperatures (around

135°C), which can cause damage to the wool fibre. In order to overcome this

problem, carriers are introduced so that the polyester fibre swells at approximately

100-105°C and the disperse dye can penetrate the polyester fibres37. The polyester

fibres used in the wool/polyester blend are of low crystallinity. Although this reduces


the tensile strength of polyester, it increases the affinity of these fibres towards

disperse dyes. In the Raman spectrum the crystallinity of PET can be observed in the

narrowing of the carbonyl band near 1730 cm-1. Adsorption of the disperse dye in the

polyester fibre is through π-binding via the aromatic nuclei and hydrogen bonding

with the polyester fibre.

Figure 1.4 Some Disperse Dyes

N

N

OH

CH3

NH

CH3

O

O

O

NH2

NH2

NH2

NH2

(i) CI Disperse Yellow 3 (ii) CI Disperse Blue

Irradiated Dyed Wool Fabric

In the textile industry, light fastness is one of the most important properties of a dyed

fabric. This is dependent on many factors, such as the photo-stability of the dye

chromophore and the effect of the chemical environment of the substrate on its

stability38. For example, one of the problems faced by dyers is that the tip of the wool

fibre is weathered and therefore it does not have the same affinity for the dye as the

rest of the fibre39.

The Lanasol group of dyes offer excellent light fastness. The fixation of this dye to

the wool fibre is achieved by the formation of covalent bonds between the reactive


group of the dye and one or more of various sites on the wool fibre40, 41. Of these sites,

sulphur-containing and basic amino acids are highly sensitive to photo-oxidation

followed by aromatic amino acids and hydrophobic amino acids respectively42. In the

process of photo-degradation, the original colour on the fabric fades, indicating

chemical changes in the dye molecule. It has been shown41 however that the actual

bonds formed between the wool fibre and the reactive dyes are not broken.

Baumann43,44, has indicated that certain reactive dyes bonded to the side chain of wool

slow the damage of the fibre caused by photo-oxidation, while they can accelerate it if

attached to the skeletal polypeptide chain. Baumann has also argued that the results

obtained by various research groups may vary to some extent since the irradiation

conditions would not be the same causing very small differences in the amino acid

content of the wool. Rusznák et al.41 have demonstrated that the number of amino acid

side chains increasing after irradiation are arginine, aspartic acid, glutamic acid,

serine, threonine; while the number of amino acid side chains decreasing after

irradiation of dyed wool are cystine, tryptophan and tyrosine. They have also

indicated that leucine, isoleucine, methionine, serine, tyrosine and valine become

protected against photo-degradation when the wool fabric is dyed with reactive dyes.

Therefore, it is likely that studying the vibrational spectra of the irradiated dyed

fabrics may reveal these changes. In a previous study43 on fading the dyed samples

were visually evaluated against a series of commercial standards. It was concluded

that such evaluations are very difficult since wool is strongly yellowed and the hue of

the coloured samples was perceptibly greener after irradiation. It may, therefore, be

preferable to study fading with vibrational spectroscopy and chemometrics, since

there is no sample preparation and it can separate substance and dye effects out.


1.6 Colour Matching

What is Colour?

International Lighting vocabulary defines colour as: “ The characteristics of visual

sensation which enables the observer to distinguish differences in the quality of the

sensation of the kind which can be caused by differences in the spectral composition

of the light.”

Prediction of colour according to its spectrum only, is not always conclusive. This is

because the sensitivity of the human eye changes over the visible spectral range.

Two objects may have quite different visible spectra but may appear to have the same

colour under certain lighting conditions. Under different lighting conditions, these

same two objects may appear to have very different colours.

Billmeyer and Saltzman have described colour as “the spectral power distribution

curve of the light source multiplied by the spectral transmittance or reflectance curve

of an object multiplied by the spectral response curve of the eye”45.

Colour and Colour Measurement in Various Industries

In the 19th Century after the first synthetic dye was produced, a whole new chapter in

various industries begun. Colour photography was discovered in 1907. Colour

television was invented in 1928 bringing colour into every living room.

Today different industries use the psychological influences of colour to manipulate

consumers with the latest shades for cosmetics, fashions and cars46. The colour of the

product, therefore, is an important property of it.


Colour has become an important property to be considered in industry. It is important

for producers to be able to control the shade of colour on their product as well as

being able to match the colour desired by the consumer. The exact imitation of colour

today is a combination of art, skill and science.

For example, the paints and coating industry needs a fast and accurate colour

formulating process since no visual distinction between the original and the finished

product is possible47. In the ink industry the equation exists: the more colour control,

the less waste and the greater productivity48.

In the leather industry, the use of colour-measurement instrumentation is well

established49. In the textile industry, the market demand for more attractive colours

has caused the producers and designers to concentrate more on this part of the

manufacturing. Colour matching of textiles is also an important field of forensic

science.

Colorimetry

Although colour is a sensory property dependent upon the properties of the eye and

the brain of the observer, it is essential to be able to define it quantitatively. The

science of quantitatively defining and measuring the sensation of colour is referred to

as colorimetry.

The CIE system defined in 1931 by the International Commission on Illumination50

describes a colour in terms of the relative amounts of the three primary colours: red,


green and blue. Because these are not always additive, three virtual colours X,Y and

Z — the tristimulus values —are used. The tristimulus values are found using the

CIE distribution coefficients λx , λy and λz which are defined such that a mixture of

red, green and blue in the proportions λx : λy : λz is perceived as identical to

monochromatic light of wavelength λ.

The virtual primary colours may be obtained from the tristimulus values by

integrating over to visible spectrum as:

∫= λλxREX λλλ d

∫= λλyREY λλλ d

∫= λλzREZ λλλ d

where Eλ is the illumination energy at λ

and Rλ is the reflectivity at λ.

In order to show colours on a two dimensional plot, chromaticity coordinates x, y, z

are defined as x=X/(X+Y+Z), y=Y/(X+Y+Z) and z=Z/(X+Y+Z). The plot of x versus

y as shown in Figure 1.5 is known as the CIE chromaticity diagram.


Figure 1.5 The CIE Chromaticity Diagram

The colours of the visible spectrum lie on a parabolic curve in the chromaticity

diagram and the achromatic colours (white, black and grey) are at the centre. The

straight line at the bottom of the diagram represents the non-spectral purple colours. A

luminosity coordinate may be added perpendicular to this diagram to create the CIE

colour solid.

Before the introduction of automation to colour matching, when a new target colour

was to be matched, the master shader would compare the given colour with colours on

file. Based on the visual assessment of the colour differences, a recipe closest to the

target colour was chosen and then adjusted until an acceptable match was obtained.

0.5 1.0

0.5

1.0 550

700

x

purple blue

green

λD

400 y

w

C

F


It is now possible to measure the reflectance over the visible spectrum of a sample

panel using a spectrophotometer and, using a microcomputer, to determine CIE

tristimulus value with standard observer and the standard illuminant51. This procedure

still has its faults and a skilled colour master is still needed to make the final decision

and fine alterations.

In this project vibrational spectroscopy combined with chemometrics were used in

order to explore the colour matching of dyed wool.

1.7 Vibrational Spectroscopy

FT-IR spectroscopy

Vibrational spectroscopy has found extensive use in the study of biological materials.

Human skin, hair and nail as well as animal fibres and tissues have been investigated

using various vibrational spectroscopic methods such as attenuated total reflectance

(ATR), Photoacoustic spectroscopy (PAS) and infrared microscopy. A great

advantage of these techniques is that they are non-destructive and require little or no

sample preparation. Samples can be studied in situ, which is important in the

investigation of ancient artefacts or forensic evidence.

PAS and ATR have been used for quantitative and qualitative analysis of polymer

finish on wool.52 Of these two methods, PAS was found to be superior for the

characterisation of the bulk of the sample while ATR is more sensitive to the surface.

ATR was also able to detect a much lower percentage of fluorocarbon polymers on

wool than PAS. The mirror velocity used in this study was 0.05cm.s-1, which


according to the Rosenewaig equation53 corresponds to a thermal depth of 10.5µm at

1200cm-1.

The FT-IR PAS and FT-Raman vibrational spectroscopic techniques were employed

in this project.

Rosencwaig developed photo-Acoustic Spectroscopy54 in the 1970s. It is particularly

useful for obtaining mid-infrared spectra of optically opaque or highly scattering

solids and semi-solids55. The principle of spectrum acquisition in PAS is as follows:

The sample is placed in a chamber connected to a very sensitive microphone. It is

then purged using an IR-transparent gas (usually high-purity helium). Irradiation of

the sample with modulated infrared light causes absorption of radiation by the sample.

This causes an increase the temperature of the surface of the sample and creates a

thermal wave through the sample. The release of the thermal energy into the

immediate environment (i.e. the He gas) generates a pressure variation in the gas,

which is detected by the microphone. The signal from the microphone is recorded as a

function of the wavelength of the incident light56. Carbon black is used as a reference

to correct for the system response.


Figure 1.6 Schematic layout of the PAS Sample Compartment

Microphone

νSampleGas

dx

x

The intensity of the photo-acoustic signal is primarily determined by the optical and

thermal properties of the sample. Rosencwaig and Gersho have defined the optical

absorption length as:

( ) ( )21

21

22ss

ssc

ks ρωω

αµ =

Where α is the thermal diffusivity (cm2s-1)

ω is the angular modulation frequency (rad/s)

µs is the thermal diffusion depth

ks is the thermal conductivity

ρs is the density (g/cm3)

cs is the specific heat capacity (cal. g-1.K-1)

Because the angular modulation frequency ω is determined by the mirror velocity (ν)

and the radiation wave number (ν) as ω = (4πν ν), the thermal diffusion depth is


also a function of the mirror velocity. This allows depth profiling whereby the mirror

velocity is varied to measure the spectrum at different depths from the surface of the

sample. When the mirror velocity is increased, it reduces the diffusion length and

hence causes the spectrum to be recorded from a layer closer to the surface of the

sample.

This technique has been used to study the surface of wool fibres57,58,59. The

near-surface layers of wool have been shown to have different spectra to the bulk of

the wool fibre and it has been postulated that this is due to the difference in protein

composition between the cuticle and the cortical cells57,58.

PAS has also been used to study the surface of treated wool fibres and fabrics 59. This

is achieved by depositing a thin layer of polymer on the surface of the wool fibre.

The PAS studies show that the polymer is mainly accumulated on the surface with

very little penetrating the bulk of the fibre. PAS used in conjunction with spectral

subtraction was also used to measure the total amount of polymer deposited on the

fibre.

In 1980 L.H. Lee56 used PAS along with UV-Visible spectroscopy to study the

adhesion and adsorption of dyes onto polymers. This paper foreshadows the potential

of this technology to become useful in the fields of coatings, inks and dyes on textiles.

PAS studies60,61 of cotton, wool and polyester as well as wool/polyester blend fabrics

show that these materials have different spectra with the spectrum for the blend

possessing bands from both wool and polyester. It was also shown that the yellowing


of the sample by exposure to sunlight could be explained by the disappearance of

fluorescence whitening agent (FWA) bands from the photo-acoustic spectrum.

Raman Spectroscopy

In 1928, Sir Chandrasekharta Venkata Raman discovered, experimentally, the effect

of change in the frequency of scattered radiation of the liquids for which he was later

awarded a Nobel Prize62. Since then different ways of applying this theory to study

different molecules have been developed. Since the 1970’s lasers have been used as a

source of intense monochromatic radiation for Raman excitation.

o Theory of Raman Spectroscopy

In Raman spectroscopy, intense monochromatic light is irradiated onto the sample.

The light scattered from the sample is then studied.

Most of the scattered light is due to elastic scattering known as Rayleigh scattering.

Rayleigh scattering has the same frequency as the incident light. A much smaller

amount of the light is also scattered inelastically by Raman scattering. Raman

scattering is very much weaker than Rayleigh scattering and is generally on the order

of 10-5 of the intensity of the incident radiation. The frequency of the Raman scattered

light is shifted slightly from that of the incident light. Its frequency is given as νo±νm;

where νm is one of the vibrational frequencies of the molecule. The set of scattering

lines with frequencies νo-νm are called Stokes lines and those at νo+νm are called

anti-Stokes lines. These transitions are illustrated in Figure 1.7.


In Rayleigh scattering, the molecule is raised from the ground state to the excited state

and then relaxes to the same ground state and emits a photon of the same energy. In

Raman scattering, the molecule is excited to a higher energy state but when it returns

to the ground electronic state, it goes into a different vibrational state and hence the

emitted photon has a different frequency to the absorbed photon. A Stokes line is

produced when the molecule is excited from the ground vibrational state and returns

to a higher vibrational state. An anti-Stokes line is produced when the molecule is

excited from a higher vibrational state and returns to the ground state.

According to the Maxwell-Boltzman distribution law, the population of the ground

vibrational state is much greater than the population of any of the higher vibrational

states. Since only molecules in excited vibrational states can produce anti-Stokes

lines the Stokes lines are stronger than the anti-Stokes lines.


Figure 1.7 Energy Level Transitions of Rayleigh and Raman

Scattering

RaylieghScattering

GroundElectronic State

ExcitedElectronic State

Raman Scattering

νονο νο νο−νm

vibr

atio

nal

stat

es

vibr

atio

nal

stat

es

νο νο+νm

anti-StokesStokes

Raman spectroscopy may also be explained in classical theory. The electric field

strength (E) of the beam, which is an electromagnetic wave, varies with time (t) as:

tEE oo πν2cos=

Where Eo = The vibrational amplitude

νo = The frequency of the laser

Now, if this light irradiates a diatomic molecule, an electric dipole moment is

produced in the molecule:

tEEP oo πναα 2cos== (1)

Where α is the polarisability.


If the molecules are vibrating with frequency νm, the nuclear displacement (q) can be

written as:

tqq mo πν2cos= (2)

where qo is the vibrational amplitude.

For a small qo, the polarisability is a linear function of q. That is:

oo

o qq

∂∂

+=ααα (3)

Where αo is the polarisability at the equilibrium position and (∂α / ∂q )o is the rate of

change of α with respect to the change in q at the equilibrium position.

Combining equations (1), (2) and (3) we obtain:

[ ]})(2cos{})(2cos{)(212cos ttE

qtEP momoooooo ννπννπαπνα −++

∂∂

+=

Where the first term stands for an oscillating dipole which radiates light of frequency

νo (Rayleigh scattering) while the second term represents the Raman scattering

anti-Stokes (νo + νm ) and Stokes line ( νo - νm ).

If the ratio (∂α / ∂q )o is zero (i.e. if the polarisability does not change with the

displacement of a vibrational mode), the second term will not exist and hence the

vibration will not be Raman active.


Raman spectroscopy has been used for the study of a wide variety of subjects. For

example, biological material such as proteins, polypeptides and amino acids63 were

investigated using Raman spectroscopy.

The first detailed study of wool using this technique was by Lin and Koenig64.

Shishoo et. al.65 studied un-stretched, stretched and annealed wool fibre using Raman

spectroscopy, in order to find out the structural changes in the fibre upon annealing.

Hsu et. al.66 investigated the conformation of the wool fibre using Raman

spectroscopy.

Studies67,68 have also been done on the chemical interactions between the wool fibre

and various chemicals used in different chemical treatments of wool.

All the above-mentioned studies have been conducted using Raman spectroscopy.

Since the introduction of FT-Raman however, a few papers have also been published

on FT-Raman spectroscopy of wool69 and UV-treated wool70,71.

Lee-Son and Hester72 studied the dye-fibre interaction between two Lanasol reactive

dyes with untreated wool using laser Raman spectroscopy. In this paper, they argued

that this technique is particularly a useful one for the analysis of dyes; since the dye’s

intense absorption of visible light leads to enhanced sensitivity. They have also

identified the new peaks arising from the dye molecule on the wool fibre

demonstrating that it is plausible to study dye-wool fibre system in situ.


Later, Keen73 demonstrated that using FT-Raman micro-probe it is possible to obtain

spectra in Situ of dyed wool/ polyester fabric. She argued that although the spectra are

quite similar, some differences were observed in the dye region.

Cheng74 on the other hand, investigated the same set of samples using DRIFT

spectroscopy. She concluded that there is hardly any visual difference among the

spectra. She then tried to study these samples using chemometrics, whereupon she

concluded that it is not possible to discriminate the samples using PCA and PLS,

either.

1.8 Chemometrics

The principles behind chemometrics are explained in detail in chapter two. Briefly,

chemometrics is the application of numerical analysis to chemical data. In this study,

Vibrational Spectra data are analysed using Principle Component Analysis (PCA) as

well as Partial Least Square (PLS). In both cases the goal is to extract useful

information from a large mass of spectral data.

PCA is a qualitative method in which a large number of samples are separated into

one or more classes on the basis of the similarities of their spectra.

PLS is a quantitative method in which the concentration of multiple analytes may be

determined spectroscopically. It requires an initial calibration with a training set of

samples of known analyte concentrations.


In this thesis, woollen and wool/ polyester fabrics (both dyed and undyed) were

investigated using vibrational spectroscopy; namely FT-IR PA and FT-Raman. Some

of these samples were then UV-irradiated and studied with the same techniques. The

spectral data were also investigated quantitatively as well as qualitatively using

chemometrics. A literature review indicates that this is the first time such an approach

has been taken.


1.9 References

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Richards (ed.), 27, (New York: Academic Press, 1973), p. 111. 6 P.G. Gohl, L.D. Vilensky, ‘Textile Science’, p.44. 7 H.M. Appleyard, C.M. Grevelle, Nature, 166, 1950, 1031. 8 E. Lehman, Mellinand Textilber, 22, 1941, 145. 9 R.D. Fraser, N.L. Jones, T.P. Macrae, E. Suzuki, P.A. Tulloch, ‘Proc. 6th internat. Wool Text.

Res. Conf.’, Pretoria, Vol. 1, 1980. 10 J.H. Bradbury, K. F. Ley, Australian J. Biol. Sci., 25, 1972, 1235. 11 J. L. Bradbury, G.V. Chapman, N.L.R. King, Australian J. Biol. Sci., 18, 1965, 353. 12 J.A. MacLaren, B. Milligan, ‘Wool Science, The Chemical Reactivity of the Wool Fibre’,

Science Press, Ch.1. 13 J.D. Leeder, Wool Sci. Rev., 63, 1986, 3 14 P.R. Blakey, R.Guy, F. Happey and P. Lockwood, ‘The Effect of Chemical Modifications on

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15 T.Kondo, Text. Res. J., 23, 1953, 373. 16 E.H. Mercer, Text. Res. J., 23, 1953, 388. 17 J.H. Dusenbury, A.B. Coe, Text. Res. J., 25, 1955, 354. 18 J. Menkart, A.B. Coe, Text. Res. J., 28, 1958, 218. 19 R.O. Hall, J. Soc. Dyers Col., 53, 1937, 341. 20 J.D. Leeder, J.A. Rippon, F.E. Rothery and I.W. Stapleton; Proc. 7th Internat. Wool Text. Res.

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39 L.A. Holt, L.N. Jones, I.W. Stapleton, ‘Interactions Between Wool Weathering and Dyeing’, Proceedings of the 8th International Wool Textile Research Conference, N.Z., Vol. IV (1990), p. 117.

40 D.M. Lewis, J. Soc. Dyer Color. , 98 (5-6), 1982, 165-176. 41 I.Rusznàk, J. Frankl, J. Gombkötő, J. Soc. Dyer Color., 101, 1985, 130-136.


42 A.S. Davie, W.H. Leong, D.J. Tucker, J.S. Church, ‘FT-Raman and FT-Infrared Studies of

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43 H. Baumann, J. Soc. Dyer Color., 90, 1974, 125-129. 44 H. Baumann, ‘The Effect of Reactive Dyes on the Main-chain Scission of Wool on Exposure

to Light’, J. Soc. Dyer Color., 90, 1974, 326-328. 45 F.W. Billmeyer, Jr., M. Saltzman; ‘Principles of Colour Technology’, 2nd ed. (John Wiley &

Sons, 1981), p.17. 46 National Geographic, Vol. 196, No. 1, 1999. 47 D. Spitzer, R. Gottenbos, P. van Hensbergen, M. Lucassen, 29, 1996, 235-238. 48 P. Pugh, American Ink Maker, 75(6), 1997, p.p. 65-66. 49 J.C. Crowther, J. of the Am. Leather Chemists Association, 84(6), 1989, 184-199. 50 R. Zbinden, ‘Principles of Colorimetry’, Colorimetry, (1962,Basle). 51 C.J. Sherman, Polymer Paint Colour Journal, 177, 4190, 1987, 296. 52 J.S. Church, W-H. Leong; ‘ The Analysis of Wool Textile Blends by FT-IR PAS and FT-

Raman Spectroscopies’, ‘9th Intn. Wool Text. Res. Conf.’, Biella, Italy, IV (1995), pp. 114-122.

53 A. Rosencwaig, E. Pines, Biochimica Biophysica Acta, 493, 1977, 10-23. 54 A. Rosencwaig, ‘Photoacoustics and Photoacoustic Spectroscopy’, (John Wiley and Sons,

New York, 1980), pp. 94-124. 55 J.M. Chalmers, M. W. Mackenzie in ‘Advances in Applied Fourier Transform Infrared

Spectroscopy’, M.W. Mackenzie (ed.), (John Wiley & Sons Ltd., 1988), Ch. 4. 56 L.H. Lee, “Photoacoustic Spectroscopy For the Study of Adhesion and Adsorption of Dyes

and Polymers”, Polymer Science and Technology Series, 12(A), 1980, pp. 87-102. 57 L.E. Jurdana, K.P. Ghiggino, I.H. Leaver, P. Coleclarke; Applied Spectroscopy, 49 (3), 1995,

361-366. 58 L.E. Jurdana, K.P. Ghiggino, I.H. Leaver, C.G. Barraclough, P. Coleclake; Applied

Spectroscopy, 48 (1), 1994, 44-49. 59 E.A. Carter, P.M. Fredericks, J.S. Church; Textile Res. J., 66, 1996, 787-794. 60 W.P. McKenna, D.J. Gale, D.E. Rivett, E.M. Eyring, Spect. Lett., 18 (2), 1985, 115-122. 61 R.S. Davison, D. King, J. Soc. Dyer Color, 99, 1983, 123-126. 62 J. Twardowski, P. Anzenbacher, ‘Raman and Infrared Spectroscopy in Biology and

Biochemistry”, 1st Ed., (Polish Scientific Publishers, Warsaw, 1994). 63 C.L. Putzig, M.A. Leugers, M.L. McKelvy, G.E. Mitchell, R.A. Nyquist, R.R. Papenfuss, L.

Yurga; Anal. Chem., 64 (3), 1992 64 B.G. Froshour, J.L. Koenig in ‘Advances in Infrared and Raman Spectroscopy’, R.J. Clark ,

R.E. Hester (ed.), Volume 1, (Heyden, London, 1975). 65 R. Shishoo, M. Lundell, Journal of Polymer Sciences, 14, 1976, 2535-2544. 66 H.L. Hsu, W.H. Moore, S. Krimm, Biopolymers, 15, 1976, 1513-1528. 67 R.H. Fish, J.R. Scherer, E.C. Marshal;, S. Kint, Chemosphere, 1 (6), 1972, 267-272. 68 I.H. Leaver, R.E. Hester, R.B. Girling, Text. Res. J., 58 (2), 1988, 182-184. 69 E.A. Carter, P.M. Fredericks, J.S. Church, R.J. Denning, Spectrochim. Acta, 49A, 1994, 1927-

1936. 70 L.J. Hogg, H.G.M. Edwards, D.W. Farwell, A.T. Peters, J. Soc. Dyer Color., 110, 1994, 196-

199. 71 J.S. Church, K.R. Millington, Biospectroscopy, 2, 1996, 249-258. 72 G. Lee-Son, R.E. Hester, J. Soc. Dyer Color., 106, 1990, 59-63. 73 I. Keen, ‘Forensic Application of Raman Microprobe’, Masters Thesis, Queensland University

of Technology, (1998), Chapter 4. 74 J. Cheng, ‘Characterisation of Wool Treated with Metal Ions’, Master Thesis, Queensland

University of Technology, (1993), Chapter 6.

Chapter 2 Experimental 40

Chapter 2

Experimental

2.1 Materials

Fabrics

Two types of fabric were used in these studies. The CSIRO Textile and Fibre Technology

located in Geelong, Victoria provided a set of twenty-four samples. These were plain weave

Merino woollen fabrics dyed with three types of Lanasol dyes: red, blue and yellow. Each

sample was dyed with various ratios of two or three of these colours, so that the ratios added to

10. For example, a sample referred to from here on as XYZ means that there are X parts of red,

Y ratios of blue and Z ratios of yellow dyes on the fabric, and where X+Y+Z = 10 ratios.

The other set of fabrics studied consisted of a 55:45 wool/polyester blend. They were

plain-weave fabrics dyed with Forosyn dyes (Sandoz Ltd., Switzerland) pre-set for 30 seconds at

190°C.

Three colour squares (or diamonds) were studied here. Each diamond has samples with various

combinations of three out of four colours present in that particular diamond. Appendix 1 shows

Diamonds 1, 2 and 3. As seen, each sample has a reference, which consist of three numbers. The

first one indicates the number of parts of one colour (yellow or green), while the next two give

the number of part of the other two colours (brown and grey, respectively). For example, a

reference number 600 represents Yellow colour, which is equal to 1.8% Forosyn Yellow 2RL.


The same rule may be applied to the other two diamonds as well. The colours available in each

diamond are as follows:

I) Diamond 1:

Forosyn Yellow 2RL, Forosyn Brown RL, Forosyn Green 2GL, Forosyn Grey 2BL

II) Diamond 2:

Forosyn Brown RL, Forosyn Yellow 2RL, Forosyn Grey 2BL, Forosyn Red BL

III) Diamond 3:

Forosyn Brilliant Orange R, Forosyn Green 2GL, Forosyn Red BL, Forosyn Brown RL

Fading of Colour on Woollen Fabrics by UVA Irradiation

Swatches of the pure wool samples described in section 1.1, were used to study the fading of the

colours on these samples. UVA irradiation was applied to nine samples selected out of the

twenty-four. These samples were selected so that they had various ratios of red dye. The colour

chosen was however picked out of the three at random and not for a particular reason. The

samples were irradiated for a period of seven and twenty-one days respectively. They were

observed at 24 hour intervals to monitor the fading rate of the colours. After the completion of

each irradiation period, the samples were stored in the dark until they were studied by

vibrational spectroscopy.


2.2 Instrumentation

UV Accelerated Weathering Instrument

The equipment used for this purpose was called SUNTEST CPS+ (W.C. Heraeus GmbH,

Germany).

The test chamber, which consists of a parabolic reflector, sample table, xenon lamp and photo

diode to measure the radiation of the lamp. The lamp and test chamber are air-cooled. The

specifications of the instrument are shown in Table 2.1

Table 2.1- Specifications for the Accelerated Weathering Instrument

Dose 453600kJ/m2

Distance from lamp axis to sample level Approx. 230mm

Irradiance for the wavelength range below 800nm for filter system "max. UV"

765W/m2

Sample swatches of the woollen fabric approximately 2×9cm were stapled onto a cardboard

mount and then placed in the sample holder of the instrument such that the fabric was 23cm

away from the illumination tubes.

2.3 Vibrational Spectroscopy Analysis

Both sets of samples were analysed using FT-IR Photoacoustic Spectroscopy (FT-IR PAS) and

FT-Raman spectrometry.


Sample Preparation

Circular pieces of dyed woollen fabric of approximately one centimetre in diameter were

prepared with a wad punch. These were then arranged in various manners with careful

attention to the direction of the warp and weft.

Three layers of samples were put on the top of each other such that their respective warp and the

weft were running in the same direction. Meyer1 has previously shown that samples with 90°

and 180° warp and weft orientation exhibit the smallest difference in energy in PAS, and give

identical spectra. While other orientations, such as when a sample is positioned with its warp at

45° angle to the next one, spectra with slightly different intensities were obtained.

Wool/polyester blend fabrics were also studied using FT-IR PAS. Three small swatches (approx.

5×5mm) were positioned in the sample holder. Again, the samples were positioned so that warp

and weft of each fabric was aligned with respect to those of the sample underneath. These

sample stacks were put in the middle of the phto-acoustic cell. The alignment of the sample with

the IR beam was aided by the use of a liquid crystal thermal imaging sheet supplied by the

manufacturer of the instrument.

After the acquisition of each spectrum, the top swatch was moved to the bottom of the stack

hence exposing the next sample for measurement.


Vibrational Spectroscopy Instrumentation

FT-IR Photo-Acoustic Spectroscopy

PA spectra were acquired with an MTEC Model 200 standard sample gas-microphone accessory

with an accompanying MTEC pre-amplifier power supply. The spectral signal was then

transferred to the FT-IR series 2000 signal processor.

A background spectrum of finely powdered pressed carbon black was collected at the beginning

of each session. Both the background material and the samples were purged for 10 minutes at

20cc.min-1 with UHP grade Helium. The chamber was then sealed and the spectrum recorded.

The correct purging time for the samples was determined by first testing different times until an

acceptable spectrum could be recorded. That was done on the basis of the water vapour content

in the spectrum.

The conditions at which the spectra were recorded are tabulated below and unless indicated

otherwise, these conditions were kept constant throughout the studies, for both types of fabric.


Table 2.2 Specification and operating parameters for the PE-2000 FT-IR spectrometer Detector MTEC

Wave number range 4000-450cm-1

Number of Scans 128

Apodisation Strong

Resolution 8.0cm-1

OPD velocity 0.2cm.s-1

Interferogram Bi-directional, double sided

Acquisition time 10 min

MTEC Pre-AMP Gain 2.0

Phase correction Self, 256 pts

Three swatches were taken from each fabric to ensure that there was no variation across the area

of the fabric. Spectra were recorded for each of these swatches on two different days to ensure

that there was no variation over time.

FT- Raman Spectroscopy

The samples prepared for FT-IR PAS method were also examined by FT-Raman spectroscopy.

Samples were studied using Perkin Elmer System 2000 NIR/ FT-Raman spectrometer. This

instrument is equipped with Nd:YAG laser emitting at 1064nm.

Various conditions such as different laser power and mirror velocities were tried as well but it

was found that the below conditions were optimal. The spectra were then recorded six times for

three pieces of the same sample. The conditions of the recording spectra are tabulated below:


Table 2.3 Specification and operating parameters for the PE-2000 NIR/ FT-Raman

spectrometer

Laser Type Nd-YAG (1064 nm)

Laser Power 300mW

Phase correction Self

Interferogram type bi-directional, double sided

Detector InGaAs

Beam splitter Quartz

Gain 1.0

Wavenumber Range 3800-200cm-1

Resolution 8cm-1

Number of Scans 300

Mirror Velocity 0.2cm.s-1

Beer Norton Apodisation Strong

The optimum sample alignment was determined using BaSO4 (Hopkin and William, London).

2.3 Data Processing

Data Manipulation

All data manipulations in these studies were performed using the Grams 32 software package2,

Excel 97 and SIRIUS3 6.0. Data collected were transferred to Grams 32 and changed to ASCII

file type. The baseline was flattened and zeroed. (ie. a linear baseline was subtracted to bring

both ends of the spectrum to zero intensity). The data interval was increased by averaging over 4

cm-1. Since the region 1800-800cm-1 contained most of the peaks for wool, the spectra were

truncated to this region. Further studies of the spectra have revealed that the changes due to


various colours occur at ca.1500-925cm-1 for FT-IR PAS. Therefore, the data were studied in

this region.

Both the photoacoustic and the Raman spectra were also analysed as their first derivatives. First

derivative (9 Points, 7 degrees) spectra were obtained using the MacroWizard package in

Grams 32.

Data Pre-treatment

Before more detailed chemometric analysis is performed, it is often useful to pre-treat the raw

spectral data to make all of the spectra compatible with the chemometric process. The type of

pre-treatment that is required depends on the origin of the data and the context of the problem4.

There are two basic types of pre-processing: transformation of variables and transformation of

samples. In variable transformation, systematic changes across the columns of the data matrix

are removed. The most common form of variable transformation is column centring where the

mean of each column in the data matrix (X) is subtracted from every value in that column. ie.

m

XXX

m

1kki

ijcentred]-[column

ij

∑=−=

Where, m is the number of rows in the data matrix.

Depending on the nature of the problem5, it may be helpful to column-standardise the data by

dividing the column-centred values by the standard deviation of the column. i.e.


m

m

XX

XX

m

1k

m

1lil

ik

centred][columnij[standard]

ij

∑∑

=

=

−

−

=

Sample transformation follows similar procedures to variable transformation with the

transformation happening across the rows of the data matrix rather than the columns.

Data may be row-centred. Since almost all data is column-centred, this is known as doubled-

centring.

n

XXX

n

1k

centred][columnin

centred][columnij

centred][doubleij

∑=

−

−− −=

Double-centred data sets have no features in common throughout the data; they only show the

data variation.

The sample data may be normalised. This may be by an internal standard in which case the

value for the standard in subtracted from the corresponding data values. Alternatively, it may be

normalised to a constant sum in which case the normalised value ijX ′′ is given by:

∑=

=′′ n

1jij

ijij

X

XX


In this study, the data were imported into the Excel 5.0 spreadsheet for pre-treatment. Once in

the spreadsheet, the data matrix was double-centred, normalised and standardised as appropriate

before being transferred to the SIRIUS 6 software package for further chemometric analysis.

2.4- Chemometrics

There are two general objectives in the chemometric analysis of spectral data: qualitative

classification and quantitative measurement of component concentrations.

Qualitative analysis involves the classification of a spectroscopic sample into one or more

classes on the basis of its spectrum. The class is defined by a set of samples of known class (the

training set). The calculation of the quality of fit of the spectrum to the class may also be

possible. In this study, principal component analysis (PCA) and subsequent use of the SIMCA

technique were used for classification of spectral data.

Quantitative measurement requires first a calibration with a training set of multiple samples with

a known concentration of multiple analyte components and then the calculation of the

concentrations in an unknown sample in such as way as to minimise prediction error. In this

study, partial least squares analysis was used for quantitative measurement.


Principal Component Analysis

Principal Component Analysis (PCA) is a multivariate reduction technique in which the original

multivariate data is reprojected onto a set of orthogonal axes. The new projections are known as

the principal components (PCs).

When the raw data (Xij) consists of n objects described by m independent variables, we may

describe the data by a set of principal components (PCij) which are linear combinations of Xij.

∑=

=m

1kjkikij XaPC 3.1

where aik is the loading of the variable k on PCi.

When there are m independent variables, there will also be m orthogonal PCs in any given set

and all m PCs will be needed to fully describe the data.

In PCA the loadings are chosen such that the maximum amount of variance is described in the

first PC (PC1). The next largest variance is described by PC2 and so forth with progressively

decreasing variance down to PCm. In this way it is often possible to give a good description of

the data objects with only the first few PCs rather than a large number of independent variables.

Because most of the variance is described by the first few PCs, it is possible to display the data

in two-dimensional plots of one PC versus another. In this way subtle relationships between the

objects, which would otherwise be concealed by the mass of data, may be revealed.


Useful information may also be obtained by inspection of the loadings. For example, if two

classes of objects are effectively distinguished by a particular PC (PCi) then an inspection of the

loadings ai1…aim may reveal which of the original variables distinguish the two classes of object.

In the raw data this relationship may be obscured by all the other variables.

When PCA is applied to spectral data the objects are the individual spectra, the independent

variables are the wavelengths or frequencies and the raw values (Xij) are the spectral intensities.

The raw data matrix is therefore:

ν1 ν2 … νm

spectrum 1 I1

1

I1

2

… I1m

spectrum 2 I2

1

I2

2

… I2m

spectrum 3 I3

1

I3

2

… I3m

: : : … :

spectrum n In

1

In

2

… Inm

In a typical experiment, the number of spectra (n) is generally less than twenty while the number

of wavenumbers (m) is generally several hundred.


When PCA is performed, this is transformed into the PC data matrix:

PC1 PC2 … PCm

spectrum 1 PC11 PC12 … PC1m



: : : … :

Spectrum n PCn1 PCn2 … PCnm

Where the PCs are described by the loading factors aij :

PCij = ai1.Ij1 + ai2.Ij2 + … + aim.Ijm

Theoretically, the data has not been reduced — it is still an n×m matrix. If the data is amenable

to PCA however, it will be adequately described by only the first few PCs.

If, for example, the data is adequately described by three PCs , the data matrix is effectively

reduced to n×3 in size.

PC1 PC2 PC3

spectrum 1 PC11 PC12 PC13



: : : :

spectrum n PCn1 PCn2 PCn3


A two dimensional PCA plot may be made of one PC versus another in which each spectrum is

represented by a single point. Information about the relationships between spectra may be seen

in the groupings on the PCA plot. When a particular PC is found to separate two classes of

spectra, the loading spectra for that PC may be examined to see in which wavenumber region

these classes differ.

SIMCA

Soft Independent Modelling of Class Analogies (SIMCA) is a technique for the supervised

classification of PC data. In SIMCA, a number of object classes are defined and the PC scores

for each object are tested to determine how well they fit each class.

The degree to which a class is defined is given by the Residual Standard Deviation (RSD)

which, is defined as:

∑= −−

=cN

1n c

2n[c]

[c] 1PNε

RSD 3.2

where: Nc is the number of classes

P is the number of PCs in the model

and εn[c] is the error for object n in class c

A small value of RSD indicates a tightly clustered class. Depending on the level of confidence,

a critical RSD value (RSDcrit) may be set to define the boundary of the model:

SDF.RSDcrit = 3.3


where: F is the critical F value

and SD is the mean standard deviation.

An object may be said to belong to a class if, when it is fitted into that class, the RSD value of

the class is less than RSDcrit.

One class (C) can be fitted into another class (D) and the RSD[C|D] calculated as:

∑ == CN

1nC

2n[D]

D]|[C Nε

RSD 3.4

This allows the distance between two classes (dCD) to be calculated:

1RSDRSDRSDRSD

d 2[D]

2[C]

2[D|C]

2D]|[C

CD −+

+= 3.5

Fuzzy Clustering

The fuzzy clustering technique is an unsupervised analysis method. In this technique the user

does not assign the objects to any class but rather the fuzzy clustering technique designates each

object with a degree of membership to a particular class. The number of classes available is

determined by the user. A membership value between zero and one is then assigned to each

object for any given cluster with a higher value indicating a high degree of similarity between

the object and the class. The sum of the membership values for each object across the whole all

of classes designated equals one. An object with close to equal membership values in each class

is referred to as a fuzzy object.


Quantitative Analysis

The simplest (univariate) way to calculate concentration spectroscopically is to make a Beer’s

law plot of absorbance versus concentration for a calibration set and then fit a regression line to

the data using a linear least squares method.

This technique can be extended to multiple analyte components and multiple frequencies and is

known as the multivariate Classical Least Squares (CLS) method.

When there are n samples containing l components and n frequencies in the spectra, the

absorbance may be written as an m×n matrix (A) and the concentrations as an m×l matrix (c).

Beer’s law is therefore:

A = cK + EA 3.6

where K is an l×n matrix of pure-component spectra

and EA is an m×n matrix of spectral noise.

The estimated component spectra ( K ) may be found from the calibration set by:

ACCCK ′′= −1)(ˆ 3.7

and the estimated concentration ( c ) may then be calculated for the unknown spectra (a) as:

aKKKc ˆ)ˆˆ(ˆ 1−′= 3.8

While CLS represents a great improvement over univariate methods7,8,9,10, it can fail to take

account of nonlinearities and baseline effects.


Many of these shortcomings can be solved by the techniques of Principal Component Regression

(PCR) and Partial Least Squares regression (PLS) in which the raw data is first converted to

principal components and the PC scores (T) are fitted to the concentrations. ie:

veTvc += 3.9

where v is a coefficient relating concentration and principal component scores. In PLS the

coefficient v is analogous to the pure component spectra k in CLS but v is an “abstract spectra”

which may have no obvious chemical significance. The v spectra in PLS may reflect baseline

variations, instrumental anomalies or non-linear chemical effects as well as analyte absorbances.

Although PLS yields generally poorer qualitative information than CLS, it produces superior

quantitative measurements.

Because the PC coefficients (v) have no obvious physical interpretation — unlike CLS k factors

which are component spectra — the problem arises of how may factors should be included in a

PLS analysis. Too few factors would fail to account for the systematic variation; too many

would overfit the data and produce spurious effects by fitting random error.

The optimal number of factors for any calibration set can be determined by the process of cross-

validation11,12,13 in which the PLS analysis is conducted with different numbers of factors to find

the model with minimal prediction error.

A model is cross validated by systematically removing one or more of the objects from the

calibration set and then measuring how well the removed values are predicted using the

remaining calibration set. This process is repeated until all of the elements of the calibration set

have been removed at least once.


Both for validation and for estimation of the error of the final concentration measurement, the

Standard Error of Estimation (SEE) is required for the calibration set and the Standard Error of

Prediction (SEP) is required for the validation set. These may be calculated for centred data

by14:

∑= −−

−=

n

1i

2ii

1n)cc(SEE

df 3.9

and

∑= −

−=

n

1i

2ii

1n)cc(SEP 3.10

where ic is the predicted concentration of sample i ci is the actual concentration of component i n is the number of samples and df is the number of factors used in the analysis (ie. the degree of freedom)


2.5 References:

1 U. Meyer, Private notes, Queensland University of Technology, 1990. 2 Grams 32, Version 4.01, level 1,1996, Galactic Industries Corp., New Hampshire, USA 3 Pattern Recognition Systems AS, 1998, Bergen, Norway. 4 O.V. Kvaleim, “Pretreatment of Multivariant Data” in “SIRIUS: A Program for Multivariant Calibration

and Classification”, T.V. Karstang, O.M. Kvalheim (ed.), (Pattern Regognition Systems, Norway, 1990) 5 A. Thilemans, D. L. Massart, Chimica, 39, 1985, 236-242. 6 O.M. Kvalheim, SIRIUS (version 2.3); Department of Chemistry, University of Bergen, Norway 7 D.M. Haarland, Easterling, R.G.; Appl. Spectrosc., 34, 1980, 539 8 D.M. Haarland, Easterling, R.G., Vopicka, D.A.; Appl. Spectrosc. 39, 1985, 73 9 M.A. Sharaf, D.L. Illman, B.R. Kowalski; “Chemometrics”, (1986), Wiley, N.Y. 10 S.N. Deeming, S.L. Morgan; “Experimental Design: a Chemometric Approach” (Elsevier, New York,

1987) 11 D.M. Haarland, E.V. Thomas; Anal. Chem., 60, 1988, 1193 12 D.M. Haarland, E.V. Thomas; Anal. Chem., 60, 1988, 1202 13 D.W. Osten; J. Chemom , 2, 1988, 39 14 D.M. Haarland in “Practical Fourier Transform Infrared Spectroscopy”, Ferraro, J.R., Krishnan, K. (ed);

Academic Press; San Diego, pp395-468

Chapter 3 FT-IR Spectroscopy of Dyed Wool 59

Chapter 3 FT-IR Spectroscopy of

Dyed Wool

3.1 Introduction

Fourier Transform Infrared spectroscopy (FT-IR) has become a more common

method of analysis than classical (dispersion) infrared measurements because it is

faster and offers spectra with significantly increased signal-to-noise ratio1,2. There are

various FT-IR methods available, such as Diffuse Reflectance Infrared Fourier

Transform Spectroscopy (DRIFTS), Fourier Transform Infrared Photo Acoustic

Spectroscopy (PAS) and FT-IR Microscopy. FT-IR spectroscopy may be used in the

far-, near- or mid-infrared regions. Studies on various textiles in the far and near

infrared region have been reported3,4,5,6.

In this work, the mid-infrared region of the spectrum using FT-IR PAS technique has

been applied.

PAS is a useful technique for studying the surface of wool samples7. The technique is

relatively fast and requires minimal sample preparation. One of its advantages over

the transmission and reflection techniques is that samples with opaque or rough

surfaces can be examined without any major difficulty. FT-IR PAS has been used for

compositional analysis of wool blends8.


PAS has also been used in studying textile mixtures quantitatively9. The blends

studied are wool/ polyester, wool/ nylon and a three-component blend of wool, nylon

and viscose rayon. It has been shown that the spectra of the various wool blend fabrics

may be quantitatively studied by spectral substraction of wool from the other

components.

Using air as the gas for purging of the chamber rather than helium (as used in this

project) results in spectral interference due to absorptions by carbon dioxide (at

ca. 2350 cm-1)10 and it has been shown that using air in the photo-acoustic chamber

causes the highest frequency available to be 3 KHz resulting in the loss of

sensitivity11. It is claimed that the sensitivity of the instrument may be improved two

or three fold if helium gas replaces the air in the chamber.

In another publication12 the possibility of quantitative determination of wool/polyester

woven fabric, using various vibrational spectroscopy methods was investigated (i.e.

FT-Raman, mid-Infrared ATR and DRIFT). It was concluded that in order to be able

to use ATR and DRIFT, sampling preparation has to be optimised.

PAS and ATR have been used for quantitative and qualitative analysis of polymer

finishes on wool13. Of these two methods, PAS was found to be superior for the

characterisation of the bulk of the sample while ATR is more sensitive to the surface.

ATR was also able to detect a much lower percentage of fluorocarbon polymers on

wool than PAS. PAS has some other advantages over ATR. For example, in ATR it is

difficult to ensure contact between the crystal and the fabric. Another advantage is


that, with PAS, depth profiling is possible while ATR is more a surface analysis

method.

ATR has also been used for the investigation of the photo-oxidation of wool and the

effect of the UV absorber Cibafast W 14. It is concluded that Cibafast W does decrease

the rate of the formation of cysteic acid and disulphide bond fission.

Another method of studying biological and polymer samples as a single fibre is FTIR

microscopy. Briefly, in this technique, a microscope accessory is mounted on the

infrared spectrometer in the place of the sample compartment. This technique can be

used to characterise a variety of chemical systems e.g. biological and semiconductor

materials15,16, polymers such as food wrappings, paint layers or polymer composite

materials in situ17. It has been shown18 that combining crystal microscopy and micro-

FT-IR is a suitable method for the analysis for the presence of illicit drugs such as

cocaine and heroin and LSD19. Micro-FT-IR may be applied to the identification of

natural as well as synthetic fibres and may offer a quicker and easier method than the

other classical techniques20.

PAS has, however, distinct advantages over FTIR microscopy. Such as:

1. In order to obtain a good micro-IR spectrum of a fibre, the fibre must be

flattened with a roller to reduce its optical density. Changes in the crystallinity

may occur with flattening and that in turn may change the intensity ratios of

absorption bands21. This is particularly important in studying forensic

evidence.


2. PAS is non-destructive and therefore the same sample may also be studied by

other techniques.

3. The problem with optical alignment in FTIR microscopy is avoided in FT-IR

PAS because, in PAS, the focal spot size is greater.

As explained in Chapter One, another advantage of PAS is that it allows depth

profiling. In brief, this may be achieved by varying the mirror velocity of the

interferometer. When the mirror velocity is increased, it reduces the thermal diffusion

length and hence causes the spectrum to be recorded from a layer closer to the surface

of the sample. This technique has been used to study the surface of wool fibres22,10

and the migration of the fibres within the fabric during wear8. The near-surface layers

of untreated wool have been shown to have different spectra to the bulk of the wool

fibre. It has been postulated that this is due to the difference in protein composition

between the cuticle and the cortical cells10.

PAS has also been used to study the surface of treated wool fibres and fabrics7.

Surface treatment is an important step in the finishing of wool and is used to achieve

various desirable properties such as shrink-proofing, stain-blocking and soft lustres.

These aesthetic properties may be achieved by depositing a thin layer of various

polymers on the surface of the wool fibre. The PAS studies show that the polymer is

mainly accumulated on the surface with very little penetration to the bulk of the fibre.

PAS was used in conjunction with spectral subtraction to measure the total amount of

polymer deposited on the fibre.

FT-IR Spectroscopy of Dyed Wool Chapter 3

Table 3.1 Assignment of the peaks in IR vibrational mode for undyed wool (Note: N.O. = Not Observed, N.R. = Not Reported) Assignment This

work Ref. 24 Ref. 25

ν (COO-) N.O. N.R. 1720 Amide I ν(C=O) α-helix

1657 1653 1600

Amide I ν(C=O) β-helix

N.O. 1624 N.R.

Amide II δ(N-H), ν(C-N) α-helix

1545 1550 1525

Amide II δ(N-H), ν(C-N) β-helix

1522 1531 N.R.

δ(CH2) (CH3) 1457 1448 1450 ν(COO-) 1398 1400 N.R. νs Cystine dioxide

1305 N.R. 1300

Amide III β-pleated sheet

1242 1244 1240

ν(S-O) Cysteine-S-sulfonate

N.O. 1194 N.R.

νa(S-O) Cysteic acid

1175 1172 1175

νs(S-O) Cystine dioxide

1126 1124 1120

νs(S-O) Cystine monoxide

1083 1076 1075

νs(S-O) Cysteic acid

N.O. 1041 1040

νs(S-O) Cysteine-S-sulfonate

N.O. 1024 N.R.


Figure 3.1 FT-IR PA spectra of undyed Merino wool fabric purged with ultra-pure Helium gas for: A) 5minutes (top spectrum), B) 10 minutes (middle spectrum) and C) 15 minutes (bottom spectrum). (Resolution= 8.0 cm-1, OPD velocity= 0.2 cm S-1, No. of Scans= 128)


Figure 3.2 FT-IR PA spectrum of undyed wool fabric.

Amide I Amide II CH deformation

Amide III ν(S-O)


Considering the advantages of PAS over the other IR techniques, in particular its

ability in depth profiling allows studying the dye-fibre system in situ; i.e. in the cortex,

where the dye molecules migrate upon dyeing the fibre. Therefore, it was decided to

use PAS to examine the dyed fabrics in this project.

Therefore, a mirror velocity of 0.2cm.s-1 was chosen. This, according to the

Rosencwaig equation23, assuming that the thermal conductivity of wool24 is 9.0531 X

10-5 cal °C-1 s-1 cm-1, corresponds to a thermal depth of 2 and 4µm at 1800 and

800cm-1 respectively. As the thickness of the cuticle layer in Merino wool is

estimated24 to be between 0.5 and 1.0 µm, it is concluded that the information

obtained in these studies was from the cortical area of the fibre.

3.2 FT-IR PA spectroscopy of undyed Wool

In this project, the samples were purged with helium for different time intervals in

order to find the optimum purging time of the sample. Figure 3.1 shows the spectra

for a simple-weave, undyed Merino wool fabric. The sample was purged for different

time intervals, namely five, ten and fifteen minutes respectively. As can be seen, the

spectrum obtained after five minutes purging is very noisy and of lower quality. In

contrast, the spectra purged for ten and fifteen minutes show minimal difference in

quality. Therefore, it is more economical and time effective to purge the sample for

only ten minutes.

Figure 3.2 shows a typical spectrum of 21µm Merino wool fabric. Visual comparison

of this spectrum with previously reported spectra24, 25 shows that they are very similar.

The major peaks for this spectrum are listed with their assignments in Table 3.1. Also


in this table, are the peak positions found in previous studies24,25,26 for comparison

purposes. All the major peaks in this spectrum have been reported previously.

However there are a few differences found between the spectra obtained in these

studies and one of the published spectra24; for example, in the spectrum recorded by

Carter, the peaks near 1400 and 1300cm-1 are not found. These peaks have been

assigned to ν(coo-) and νs-o bands of cystine dioxide respectively. Other published

spectra 25 however, do show these peaks. The peak at 1720 cm-1, is assigned by

Carter24 to ν (coo-). In the spectrum acquired here, this peak may be obscured by the

dominant Amide I band, which is centred at 1667 cm-1 and has a shoulder at around

1720 cm-1. The peaks for cysteine-S-sulfonate present in Carter’s spectrum are

missing from Figure 3.2.

The lack of these peaks and the presence of the peaks for cystine monoxide and

cystine dioxide indicates that probably the surface of the fabrics studied here have

been slightly oxidised.

Chemometrics

The data were then studied qualitatively using chemometrics method of analysis in

order to investigate the regions of the spectrum where the dyed and undyed samples

differ from each other. This was done with the aid of PCA. The pre-treatment of all of

the data discussed for woollen samples in this chapter, unless stated otherwise,

consisted of normalising in MS-Excel before being submitted to SIRIUS 6.0 for

chemometric analysis.

A matrix was built consisting of 19 objects and 251 variables. The objects considered

were repeat spectra of undyed wool along with the spectra of wool dyed with a

mixture of two colours, i.e. 073, 370 and 703.


Figure 3.3 PCA and loading plot of spectra of undyed and dyed wool samples

dyed with Lanasol dyes

Comp. 1 (62.0%)

Comp. 2 (16.6%)

-2.8 -1.1 0.6 2.3 4.0 *10-3

-4.1

-2.4

-0.7

1.0

2.8 *10 -3

**** *

*

* ** ** **

*

** ***

Undyed wool

073

703

370

-0.3-0.25-0.2

-0.15-0.1

-0.050

0.050.1

0.150.2

1800

1756

1712

1668

1624

1580

1536

1492

1448

1404

1360

1316

1272

1228

1184

1140

1096

1052

1008 964

920

876

832

Loadings comp. 2

Loadings comp. 1

Loadings comp. 2


Three components were significant to explain 83% of the variance, with PC1

separating undyed wool from the dyed ones. See Figure 3.3. The loading plot of PC1

indicates that bands in the region of 1720-1380 cm-1 have greater contribution to the

undyed samples; whereas the spectral region of 1308-828 cm-1 has a greater

contribution to the dyed samples, with the peak at around 1020 cm-1 having the

greatest weight (probably corresponding to νs (S-O) at 1024 cm-1).

This indicates that the S-O bond in the oxidized cysteine is more strongly affected by

the dyeing process than amide I and II.

3.3 FT-IR Studies of dyed wool

Introduction

Dyed wool has been studied extensively using various analytical techniques,

especially UV-VIS spectroscopy. In this method the dye is normally extracted into a

solution that can then be studied spectroscopically. The fabric may also be Soxhlet-

extracted to remove any unbound lipids from the surface of the fibre. Papini27 has

studied the monochromatic reflectance, transmittance and absorbance for the solar

spectra for undyed as well as dyed wool and cotton fabrics; and found that the dye

does not influence the NIR spectra, while it does influence the UV-VIS spectrum. It

was shown that the influence of the dye on the UV-VIS spectrum is greater for the

cotton than for the wool fabric.

Church et al.28 have studied Lanasol dyes, the general formulas of which are shown in

Figure 3.4. They have concluded that lysine, cysteine and histidine are the reaction


Table 3.2 Table of peak positions and relative intensities in IR vibrational mode

for undyed wool and wool dyed with Lanasol dyes (Note: str. = strong, vw = very weak, sh = shoulder, shp = sharp, br = broad, m = medium)

Sample Peak Peak Peak Peak Peak Peak Peak Peak Peak Peak Peak Peak Peak Peak Peak Peak Peak

000 undyed

1657 1545 1457 1398 1305 1242 1175 1126 1110 1083 934 m

921 w, sh

881 vw

019 1654 1544 1457 1398 1310 1245 1177 1122 1083 937 923 sh

897

046 1656 1545 1456 1399 1305 1242 1127 1083 932 894

073 1657 1544 1457 1398 1306 1241 1126 1084 934 923 sh

127 1662 1542 1456 1396 1310 1241 1126 1107 1084 932 920 sh

902 888 sh

154 1656 1546 1458 1399 1305 1241 1122 1083 934 str.

901 887 878

181 1656 1544 1457 1398 1306 1241 1118 1083 928 str.

886

208 1656 1544 1457 1397 1306 1240 1118 1082 928 str.

878

213 1654 1544 1457 1398 1302 1245 1123 1083 934 str.

217 1651 1542 1457 1397 1311 1244 1082 930 str.

235 1655 1545 1457 1397 1307 1239 1126 1108 1082 931 str.

906 m

244 1658 1545 1455 1398 1308 1242 1126 1108 1083 930 str.

935 sh

307 1657 1543 1458 1399 1306 1244 1084 934 str.

316 1655 1545 1455 1399 1311 1241 1125 1083 933 m, br

343 1657 1544 1455 1398 1312 1242 1126 1109 1082 954 vw

940 sh

928 m

905 vw

878

361 1654 1543 1457 1398 1301 1243 1125 1109 1082 930 sh

925 str., br

908 vw

893 874

370 1655 1543 1458 1398 1305 1239 1109 1082 954 vw

929 br, str.

895 vw

424 ???? 1546 1454 1397 1306 1242 1082 940 sh

930 str, shp

433 1657 1545 1454 1397 1302 1126 1083 948 sh

932 w, br

898 w

872

532 1658 1548 1457 1398 1301 1238 1126 1082 926 vbr, str.

899 vbr, w

880

550

1655 1543 1458 1398 1242 1083 924 vstr, br

613

1655 1544 1457 1397 1306 1240 1124 1115 1082 933 m, br

917 sh

898 w

878 vw

703

1657 1543 1457 1397 1301 1237 1126 1082 932 str, sh

899 w,br

730

1655 1543 1456 1398 1241 1123 1082 959 w,br

930 str. ,br

811

1658 1546 1457 1398 1303 1238 1082

930 vstr.,sh

896 w, sh

860 vW


O

NH

Br

Br

Chromophore CH2

Br

NHChromophore

CH2

or

Figure 3.4 The Lanasol dye containing chromophore attached to α,β-

dibromopropionylamido or α-monobromoacrylamido groups respectively.

NH

N

amino-acid

O

Chromophore

aziridine ring

Figure 3.5 The Lanasol dye product with lysine.


Figure 3.6 FT-IR PAS spectra of wool samples undyed and dyed with

different ratios of Lanasol dyes.

10

20

30

40

50

60

70

1800 1600 1400 1200 1000

Wavenumber (cm-1)

730

307

073

000 (Undyed)

343

800

Regions where spectra show differences.


sites and observed that the other potential sites of reaction, namely tryptophan,

tyrosine or serine, were not involved at all. They also confirmed previous studies

indicating that the dye forms an aziridine ring with lysine side chain groups as

illustrated in Figure 3.5.

In the studies conducted here, PAS was used to study dyed woollen fabrics with the

aid of chemometrics. This was done with a view to examine the dyes on the samples,

both qualitatively and quantitatively.

PAS Analysis of Woollen Fabrics Dyed with Lanasol Dyes

Figure 3.6 shows PAS spectra of undyed wool fabric (i.e. 000) along with the 343,

073, 307 and 730 dyed samples. Table 3.2 lists the main peaks found. Unlike the

FT-Raman spectra of the same samples (Cf. Chapter 4) no peaks were observed that

could be assigned exclusively to the dye molecules. This might be due to the low total

percentage of dye on the fibre (~2-3%) or to the greater IR activity of the wool

groups. Visual comparison of these spectra indicates that there are some minor

differences between the undyed wool spectrum and those of the dyed ones. Apart

from these minor differences however, keratin peaks dominated the spectrum. The

changes are particularly obvious in the region of 1200-900 cm-1. This area is marked

in Figure 3.6 with arrows. The main wool keratin peaks are present and can be

assigned. It must be noted that due to saturation, it was anticipated that the peaks for

Amides I and II, at around 1657 and 1547cm-1, would not be very informative.


Therefore, apart from acknowledging their correct position on the spectrum they were

disregarded. Changes were, however, observed in the 1400-800 cm-1 region.

The spectra were next submitted to chemometrics for further analysis, both

qualitatively and quantitatively.

3.4 Chemometrics studies of dyed wool

Chemometrics has been used to interpret the spectral data obtained using various

analytical methods. It is especially useful when there are no obvious differences

between the spectra obtained for quite similar samples. Chemometrics has been

applied to DRIFT spectra of dye mixtures extracted from a polyester/cotton shirt29.

Raw data and pre-treated spectral data matrices were studied using PCA, SIMCA and

Fuzzy Clustering. It was concluded that in PCA the objects cluster according to their

position on the garment, therefore offering more information than previously used

methods such as TLC.

Kokot and co-workers30 have demonstrated that DRIFT combined with chemometrics

can classify samples not only according to the quality of the cotton fabrics but also by

its stages in a processing sequence. In 1994, the same authors31 reported the

possibility of the application of chemometrics to DRIFT spectra of the dye mixtures

of worn clothing. In these studies the dyes were extracted from the sample. It was

shown that, by interpreting the spectral data using PCA and SIMCA, it is possible to

discriminate and match unknown dye mixtures extracted from microscopic samples of

worn fabrics as well as unknown dye mixture extracted from the same fabric in a

wash and wear situation.


Figure 3.7- PCA plot of samples with a mixture of two and three dyes

Note: Brown = 046, Light Orange = 073, Sea Green = 208, Lime = 307, Pink = 550, Rose = 703, Grey 40% = 343, plum = 127, Indigo = 154, Aqua = 181, Sea green = 208, Violet = 213, Blue-Grey = 217, Red = 235, Dark Red = 244, Dark Green = 361, Olive Green = 424, Dark Yellow = 433, Sky Blue = 532, yellow = 613, Pale blue = 811.

Comp. 1 (49.9%)

Comp. 2 (19.9%)

-3.4 -1.7 -0.0 1.7 3.3*10-3-3.1

-1.4

0.3

2.0

3.7 *10

-3

**

** **

**

*

*

**370a

*

** ** * *

*

**

*

**

**

**

235b**235e*

*

244b

** *

***

**

*

*

*

**433d

*

** *424c

* *

*

**

*

***

* *** *

*

*

***

*** *

**

*

*

***

*

*

**

*

**

**

**

*

*

*

****

*

***

***** *

*

**

**

*

*

**

*

*

**

**

*

**

*

*

****

***


Figure 3.8 PCA plot of samples with a mixture of two dyes

Note: Brown = 046, Light Orange = 073, Sea Green = 208, Lime = 307, Pink = 550, Rose = 703

Comp. 1 (50.6%)

Comp. 2 (19.1%)

-2.0 -1.0 0.0 1.0 2.0*10

-3-2.0

-1.0

0.0

1.0

2.0 *10 -3

**

*

*

**

**

*

**

**

*

*

*

*

*

*

* **

*

* **

*

***

*

**

*

073

046

208

307

550

703

Figure 3.9- PCA plot of samples with a selection of samples Note: Light Orange = 073, Lime = 307, Rose = 703, Grey 40% = 343, Aqua = 181, Pale Blue =

811, Blue- Grey = 217.

Comp. 1 (62.0%)

Comp. 2 (16.6%)

-2.2 -1.0 0.2 1.4 2.6*10

-3-2.1

-0.9

0.3

1.5

2.7 *10

-3

*

****

**

*

*

** * * *

*

*

**

*

**

*

*

**

*

*

*

**

*

*

* **

*

* **

*

*

073307

811

181

703

217 343


In view of these results the spectral data were submitted to PCA for pattern

recognition followed by PLS for the prediction of the ratio of dyes. The advantage of

this procedure to the previous studies mentioned above is that the dyes were examined

in situ. It is anticipated that by doing so more information about the dye-wool fibre

may be obtained, and that it would eliminate the lengthy procedure of dye extraction.

PCA

A data matrix consisting of repeated PA spectra of all the samples studied here was

submitted to PCA. The matrix consisted of 141 objects and 151 variables. Three

components accounted for 76% of the total variance. Figure 3.7 shows PC1 versus

PC2 plot for these data, indicating that there are too many data points to be

conclusive.

Therefore, smaller groups of samples were considered in PCA analysis.

Next, a matrix consisting of 34 objects and 151 variables was built using samples

containing only two colours. The data matrix was then analysed with PCA. 75% of

the total variance was explained using three components. Figure 3.8 shows the plot of

PC1 versus PC2. As it can be seen in this figure, even though the repeated spectra of

the same sample have not clustered together tightly, a general pattern is observed with

respect to the amount of red dye. Starting with zero red dye on the right-bottom of the

plot and moving anti-clock wise.

Another group considered was with a mixture of two or three dyes present on the

sample. The samples chosen for this purpose were 073, 181, 217, 307, 343, 730, 811.


Table 3.3: Predicted and Measured Values for the Validation Sets

(For Dependent Variables Red, Blue and Yellow respectively)

Red Blue Yellow Name Pred. Meas. Pred. Meas. Pred. Meas. 343a 3.5 (4) 3 5.2(5) 4 4.2 (4) 3 343b 4.3(4) 3 5.2(5) 4 1.3(1) 3 343c 3.8(4) 3 4.6(5) 4 1.9(2) 3 343d 3.9(4) 3 4.4(4) 4 2.7(3) 3 343e 2.5(3) 3 4.6(5) 4 5.4(5) 3 343f 2.3(2) 3 4.4(4) 4 5.9(6) 3 073a -1.7(-2) 0 5.3(5) 7 5.6(6) 3 073b -1.6(-2) 0 5.9(6) 7 5.6(6) 3 073c 0.13(0) 0 5.1(5) 7 6.4(6) 3 073d -3.2(-4) 0 5.3(5) 7 5.7(6) 3 073e -1.5(-2) 0 5.5(6) 7 5.5(6) 3 073f 0.20(0) 0 6.2(6) 7 4.0(4) 3 811a 8.3(8) 8 5.2(5) 1 -0.70(-1) 1 811b 10.4(10) 8 4.2(4) 1 -0.82(-1) 1 811c 10.4(10) 8 4.9(5) 1 -1.5(-2) 1 811d 8.1(8) 8 4.5(5) 1 0.18 (0) 1 811e 7.7(8) 8 5.3(5) 1 0.26(0) 1 811f 9.6(10) 8 4.0(4) 1 -0.12(0) 1 181a -0.57(-1) 1 6.3(6) 8 4.5(5) 1 181b 1.8(2) 1 6.0(6) 8 2.2(2) 1 181c 1.9(2) 1 5.7(6) 8 1.8(2) 1 181d 1.7(2) 1 6.1(6) 8 1.4(1) 1 181e 0.75(1) 1 5.8(6) 8 3.8(4) 1 181f 3.7(4) 1 6.8(7) 8 0.20(0) 1 217a 1.6(2) 2 2.8(3) 1 6.2(6) 7 217b 2.2(2) 2 2.5(3) 1 7.9(8) 7 217c 2.3(2) 2 3.1(3) 1 5.8(6) 7 217d 2.5(3) 2 3.2(3) 1 5.9(6) 7 217e 2.1(2) 2 2.6(3) 1 5.1(5) 7 217f 2.9(3) 2 2.6(3) 1 6.7(7) 7 730a 4.5(5) 7 2.2(2) 3 4.0(4) 0 730b 4.5(5) 7 3.2(3) 3 3.3(3) 0 730c 6.4(6) 7 3.1(3) 3 -0.65(-1) 0 730d 3.9(4) 7 3.7(4) 3 4.9(5) 0 730e 2.5(3) 7 2.7(3) 3 3.5(4) 0 730f 1.6(2) 7 2.4(2) 3 5.9(6) 0 307a 1.9(2) 3 6.2(6) 7 307b 2.3(2) 3 4.4(4) 7 307c 4.0(4) 3 5.5(6) 7 307d 0.99(1) 3 7.5(8) 7 307e 3.5(4) 3 4.9(5) 7 307f 2.3(2) 3 4.6(5) 7


)

Figure 3.10- Plot of predicted versus measured values for the calibration set (dep. Var. = red)

Measured (Red)

Predicted (Red)

0.0 2.0 4.0 6.0 8.0 -2.0

0.0

2.0

4.0

6.0

8.0

370a

370c

370d370e235a

235b 235c 235d 235e

235f 244a 244b 244c 244d 244e 244f

532a532b532c

532d

532e532f

433a

433b

433c

433d

433e

433f

424a424b

424c

424d

424e

424f

703a

703b 703c 703d 703e

703f 613a

613b613c613d613e

613f

361a361b

361c361d361e361f

154a 154b 154c 154d 154f 127a 127b 127c 127d 127e 127f

213a

213b

213c

213d

213e

213f

019a 019b 019c

019d

019e

046a

046b 046c

046d 046e

316a

316b316c316d316e316f

550a

550b

550c

550d

550e

550f

208a

208b

208c 208d

208e 208f

Slope =

0.884

Interc. =

0.358

Corr. =

0.940


These samples were chosen since they were the same samples to be UV-irradiated at a

later stage. Hence, their behaviour in PCA was of importance. Figure 3.9 shows PC1

versus PC2 plot, explaining 79% of the total variance. In this plot PC1 has separated

samples with three and one portions of yellow (positive) from seven and zero ratios

(negative).

The PCA analysis of these FT-IR PAS spectra has the repeats of each spectra loosely

clustered together. Overall, however, the spectra of different samples do not from any

informative pattern.

PLS

Next, the data were submitted to PLS method of analysis for quantitative studies:

Validation Set: 073, 181, 217, 307, 343, 730, 811.

Calibration Set: all the rest of the samples

Independent Variables: 1400-800 cm-1

Dependent Variables: ratios of red, blue and yellow

Pretreatment: Normalization

Dependent Variable: Red

A calibration set consisting of 97 objects and 152 dependent variables was built and

submitted to PLS for cross-validation. Five factors were significant to explain 88.4%

of the dependent and 81% of the independent variance. The first four factors

explained 76% of the total dependent variance. The high number of factors being

significant, with respect to the number of dependent variables may be explained by

the interactions that might exist between three dyes or the dye-fibre system. The

correlation between the data in this set is quite reasonable. See Figure 3.10.

Subsequently, the validation set (of 42 objects) was fitted to the PLS model. The


predicted values for the ratio of red dye in each sample are shown in Table 3.3. As it

can be seen, the ability of this PLS model to predict red ratios is quite reasonable.

Because, for example, the ratios of 2 and 3 have been almost predicted correctly each

time, while 1 and 8 ratios are predicted about half of the time. The prediction ability

for the ratios of 0 and 7 however, is quite poor. The values for SEE and SEP were

calculated to be 0.594 and 1.74 respectively. The value for SEP is about triple that for

SEE. The high value obtained for SEP indicates that the calibration set probably needs

more data, in order to obtain a more robust calibration set.

Dependent Variable: Blue

The same calibration set was used to predict ratios of blue dye. Only two factors were

significant here. The cross validation ratio (CsvSD) for these two factors indicates that

there is not a good relationship between the data for the blue dependent variable. This

can be seen from the plot of measured versus predicted values and the values of slope

(0.366), intercept (1.765) and correlation coefficient (0.605) for line fitted to these

data. See Figure 3.11(a).

It seems that zero blue is particularly poorly predicted with this model. Therefore,

those samples were taken out in order to see if there would be an improvement. 85

objects and 152 independent variables were used in the new data matrix. The results

are shown in Figure 3.11(b). Seven factors were significant explaining 75% and 69%

of the total variance of the independent and dependent variables, respectively. The

values of slope (0.474), intercept (1.671) and correlation coefficient (0.688) indicate

that there is a slight improvement for this calibration set. The validation set was then

introduced into this model. Spectra of sample 307 were taken out of the validation set


Figure 3.11- Plot of predicted versus measured values for the calibration set (Dep. Var. = blue) (a) Including samples with zero ratio of blue

Measured (Blue)

Predicted (Blue)

0.0 2.0 4.0 6.0 8.00.0

2.0

4.0

6.0

8.0

370a 370c 370d 370e

235a

235b

235c235d

235e235f

244a

244b244c244d

244e244f

532a

532b532c

532d

532e

532f433a

433b433c433d433e

433f

424a

424b424c424d

424e

424f703a

703b

703c

703d 703e 703f 613a

613b

613c 613d 613e 613f

361a

361b

361c361d361e361f

154a154b154c

154d

154f

127a127b

127c127d127e127f

213a

213b213c213d213e213f

019a 019b 019c 019d

019e

046a

046b046c046d

046e

316a

316b

316c 316d

316e

316f

550a

550b

550c

550d

550e

550f

208a

208b

208c

208d

208e

208f

Slope =

0.366

Interc.

1.765

Corr. =

0.605

(b) Excluding samples with zero ratio of blue

Measured (Blue)

Predicted (Blue)

0.0 2.0 4.0 6.0 8.00.0

2.0

4.0

6.0

8.0

370a

370c 370d 370e

235a235b235c235d

235e

235f

244a

244b244c244d

244e

244f

532a

532b532c

532d532e

532f

433a

433b

433c433d

433e

433f

424a

424b

424c

424d

424e424f613a 613b 613c 613d

613e 613f

361a

361b 361c 361d 361e 361f

154a154b

154c154d154f

127a

127b

127c

127d

127e

127f

213a213b

213c

213d213e213f019a

019b

019c 019d

019e

046a

046b

046c046d

046e

316a

316b 316c 316d 316e

316f

550a550b

550c

550d550e

550f

Slope =

0.474

Interc. =

1.671

Corr. =

0.688


since the samples with zero ratio of blue have been taken out of the calibration set as

well. 36 objects and 152 independent variables were therefore introduced to the PLS

system as the validation set.

The predicted values for blue using the model are displayed in Table 3.3. It shows that

the system predicts 3, 4 and 7 blue ratios between 50-100% (± one ratio), while the

ratios of 1 and 8 are not predicted well at all. This is understandable since in the

samples in the calibration set with ratio of one are not predicted very well either. The

calibration set also does not contain samples with 8 ratios of blue.

The values for SEE and SEP were also calculated to be 0.138 and 1.27 respectively.

There is a great improvement in the value calculated for SEE from that for dependent

variable red. This is in turn reflected in the value calculated for SEP. The relatively

high value of SEP may however be attributed to the fact that in the calibration set

samples with ratios of one and eight are not predicted well or do not exist at all.

This indicates that the ability of the PLS model used in predicting ratios for the

validation set is less than its ability of prediction of the calibration set. in reality, in

an ideal situation the prediction ability of a PLS model can only be as good as the

calibration set used for that purpose.

Dependent Variable: Yellow

The same data matrix as the one used for dependent variable red was chosen

containing 98 objects and 152 independent variables. Six factors were significant to

explain 84% and 80% of the total variance in the dependent and independent variables

respectively. The values of slope (0.784), intercept (0.899) and correlation coefficient


Figure 3.12- Plot of predicted versus measured values for the calibration set (Dep. Var. = yellow)

Measured (Yellow)

Predicted (Yellow)

0.0 2.0 4.0 6.0 8.0 10.0-0.20

0.00

0.20

0.40

0.60

0.80

1.00*10

1

370a

370c 370d 370e

235a

235b235c235d235e235f

244a244b244c244d244e

244f

532a

532b532c

532d532e532f

433a433b433c

433d433e

433f424a

424b424c

424d424e424f

703a703b703c703d703e703f

613a

613b613c613d

613e

613f

361a

361b 361c 361d 361e 361f

154a154b154c

154d

154f

127a

127b

127c 127d 127e 127f

213a213b

213c

213d

213e

213f

019a019b019c

019d

019e046a

046b

046c046d046e

316a

316b

316c

316d

316e

316f

550a

550b 550c 550d 550e 550f

208a 208b

208c 208d

208e 208f Slope =

0.784

Interc. =

0.899

Corr. =

0.885


Table 3.4: Main peaks and their assignments for dyed Wool/ polyester blend

fabrics.

(Note: N.R. = Not Reported, s = strong, m = medium, w = weak, br = broad,

v = very, shp = sharp, sh = shoulder)

Peak Position (cm-1) Found in the literature

Assignment of the peak Approximate Peak Positions (cm-1)

Found in this work

Wool24, 25 Polyester26 Wool24, 25 Polyester

1718 (s, br)

1720 1720 (vs)

ν (C=COO)

1578 (w, br)

N.R. 1577 (w)

N.R.

1559 (w, br)

1550 N.R. AmideII δ(N-H),ν(C-N)

α helix

N.R.

1543 (w, br)

1531 N.R. AmideII δ(N-H),ν(C-N) β-pleated sheet

N.R.

1507 (m, shp)

N.R. 1514 (w)

N.R.

1457 (w, br)

1450 1456 (w, br)

δ (CH2), δ (CH3)

1410 (s, shp)

1400 1410 (m, shp)

ν (COO-)

1374 (w, sh)

N.R. 1372 (w, br)

N.R.

1342 (m, shp)

N.R. 1340 (m, shp)

N.R.

~1250 (s, vbr)

1250 ~1250 (s, shp)

1173 (w, sh)

1172 N.R. νa (S-O) cysteic acid

N.R.

1107 (s, br)

1100 1099 νs (S-O) inorganic sulfate

1042 (w, sh)

1041 N.R. νs (S-O) cysteic acid

N.R.

1022 (w, shp)

1024 1018 (m, vshp)

νs (S-O) cysteine-S-sulfonate

974 (m, shp)

N.R. 973 (w,br)

N.R.

874, 850 (m, shp, doublet)

N.R. 874 (w, br, singlet)

N.R.


Figure 3.13 PA spectra of wool/ polyester samples dyed with Grey, Brown and Green Forosyn dyes.

006 Grey

060 Brown

600 Green

600 Yellow


(0.885) indicate that there is a reasonable correlation between the data in the matrix.

See Figure 3.12. Although the graph suggests that the samples with zero and six ratios

of yellow have not been predicted very well in the calibration set, when taken out the

results are worsened. Consequently they were left in the model and the validation set

was introduced to this model. 42 objects were introduced as the validation set. Seven

factors were significant. The results of the predictions are shown in Table 3.3. As it

can be seen the ability of the PLS model used for the prediction of yellow is not

particularly good. This is also shown by the values of SEE and SEP calculated for the

dependent variable yellow. SEE and SEP values were calculated to be 1.14 and 2.36.

These results however, may be justified by the poor performance of the calibration

set.

Overall, it seems that the ability of the model built for the set of data used here, is

promising. It needs, however, to be investigated further perhaps with a broader set of

data in the calibration set. On the whole, the results indicates that using the PLS

method for quantitative analysis of PA spectra of various ratios of Lanasol dye on

woollen fabrics is a viable procedure and should be investigated further.

3.5 Photo-acoustic Spectroscopy of Wool/Polyester Blends

In these studies, samples were taken from three colour diamonds, with those dyed

with a single Forosyn dye. Refer to Appendix 2. They were examined using PA

spectroscopy.

The main peaks found in these spectra are assigned in Table 3.4. As it can be seen in

this table, the spectrum contains peaks belonging to both fibres present in the fabric.


Figure 3.14 – PCA plots of PA spectral data:

(i) 1st derivative spectra, block normalised, Y-mean centred

Comp. 1 (14.3%)

Comp. 2 (13.0%)

-5.5 -1.8 1.9 5.6 9.4 *10 -1

-7.0

-3.3

0.4

4.1

7.8 *10

-1

***

*

*

**

*

***

*

* *

*

**

*

* *

*

*

*

**

*

* *

**

(ii) Spectra, block normalised, Y-mean centred:

Comp. 1 (65.8%)

Comp. 2 (22.4%)

-6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 *10

-3-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0 *10

-3

*

*** *

**

*

*

*

***

**

**

*

**

**

**

**

*

*


Some loss of information is observed as peak saturation is observed which may be

due to the OPD velocity used.

Visual comparison of the spectra reveals minor differences between them. See Figure

3.13. Thus they were submitted to chemometrics for further analysis.

Various spectral data pre-treatments were tried on this reduced data set in order to

find the one that offered the best separation of the samples in PCA. For example,

Figure 3.14 shows PCA plots obtained using first derivative spectra (i) and non-

derivative spectra (ii). Both of these sets have then been block normalised and

Y-mean centred. As it can be seen non-derivative spectra offer a much clearer PCA

plot.

Hence, the best pre-treatment for this set of data was decided to be non-derivative

spectra block normalised followed by Y-mean centring.

Subsequently, all objects and 251 variables were submitted to PCA for analysis. Three

components were significant to explain 90.5% of the variance in the data. Figure

3.14(ii) shows PC1 versus PC2. PC1 explains 65.8% of the total variance and

separates yellow and green colours (positive) from brown and grey colours (negative).

PC2 and PC3 do not separate the samples any further.

Spectral data (pre-treated in the same manner) for colours red and orange were then

introduced to the same set of data. More data for yellow, brown, grey and green were

added as well. These data were pre-treated in the same manner as the existing data.

The PCA plot is shown in Figure 3.15. 93 objects and 251 variables were introduced


Figure 3.15: PCA plot of individual dyes red, yellow, orange, green, grey and brown on wool/polyester fabric samples.

(Note: The colour of the stars in the plot corresponds directly to the colour on the fabrics)

Com p. 1 (51.9%)

Com p. 2 (35.0%)

-0.84 -0.38 0.08 0.55 1.01*10-2-1.33

-0.87

-0.41

0.05

0.51*10 -2

****

*****

* *

*

** * ** **** **

*

*

* *

*

* * *

*

**

* *

** ****

***

***

*

*

*

*

**

* ** ****

*

**

*

*

*** *

***

***

** *

***

** *

* * ** *** *


to PCA. Three components were significant to explain 93% of the total variances in

amongst the data. As it may be seen in this figure, there is a poor separation of colours

in this plot.

In summary, PA spectra of woollen fabrics dyed with Lanasol dyes and wool/

polyester fabrics dyed with Forosyn dyes were recorded and studied qualitatively

(PCA) and quantitatively (PLS).

The results show that PCA separated the woollen samples dyed with two colours

according to their position on the colour card. Samples dyed with three dyes however,

do not separate that well and although spectral repeats still clustered together, they do

not do so in any order. On the other hand, wool blend fabrics spectra were displayed

on the PCA plot totally in a random fashion.

PLS was performed on the spectral data of dyed woollen fabrics. The calculations

indicate that the results obtained are promising. They do however need to be studied

further with a much larger calibration set.

These samples were then examined further using FT-Raman spectroscopy and

chemometrics, in order to investigate if this combination would offer better solution

to the problem of colour matching.


3.6 References

1 N.M. Morris, R.A. Pittman and R.J. Berni; ‘ Fourier Transform Infrared Analysis of Textiles’, 16 (2), 1984, 30-44. 2 M.P. Fuller and P.R. Griffiths; Applied Spectroscopy, 34 (5), 1980, 533-538. 3 R.S. Davidson and D. King; J. Text. Inst., 74 (6), 1983, 382-384. 4 A. Rosencwaig, ‘Photo-acoustics and Photo-acoustic Spectroscopy’, (Interscience, New York, 1980). 5 J.S. Church and K.R. Millington; Biospectroscopy, 2, 1996, 249-258. 6 R.S. Davidson, D. King, P.A. Duffield and D.M. Lewis; J. Soc. Dyer Color., 99, 1983, 123-

126. 7 E.A. Carter, P.M. Fredericks, J.S. Church; Textile Res. J., 66, 1996, 787. 8 J.S. Church, W-H. Leong; ‘ The Analysis of Wool Textile Blends by FT-IR PAS and FT-

Raman Spectroscopies’, 9th International Wool Textile Research Conference, Biella, Italy, IV (1995), p.p. 114-122.

9 R.S. Davidson and G.V. Fraser; J. Soc. Dyer Color., 100, 1984, 167-170. 10 L.E. Jurdana, K.P. Ghiggino, I.H. Leaver, C.G. Barraclough, P. Coleclake; Applied

Spectroscopy, 48 (1), 1994, 44-49. 11 J.F. McClelland &R.W. Jones, S. Luo & L.M. Seaveron; ‘A practical guide to FTIR

photoacoustic spectroscopy’, March 1992. Reprint of a chapter to appear in Proper sample handling with today’s IR instruments. Edited by P. Coleman and published by CRC press.

12 J.S. Church and A.L. Woodhead; ‘The Vibratinal Spectroscopic Analysis of Wool/ Polyester Textile Blends’, XXX CSI (1997), Melbourne, p.p. PS54. 13 J.S. Church and D.J. Evans; J. of Applied Polymer Science, 57 (13), 1995, 1585-1594. 14 D.C. Jones, C.M. Carr, W.D. Cooke and D.M. Lewis; Textile Res. J., 68 (10), 1998, 739-748. 15 K. Krishnan, ‘Application of the FT-IR Microsampling Techniques to Some Polymer Systems’, Proceedings fro SPIE the international Society for Optical Techniques for

Industrial Inspection, 665, 1986, p.p.252-257. 16 J.M. Chalmers and N.J. Everall; ‘FT-IR Microscopy Advances in Techniques for Characterising and Structure-Property Elucidations of Industrial Materials and Chemicals’, XXX CSI (1997), Melbourne, p.p. I25. 17 D.J. Gerson and C.A. Chess, Practical Spectroscopy, 6, 1988, 73-83. 18 D. Wielbo and I.R. Tebbett, Journal of Forensic Science, JFSCA, 37 (4), 1992, 1134-1148. 19 H.A. Harris and T. Kane, Journal of Forensic Science, JFSCA, 36 (4), 1991, 1186-1191. 20 P.L. Lang, J.E. Katon, J.F. O’Keefe and D.W.Schiering, Microchemical Journal, 34, 1986,

319-331. 21 M.C. Grieve; Forensic Science Review, 6 (1), 1994, 69. 22 L.E. Jurdana, K.P. Ghiggino, I.H. Leaver, P. Coleclarke; Applied Spectroscopy, 49 (3), 1995,

361-366. 23 A. Rosencwaig,; ‘Photo Acoustic and P.A.S.’ (John Wiley & Sons, New York; 1980), pp. 94-

124 24 E. Carter, ‘Vibrational spectroscopic Studies of Wool’, PhD thesis, Queensland University of

Technology, 1998. 25 M.A. Moharram, T.Z. Abdel-Rehim, S.M. Rabie, J. App. Polym. Sci., 26, 1981, 921-932 26 D.M. Lewis in ‘Wool Dyeing’, D.M. Lewis (ed) 27 M. Papini; Infrared Phys., 29 (1), 1989, 133-137. 28 A.S. Davie, J.S. Church, P.J. Scammells and D.J. Tucker; ‘A Spectroscopic Analysis of the Dibromopropionyl/ Bromoacryl Dyes With Wool’, XXX Colloquium Spectroscopium Internationale (1997), Melbourne, p.p. C11. 29 S. Kokot, S. Carswell and D.L. Massart; Applied Spectroscopy, 46 (9), 1992, 1393-1399. 30 C.Gilbert, S. Kokot and U. Meyer; Applied Spectroscopy, 47 (6), 1993, 741-747. 31 S. Kokot and C. Gilbert; Analyst, 119, 1994, 671-675.

Chapter 4 FT-Raman Spectroscopy of Dyed Wool 76

Chapter 4 FT-Raman Spectroscopy

of Dyed Wool

4.1 Introduction

Vibrational spectroscopy has played a significant role in both structural determination

and compositional analysis of various materials. Fourier Transform Raman

Spectroscopy has been used in many studies of biological tissues. It has been shown1

that good quality spectra of Human skin, nail, hair and calluses can be obtained with

this method. Human keratotic biopolymers have been studied using FT-Raman

spectroscopy. It has been concluded that despite their functional differences, these

samples show molecular similarities.

FT-Raman spectroscopy has also been used in studying natural fibres such as cotton.

Kokot et al.2 have shown that various Australian cotton fibres may be identified using

FT-Raman and chemometrics. FT-Raman microscopy has been used3 to study the

fixed dye on the thread of a jean garment. It has been shown that the spectrum of the

dye may be observed with no chemical pre-treatment of the sample. In a related study

of reactive dyes on cotton fabric4 it was concluded that PCA could discriminate

between samples with four different dye states, as well as the individual dye

concentration subgroups. PLS analysis was also applied to predict the concentration

of the unfixed dye on the fabric.


Although much useful information may be obtained from wool fibres using

vibrational spectroscopy, traditional infrared techniques have been limited by several

practical problems5 which may be overcome by using Raman spectroscopy in the

visible or near-infrared region. In many samples visible laser excitation produces a

fluorescence signal that mask the Raman spectrum. Near-infrared excitation also

reduces the problem of fluorescence.

4.2 Dyed Wool For the past three decades keratins such as wool have been studied using vibrational

spectroscopy methods. In 1972, Raman spectroscopy was used to study the

interaction of mercuric chloride with wool6,7. In 1976, the Amide I and Amide III

band regions in unordered polypeptide chains present in feather keratin were studied8

with Raman spectroscopy, as were structural changes in the wool fibre after

annealing9. The effect of stretching on the conformation of the peptide backbone of

the wool fibre, pre-treated with sulphite, has also been investigated10 with FT-Raman

spectroscopy.

In 1994, the structure of untreated and hydrogen peroxide bleached wool samples

were studied11. The authors in this paper claim it was the first FT-Raman study on the

structure of wool. Rapid, good quality FT-Raman spectra of bundled and woven

scoured merino wool were recorded. The spectrum of a single wool fibre was

measured using an FT-Raman microscope. The authors concluded that the highest

quality spectra were obtained from the wool fabric, but there were no major


spectroscopic differences between this and the spectra obtained from the bundle of the

wool fibres.

The conditions of the instrument used in the above study are summarised in Table 4.1.

These instrumental conditions are important in order to be able to compare various

studies of the same nature.

Table 4.1 Instrumental conditions used in Hoggs11, Carter12 and this study

In the same year, untreated wool and wool in different stages of shrink-proofing

treatment was studied using FT-Raman spectroscopy12. The instrumental conditions

used in this study are also noted in Table 4.1. Two objectives were considered here:

to find the optimum conditions for FT-Raman spectral acquisition of wool in various

presentations and to study the stability of the wool sample under laser irradiation. This

was achieved by assessing the wool discolouration and by observing any spectral

changes by varying the laser power. The following conclusions were reached:

1. No damage to the wool samples was observed up to the maximum laser power

(500mW) and

Instrument Parameter

Hogg et al.11 Carter et al.12 This Study

Laser Type Nd: YAG Nd:YAG Nd:YAG Laser wavelength 1064nm 1064nm 1064nm

Laser power 200mW Varied: 50-500mW 300mW

Number of scans ≥8000 Varied: 500-1000 300

Resolution 4.0cm-1 8.0cm-1 8.0cm-1 Spectral Range 300-3400cm-1 4000-400cm-1 4000-400cm-1

Chapter 4 FT-Raman Spectroscopy of Dyed Wool

Table 4.2: Frequencies and assignments of the peaks found in FT-Raman spectra of undyed wool

This Study Hogg, Edwards et al.11

Frequency /cm-1 Assignment

Carter et al.12 /cm-1

Frushour & Koenig /cm-1

Frequency /cm-1

Assignment

1653 (vs)

Amide I 1653 1658 1654 Amide I [ν(C-O)]

1613 (s) Tyr And Trp 1614 1614 N.R. N.R. 1552 (vw,sh) Trp 1553 1558 N.R. N.R. 1447 (vs)

CH2 and CH3 bending mode

1448 1450 1449 δ(CH2)

1337 (s)

CH2 bend, Trp 1338 1340 1340 δ(CH2), Amide III

1314 (s) Cα-H bend 1318 1316 N.M.S. N.M.S. 1272 (vw)

Amide III (α) 1281 1271 1274 Amide III (α)

1260 (vw)

Amide III (α) 1259 N.O. 1250 (vw)

Amide III (α), indicating some disorder

1246 (vw)

Amide III (unordered)

1244 1245 N.M.S. N.M.S.

1203 (w)

Tyr and Phe 1207 1209 1200 δ(CH2), Amide III

1175 (w) T yr 1176 1180 N.R. N.R. 1155 (w) C-N stretch 1155 1158 N.R. N.R. 1124 (w)

C-N stretch 1126 1126 1126 (broadened between 1031-1126cm-1)

ν(C-C) stretching modes, skeletal backbone, all-trans

1096 (vw, sh)

C-N stretch 1096 1098 N.R. N.R.

1089 (w, sh)

N.R. N.R. N.R. 1089 ν(C-C) stretching modes, skeletal backbone, random liquid-like conformation

1079 (vw, sh)

C-N stretch 1079 1080 N.R. N.R.

1060-1062 (w, br, sh)

N.R. N.R. N.R. 1064 ν(C-C) stretching modes, skeletal backbone, all-trans conformation

1031 (m) Phe 1031 1034 N.R. N.R. 1001 (s)

Phe and Trp 1002 1006 1003 Aromatic ring breathing mode

955 (m) CH2 rock 952 959 N.R. N.R. 931 (m)

Skeletal C-C stretch (α)

934 935 N.R. N.R.

N.O. Skeletal C-C stretch (α)

905 N.O. N.R. N.R.

897 (m)

Skeletal C-C stretch (α)

897 N.O. N.R. N.R.

876 (w, sh) Trp 881 883 N.R. N.R. 851 (s) Tyr 851 852 N.R. N.R. 826 (m, v br, broadened between 835-826cm-1)

Tyr 828 835 N.R. N.R.

801 (w) 801 811 N.R. N.R.

Notes:

N.O. (Not Observed): the reference does not acknowledge the peak at all N.M.S. (Not Mentioned Specifically): there is a reference to the range where the peak is found but it has not been mentioned and assigned individually. N.R. (Not Reported): the range in which that peak was to be found has not been discussed at all by the authors.


2. Optimum quality spectra for various sample holders were obtained using different

conditions. For example, the best spectrum for a dense plug of dry wool fibres in a

glass cuvette was obtained using the laser power of 200mW with 500 scans while

frame held fibres require 300mW and 1000 scans.

Their band frequencies and assignments are also shown in Table 4.2.

Reactive dyes form a covalent bond with the wool fibre substrate, giving rise to the

high wet fastness of these dyes13,14,15. The reactive dyes available for dyeing wool can

be divided into two types: those reacting via nucleophilic substitution reactions and

those by reacting via Michael additions. Lanasol dyes, the most successful reactive

dyes, belong to the second type. The chemistry of reactive dyes has been explained

in chapter one, hence it suffices here to point out their general chemical structure:


NHCH2

Br

OD NH2peptide

NHCH2

NH

OD

peptide

NH NH

BrO

peptide

D

NH

NpeptideO

D

substitution

reaction

addition

reaction

NHpeptide

NHD

O NHpeptide

NH2 peptide

NHBr

OD

Br

H2O

HBr

+

+

Using model compounds consisting of simple chromophores attached to the reactive

sites of Lanasol dyes, it has been further shown16 that lysine, cysteine and histidine

are the amino acid providing reaction sites. Tryptophan, tyrosine and serine on the

other hand were not involved in the reaction of the wool fibre with the Lanasol dyes.

In this paper, the authors claim that they have observed the presence of an aziridine

ring in the product of the dye and the lysine amino acid, while they found no evidence

of two amino acids covalently binding to one dye molecule. The observations

challenge the previous assumption17 that the Lanasol dyes are bifunctional (ie. they


Table 4.4 FT-Raman dye peaks found in each fabric dyed with two dyes (1800-400cm-1) Lee-Son and Hester have assigned all these bands as Dye On Fibre (D.O.F.)

Table 4.5 FT-Raman dye peaks found in selected fabrics dyed with three

dyes, studied in this project (1800-400 cm-1)

Sample 811 Sample 343 Sample 361 Sample 217 Sample 181 Sample 127 Reported by Lee-Son & Hester5

1474 1478 1478 1471 1476 1475 1475 1430

1370 1369 1370 1371 1375 1368 1366 1367 1362

1343 1340 1264 1262 1267 1262 1260 1260

1124 1124 1123 1125 1123 1121 1127 1034 1034 1035 1034 1036 1030

897 917 796 695-690

From this study sample

019 sample

046 sample

073 sample

550 sample

730 sample

370 sample

703 sample

208 sample

307

Reported by Lee-Son &

Hester5

Blue 3G, Yellow 4G Red 6G, Blue 3G Red 6G, Yellow 4G Red 6G, Yellow 4G

1478 1478 1475 1477 1475 1475 1425 1425 1432 1430

1369 1372 1370 1371 1372 1375 1361 1366 1368 1368 1364 1362 1343 1343 1340 1344 1339 1346 1340

1273 1267 1263 1267 1268 1263 1265 1265 1264 1260 1124 1125 1124 1124 1124 1123 1127

1034 1034 1034 1034 1030 905 902 917 796 695-690


can react with two nucleophilic groups of the fibre), thus increasing the tensile

strength of the wool fibre by cross linkage between the peptide chains.

In 1990, Lee-son and Hester5 studied the interaction of two representative Lanasol

dyes on the 21-micron Australian Merino wool fibre using conventional Raman

spectroscopy. They studied the interaction of these dyes with untreated wool and wool

treated with light stabiliser suitable for this type of dyes. The reactive dyes used were

Lanasol Red 6G (CGY, C.I. Reactive Red 84) and Lanasol Yellow 4G (CGY, C.I.

Reactive Yellow 39). They report that in order to obtain a reasonable spectrum, they

had to remove the cuticles according to the method described previously by Bradbury

and Peters18. They found that using the wool fibre with the cuticle swamps the

spectrum with fluorescence. The conditions used by them are shown in Table 4.3.

Table 4.3 Summary of the conditions used by Lee-Son and Hester5

Laser power 100mW at the sample Scan speed 100cm-1min –1 Resolution 6.0cm-1 Wavenumber range 1800-400cm-1

They studied the two dyes in various environments, viz. pure dye in aqueous solution,

pure dye in solid state and dye on the wool fibre as a substrate.

The peak position observed by Lee-Son and Hester are shown in Table 4.4 together

with the peak position found in this study for the same dye combinations. All of the

bands found by Lee-Son and Hester were also found here except for those in the

region 1030-690cm-1. These bands may be obscured by fluorescence which Lee-Son


and Hester eliminated by removing the cuticle. They argue that the bands found at

796,1260, 1340 and 1475cm-1 are due to dye bound to the hydrophobic section of the

wool substrate while those at 917, 1127,1362, 1375 and 1430cm-1 are due to dye

bound to the hydrophilic part.

They also postulated that the bathochromic shift of the band found at 1038cm-1 and

assigned to the S-O stretch of the water-solubilising sulphonate group in the solid dye

shows that there is hydrogen bonding in this region, and that there is a decrease in the

S-O bond order. With the shift of this band to an even lower frequency for the dye-

wool substrate, there is an indication, that there is steric hindrance by the protein fibre.

Overall, they have concluded that some kind of chemical reaction must have occurred

between the dye molecule and wool substrate. This is shown by fluorescence, by the

shift in the position of some of the bands and by the appearance of a weak new band

at 690cm-1. The band at 690cm-1 is argued to originate from a three-membered ring

with a hetero-atom (probably sulphur of the cystine or cysteine residue of the wool

substrate)

In this work, FT-Raman spectra of dyed wool and wool/polyester blends have been

studied using chemometrics data analysis; with the view to predict the ratio of the

dyes applied to the fabrics both qualitatively and quantitatively.

In the present work, FT-Raman spectrum was obtained from undyed Australian

Merino wool in the form of a plain weave fabric, and the bands were assigned.


Figure 4.1 Typical Raman Spectrum of undyed woollen fabric


Figure 4.1 shows a typical spectrum. The instrumental conditions are shown in

Table 4.1.

The bands found in the spectrum in Figure 4.1 along with their assignment are shown

in Table 4.2. For comparison, bands found in the literature are included. A

comparison of the spectra by Lin and Koenig11 with those by Carter12 shows that

Fourier transform Raman spectroscopy offers improvement in the resolution.

It is clear from Table 4.2 that the Raman vibration frequencies of wool found in this

study and in three previous reports are all in general agreement. All of the peaks

found here agree within instrumental resolution with those reported by Carter et al.12.

Some of the weaker band reported here were either not seen or not noted in some of

the earlier reports but all of the bands here have been reported and assigned by at least

one group.

Next, plain weave Merino wool fabrics dyed with Lanasol dyes of two or three

colours, viz. red, blue and yellow were studied. The FT-Raman spectra of samples

with a various combination of two of these dyes were considered. For their Raman

scattering frequencies and assignments see Table 4.4.

Table 4.4 reveals that the information obtained is very similar to those found by Lee-

son and Hester 5, except for a few differences. For example, peaks assigned to dye on

fibre at 1475 and 1430cm-1 were not observed in any of the spectra recorded in these

studies. Samples with no yellow dye are also missing the peaks at, 1375, 796 and


690cm-1. Lee-Son and Hester have assigned all these peaks as “dye on fibre” and not

to any specific chemical bonding.

To remove the underlying wool signal, difference spectra were obtained by

subtracting the spectrum of undyed wool. The spectrum of undyed wool was

multiplied by a factor before subtraction. This factor was varied to minimize the wool

peaks and was different for each sample.

Subtracting the peaks belonging to the wool structure should theoretically leave the

dye peaks. This proved difficult for some of the samples, possibly due to the small

amount of dyes on the fabrics. The peak positions and assignments of the dyed and

undyed wool spectra are reported in Table A2-0. See Appendix 2. This practice,

although useful in a sense that it revealed some dye peaks, did not offer clear spectra

of the dyes.

From Table 4.4, it seems that there are peaks that belong to one or two sets of samples

and not to another. For example, all of the samples studied here did not display peaks

at 796 and 695-690cm-1. The peak at 1430cm-1 is missing in the spectra of samples

0BY and RB0, suggesting that this peak is indirectly related to the presence of blue

dye.

Samples with a combination of the three colours were considered next. Again the

FT-Raman spectrum of wool substrate was subtracted from the spectra recorded for

these samples. Samples 325 and 316 were not considered here since the resulting

subtracted spectra became too noisy. The peak positions for these spectra are


Figure 4.2 PCA Plot of the spectra of wool fabric samples (24) dyed with Lanasol dyes

Comp. 1 (26.4%)

Comp. 2 (16.3%)

-6.0 -3.0 0.0 3.0 6.0 -6.0

-3.0

0.0

3.0

6.0

019 ***

*****046

*154

* 127 **

200 *217

235

***361**

316 *325 **

**

307

*343

*370

****

424 *433 * **532 550

**

613

**


Figure 4.3-The approximate position of samples in Group (i)

730 •

073 •

181 •

811 •

343 •

217 •

307 •


displayed in Table 4.5. Peaks at 1430, 796 and 695cm-1 were not observed for any of

the samples. This is understandable since 796 and 695cm-1 peaks have not been

observed at all for any of the samples studied in this project, while the peak at

1430cm-1 is missing for all the samples containing blue dye in Table 4.4. Samples in

Table 4.5 however, all contain blue dye and consequently lack 1430cm-1 peak. On the

other hand, the peaks at 1127 and 1340cm-1 are directly dependent on the presence of

red dye.

In summary, in the Raman spectra the dye peaks are visible above the substrate,

whereas, in PA spectra the dye peaks are masked by the substrate. The presence or

absence of some of the peaks is also influenced by an individual dye. It would have

been useful to compare these spectra to the spectra of the pure dyes off-fibre.

Unfortunately these were not available.

4.3 Chemometrics

Chemometrics for Mixture of the Dyes

Discrimination of reactive dyes on cotton fabric has also been investigated19 using

FT-Raman spectroscopy and chemometrics. PCA has been successfully used in order

to discriminate the different states of the dye on the fabric as well as distinguishing

these from the undyed cotton fabric. PLS has also been used for the prediction of ratio

of dye concentration on the cotton fabric. It has been subsequently suggested that

FT-Raman spectroscopy may be a suitable quantitative method for the prediction of

the amount of the dye on cotton fabrics.

In the studies described in this thesis, the application of FT-Raman spectroscopy in

combination with chemometric methods such as PLS and PCA for the discrimination


Figure 4.4 PCA and the loading plots for Group (i)

Comp. 1 (52.0%)

Comp. 2 (28.0%)

-6.0 -3.0 0.0 3.0 6.0-7.0

-4.0

-1.0

2.0

5.0

*********

**

**

**** * ** **

307

811

073

181 343

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

1524

1448

1372

1296

1220

1144

1068 99

2

916

840

764

688

612

536

Loadings Comp. 1


Figure 4.5 PCA plot and the loading plots of Group (ii)

Comp. 1 (52.2%)

Comp. 2 (19.4%)

-4.0 -2.0 0.0 2.0 4.0-4.0

-2.0

0.0

2.0

4.0

**

**

* ********

***

** *** ******

***

****

073

811343

703

154

235433 532

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

1524

1460

1396

1332

1268

1204

1140

1076

1012 94

888

482

075

669

262

856

4

Loadings Comp. 1


Figure 4.6 Approximate position of samples chosen in Group (ii)

532 •

073 •

154 •

811 •

343 •

703 •

433 •

235 •


and quantitative prediction of wool dyed with reactive dyes in various ratios was

investigated.

Twenty-four samples were studied. Six spectra for each sample were recorded. The

dye spectral region of 1524-524 cm-1 was chosen. A matrix consisting of 166 objects

and 251 variables were subsequently submitted to PCA. The plot obtained is shown in

Figure 4.2. (Note: unless otherwise stated, the pre-treatment of FT-Raman spectral

data used for woollen fabrics in this chapter is normalised in MS-Excel and Y-mean

centred in SIRIUS 6.0)

Even though samples are clustered, they are not well separated and it is not in a very

easily visualised format; and there are too many samples contributing to each PC in

order to make a conclusion based on this picture. Consequently, a selection of smaller

sets was chosen for further studies.

Group (i) The group chosen were the same as the ones that were selected for UV-treatment as

discussed in Chapter 5. They are: 073, 307, 730, 181, 811, 343, 217. These samples

are arranged in the set as shown in Figure 4.3.

A matrix was made of these samples consisting of 23 objects and 251 variables

covering the spectral region of the dye peaks (1524-524 cm-1). The matrix was

submitted to PCA. Spectra belonging to samples 730 and 217 were taken out as

outliers, since the spectra were noisy. The PCA results are shown in Figure 4.4.


Figure 4.7: Predicted vs. Measured values for red as dependent variable in (i) the calibration and (ii) the validation set for Group (i)

(i) Calibration Set:

Measured (%R)

Predicted (%R)

0.0 2.0 4.0 6.0 8.0 -2.0

0.0

2.0

4.0

6.0

8.0

019Ra 019Rb

019Rc 019Re

019Rf

046Ra

046Rb

046Rc

046Re

154Ra 154Rb 154Rc 154Rd 154Re 154Rf 127Ra 127Rb 127Rc 127Rd 127Re 127Rf 235Ra 235Rb 235Rc

235Rd 235Re 235Rf 244Ra 244Rb 244Rc 244Rd 244Re 244Rf 361Ra361Rb361Rc

361Rd361Re361Rf208Ra

208Rb 208Rc 208Rd 208Re 208Rf

316Ra316Rb316Rc316Rd316Re316Rf

325Ra325Rb325Rc

325Rd

325Rf

370Ra370Rb370Rc

370Rd

370Re

370Rf

424Ra424Rb424Rc

424Rd

424Rf433Ra433Rb433Rc

433Rd433Re433Rf

532Ra532Rb532Rc

532Rd532Re532Rf

550Ra

550Rb

550Rc

550Rd

550Re


613Rd

613Re

613Rf

703Ra 703Rb 703Rc 703Rd 703Re 703Rf

Slope = 0.699

Interc. = 0.927

Corr. = 0.836

(ii) Validation Set:

Measured (%R)

Predicted (%R)

0.0 2.0 4.0 6.0 8.0 -0.50

0.00

0.50

1.00

1.50 *10

1

073Ra 073Rb 073Rc 073Rd 073Re

181Ra 181Rb 181Rc 181Rd 181Re 181Rf

217Ra 217Rb 217Rc

217Re 217Rf

307Ra

307Rb307Rc307Rd307Re307Rf

343Ra343Rb343Rc343Rd

343Re

343Rf

811Ra

811Rb 811Rc 811Rd 811Re 811Rf

730Ra

730Rb

730Rc

730Re

730Rf Slope =

1.002 Interc. =

0.597

Corr. = 0.881


The samples are separated in Figure 4.4 according to their position on the colour

triangle in Figure 4.3. The 811 sample is furtherest away from 073 and 307, followed

by the sample 343, and 181, which is correctly closer to 073.

Component 1 explains 52% of variance, distinctly separating samples 811 and 073

from the rest. The loading plot for PC1 shows that this separation is mainly due to the

peaks in the range 1400-1100cm-1 and mainly due to blue colour. e.g. peak at 1124,

1264 and 1375 are not present in the spectra for samples 073, 811 and 181. Refer to

Figure 4.4 for details.

PC2 has separated 307 samples from all the rest. This is due to its high ratio of

yellow dye on the fabric and the fact that this sample does not contain any blue dye.

Group (ii)

The next subset to be examined was a matrix consisting of eight samples, from zero

ratio of red to 8 ratio of red. Because the full range of various amounts of red dye are

represented in this subset, it is assumed that it correctly models the red dye spectra for

the full data set.

A matrix comprising of 35 objects and 251 variables was built and submitted to PCA.

Figure 4.5 shows PC1 versus PC2 for this group. PC1 has separated 0, 1, 2 and 4

ratios of red from 3, 5,7 and 8. It is also apparent that the samples are positioned from

zero ratio of red followed by 1, 2, 3, 4, 5, 7 and 8 ratios. Refer to Figure 4.6.

The loading plot for PC1 indicates that the differences are mainly due to the region

1490-1152cm-1. Samples with 3, 5, 7 and 8 ratios of red have positive scores on PC1,


which according to the loading plot (Figure 4.5) are due to the peaks with maxima

around 1490, 1438, 1384, 1244, 1180 and 1152cm-1.

It has therefore been demonstrated here that PCA has separated the samples into

clusters, which are positioned in order according to their position in the sample

arrangement.

It is also shown that the dye peaks for blue colour are in the 1500-1000cm-1 region

and that this region separates samples with higher than 4 ratios of blue from the rest.

For the red dye, the peaks are located mainly in the region 1490-1152cm-1. It has

also been demonstrated here that the same principle can be applied to samples with a

mixture of two dyes, as for three.

So far the data have been successfully studied qualitatively, it is therefore of interest

to investigate the possibility of studying them quantitatively. Hence, the data were

submitted to PLS to investigate the ability of the system to predict dye ratios on some

of the samples (the validation set), with the aid of the rest as calibration set.

• PLS

In this study, the multivariate calibration application called direct calibration was

applied. An estimate of the unknown dye ratios (in validation set) was obtained from

samples with known ones20 (calibration set).

A calibration model was built and significant factors were selected by cross-

validation. Cross-Validation is a validation technique, which determines the number


of latent variables that are to be considered in a classification or regression method21.

Cross-Validation is applied so that the model with the best predictive ability is

chosen. Subsequently, the Raman spectra of various groups of samples were

submitted as calibrations sets to PLS analyses while the rest of samples were

introduced as validation. The same groups of samples chosen as Groups (i) and (ii)

previously have been considered here as the validation set. In order to find the best

PLS model for these samples, the spectral region 1524-524cm-1 was investigated as

the independent variables.

In these studies a question to be considered was whether a PLS1 or PLS2 regression

analysis should be applied to these data. The theory and detailed explanation of these

methods is well covered in other literature22,20. PLS calibration for a multi

components analysis may be performed in two different ways:

PLS1- a separate regression is performed for each of the analytes.

PLS2- all the analytes are modelled as one and the regression is then

performed.

Each of these regression methods have some advantages as well as disadvantages. For

example, PLS2 utilises only one set of factors for all of the Y-variables and if there is

a large number of them, it is quicker to develop a single PLS2 model. In addition, if

the Y-variables are interrelated, PLS2 might be more robust. On the other hand, in

terms of predictive accuracy, PLS1 calibration usually performs just as well or

sometimes even better22.

In these studies therefore, both systems were tried in order to find the preferred PLS

method and it was concluded that using the PLS1 method gives better results. This is

not unexpected, since the ultimate requirement of this calibration study is to develop


Table 4.6 Results of PLS Model applied to Group (i), dependent variables red, blue and yellow

Red Blue Yellow

Name Measured Predicted Name Measured Predicted Name Measured Predicted

073Ra 0.00 0.34 (0) 073Ra 7.00 8.92 (9) 073Ra 3.00 1.88 (2)

073Rb 0.00 0.18 (0) 073Rb 7.00 8.86 (9) 073Rb 3.00 2.00 (2)

073Rc 0.00 -0.05 (0) 073Rc 7.00 9.99 (10) 073Rc 3.00 1.58 (2)

073Rd 0.00 -0.79 (-1) 073Rd 7.00 10.5 (11) 073Rd 3.00 1.81 (2)

073Re 0.00 -2.04 (-2) 073Re 7.00 11.1 (11) 073Re 3.00 1.72 (2)

181Ra 1.00 1.91 (2) 181Ra 8.00 7.71 (8) 181Ra 1.00 1.48 (2)

181Rb 1.00 2.17 (2) 181Rb 8.00 7.63 (8) 181Rb 1.00 1.34 (1)

181Rc 1.00 2.42 (2) 181Rc 8.00 7.57 (8) 181Rc 1.00 1.33 (1)

181Rd 1.00 1.33 (1) 181Rd 8.00 11.8 (12) 181Re 1.00 1.18 (1)

181Re 1.00 1.98 (2) 181Re 8.00 8.34 (8) 181Rf 1.00 1.08 (1)

181Rf 1.00 2.07 (2) 181Rf 8.00 8.18 (8) 217Ra 7.00 4.56 (5)

217Ra 2.00 3.98 (4) 217Ra 1.00 1.72 (2) 217Rb 7.00 7.52 (8)

217Rb 2.00 4.04 (4) 217Rb 1.00 -0.84 (-1) 217Rc 7.00 5.94 (6)

217Rc 2.00 3.44 (3) 217Rc 1.00 0.29 (0) 217Rf 7.00 4.27 (4)

217Re 2.00 0.34 (0) 217Rf 1.00 3.46 (4) 307Ra 7.00 5.69 (6)

217Rf 2.00 1.90 (2) 307Ra 0.00 0.33 (0) 307Rb 7.00 5.44 (5)

307Ra 3.00 4.22 (4) 307Rb 0.00 0.40 (0) 307Rc 7.00 5.03 (5)

307Rb 3.00 2.80 (3) 307Rc 0.00 1.36 (1) 307Rd 7.00 6.38 (6)

307Rc 3.00 2.96 (3) 307Rd 0.00 -0.25 (0) 307Re 7.00 6.13 (6)

307Rd 3.00 3.08 (3) 307Re 0.00 -0.05 (0) 307Rf 7.00 4.95 (5)

307Re 3.00 2.98 (3) 307Rf 0.00 2.58 (3) 343Ra 3.00 2.61 (3)

307Rf 3.00 2.41 (2) 343Ra 4.00 3.32 (3) 343Rb 3.00 2.40 (2)

343Ra 3.00 4.54 (5) 343Rb 4.00 3.19 (3) 343Rc 3.00 2.42 (2)

343Rb 3.00 4.83 (5) 343Rc 4.00 3.23 (3) 343Rd 3.00 2.43 (2)

343Rc 3.00 5.20 (5) 343Rd 4.00 2.58 (3) 343Re 3.00 2.13 (2)

343Rd 3.00 5.34 (5) 343Re 4.00 2.19 (2) 343Rf 3.00 2.41 (2)

343Re 3.00 6.71 (7) 343Rf 4.00 3.63 (4) 811Ra 1.00 2.05 (2)

343Rf 3.00 4.54 (5) 811Ra 1.00 -2.81 (-3) 811Rb 1.00 2.48 (3)

811Ra 8.00 11.1 (11) 811Rb 1.00 -0.58 (-1) 811Rc 1.00 2.48 (3)

811Rb 8.00 8.27 (8) 811Rc 1.00 -1.15 (-1) 811Rd 1.00 1.96 (2)

811Rc 8.00 8.83 (9) 811Rd 1.00 -1.05 (-1) 811Re 1.00 2.05 (2)

811Rd 8.00 9.29 (9) 811Re 1.00 -1.98 (-2) 811Rf 1.00 2.47 (3)

811Re 8.00 10.3 (10) 811Rf 1.00 -2.28 (-2) 730Ra 0.00 3.76 (4)

811Rf 8.00 9.83 (10) 730Ra 3.00 1.62 (2) 730Rb 0.00 5.31 (5)

730Ra 7.00 4.68 (5) 730Rb 3.00 -2.70 (-3) 730Rc 0.00 3.73 (4)

730Rb 7.00 7.57 (8) 730Rc 3.00 1.81 (2) 730Re 0.00 4.79 (5)

730Rc 7.00 4.18 (4) 730Re 3.00 -1.91 (-2) 730Rf 0.00 3.52 (4)


the best possible prediction system. This also agrees with Massarat et al.22 finding that

if the purpose of the regression study is to find the best possible predictions, a

separate PLS1 regression for each Y-variable should be used.

Group (i)

Validation Set: 073, 181, 217, 307, 343, 730 and 811 (six spectra of each).

Dependent Variables: Red, Blue, Yellow colour

Independent Variable: 1524-524 cm-1

a) PLS1 Calibration for Red Dependent Variable

A PLS model was developed for the FT-Raman spectra of the woollen samples, with

the calibration set consisting of six repeats of spectra for 17 samples (97 objects). The

other seven samples (39 objects) were introduced as the validation set. Two of the

spectra (217Rd and 073Rf), were found to be outliers and hence they were removed

from the set.

Five components were found to be significant, explaining a total of 79.10% of the

variance of the independent and 69.94% of the variance of the dependent variables.

Figure 4.7 shows the plots of predicted versus measured values for these calibration

and the validation sets. The predicted values for the calibration objects are not very

close to the measured ones. This can be seen as the set of the samples are spread


Figure 4.8: Predicted vs. Measured values for (i) calibration and (ii) validation sets for blue as the dependent variable.

(i) Calibration set:

Measured (%BL)

Predicted (%BL)

0.0 2.0 4.0 6.0 8.0 -0.50

0.0

0.5

1.0*10

1

019Ra 019Rb 019Rc 019Re 019Rf

046Ra046Rb046Rc046Re


127Ra 127Rb 127Rc 127Rd 127Re 127Rf

235Ra235Rb235Rc235Rd235Re235Rf 244Ra244Rb

244Rc244Rd244Re244Rf361Ra361Rb361Rc361Rd361Re361Rf

208Ra 208Rb 208Rc

208Rd 208Re 208Rf

316Ra 316Rb 316Rc

316Rd 316Re 316Rf 325Ra

325Rb 325Rc 325Rd 325Rf

370Ra 370Rb 370Rc

370Rd 370Re 370Rf

424Ra 424Rb 424Rc 424Rd 424Rf

433Ra433Rb433Rc

433Rd433Re433Rf532Ra532Rb532Rc532Rd532Re532Rf


550Re550Rf

613Ra 613Rb 613Rc

613Rd 613Re 613Rf

703Ra 703Rb 703Rc 703Rd 703Re 703Rf

Slope = 0.825

Interc. = 0.507

Corr. = 0.908

(ii) Validation set:

Measured (%BL)

Predicted (%BL)

0.0 2.0 4.0 6.0 8.0 -0.50

0.00

0.50

1.00

1.50 *10

1

073Ra 073Rb 073Rc 073Rd 073Re

181Ra 181Rb 181Rc

181Rd

181Re 181Rf

217Ra

217Rb 217Rc

217Rf

307Ra 307Rb 307Rc 307Rd 307Re

307Rf 343Ra343Rb343Rc343Rd343Re

343Rf

811Ra 811Rb 811Rc 811Rd 811Re 811Rf

730Ra

730Rb

730Rc

730Re

730Rf

Slope = 1.283

Interc. = -1.320

Corr. = 0.875


Figure 4.9: Predicted vs. Measured values for (i) calibration and (ii) validation sets for the yellow dependent variable.


Measured (%Y)

Predicted (%Y)

0.0 2.0 4.0 6.0 8.0 10.0 -0.50

0.00

0.50

1.00

1.50 *10

1

019Ra

019Rb 019Rc 019Re

019Rf

046Ra

046Rb

046Rc

046Re154Ra154Rb154Rc154Rd154Re154Rf


235Ra235Rb235Rc235Rd235Re235Rf244Ra244Rb244Rc244Rd244Re244Rf361Ra 361Rb 361Rc 361Rd 361Re 361Rf

208Ra

208Rb208Rc

208Rd208Re208Rf

316Ra316Rb316Rc

316Rd

316Re316Rf325Ra325Rb325Rc

325Rd

325Rf

370Ra 370Rb 370Rc 370Rd 370Re 370Rf

424Ra424Rb424Rc424Rd424Rf433Ra433Rb433Rc433Rd433Re433Rf532Ra 532Rb 532Rc 532Rd 532Re 532Rf 550Ra

550Rb

550Rc 550Rd

550Re

550Rf 613Ra613Rb613Rc613Rd613Re613Rf703Ra703Rb703Rc703Rd703Re703Rf

Slope = 0.653

Interc. =

1.396

Corr. = 0.808


Measured (%Y)

Predicted (%Y)

0.0 2.0 4.0 6.0 8.00.0

2.0

4.0

6.0

8.0

073Ra073Rb073Rc073Rd073Re

181Ra 181Rb 181Rc 181Re 181Rf

217Ra

217Rb

217Rc

217Rf

307Ra 307Rb 307Rc

307Rd 307Re

307Rf


811Ra 811Rb 811Rc 811Rd 811Re 811Rf

730Ra

730Rb

730Rc

730Re

730Rf

Slope = 0.445

Interc. = 1.884

Corr. = 0.666


across the line of the best fit. The values for SEE and SEP were calculated to be 1.1

and 2.1.

Table 4.6 shows details of the values for predicted and measured for members of the

validation set.

Another model was built in order to verify that the calibration set used is the preferred

one. This was done by detecting the outliers of the first model with the SIRIUS 6.0

software. They were then eliminated from the calibration set and then another cross-

validation was performed on the data. This model was found to have a better

correlation between the samples in the calibration set, and the values obtained for the

slope and the intercept of the line of best fit improved accordingly. The same

calibration set that was applied to the previous model was introduced here in order to

predict the values for the red dependent variable for the system. It was found that the

model behaves less favourably here and therefore the new calibration set was not

used.

The plots of predicted versus measured for the calibration and the validation set are

displayed in Appendix 2 (Figure A2-1).

Therefore, it was concluded that the first model built is the preferred one in this case.


Table 4.7: Results of PLS model applied to Group (ii) with the red dependent variable

Object

Name

Measured

Value

Predicted Value

(nearest integer)

073Ra 0.00 0.96 (1)

073Rb 0.00 1.23 (1)

073Rc 0.00 0.83 (1)

073Rd 0.00 0.28 (0)

073Re 0.00 0.76 (1)

154Ra 1.00 2.36 (2)

154Rb 1.00 2.45 (3)

154Rc 1.00 2.34 (2)

154Rd 1.00 1.95 (2)

154Re 1.00 1.99 (2)

154Rf 1.00 2.43 (2)

235Ra 2.00 2.78 (3)

235Rb 2.00 2.61 (3)

235Rc 2.00 2.77 (3)

235Rd 2.00 2.46 (3)

235Re 2.00 2.45 (3)

235Rf 2.00 2.83 (3)

343Ra 3.00 3.19 (3)

343Rb 3.00 3.33 (3)

343Rc 3.00 3.44 (3)

343Rd 3.00 3.49 (4)

343Re 3.00 3.58 (4)

343Rf 3.00 3.30 (3)

433Ra 4.00 2.98 (3)

433Rb 4.00 2.93 (3)

433Rc 4.00 3.00 (3)

433Rd 4.00 3.08 (3)

433Re 4.00 3.21 (3)

433Rf 4.00 3.22 (3)

532Ra 5.00 3.41 (3)

532Rb 5.00 3.17 (3)

532Rc 5.00 3.26 (3)

532Rd 5.00 3.31 (3)

532Re 5.00 3.34 (3)

532Rf 5.00 3.12 (3)

811Ra 8.00 6.17 (6)

811Rb 8.00 5.10 (5)

811Rc 8.00 5.15 (5)

811Rd 8.00 5.54 (6)

811Re 8.00 5.98 (6)

811Rf 8.00 5.75 (6)

703Ra 7.00 4.27 (4)

703Rb 7.00 3.94 (4)

703Rc 7.00 3.83 (4)

703Rd 7.00 4.03 (4)

703Re 7.00 3.80 (4)

703Rf 7.00 4.18 (4)


Figure 4.10: Predicted vs. Measured values for (i) calibration and (ii) validation sets for Group (ii) (red dependent variable).


Measured (%R)

Predicted (%R)

0.0 2.0 4.0 6.0 8.0-0.20

0.00

0.20

0.40

0.60

0.80

1.00 *10

1

073Ra 073Rb 073Rc 073Rd 073Re

154Ra 154Rb 154Rc

154Rd

154Re 154Rf 181Ra 181Rb 181Rc

181Rd

181Re 181Rf

217Ra

217Rb

217Rc 217Rd 217Re 217Rf 244Ra 244Rb 244Rc 244Rd 244Re 244Rf 361Ra361Rb361Rc

361Rd361Re361Rf

208Ra 208Rb 208Rc 208Rd 208Re 208Rf

316Ra316Rb316Rc

316Rd

316Re


325Rd

325Re

325Rf370Ra370Rb370Rc370Rd370Re

370Rf

424Ra424Rb424Rc424Rd424Rf

613Ra613Rb613Rc

613Rd

613Re613Rf

703Ra 703Rb 703Rc 703Rd 703Re 703Rf

730Ra

730Rb

730Rc 730Re 730Rf

Slope = 0.683

Interc. = 0.990

Corr. =

0.827


Measured (%R)

Predicted (%R)

0.0 2.0 4.0 6.0 8.0 0.00

0.20

0.40

0.60

0.80

1.00 *10

1

127Ra 127Rb 127Rc 127Rd 127Re 127Rf 235Ra

235Rb 235Rc 235Rd 235Re 235Rf 307Ra307Rb307Rc307Rd307Re

307Rf




550Ra

550Rb550Rc

550Rd

550Rf

811Ra

811Rb 811Rc 811Rd 811Re 811Rf

Slope = 0.671

Interc. =

1.499

Corr. = 0.818


b) PLS1 Calibration for Blue Dependent Variable

The same data set was used to study blue dependent variable. The plot of predicted

versus measured values for the calibration and the validation set are displayed in

Figure 4.8. As indicated by these plots, even though the relationship between the

values in the calibration set is reasonable, when it is applied to the validation set it

was not successful. See Table 4.6. SEE and SEP values were found to be 0.86 and 2.3

respectively. The value of SEP is very high.

c) PLS1 calibration for yellow dependent variable

Another PLS1 calibration model was built with the same calibration and validation set

for group (i) but with the yellow colour chosen as the dependent variable. Figure 4.9

shows the plot of predicted versus measured values for both calibration and validation

sets.

The calibration model was obtained employing the cross-validation method. The

validation set was then introduced to this model in order to predict the yellow

dependent variable. Figure 4.9(ii) shows the plot of predicted versus measured values

for the validation set. The spread of these samples on fitted line shows that the

predicted values are not close to the measured ones. Table 4.6 shows the predicted,

measured values for this model. The SEE and SEP values for this model were

calculated to be 1.5 and 2.1 respectively.

Group (ii)

Validation Set: 046, 127, 235, 307, 343, 433, 532, 550 and 811


Dependent Variable: Yellow, blue, red colours

Independent Variable: 1524-524 cm-1

a) PLS1 calibration for red dependent variable

The PLS model developed for this group, consist of a calibration set of 14 samples

and a validation set of 9 samples. As before, six repeated spectra of each sample were

used. A total of 81 objects for the calibration set, 47 objects for the validation set and

252 variables were submitted to the model. Three components were significant,

explaining a total of 85.4% and 68.3% of the independent- and dependent variable

variance respectively.

Figure 4.10 plots the predicted versus measured values for the calibration and

validation sets. As can be seen the samples in the calibration set are spread about the

line of best fit and the correlation between the samples in this set is not very good.

The same is observed in the plot for the validation set. The SEE and SEP values for

this dependent variable were found to be 1.2 and 1.4 respectively. Table 4.7 shows

the predicted and measured values for members of the validation set for this model.

b) PLS1 calibration for blue dependent variable

A model consisting of calibration set (81 objects) and validation set (42 objects) was

built. Samples 046 and 550 were found to be outliers and hence were taken out of the

validation set. Six factors were found to be significant, with the first three explaining

75% and 62% out of 84.3% and 82.0% of the total variance in the independent and

dependent variables respectively. The plot of the predicted versus measured values

shows that the values predicted are not close to the measured ones for the validation


set. Predicted and measured values for the validation set are tabulated in Appendix 2

(Table A2-2). The values for SEE and SEP are 1.2 and 0.85 respectively. It is unusual

to obtain a SEP value that is lower than that of SEE especially since the predicted

values for the validation set were not close to the measured ones. This might be

explained by the fact that the calibration and validation set were not of adequate size.

c) PLS1 calibration for yellow dependent variable

The same data set was used for the dependent variable yellow. Eight factors were

significant with the first four explaining 80% and 68% of the total variance in the

independent and dependent variables respectively. The poor results obtained for

sample 046 may be attributed to the fact that sample 316 in the calibration set has not

been predicted successfully either. i.e. if the PLS model has not been able to

successfully predict a ratio in the calibration set, then it is justifiable that predicting

the very same ratio in the validation set with respect to the previous sample would be

poor. Therefore it is not unexpected that the model fails to predict this particular

ratio. The SEE and SEP values calculated were found to be 0.97 and 1.3 respectively.

The plot of predicted versus measured for the calibration and the validation sets along

with a table containing these values may be found in Appendix 2 (Figure A2-3 and

Table A2-3). Sample 550 seems to have some other problem since the model has

shown poor predicting ability for it with both blue and yellow dependent variables.

Group (ii) has demonstrated better predictability despite the fact that there are more

calibration objects in group (i). This might be due to the fact that there are a greater

variety of samples in group (ii). Overall however, the values of SEE and SEP for both


group (i) and (ii) indicate that those predictions have not worked well. This may be

because the dye values do not reflect the amount of dye actually on the fibre.

4.4 Wool/ Polyester Blend Dyed With Foron and Lanasyn dyes

The wool/polyester fabrics studied here were a 45:55 blend of these fibres

respectively (the most popular wool blend). The dyes used here were a mixture of

disperse dyes and wool dyes produced and patented by Sandoz called Foron and

Lanasyn respectively. The chemical structures of these dyes are not disclosed to the

public. The wool dyes are metal complexes and acid dyes with their chromophores

comprising of azo and anthraquinone groups.

Appendix 1 shows the arrangement of the samples studied in the three colour squares

(or diamonds). In these diamonds each sample is identified by a reference number

displaying three figures used to produce that shade.

For example, in one of the diamonds, the first figure gives the number of parts of

Forosyn Yellow 2RL or Forosyn Green 2GL, while the second figure shows the parts

of Forosyn Brown PL and the third one refers to the parts of Forosyn Grey 2BL.

Undyed Wool/ Polyester Blend Fabrics

Blends are very important in the textile industry, covering a large part of the market

since they provide physical and economical advantages over pure materials. Wool is

one of the most expensive textile materials. Therefore it is often blended with cheaper

synthetic fibres such as acrylic or polyester fibres with the wool/polyester blend being

the most widely used. These fibres offer better aesthetic properties to the end product.


It must be noted that although there are a few types of polyester, they all belong to the

same group of fibres called Poly Ethylene Tetraphthalate (PET). Refer to the general

formula for PET below. Therefore, one would expect their spectra to be very much

the same. Indeed, it is reported in the literature23 that the changes in the crystallinity

and heat of the polyester fibre causes changes only in the intensity of the vibrational

bands.

O

OCH3 O CH3

O

OH OH

O

OCH3 O

O

O H CH3 OH

n

n + n

+ (2n-1)

In FT-Raman the changes in the polarizability ellipsoid causes bands to appear.

Hence, while in FT-IR spectroscopy, functional groups such as C=O give rise to

strong bands, in FT-Raman C=C and C-C of the backbone display intense peaks.

However, these two analysis methods, give complementary information about the

sample and it is of great advantage to be able to study a sample using both methods.

One of the advantages of using FT-Raman analysis has been shown in a recent

forensic study of single polymeric fibres, where Miller et al.24 have shown that

FT-Raman spectroscopy may be used for the identification of these fibres even when

these are mounted on a microscope slides. The disadvantage of studying the polymer

structure using FT-Raman might be the sensitivity of this technique to dyes

(especially under high laser power where the sample might burn) and the degree of


crystallinity of the polymer fibre under study25 (causing changes in the position and

the shape of the peaks in the spectrum).

Vibrational spectroscopy has been used in various studies involving polymer fibres,

such as polyester26, 27, 28 and biopolymer29 blend textiles.

Church et al.25 have compared various vibrational spectroscopic methods for the

determination of the ratio of wool/polyester in textile blends. They compared FT-

Raman, mid-infrared ATR and DRIFT and near-infrared diffuse reflectance

techniques using classical least squares and partial least squares analysis. Of these

spectroscopic methods it was concluded that first derivatives of ATR in the NIR

region gives better results with respect to DRIFT and mid-infra-red region.

It has been shown25 that the use of vibrational spectroscopy methods for both

qualitative and quantitative analysis of blends is much more suitable than the

conventional analysis methods because vibrational spectroscopy methods are quicker,

more robust and no sample preparation is needed.

The authors suggested that vibrational spectroscopy may be used for the

determination of the percentage of wool in wool blends textiles. And they report that

the percentage of the wool in the blend has been determined by subtracting the

spectrum of pure polyester from that of the blend. The percentage of the wool in the

blend was then determined from the ratio of the C-H stretching band area of the wool

component obtained above with the area of the same band in the original blend

spectrum.


Table 4.9: FT-Raman peaks assigned to the polyester component in the

spectra of a (45:55) wool/polyester sample in the region 3800-400cm-1

* The peaks assigned in the spectrum of a polyester sample d = doublet N.R. = Not Reported Peaks (cm-1) found in this study

Peaks (cm-1) from Ref. 27*

Assignment of the peaks found in Ref. 30* and 31 and 32

3079 (m, br) N.R. νC-H 2963-2933 (m, d) N.R. νC-H 1725 (s, shp) 1732 νC=O 1612 (vs, shp) 1616 Benzene ring

(νC=C) 1453 (w, br) N.R. δCH2 1413 (w, sh) N.R. Benzene ring

(para substituted) 1286 (m, shp) 1286 νC-O 1180 (vw) N.R. Benzene ring

(para substituted) 1114 (w, d) N.R. Benzene ring 1094 (w, d) N.R. νC-O 999 (vw) N.R. νOCH2 855 (m, shp) 857 Benzene ring 700 (vw) N.R. Benzene ring 630 (m, shp) 632 Benzene ring


Figure 4.11: Typical FT-Raman spectrum of undyed wool/polyester fabric


The conditions used in this paper are summarised in Table 4.8.

Table 4.8: Summary of the conditions used in Church et al.25 and this study

Conditions used Reference 25 In this study

Type of laser Nd:YAG (1064nm) Nd:YAG (1064nm)

Laser power 300mW 300mW

Number of Scans 500 300

Resolution 4.0cm-1 8.0cm-1

Frequency range 4000-400cm-1 3800-400cm-1

The FT-Raman spectrum of 45:55 wool/polyester fabric is displayed in Figure 4.11.

Visual comparison of this spectrum with the Raman spectra of PET in the literature30

indicates that they are very similar. No fluorescence is apparent and the peaks are very

well resolved. The peaks positions and descriptions are listed in Table 4.9 along with

the assignments of polyester fibre from the literature30,31, 32. In Figure 4.11 the bands

belonging to the wool fibres are not all apparent since some are hidden under those for

polyester fibres.

Previous Studies

Dyed wool/polyester fabrics have been previously studied using various spectroscopy

methods, especially FT-IR spectroscopy33,34,35. The same wool/polyester pattern card

samples have been analysed by first extracting the dyes and then studying them using

TLC and FT-IR (DRIFT) methods34. The spectral matrix was then analysed by PCA.

It was concluded that the application of PCA to these results is not successful and that

the duplicates recorded for each sample do not cluster together since there are

variations between them.


As mentioned in Chapter 3, Cheng34 has studied the same set of the samples as used

here. She looked at samples dyed with a Forosyn Brown, Yellow, Grey and their

combinatory colours using FT-IR (DRIFT) and concluded that the spectra of the

fabrics dyed with one colour were similar visually, but clustered successfully in a

PCA plot. Thus it was argued that the difficulties faced in earlier studies were

overcome by taking the dye extraction step out of the analysis. She also found that

when the same procedure was applied to the samples dyed with a combination of

these main colours, the PCA plot “shows almost random distribution of scores”.

Keen35 studied the same samples using infrared microscopy and Raman microprobe

spectroscopy and analysed the data by PCA. Wool and polyester fibres were pulled

out of the fabric sample for study. In the case of dyed polyester fibre, although the

spectra are similar along one fibre, there are some slight differences in the dye region

of the spectrum. When these spectra were subjected to PCA analysis, after removing

the outliers, the duplicates were separated quite reasonably; and that the darker

colours clustered in a more spread out manner which was attributed to the

fluorescence produced by these samples.

In summary these studies have shown that:

a) Taking spectra directly of the dye and substrate may eliminate dye extraction

step.

b) DRIFT spectroscopy followed by PCA does not separate samples with a

combination of these dyes successfully, while it does separate the samples

dyed with one dye only.


c) FT-Raman microprobe spectroscopy followed by PCA has shown promising

results, as the objects have separated in PCA according to the Forosyn

pattern card.

In this work, the FT-Raman spectra of these samples were analysed by PCA. The

results were also submitted to PLS to test the possibility of predicting the percentage

of a dye on a fabric sample.

FT-Raman Spectroscopy of Dyed Wool/Polyester Blend

A Comparison of the Spectra of Single Forosyn Dyes

In these studies, the spectrum of the undyed wool/polyester was subtracted from the

spectra of the samples dyed with one colour. In the subtraction process, scale factors

were chosen so as to minimize the strong νC=O and νC=C polyester bands at 1612cm-1

and 1725cm-1 respectively.

Three differently dyed diamonds were studied here. For further information about the

samples in the diamonds, refer to Chapter 2, section 2.1, and Appendix 1. It must be

noted that when referred to the four corners of a diamond, it means the four individual

colours. Each side of a diamond has been numbered clockwise (starting from left

corner) to sides 1, 2, 3 and 4. Side 5 in the meantime, refers to the samples situated in

a line in the middle of the diamond, cutting it to two triangles (top and bottom).

The undyed wool/ polyester FT-Raman spectrum was subtracted from all the

FT-Raman spectra of the dyed samples. An example of this subtraction is shown in


Figure 4.12: FT-Raman difference spectra for dyed (006) and undyed wool/polyester blend.

-10

0

10

20

3400 3200 3000 2800 2600 2400 2200 2000 1800 1600 1400 1200 1000 800 600 400 200

Dyed

Undyed

Difference




0

10

20

30

40

50

60

3400 3200 3000 2800 2600 2400 2200 2000 1800 1600 1400 1200 1000 800 600 400 200

Dyed

Undyed

Difference

0

20

40

60

80

3400 3200 3000 2800 2600 2400 2200 2000 1800 1600 1400 1200 1000 800 600 400 200

Dyed

Undyed

Difference


Table 4.11: Dye peaks found in the FT-Raman spectra of dyed (45:55) wool/polyester Using a mixture of various colours after subtraction of the substrate spectrum

Sample Peak (cm-1)

Peak (cm-1)

Peak (cm-1)

Peak (cm-1)

Peak (cm-1)

Peak (cm-1)

Peak (cm-1)

Peak (cm-1)

Peak (cm-1)

402 1465 (vw)

1423 1403 1377 1183 1127

501 1465 (vw)

1424 1403 1378 1349 1182 1128

600 (Brown)

1464 1424 1403 1377 1348 1182 1128

015 N.O. 1421 1404 1379 1337 1184 1143 (vw, sh)

1127

024 N.O. 1425 1403 1380 N.O. 1183 1128 033 1465 1425 1402 N.O. N.O. 1182 1128 042 1464 1426 1402 N.O. N.O. 1182 1128 051 1464 1426 1402 N.O. N.O. 1182 1128 060 (Yellow)

1464 1426 1402 N.O. N.O. 1182 1128

105 N.O. Noisy N.O. 1378 Noisy Noisy N.O. 1144 1125 204 N.O. Noisy N.O. 1377 Noisy Noisy N.O. 1144 1125

(w) 303 N.O. Noisy Noisy 1377 Noisy Noisy Noisy 1144 1125 402 N.O. Noisy Noisy 1376 Noisy Noisy Noisy 1143 1127 501 N.O. Noisy Noisy 1376 Noisy Noisy Noisy 1143 1127 600 (Grey)

N.O. Noisy Noisy 1376 Noisy Noisy Noisy 1142 1128


Table 4.11: Dye peaks found in the FT-Raman spectra of dyed (45:55) wool/polyester

Using a mixture of various colours after subtraction of the substrate spectrum

Sample Peak (cm-1)

Peak (cm-1)

Peak (cm-1)

Peak (cm-1)

Peak (cm-1)

Peak (cm-1)

Peak (cm-1)

Peak (cm-1)

Peak (cm-1)

Diamond 1 006 (Grey)

N.O. 1416 1404 1377 N.O. 1127

015 N.O. 1417 1403 1377 1241 N.O. 1128 024 N.O. 1421 1403 1377 1183 1127 033 N.O. 1423 1403 1377 1183 1127 042 1465 1427 1403 1376 1345 1182 1127 051 1464

(vw) 1423 1402 1377 1181 1127

060 (Brown)

1464 1425 1401 1376 1182 1128

105 N.O. 1423 1404 1377 N.O. 1128 150 1465 1424 1403 1378 1182 1128 204 N.O. 1424 1403 1377 1182 1128 213 1465 1425 1403 1377 1182 1128 222 1464 1425 1403 1376 1182 1128 231 1464 1425 1403 1376 1182 1128 240 1464 1425 1402 1378 1182 1128 303 1464 1425 1402 1378 1182 1128 312 1464 1425 1402 1378

(w, sh) 1182 1128

321 1464 1425 1402 1378 (w)

1182 1128

330 1464 1425 1402 N.O. 1182 1128 402 1464 1426 1402 N.O. 1182 1128 411 1464 1426 1402 N.O. 1182 1128 420 1464

(sh) 1426 1402 N.O. 1182 1128

501 1464 1426 1402 N.O. 1182 1128 510 1464 1426 1402 N.O. 1182 1128 600 (Yellow)

1464 1426 1402 N.O. 1182 1128

105 Noisy Noisy Noisy 1378 Noisy Noisy 150 Noisy 1424 1402 1379 Noisy 1127 204 1466

(w) 1423 1402 1377 1183 1128

240 1464 1425 1402 1378 1182 1128 303 N.O. 1424 1403 1377 1182 1128 330 1464 1425 1402 1378

(w, sh) 1182 1128

402 N.O. 1425 1402 1378 1182 1128 420 1464 1425 1402 1378

(w, sh) 1182 1128

501 1464 1426 1402 1378 1182 1128 510 1464 1425 1402 N.O. 1182 1128 600 (Green)

1464 1425 1402 N.O. 1182 1128

Diamond 2 006 (Red)

N.O. 1417 N.O. 1378 1337 1229 1192 1144 1123

105 N.O. 1418 N.O. 1379 1337 1231 1188 1143 1126 204 N.O. 1420 N.O. 1378 1339 1235 1184 1144 1127 303 1465

(vw) 1422 1404

(vw,sh) 1379

1340 (vw,sh)

1237 1184 1144 1127


Figure 4.12. The dye peaks found after subtraction are presented in Table 4.10. The

peaks for Forosyn dyes are mainly located in the 1500-1000cm-1 region.

Table 4.10: Dye peaks found in the FT-Raman spectra of dyed (45: 55)

wool/polyester after subtraction of the peaks for the substrate

Mixture of Two and Three Colours

The spectra of samples dyed with two and three different colours were then studied.

The spectrum of the undyed fabric was subtracted from the spectra. Examples of this

subtraction are shown in Figure 4.13 and 4.14. This revealed the peaks belonging to

the dyes only. This method, although susceptible to interference, may reveal the main

differences between these spectra. The positions of the peaks found are shown in

Table 4.11.

The following observations are made from Table 4.11:

Diamond 1

• Samples from 006 to 060

Forosyn

Dye

Peak

Position

(cm-1)

Peak

Position

(cm-1)

Peak

Position

(cm-1)

Peak

Position

(cm-1)

Peak

Position

(cm-1)

Peak

Position

(cm-1)

Peak

Position

(cm-1)

Peak

Position

(cm-1)

Peak

Position

(cm-1)

Peak

Position

(cm-1)

Peak

Position

(cm-1)

Peak

Position

(cm-1)

Vibration

CH2 and

CH3

deform.

CH2 and

CH3

deform.

CH

deform.

CC bridge

bond str. CC

str.

Ring

breathing

Sym. Str.

C-C bond

Red 1417 1378 1144 1122 Yellow 1464 1426 1402 1182 1128 1095 Green 1464 1426 1242 1128 Brown 1464 1425 1377 1350 1182 1128 Orange 1446 1422 1392 1338 1289 1192 1139 1105 Grey 1376 1128

(vw, sh)


The peak at 1465cm-1 appears when there are four or more portions of

brown and less of the grey colour on the sample while the peak at

1182cm-1 appears when two or more portions of brown colour are

present.

The peaks at 1464cm-1 and 1183cm-1 are affected by higher portions

of the brown colour and not by grey.

When there are only green and grey colours present, the subtracted

spectra are very noisy since there is very little difference between the

dyed and undyed spectra.

For the samples dyed with the three dyes, all the peaks are present.

The peak at 1378cm-1 becomes weak for the samples with higher

portion of yellow. This peak disappears totally for samples with four

or more portions of yellow.

The intensity of the peak positioned at 1378cm-1 displays a reverse

relationship with the portion of green dye present on the samples.

Diamond 2

The spectra resulting from the subtraction of the spectrum of the

undyed fabric from that for samples dyed with various percentages of

brown and red show that the peaks at 1466cm-1 and 1402cm-1 are not

present for the samples with six to four portions of red. These peaks

appear for the samples with zero to three portions of red. This

observation may also be interpreted in the opposite direction for the

colour of brown. The above results are further verified by the fact the


subtracted spectra of the samples dyed with red and grey show that the

peak 1464cm-1 is not observed at all for various percentages of red dye

on the fabric. This peak is still absent for high portions of red (6-4) and

lower portions of yellow.

Therefore, the presence of this peak (1464cm-1) for the samples dyed with 3-6

portions of brown indicates that there might be some kind of interaction between

the colours brown and red, and red and yellow (to a lesser extent). This

interaction is not present in between the colours of red and grey.

The peak at 1380cm-1 is not present for the samples with 3-6 portions of

yellow and 3-0 portions of red. This indicates that there this peak is due to

the red colour and not yellow. The same reasoning can be applied to the

peak at 1337 cm-1, where the peak disappears at 4-0 portions of red and

hence 2-6 portion of yellow.

When the colour grey is present with any other colour, except red, the

subtracted spectra do not look noisy and they display well-resolved peaks.

This may indicate that there are some kinds of interactions between the

colours grey and red that are not present when grey is mixed with the other

colour.

As mentioned previously, even though there are differences between these results

it is very hard to come to any solid conclusion about the dyes and their

interactions in amongst themselves and of that with the substrate. It is therefore,

proposed that Chemometrics may help in realising differences within these

samples.


Figure 4.15: PC1 vs. PC2 and PC 2 vs. PC3, For Orange, Green, Brown and Red of Diamond 3 (D3)

DataSet: r4cD3nsrs, Subset: a3, Scores 1 vs 2

Comp. 1 (57.7%)

Comp. 2 (29.8%)

-1.00 -0.50 0.00 0.50 1.00 *10-2

-0.50

0.00

0.50

1.00

1.50 *10

-2

****** ******

** *

***** *

600 Orange

600 Red

006 Brown

060 Green


Comp. 2 (29.8%)

Comp. 3 (11.9%)

-0.50 0.00 0.50 1.00 1.50 *10 -2 -1.00

-0.50

0.00

0.50

1.00 *10 -2

******

*********** * ** *

600 Orange

006 Brown

600 Red

060 Green


Figure 4.16: Superimposed Loading Plot for PC2 and PC3 in PCA plot for

Diamond 3 Series 2= PC2 (coloured orange, separating orange from the rest) Series 3= PC3 (coloured brown, separating brown from the rest)

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

1476

1452

1428

1404

1380

1356

1332

1308

1284

1260

1236

1212

1188

1164

1140

1116

1092

Series3Series2


Figure 4.17: PC1 vs. PC2 for the four corners of Diamond 2


Comp. 1 (89.8%)

Comp. 2 (8.6%)

-1.29 -0.36 0.56 1.49 2.42*10-2-1.95

-1.02

-0.09

0.84

1.77 *10 -2

*

************************

*** * * **** * * ****2********

*

600 grey

006 red

060 yellow

600 brown

Figure 4.18: Loading vs. Variables for PC1, PC2 and PC3 for the four corners of

Diamond 2 Series1 = (PC1, brown & yellow, positive) Series2 = (PC2, brown & grey, positive) Series3 = (PC3, yellow & grey, positive)

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

1476

1444

1412

1380

1348

1316

1284

1252

1220

1188

1156

1124

1092

Series3Series2Series1


4.5 Chemometrics

In this study, six spectra of each of the dyed and an undyed wool/polyester fabric

were recorded. These spectra were reproducible.

Four Corners of Diamonds 1, 2 and 3

• PCA of the Four Corners of Diamond 3

A matrix consisting of 21 objects and 97 variables was then submitted to PCA. The

result is shown in Figure 4.15. The objects studied were the samples dyed with a

single dye namely, orange, green, red and brown. The dye spectral region

(1476-1092cm-1) was used. 99.5% of the total variance has been explained by PC1,

PC2 and PC3.

• PC1 explaining 57.8% separates green and brown colours from red and

orange. The loading plot for component 1 indicates that this separation is

mainly due to 1348-1268cm-1 and 1116-1092 cm-1. This is in agreement with

the conclusion drawn in the last section, where it was found that the intensity

of the peak at 1337cm-1 is directly proportional to the amount of red dye

present on the fabric.

• PC2 explains 29.8% of the variance and separates orange samples from the

rest. The loading plot for this component suggests that the spectral region of

1160-1138cm-1 followed by the regions 1410-1310 and 1120-1092cm-1 are

where the peak intensities increase as the ratio of orange dye on increase.

• Finally, PC3 explains 11.9% of the variance and separates brown samples

from the rest. The regions directly influencing this separation are: 1318-1268,

1156-1110 and 1470-1400cm-1 while the intensity of the peaks in the regions


1280-1190, 1174-1154, 1346-1316cm-1 are inversely influenced by the ratio of

brown dye on the sample. As mentioned in the previous section, the intensity

of the peaks at 1466 and 1402cm-1 are directly proportional to the amount of

dye present on the fabric. See Figure 4.16

PCA of the Four Corners of Diamond 2

A matrix containing 49 objects (replicate spectra of fabrics dyed with single colours

brown, yellow, grey or red), and 97 variables (the 1476-1092cm-1, dye region) was

submitted to PCA. The result is shown in Figure 4.17. Three components were

extracted, explaining a total variance of 99.6%.

• PC1 explained 89.8% of the total variance. It separates red and grey coloured

samples from brown and yellow.

• PC2 explaining 8.6% of the variance separated the objects into red and yellow

(with negative scores) and brown and grey (positive scores).

• PC3 only explained 1.3% of the total variance, separating brown and red

(negative) from grey and yellow (positive). Although this component was

considered, it was not an important contributor since the first two components

explain 98.4% of the total variance.

The loading plot for PC1, PC2 and PC3 are shown in Figure 4.18. Although no

conclusion can be drawn regarding the individual colours, the PC plots do give an

indication of the direction which each two-colour combination drive the intensity of

the peaks in the region.


Figure 4.19: (i) PCA (PC1 vs. PC2) for the four corners of Diamond 1


Comp. 1 (87.2%)

Comp. 2 (10.3%)

-2.0 -1.0 0.0 1.0 2.0*10-2-2.0

-1.0

0.0

1.0

2.0 *10 -2

** * * * * * * * * * *** * * * * ********* ***************

**************************** **********

*

600 Yellow

006 Grey

600 Green

060 Brown

Figure 4.19: (ii) PCA Loading Plot for PC1, PC2 and PC3 for the four corners of

Diamond 1

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

1476

1448

1420

1392

1364

1336

1308

1280

1252

1224

1196

1168

1140

1112

Loadings Comp. 3Loadings Comp. 2Loadings Comp. 1


• PCA of the Four Corners of Diamond 1

A matrix consisting of 81 objects and 97 variables (the dye region of the spectrum)

was submitted to PCA for analysis. It must be noted that for this analysis a greater

number of spectral repeats than the usual 6 repeats was tried. This is simply because

there were more spectral repeats recorded due to the diamonds having individual

colours in common. Also, because there were only four samples being studied it was

decided to use as many repeats as possible in order to make it a more robust. Figure

4.19 shows the separation of the four colours (yellow, brown, green and grey) by PC1

and PC2. The total variance explained by four components was 99.6%, with the first

two components explaining 97.5%. PC1 separates brown and yellow colours from the

rest, while PC2 and PC3 separate green and brown respectively.

The loading plot for component 1 and the distribution of variance and residuals for

each variable shows that yellow and brown colours directly influence the intensity of

the peaks at 1140-1100cm-1, and 1470-1310cm-1 to a lesser extent while peaks in the

region of around 1300-1200cm-1 vary in direct proportion to PC2.

Component 3 however explains only 1.7% of the total variance. The loading plot for

this component shows that as the concentration of brown colour is increased on the

fabric the spectral regions of 1150-1192 and 1384-1336cm-1 also increase in intensity.

The loading plot for component 4, explaining 0.4% of the total variance, indicates that

it distinguishes some variation in amongst the repetitive spectra for one sample. This

might suggest that the dye uptake on the fabric is not homogeneous.


Figure 4.20: PCA Plot for Sides 1, 4 and 5 of the Top Triangle in Diamond 2

Note: Some clusters have been coloured in purple for clarity.

DataSet: r145D2nsrs, Subset: b1, Scores 1 vs 2

Comp. 1 (92.2%)

Comp. 2 (6.3%)

-2.0 -1.0 0.0 1.0 2.0 *10 -2 -2.0

-1.0

0.0

1.0

2.0 *10-2

******************

******************* * * * * * ** * * * * * * * * * * * * * * * * ****** ****** ******

****** ****** ************ *

***************************** ******

****** ****** ******** ** *****5********

*

015

105

024

204 303

402 420501

033

330240

042051

150

060 Yellow 006

Red

600 Brown 510


Figure 4.21: PCA Plot for Side 1, 4 and 5 of the Top Triangle in Diamond 1

D ataS et: tt2kn , S u b se t: a5 , S co res 1 vs 2

C o m p . 1 (94 .4% )

C o m p . 2 (3 .4% )

-2 .8 -1 .3 0 .2 1 .8 3 .3*10-2-3 .2

-1 .7

-0 .1

1 .4

3 .0 *10 -2

*********** ************** ***

*** *

** ******

************* ***

* * * * ** * * ** * * * * **

6 0 0 Y ellow

5 0 1

5 1 0 4 2 0

2 4 0

3 3 0 /4 0 2 3 0 3

0 6 0 B row n

1 5 0

0 5 1

2 0 4

0 4 2

0 3 3

0 2 4

1 0 5

0 1 5

0 0 6 G rey

Figure 4.22: PCA Plot For The Top Triangle In Diamond 1 The position of some of the samples (in Pink) located in the middle of the triangle are shown in relation to the ones seen in Figure 4.21.

DataSet: tt2kn, Subset: a7, Scores 1 vs 2

Comp. 1 (93.2%)

Comp. 2 (4.6%)

-2.8 -1.3 0.2 1.7 3.2 *10-2-3.0

-1.5

-0.0

1.5

2.9 *10 -2

* **********

*****************

*******

****

********* *******

****** **************

************

006 Grey 015

105

033051

303411

600 Yellow

231

321

312 501

510

330

240

150

420 060 Brown

****


Top Triangles of D1 and D2

PCA for Three Sides of Top Triangle of D2

A matrix consisting of 174 objects (sides 1,4 and 5) and 97 variables (1476-1092cm-1)

was analysed using PCA. The objects in this set include samples with both single dyes

and with mixtures of two dyes. Figure 4.20 shows PC1 vs. PC2 plot. A total of 99.0%

variance was explained by three components. PC1 explained 92.4% and PC2 6.3% of

the total variance.

The PCA analysis conducted here has been used as an indication of the location of the

samples only. As it is seen in Figure 4.20, there are too many samples and too closely

spaced for a complete separation of the samples into positive and negative

components.

The PCA plot however, clearly shows that the repeated spectra of each sample are

clustered together and that they are positioned regularly according to the ratio of each

of the three dyes.


PCA for Top Triangle of D1

A matrix of 72 objects and 126 variables representing three sides was submitted to

PCA for analysis. The results are shown in Figure 4.21. PC1 explained 94.4% of the

variance while PC2 explained 3.4%.

Overall, the samples are separated and positioned according to their location on the

diamonds, with the three main colours well separated. The samples with a

combination of two dyes are clustered on the PCA plot, in the appropriate locations

between their component colour.

In Figure 4.22 a few of the samples with a combination of three dyes have been

included in the matrix composed and analysed in Figure 4.21. Again the plot is an aid

to view the position of these samples in the context of the triangle studied before.

From Figure 4.21 it can be concluded that the samples are located in the right position

according to their dye ratios. For example, sample 231 is situated before sample 240.

Samples 312 and 411 are also correctly positioned before samples 330 and 420

respectively.

Therefore, the overall conclusion is that PCA plots can separate the samples in

accordance to their dye ratios of one, two or three dyes on the substrate thus allowing

the data to be studied qualitatively.

The data were then analysed quantitatively by Partial Least Squares.


Table 4.12: Results of PLS Model for the prediction of brown dependant

variable in the regions 1476-1092cm-1 and 1800-800cm-1 Name Predicted

(1476-1092cm-1)

Predicted

(1800-800cm-1)

Measured

303B212 2.7 2.9 3.00

303B213 3.21 3.2 3.00

303B215 3.2 3.1 3.00

303B211 2.8 3.1 3.00

033B221 -0.10 0.17 0.00

033B223 -0.29 -0.17 0.00

033B224 0.13 0.13 0.00

033B225 0.03 -0.00 0.00

033B226 -0.28 -0.09 0.00

330Y11 2.3 2.9 3.00

330Y12 2.6 3.0 3.00

330Y13 2.2 2.5 3.00

330Y14 2.2 2.6 3.00

330Y15 2.2 2.7 3.00

330Y16 2.0 2.7 3.00


Figure 4.23: Predicted vs. Measured values for (i) calibration and (ii) validation sets for Sides 1, 4 and 5 of Diamond 2 respectively, dependent variable: Brown.


Measured (Brown)

Predicted (Brown)

0.0 2.0 4.0 6.0-2.0

0.0

2.0

4.0

6.0

8.0

600B26 600B211 600B212 600B213 600B214 600B215 600B216 600B22 600B23 600B24 600B25 600B21

501B216501B212501B214501B215501B211

402B216402B212402B213402B214402B215402B211

204B216204B212204B213204B214204B215204B211

105B216 105B212 105B213 105B214 105B215 105B211

060B26 006B211 006B212 006B213 006B214 006B215 006B216 006B22 006B221 006B222 006B223 006B224 006B225 006B226 006B23 006B24 006B25 006B26 015B221 015B222 015B223 015B224 015B225 015B226 024B221 024B222 024B223 024B224 024B225 024B226 042B221 042B222 042B223 042B224 042B225 042B226 051B221 051B222 051B223 051B224 051B225 051B226 060B21 060B22 060B221 060B222 060B223 060B224 060B225 060B226 060B23 060B24 060B25 006B21 600Y316

060Y111 060Y112 060Y113 060Y114 060Y115 060Y116 060Y12 060Y13 060Y14 060Y15 060Y16 060Y211 060Y212 060Y213 060Y214 060Y215 060Y216 060Y311 060Y312 060Y313 060Y314 060Y315 060Y316 150Y11

150Y12150Y13150Y14150Y15

240Y11240Y12240Y13240Y14240Y15240Y16

420Y11420Y12420Y13420Y14420Y15420Y16

510Y11 510Y12 510Y13 510Y14 510Y15 510Y16

600Y11 600Y12 600Y13 600Y14 600Y141 600Y142 600Y143 600Y144 600Y145 600Y146 600Y15 600Y16 600Y311 600Y312 600Y313 600Y314 600Y315

060Y11

Slope = 0.982 Interc. = 0.041

Corr. = 0.991


Measured (Brown)

Predicted (Brown)

0.0 1.0 2.0 3.0-1.00

0.00

1.00

2.00

3.00

4.00

303B212 303B213 303B215 303B211

033B221 033B223 033B224 033B225 033B226

330Y11 330Y12 330Y13 330Y14 330Y15 330Y16

Slope =

0.957

Interc. = 0.007

Corr. = 0.990


PLS

Two PLS1 models were built for diamonds 1 and 2 (see Appendix 1), using the sides

of the top triangle of diamonds 1 and 2 respectively. Another PLS1 model was built

using all of the points in the top triangle of diamond 1. Two ranges of independent

variables were studied for each: the whole spectrum (1800-800cm-1) and the dye

region (1500-1000cm-1).

Sides 1, 4 and 5 of Diamond 2

Validation Set: 033, 303, and 330

Dependent Variable: Brown, Yellow, Red

Independent Variable: 1800-800cm-1

PLS Analysis for the Brown Dependent Variable

A PLS model consisting of 154 objects in the calibration set was built.

These samples were then analysed with the model and a cross-validation was

performed again. Eight factors were found to be significant and explained 99.46% of

the independent variables and 98.18% of the dependant variables. The last three

factors however, explained only 4.02% of the total variance of the dependent

variables. Thus, the model consisted of 5 factors explaining ~94% variance.

A validation set consisting of 15 objects was introduced to this model. The plots of

measured versus predicted values for (i) the calibration and (ii) the validation sets are

shown in Figure 4.23. The values for the slope, intercept and correlation coefficient

show that there is a good correlation between the objects on the calibration set and

that this set may be used with confidence. The results of the prediction for the

validation set are tabulated in Table 4.12.


SEE and SEP values were also calculated to be 0.35 and 0.37 respectively.


Validation Set: 033, 303, and 330


Independent Variable: 1476-1092cm-1 (dye region)

PLS Analysis for Dependent Variable Brown

Another PLS1 model was built using the same calibration and validation sets. The

independent variable region chosen however was 1476-1092cm-1, which is the region

of the spectrum containing the dye peaks. Five factors were found to be significant,

explaining 96.92% of the variance for the dependent variables. The results for the

prediction of the validation set are presented in Table 4.12. These show that the first

model has a better predictive ability and that the whole 1800-800cm-1 region should

be used as independent variables. This may be the result of interactions between the

dye molecule and the substrate. The SEE and SEP values were calculated to be 0.79

and 1.2 respectively.

Based on these results the rest of the models were constructed using the whole region

of the spectrum as independent variable.

Hence, the PLS model with independent variables 1800-800cm-1 for yellow and red as

dependent variables were also studied:

PLS Analysis for the Yellow Dependent Variable


Table 4.13 Results of PLS model for Sides 1, 4 and 5 of Diamond 2 for the prediction of yellow and red dependant variable in the region 1800-800cm-1 for smaller validation set.

Name Predicted Measured Name Predicted Measured

303B212 0.01 (0) 0.00 303B212 3.31 (3) 3.00

303B213 -0.06 (0) 0.00 303B213 3.11 (3) 3.00

303B215 -0.04 (0) 0.00 303B215 2.96 (3) 3.00

303B211 0.037 (0) 0.00 303B211 3.45 (4) 3.00

033B221 3.43 (3) 3.00 033B221 3.16 (3) 3.00

033B223 3.30 (3) 3.00 033B223 3.28 (3) 3.00

033B224 2.98 (3) 3.00 033B224 2.99 (3) 3.00

033B225 3.42 (3) 3.00 033B225 3.01 (3) 3.00

033B226 3.37 (3) 3.00 033B226 3.18 (3) 3.00

330Y11 2.78 (3) 3.00 330Y11 0.72 (1) 0.00

330Y12 2.69 (3) 3.00 330Y12 0.65 (1) 0.00

330Y13 2.88 (3) 3.00 330Y13 0.76 (1) 0.00

330Y14 2.88 (3) 3.00 330Y14 0.63 (1) 0.00

330Y15 2.85 (3) 3.00 330Y15 0.85 (1) 0.00

330Y16 2.89 (3) 3.00 330Y16 0.97 (1) 0.00


Five components were significant for this model, explaining 98.01% of the total

variance of the dependent variable, with the first two explaining 94.24%. The plot of

predicted versus measured values gives a slope of 1.02, intercept of (-0.015) and a

correlation coefficient of 0.986. Thus indicating that the predicted values are

reasonably close to the measured ones. The results of the prediction are shown in

Table 4.13. SEE and SEP values for this model are 0.35 and 0.37 respectively.

PLS Analysis for the Red Dependent Variable

Six factors were significant here, with the first three explaining 94.83% of the total

variance of the dependent variable. The objects in the calibration set show a good

correlation indicating that this set may be used with confidence. The slope of the line

of best fit is found to be 0.989. This line intercepts the axis at 0.017.

When the validation set is introduced to this model however, the slope (0.799) and the

intercept (0.768) for the fitted line are not as favourable as for the calibration set. This

is reflected in the predicted values for the validation. SEE and SEP values for this

model were calculated to be 0.24 and 1.1 respectively. The high value for SEP

confirms that the predictive ability of this model for red dye is not good.

Since overall this model shows a reasonable ability to predict the values for the

dependent variables it was decided to expand the number of objects in the validation

set:

Validation Set: 600, 060, 006, 033, 303, 600, 060, 330 and 600


Independent Variable: 1800-800cm-1



The next model studied consisted of 72 objects in the calibration set and 102 in the

validation set. Six factors were significant explaining a total of 99.05% variance of the

dependent variable.

A cross-validation was performed on the calibration set. The values for the slope

(0.990), intercept (0.019) of the predicted versus measured plot and the correlation

coefficient (0.995) indicate that the set chosen is a suitable one. The validation set was

then introduced to this model. Here, the values for the slope (0.784), intercept (0.161)

and the correlation coefficient (0.973) indicate that there is a reasonable correlation

between these objects. See Appendix 2 (Figure A2-4). This is also confirmed by the

actual values obtained for the prediction of the dependent variable brown, shown in

Table 4.14. SEE and SEP values for this model are 0.19 and 0.83 respectively. The

value for SEP is considerably high. The calibration and validation sets studied here

were used for the dependent variables yellow and red.


A model consisting of 72 objects in the calibration set and 102 in the validation set

was built. Five factors explained 99.24% of the total variance of this dependent

variable, with the first three explaining 98.13%. The calibration set showed a good

correlation between its objects. The correlation between the validation set, although

not as good as for the calibration set, is quite favourable, as is shown by the predicted

values for its objects. See Appendix 2, Figure A2-5 and Table 4.14. The SEE and SEP

values were calculated to be 0.17 and 0.65 respectively. The value for SEP also shows

that the prediction for this dependent variable yellow is better than the one for brown.


Table 4.14: Results of PLS Model for Sides 1, 4 and 5 of Diamond 2 for the prediction of dependant variables brown, yellow and red in the region 1800-800cm-1 with a larger validation set

Name Predicted Measured Name Predicted Measured Name Predicted Measured 060Y111 4.4 (4) 6.0 060Y111 1.4 (1) 0 060Y111 0.14 (0) 0 060Y112 5.0 (5) 6.0 060Y112 0.80 (1) 0 060Y112 0.26 (0) 0 060Y113 4.4 (4) 6.0 060Y113 1.4 (1) 0 060Y113 0.16 (0) 0 060Y114 4.9 (5) 6.0 060Y114 0.54 (1) 0 060Y114 0.36 (0) 0 060Y115 4.2 (4) 6.0 060Y115 1.5 (2) 0 060Y115 0.076 (0) 0 060Y116 4.1 (4) 6.0 060Y116 1.5 (2) 0 060Y116 0.21 (0) 0 060Y12 4.1 (4) 6.0 060Y12 1.6 (2) 0 060Y12 0.22 (0) 0 060Y13 5.0 (5) 6.0 060Y13 0.78 (1) 0 060Y13 0.24 (0) 0 060Y14 4.1 (4) 6.0 060Y14 1.7 (2) 0 060Y14 0.23 (0) 0 060Y15 4.8 (5) 6.0 060Y15 0.86 (1) 0 060Y15 0.38 (0) 0 060Y16 4.1 (4) 6.0 060Y16 1.6 (2) 0 060Y16 0.29 (0) 0 060Y211 4.3 (4) 6.0 060Y211 1.4 (1) 0 060Y211 0.17 (0) 0 060Y212 4.8 (5) 6.0 060Y212 0.87 (1) 0 060Y212 0.35 (0) 0 060Y213 4.1 (4) 6.0 060Y213 1.5 (2) 0 060Y213 0.29 (0) 0 060Y214 4.5 (5) 6.0 060Y214 0.87 (1) 0 060Y214 0.58 (1) 0 060Y215 4.3 (4) 6.0 060Y215 1.6 (2) 0 060Y215 0.14 (0) 0 060Y216 4.4 (4) 6.0 060Y216 1.3 (1) 0 060Y216 0.081 (0) 0 060Y311 4.0 (4) 6.0 060Y311 1.6 (2) 0 060Y311 0.25 (0) 0 060Y312 5.2 (5) 6.0 060Y312 0.73 (1) 0 060Y312 0.11 (0) 0 060Y313 4.3 (4) 6.0 060Y313 1.6 (2) 0 060Y313 0.01 (0) 0 060Y314 4.7 (5) 6.0 060Y314 1.3 (1) 0 060Y314 -0.19 (0) 0 060Y315 5.1 (5) 6.0 060Y315 0.65 (1) 0 060Y315 0.16 (0) 0 060Y316 4.4 (4) 6.0 060Y316 1.6 (2) 0 060Y316 -0.011 (0) 0 330Y11 2.6 (3) 3.0 330Y11 3.2 (3) 3 330Y11 0.21 (0) 0 330Y12 2.8 (3) 3.0 330Y12 3.2 (3) 3 330Y12 0.15 (0) 0 330Y13 2.5 (3) 3.0 330Y13 3.3 (3) 3 330Y13 0.37 (0) 0 330Y14 2.4 (2) 3.0 330Y14 3.4 (3) 3 330Y14 0.34 (0) 0 330Y15 2.6 (3) 3.0 330Y15 3.3 (3) 3 330Y15 0.24 (0) 0 330Y16 2.6 (3) 3.0 330Y16 3.2 (3) 3 330Y16 0.32 (0) 0 600Y11 0.75 (1) 0.0 600Y11 5.7 (6) 6 600Y11 -0.53 (1) 0 600Y12 0.87 (1) 0.0 600Y12 5.2 (5) 6 600Y12 -0.38 (0) 0 600Y13 -0.10 (0) 0.0 600Y13 6.0 (6) 6 600Y13 0.13 (0) 0 600Y14 0.77 (1) 0.0 600Y14 5.6 (6) 6 600Y14 -0.42 (0) 0 600Y141 -0.038 (0) 0.0 600Y141 5.8 (6) 6 600Y141 0.071 (0) 0 600Y142 0.82 (1) 0.0 600Y142 5.7 (6) 6 600Y142 -0.53 (0) 0 600Y143 0.50 (1) 0.0 600Y143 5.8 (6) 6 600Y143 -0.36 (0) 0 600Y144 0.87 (1) 0.0 600Y144 5.6 (6) 6 600Y144 -0.66 (-1) 0 600Y145 1.1 (1) 0.0 600Y145 5.4 (5) 6 600Y145 -0.46 (-1) 0 600Y146 0.0 (0) 0.0 600Y146 5.9 (6) 6 600Y146 -0.02 (0) 0 600Y15 0.8 (1) 0.0 600Y15 5.4 (5) 6 600Y15 -0.30 (0) 0 600Y16 -0.16 (0) 0.0 600Y16 5.9 (6) 6 600Y16 0.20 (0) 0 600Y311 1.0 (1) 0.0 600Y311 5.5 (6) 6 600Y311 -0.70 (-1) 0 600Y312 0.76 (1) 0.0 600Y312 5.6 (6) 6 600Y312 -0.45 (-1) 0 600Y313 1.04 (1) 0.0 600Y313 5.2 (5) 6 600Y313 -0.52 (-1) 0 600Y314 0.78 (1) 0.0 600Y314 5.5 (6) 6 600Y314 -0.47 (-1) 0 600Y315 1.1 (1) 0.0 600Y315 5.6 (6) 6 600Y315 -0.78 (-1) 0 060Y11 4.2 (4) 6.0 060Y11 1.6 (2) 0 060Y11 0.19 (0) 0


Table 4.14: Results of PLS Model for Sides 1, 4 and 5 of Diamond 2 for the

prediction of dependant variables brown, yellow and red in the region 1800-800cm-1 with a larger validation set

Name Predicted Measured Name Predicted Measured Name Predicted Measured

600B26 5.8 (6) 6.0 600B26 0.41 (0) 0 600B26 -0.24 (0) 0 600B211 5.4 (6) 6.0 600B211 0.51 (1) 0 600B211 0.099 (0) 0 600B212 5.6 (6) 6.0 600B212 0.49 (1) 0 600B212 -0.003 (0) 0 600B213 4.8 (5) 6.0 600B213 0.71 (1) 0 600B213 0.55 (1) 0 600B214 6.1 (6) 6.0 600B214 0.20 (0) 0 600B214 -0.26 (0) 0 600B215 6.3 (6) 6.0 600B215 0.074 (0) 0 600B215 -0.40 (0) 0 600B216 6.4 (6) 6.0 600B216 0.081 (0) 0 600B216 -0.44 (0) 0 600B22 5.4 (5) 6.0 600B22 0.47 (1) 0 600B22 0.15 (0) 0 600B23 5.2 (5) 6.0 600B23 0.76 (1) 0 600B23 0.16 (0) 0 600B24 5.2 (5) 6.0 600B24 0.57 (1) 0 600B24 0.24 (0) 0 600B25 4.8 (5) 6.0 600B25 0.69 (1) 0 600B25 0.57 (0) 0 600B21 5.7 (6) 6.0 600B21 0.38 (0) 0 600B21 -0.13 (0) 0 303B216 2.6 (3) 3.0 303B216 0.11 (0) 0 303B216 3.4 (3) 3 303B212 2.6 (3) 3.0 303B212 0.28 (0) 0 303B212 3.2 (3) 3 303B213 2.9 (3) 3.0 303B213 0.24 (0) 0 303B213 2.9 (3) 3 303B214 2.9 (3) 3.0 303B214 0.24 (0) 0 303B214 2.8 (3) 3 303B215 2.9 (3) 3.0 303B215 0.26 (0) 0 303B215 3.0 (3) 3 303B211 2.9 (3) 3.0 303B211 0.12 (0) 0 303B211 3.1 (3) 3 060B26 -0.04 (0) 0.0 060B26 5.9 (6) 6 060B26 0.078 (0) 0 006B211 -0.26 (0) 0.0 006B211 -0.20 (0) 0 006B211 6.4 (6) 6 006B212 -0.18 (0) 0.0 006B212 -0.21 (0) 0 006B212 6.4 (6) 6 006B213 0.003 (0) 0.0 006B213 -0.29 (0) 0 006B213 6.4 (6) 6 006B214 -0.04 (0) 0.0 006B214 -0.081 (0) 0 006B214 6.2 (6) 6 006B215 -0.031 (0) 0.0 006B215 -0.15 (0) 0 006B215 6.2 (6) 6 006B216 0.015 (0) 0.0 006B216 -0.25 (0) 0 006B216 6.2 (6) 6 006B22 -0.16 (0) 0.0 006B22 -0.19 (0) 0 006B22 6.5 (7) 6 006B221 -0.18 (0) 0.0 006B221 -0.19 (0) 0 006B221 6.4 (6) 6 006B222 -0.24 (0) 0.0 006B222 -0.039 (0) 0 006B222 6.3 (6) 6 006B223 -0.037 (0) 0.0 006B223 -0.20 (0) 0 006B223 6.2 (6) 6 006B224 -0.28 (0) 0.0 006B224 -0.10 (0) 0 006B224 6.4 (6) 6 006B225 -0.08 (0) 0.0 006B225 -0.17 (0) 0 006B225 6.3 (6) 6 006B226 0.064 (0) 0.0 006B226 -0.16 (0) 0 006B226 6.2 (6) 6 006B23 -0.33 (0) 0.0 006B23 -0.062 (0) 0 006B23 6.5 (7) 6 006B24 -0.48 (1) 0.0 006B24 -0.049 (0) 0 006B24 6.6 (7) 6 006B25 -0.31 (0) 0.0 006B25 -0.054 (0) 0 006B25 6.5 (7) 6 006B26 -0.32 (0) 0.0 006B26 -0.14 (0) 0 006B26 6.6 (7) 6 033B221 0.095 (0) 0.0 033B221 3.0 (3) 3 033B221 2.9 (3) 3 033B222 -0.21 (0) 0.0 033B222 3.1 (3) 3 033B222 3.2 (3) 3 033B223 0.025 (0) 0.0 033B223 3.0 (3) 3 033B223 3.0 (3) 3 033B224 -0.020 (0) 0.0 033B224 3.0 (3) 3 033B224 3.0 (3) 3 033B225 -0.21 (0) 0.0 033B225 3.1 (3) 3 033B225 3.1 (3) 3 033B226 0.038 (0) 0.0 033B226 3.0 (3) 3 033B226 3.0 (3) 3 060B21 0.11 (0) 0.0 060B21 5.8 (6) 6 060B21 0.028 (0) 0 060B22 -0.073 (0) 0.0 060B22 6.0 (6) 6 060B22 0.059 (0) 0 060B221 0.11 (0) 0.0 060B221 5.8 (6) 6 060B221 -0.038 (0) 0 060B222 -0.014 (0) 0.0 060B222 5.8 (6) 6 060B222 0.11 (0) 0 060B223 -0.39 (0) 0.0 060B223 5.9 (6) 6 060B223 0.34 (0) 0 060B224 0.11 (0) 0.0 060B224 5.8 (6) 6 060B224 -0.036 (0) 0 060B225 -0.09 (0) 0.0 060B225 5.9 (6) 6 060B225 0.094 (0) 0 060B226 -0.23 (0) 0.0 060B226 5.9 (6) 6 060B226 0.19 (0) 0 060B23 -0.072 (0) 0.0 060B23 5.9 (6) 6 060B23 0.053 (0) 0 060B24 0.027 (0) 0.0 060B24 5.9 (6) 6 060B24 -0.12 (0) 0 060B25 -0.19 (0) 0.0 060B25 5.8 (6) 6 060B25 0.28 (0) 0 006B21 -0.24 (0) 0.0 006B21 -0.21 (0) 0 006B21 6.6 (7) 6 600Y316 0.88 (1) 0.0 600Y316 5.4 (5) 6 600Y316 -0.44 (0) 0


PLS Analysis for the Red Dependent Variable

Another model was built using the same set of calibration and validation as the

dependent variable red. Seven factors were significant to explain 99.32% of the

dependent variable, with the first four already explaining 96.25%. The plot of

predicted versus measured values for the calibration set show that the set chosen is a

suitable one. This is confirmed by the validation set. An excellent correlation exists

for this set with slope of 1.055, intercept of 0.004 and correlation 0.993. This is also

confirmed by the minimal spread of the objects of this set on the line of best fit. See

Appendix 2- Figure A2-6. This is further seen in the predicted values for this set. For

details refer to Table 4.14. SEE and SEP values were calculated to be 0.16 and 0.49

respectively. The lower value for SEP confirms the fact that the predictive ability of

this model for red is greater than for the previous dependent variables.

Overall, it is concluded that the PLS1 models studied here are quite feasible for

predicting the ratio of the dyes on each fabric. It is also interesting to note that the

predicted values tend to indicate that the predictive ability of the system for some

colours is better than others even though the same calibration and validation sets are

used. This might be due to the fact that it was more difficult to record the spectra of

the fabrics with brown dye on them, because they showed a large amount of

fluorescence. Therefore, despite all the effort made to level and zero-baseline the

spectra there will probably be some fluorescence or noise that has not been taken

care of.


Table 4.15: Results of PLS Model for Sides 1, 4 and 5 of Diamond 1 for the prediction of the yellow dependant variable in the region 1800-800cm-1.

Name Predicted Measured Name Predicted Measured 006Y112 0.12 (0) 0.0 060Y214 0.7 (1) 0.0

006Y111 0.17 (0) 0.0 060Y215 1.5 (2) 0.0

006Y213 0.097 (0) 0.0 060Y216 1.4 (1) 0.0

006Y214 -0.049 (0) 0.0 060Y311 1.6 (2) 0.0

006Y215 0.092 (0) 0.0 060Y312 0.6 (1) 0.0

006Y216 0.13 (0) 0.0 060Y313 1.7 (2) 0.0

006Y241 -0.011 (0) 0.0 060Y314 1.5 (2) 0.0

006Y242 -0.058 (0) 0.0 060Y315 0.69 (1) 0.0

006Y243 -0.24 (0) 0.0 060Y316 1.6 (2) 0.0

006Y244 -0.00 (0) 0.0 303Y141 3.0 (3) 3.0

006Y245 0.13 (0) 0.0 303Y142 3.1 (3) 3.0

006Y246 0.11 (0) 0.0 303Y143 3.0 (3) 3.0

006Y311 0.14 (0) 0.0 303Y144 3.0 (3) 3.0

006Y312 0.22 (0) 0.0 303Y145 3.0 (3) 3.0

006Y313 0.18 (0) 0.0 303Y146 3.0 (3) 3.0

006Y314 0.17 (0) 0.0 330Y11 3.2 (3) 3.0

006Y315 0.18 (0) 0.0 330Y12 3.1 (3) 3.0

006Y316 0.23 (0) 0.0 330Y13 3.2 (3) 3.0

033Y211 -0.13 (0) 0.0 330Y14 3.2 (3) 3.0

033Y212 -0.02 (0) 0.0 330Y15 3.1 (3) 3.0

033Y213 -0.11 (0) 0.0 330Y16 3.2 (3) 3.0

033Y214 0.11 (0) 0.0 600Y11 6.1 (3) 6.0

033Y215 -0.082 (0) 0.0 600Y12 5.9 (6) 6.0

033Y216 0.19 (0) 0.0 600Y13 6.2 (6) 6.0

060Y11 1.46 (2) 0.0 600Y14 6.0 (6) 6.0

060Y111 1.34 (1) 0.0 600Y141 6.2 (6) 6.0

060Y112 0.52 (1) 0.0 600Y142 5.9 (6) 6.0

060Y113 1.29 (1) 0.0 600Y143 6.1 (6) 6.0

060Y114 0.55 (1) 0.0 600Y144 6.1 (6) 6.0

060Y115 1.52 (2) 0.0 600Y145 5.8 (6) 6.0

060Y116 1.5 (2) 0.0 600Y146 6.1 (6) 6.0

060Y12 1.5 (2) 0.0 600Y15 5.8 (6) 6.0

060Y13 0.5 (1) 0.0 600Y16 6.2 (6) 6.0

060Y14 1.6 (2) 0.0 600Y311 6.1 (6) 6.0

060Y15 0.7 (1) 0.0 600Y312 6.1 (6) 6.0

060Y16 1.6 (2) 0.0 600Y313 6.1 (6) 6.0

060Y211 1.5 (2) 0.0 600Y314 6.1 (6) 6.0

060Y212 0.5 (1) 0.0 600Y315 6.2 (6) 6.0

060Y213 1.4 (1) 0.0 600Y316 5.9 (6) 6.0


Figure 4.24: Predicted vs. Measured values for (i) the calibration and (ii) the

validation set, for Sides 1, 4 and 5 of Diamond 1, dependant variable: yellow.

(i) Calibration Set

Measured (Yellow)

Predicted (Yellow)

0.0 1.0 2.0 3.0 4.0 5.0-2.0

0.0

2.0

4.0

6.0


105Y141 105Y142 105Y143 105Y144 105Y145 105Y146 150Y11 150Y12 150Y13 150Y14 150Y15 150Y16

204Y141204Y142204Y143204Y144204Y145204Y146240Y11240Y12240Y13240Y14240Y15240Y16

402Y141402Y142402Y143402Y144402Y145402Y146420Y11420Y12420Y13420Y14420Y15420Y16

501Y141 501Y142 501Y143 501Y144 501Y145 501Y146 510Y11 510Y12 510Y13 510Y14 510Y15 510Y16

Slope = 0.993

Interc. = 0.015

Corr. =

0.996

(ii) Validation Set

Measured (Yellow)

Predicted (Yellow)

0.0 2.0 4.0 6.0-2.0

0.0

2.0

4.0

6.0

8.0



303Y141303Y142303Y143303Y144303Y145303Y146330Y11330Y12330Y13330Y14330Y15330Y16

600Y11 600Y12 600Y13 600Y14 600Y141 600Y142 600Y143 600Y144 600Y145 600Y146 600Y15 600Y16 600Y311 600Y312 600Y313 600Y314 600Y315 600Y316

Slope =

0.897 Interc. =

0.606

Corr. = 0.974



Validation Set: 600, 060, 006, 330, 033, 303


A calibration set consisting of 72 objects was built and cross-validated. The predicted

versus measured plot for this set is shown in Figure 4.24(i). The slope (0.993) and

intercept (0.015) of the line of best fit indicates that there is a reasonable correlation

coefficient (0.996) between these objects. A validation set was then introduced into

this model consisting of 78 objects. The plot of predicted versus measured values is

displayed in Figure 4.24 (ii). The values for the slope (0.897) and the intercept (0.606)

as well as the correlation coefficient (0.974) indicate that the correlation between

these data is not as favourable as the one for the calibration set. This was however

expected since samples containing no yellow dye in the validation set are widely

spread. This is attributed in turn to the fact that the same type of samples have not

been predicted very well in the cross validation of the calibration set. The values for

the yellow dependent variable in the validation set are tabulated in Table 4.15. SEE

and SEP were calculated to be 0.17 and 0.66 respectively.


Another PLS1 model was built with the same calibration and validation sets as in the

previous section. The cross-validation results for the calibration set indicated that the

validation set might be used with confidence. Six factors explained 98.7% and 98.6%

of the total variances of the independent and the dependent variables respectively.

The same validation set was introduced to this model. For the plot of predicted versus

measured values see Appendix 2, Figure A2-7. The slope and the intercept of the line

of best fit indicate that there is an acceptable correlation between the predicted and


measured values for these objects. The predicted values along with measured values

are shown in Table A2-4. The values for SEE and SEP were calculated to be 0.23 and

0.75 respectively.

Inspection of the spectra of brown indicates that the predicted values for the brown

independent variable are again worse than those predicted for the other dependent

variables. Therefore, the results from both Diamond 1 and Diamond 2 tend to

indicate that there is something about the spectra of the samples containing this the

brown dye that causes inaccuracy in the prediction of the dye ratio.

PLS Analysis for the Grey Dependent Variable

A model with 72 objects as the calibration set was built. The spectral range chosen

for the independent variables was 1800-800cm-1. Six factors were found to be

significant explaining 98.62% of the independent variables and 99.36% of the

dependent variables. The first three factors explained 95.93% of the total variance for

the dependent variables. The plot of the predicted and measured values from cross-

validation performed on the validation set shows a good relation between these values

with a slope of 0.994 and intercept of 0.013. See Appendix 2, Figure A2-8 (I).

A validation set consisting of 78 objects was then introduced to this model. The

predicted and measured values were plotted for this set in Appendix 2, Figure A2-8

(II). The correlation coefficient of the line of best fit along with the slope (0.976) and

the intercept (0.080) of this line indicate that the predicted values for these objects are

quite close to their true ones (coefficient correlation= 0.997). The values calculated

for SEE and SEP are 0.16 and 0.30 respectively. The values predicted for dependent

variable grey are tabulated in Table A2-5 of Appendix 2.


Overall, these results indicate that the models built using the FT-Raman spectral

information of wool/polyester blend fabrics dyed with Forosyn dyes, have a good

ability for prediction of the ratio of various dyes on the fabric. It is also concluded

that some colours are predicted better than others.

So far, it has been shown that PLS1 models built for samples with one or two dye

combination are able to predict these ratios with success. There are some colours that

are produced industrially using various ratios of three dyes. Therefore, it is of interest

to find out how well these models work for three-dye mixtures.

Sides 1, 4 and 5 plus the samples in the middle of

Diamond 1

Another PLS1 model was built using the guidance in reference 20 which suggests that

the best model for these data would be a calibration set with objects from sides 1, 4

and 5 with some points from the middle of the diamond and a validation set with

objects chosen from the samples in the middle.


A calibration consisting of 168 objects was built. A cross-validation was performed

on it. Four factors were significant, explaining 97.51% and 95.83% of the total

variance of the independent and dependent variables respectively. The first two

factors explained nearly 94.0% of the total variance. The slope (0.958) and intercept

(0.082) of the line of the best fit (with R=0.974) indicates that the correlation between


Figure 4.25: Predicted vs. Measured values for (i) the calibration and (ii) the

validation set, for the top triangle of Diamond 1, dependant variable: yellow.

(i) Calibration Set

Measured (Yellow)

Predicted (Yellow)

0.0 2.0 4.0 6.0 -2.0

0.0

2.0

4.0

6.0

8.0

006Y112006Y111006Y213006Y214006Y215006Y216006Y241006Y242006Y243006Y244006Y245006Y246006Y311006Y312006Y313006Y314006Y315006Y316015Y211

015Y212

015Y213015Y214015Y215015Y216024Y211024Y212024Y213024Y214024Y215024Y216033Y211033Y212033Y213033Y214033Y215033Y216042Y211042Y212042Y213042Y214042Y215042Y216051Y211051Y212051Y213051Y214051Y215051Y216

060Y11060Y111060Y112

060Y113

060Y114

060Y115060Y116060Y12

060Y13

060Y14

060Y15

060Y16060Y211

060Y212

060Y213

060Y214

060Y215060Y216060Y311

060Y312

060Y313060Y314

060Y315

060Y316105Y141 105Y142 105Y143 105Y144 105Y145 105Y146 150Y11 150Y12 150Y13 150Y14 150Y15 150Y16

204Y141204Y142204Y143204Y144204Y145204Y146213Y311213Y312213Y313

213Y314213Y315213Y316231Y311231Y312231Y313231Y314231Y315231Y316240Y11240Y12240Y13240Y14240Y15240Y16

303Y141303Y142303Y143303Y144303Y145303Y146312Y311312Y312312Y313312Y314312Y315312Y316330Y11330Y12330Y13330Y14330Y15330Y16

402Y141402Y142402Y143402Y144402Y145402Y146420Y11420Y12420Y13420Y14420Y15420Y16

501Y141 501Y142 501Y143 501Y144 501Y145 501Y146 510Y11 510Y12 510Y13 510Y14 510Y15 510Y16


Slope = 0.958Interc. 0.082

Corr. = 0.979

(ii) Validation Set

Measured (Yellow)

Predicted (Yellow)

2.0 2.5 3.0 3.5 4.01.00

2.00

3.00

4.00

222Y311

222Y312

222Y313

222Y314

222Y315222Y316

321Y311321Y312321Y313321Y314321Y315

321Y316

411Y311 411Y312 411Y313 411Y314 411Y315 411Y316

Slope = 1.006

Interc. = -0.280

Corr. =

0.974


these objects is not as good as the one built for samples with one and two dyes.

However, these results show that it is still a reasonable correlation. A validation set

consisting of samples 222, 321 and 411 was then submitted to this model for

prediction. Figure 4.25(ii) shows that the data for sample 222 in the validation set are

quite spread. The SEE and SEP values were calculated to be 0.44 and 0.51

respectively for this model.

The same calibration and validation sets were then used for dependent variables

brown and grey.


A calibration set containing the same number of objects for the calibration set and the

independent variables was built. The cross validation of the calibration set indicated

that there is a reasonable correlation between the objects in this set. The validation set

was then introduced into the model. Six factors were found to be significant

explaining 93.96% of the variance in the dependent variable. The last two factors

explained only 3.87% of the total variance. Again it was found that the correlation

between the objects in the model with brown dependent variable is not as good as the

one for the other variables. See Appendix 2, Figure A2-9 (I, II).

The values for the brown independent variable were then predicted using SIRIUS 6.0.

These values are displayed in Appendix 2, Table A2-6. SEE and SEP values

calculated for this model are 0.55 and 0.82 respectively. These values are certainly

very much higher than what has been calculated before.


On introduction of the validation set, it was found that the correlation between these

objects is not very favourable. The values for the slope (1.156) and the intercept

(-0.094) for the plot of predicted versus measured values for the validation set

indicated that three of the spectral repeats for sample 222 might be outliers. Therefore,

they were taken out. This caused an improvement in the values for the slope (0.784)

and the intercept (0.278).

The predicted values for the objects in the validation set (after the outliers have been

taken out) are still not as accurate as the one for yellow however confirming that the

brown dye interferes with the predictive ability of the PLS1 model. See Appendix 2,

Figure A2-10 (III) and Table A2-6. In this model SEP was found to be 0.28. This is a

great improvement on SEP value calculated previously. This also confirms that the

three samples should be treated as outliers.

PLS Analysis for the Grey Dependent Variable

A calibration set consisting of the same number of objects was built. Cross validation

calculations on this set showed that there is a good correlation (0.989) between its

objects. Six factors were significant explaining a total of 97.74% of the variance for

the latent variable. The first three factors explained 95.33% of the total variance of the

grey dependent variable. The validation set consisting of 15 objects was then

introduced into this model. The values for the slope (0.517) and the intercept (0.655)

of the plot of predicted verses measured values indicated that there is not a good

correlation between these values. See Appendix 2, Figure A2-11. The actual predicted

values are shown in Table A2-7. The values for SEE and SEP are 0.32 and 0.45

respectively.


Overall, the results indicate that the PLS models built using FT-Raman spectra of

these textile samples are a promising method of quantitatively measuring the ratio of

various dyes on each fabric. It is however necessary to study a larger population in

order to find out how robust this model is. Some dyes offer a better predictive ability

in these systems than others. This might be due to the fact that the dye peaks are less

intense because of the presence of fluorescence in those spectra. Therefore, it is

proposed to study a large population as well as to investigate a wider range of dyes

as dependent variables.

It is interesting to note that the PLS has worked quite well for the polyester blend, but

failed to do equally well for the dyed wool even though the dye peaks are easily

visible in the spectra of dyed wool without the aid of chemometrics. It may be that the

failure of the chemometrics to correlate the dye peaks to the amount of dye is due to

some, unknown, interaction between the different dyes. It is perhaps more likely that

the given dye values do not reflect the amount of dye actually on the fibres.


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Acoustic and FT-Raman Spectroscopies’, Proc. 9th int. Wool Text. Res. Conf., Biella, 4 (1995), p.p. 114-122.

30 J.L. Koenig, “Spectroscopy of Polymers”, (American Chemical Society, Washington D.C. 1992), p.120.

31 E.D. Lipp, M.A. Leugers in ‘Analytical Applications of Raman Spectroscopy’, M.J. Pelletier (ed), 1st ed. (1999), Ch.3.

32 D. Lin-Vien, N. B. Colthup, W.G. Fateley, J.G. Grasselli, “The Handbook of Infrared and Raman Charactristic Frequencies of Organic Molecules”, (Academic Press, 1991), pp.485-490.

33 S. Carswell, “Microanalysis of Dyes From textiles”, Master Thesis, Queensland University of Tecnology, 1991.

34 J. Cheng, ‘Characterisation of Wool Treated with Metal Ions’, Master Thesis,Queensland University of Tecnology, 1993.

35 I. Keen, ‘Forensic Application of Raman Microprobe’, Masters Thesis, Queensland University of Tecnology, 1998.

Chapter 5 FT-Raman Spectroscopy of Irradiated Dyed Wool 121

Chapter 5 FT-Raman Spectroscopy of Irradiated Dyed Wool

5.1 Introduction

Sunlight is an important cause of physical and chemical damage to polymeric

materials such as plastics, textiles and paints. The damage is caused mainly by short

wavelength ultraviolet light1. When exposed to light, natural fibres such as wool and

cellulose undergo photo-bleaching (caused by blue light, i.e. above 400 nm), followed

by yellowing and tendering of the fibres caused mainly by the UV radiation between

290-310 nm. The degree of fibre yellowing has been shown to increase linearly with

its exposure up to 104 hours of sunlight2.

The extent of photo-degradation on the fibre may be assessed by the measurement of

the tensile strength of the fibre. Tensile strength decreases dramatically after 52 hours

of exposure of the wool fibre to accelerated light2. Simpson et al.3 have found that

both peptide and cystine cross-linkages of wool are destroyed upon photo-degradation

and a new range of ionic groups (mainly acidic) is formed. They have also reported

some success in reducing the rate of photo-degradation on wool by the use of UV

absorbers and dyes. On the other hand, chemical bleaching of the fibre and the use of

fluorescent whitening agent (FWA) on fibres accelerates photo-degradation 4.


The most likely reason5 for yellowing in the wool fabric is environmental effects on

the lipids situated on the surface of the fibre. Miller and Smith6 found that, although

some dyes accelerate the loss of strength in the fibre on exposure to sunlight, treating

the fibre with aluminium salts followed by dyeing with mordant dyes has the opposite

effect.

The chemistry of the photo-degradation of wool is not fully understood. Amino acid

analysis of UV irradiated wool fibre has indicated that, following irradiation of the

fibre, there is considerable degradation of the amino acids tryptophan, cystine,

tyrosine, phenylalanine and histidine, with tryptophan being the precursor in the

photo-yellowing of the wool fibre 2.

It has been suggested that, when wool is exposed to sunlight, a large number of

disulfide bonded cysteine residues are oxidised to cysteic acid7. This causes a

reduction in the tenacity of the fibre. The change in the elasticity of the fibre is

attributed to changes in intra- and inter-molecular forces in the α-helix with the

possible formation of new covalent cross-linkages.

Photo yellowing due to the degradation of tryptophan upon exposure to light is also

greatly influenced by oxygen. It is suggested8 that the photo-degradation of

tryptophan occurs via a cleavage of the indole ring resulting in the formation of

kynurenine derivatives some of which are yellow. The photo yellowing of wool is not

due to one particular yellow chromophore, but to melanin-like structures or

ommochrome dyestuffs.


Several significant changes occur in the molecular structure of wool when it is

exposed to stimulated sunlight, viz.:

1. Disulfide bonded cystine amino acid residues are oxidised with cysteic acid

being the major product.

2. There is a reduction in the α- helical content, while random coil or β-sheet

protein chain conformations are favoured.

3. There are structural changes in the fibre as a consequence of cystine

photo-oxidation.

4. Tryptophan amino acid residues in the wool fibre are degraded upon

exposure to light, causing photo yellowing in the fibre.

It has been suggested9 that Raman spectroscopy may be used to obtain detailed

information about UV absorbers in wool and that this technique is particularly

useful since structural information about the UV absorbers may be obtained

without any special sample preparations and since there is minimal interference

because of the relatively weak scattering by the substrate.

Various studies have also been conducted on fading of colours on different

substrates. A study on the colour fading in dehydrated flowers using photo-

acoustic spectroscopy10 concluded that the fading of the vibrant colours is due to

the humidity in the air rather than temperature. The same observation has been

made for photo-oxidation of wool, where it is suggested that atmospheric oxygen

interacts with light and humidity in the air11. Choi et al.12 have studied photo-

catalytic degradation of three Lanasol dyes and suggested that this technique may

be used for treating textile wastewater.


Irradiated Undyed Wool Fabric Church and Millington7 studied the photo-degradation of wool using Raman and

FT-IR. Wool samples were irradiated in air using various sources — UVC

(254nm), UVA (360nm) and blue light (420nm). Two photolytic reaction

mechanisms involving the cystine residues were proposed with the pathways

dependent on the wavelength of the applied radiation. The conditions used for the

Raman spectrometer are as indicated in Table 5.1.

Table 5.1 Conditions chosen in FT-Raman by Church and Millington7

Six spectra of each sample were collected, averaged and then normalised on the

strong band assigned to CH2 and CH3 bending modes at 1450 cm-1. The spectral

changes caused by various exposing wavelengths have been studied; in particular the

changes in the S-S and S-H stretching lines due to the changes in the concentration of

cysteic acid as well as tryptophan-phenylalanine band were considered.

UV-C radiation was found to cause oxidative cleavage of the S-S bond in cystine to

produce cysteic acid and the partially oxidised derivatives cystine S- monoxide and S,

S-dioxide residues. UVA and blue light produce cysteic acid and cysteine-S-sulfonate

residues. Thiol groups are also produced after UVC and UVA irradiation. Significant

halla

This table is not available online. Please consult the hardcopy thesis available from the QUT Library.


degradation of the amino acid tryptophan was also found after irradiation with UVA

and the blue light.

Lewis and co-workers2 studied the photo-oxidation of wool using FT-Raman

spectroscopy, where the samples were exposed to accelerated light exposure

conditions. The conditions for the instrument are tabulated in Table 5.2.

Table 5.2- Conditions used in FT-Raman spectrometer by Lewis and co-workers2

In order to allow comparison of changes in band intensities after photo-oxidation, they

normalised the spectra against the peak intensity of the CH2 anti-symmetric stretch at

2933cm-1 rather than the band at 1450cm-1 as done by Church because of the a strong

band associated with Cibafast W (a UV absorber) at 1454cm-1. They concluded that

differences between the irradiated and non-irradiated spectra were mainly found in the

1800-500 cm-1 region. Lewis et al. argued that changes in the intensities in the S-S

and C-S peaks confirmed that there is oxidation of cystine residues, which in turn was

supported by the appearance of a peak assigned to the S-O vibration of cysteic acid.

They have also shown that the relative intensity of the Tyrosine Fermi doublet at 830

and 854 cm-1 was affected by exposure. They have offered two explanations for that.

One is that tyrosine was photo-oxidised; another is that the intensity ratio of these two

peaks was sensitive to the strength of the H-bonding to the phenolic hydroxyl group

of tyrosine and that the increase in the intensity of the band at 854cm-1, showed that

the tyrosine residues were strongly H-bonded or buried within the hydrophobic

halla



region, being protected from further photo-oxidation. Lewis et al.2 have also

concluded that the bands belonging to tryptophan do not show any obvious changes,

despite the fact that it does degrade upon the exposure of wool to sunlight. This is in

agreement with earlier studies8, 13 indicating that tryptophan is affected by its exposure

to sunlight. In those studies they examined tryptophan amino acid in aqueous solution,

as well as by isolating it from the wool fibre using hydrolysis.

Lewis2 argued that changes in the intensities of the peaks at 1245, 1665 and 935cm-1

were indicative of an increase in the disordered content of the fibre by irradiation. The

results of Lewis’s studies are displayed in Table 5.3.

Table 5.3 Changes reported by Lewis et al. 2 in the vibrational spectra of

wool due to UV-irradiation

halla


Figure 5.1 FT-Raman spectra of undyed wool (texp= 0, 168, 504 hrs.) Note: The arrows show some of the peaks affected by UV-radiation

-0.004

-0.002

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

1500

1448

1396

1344

1292

1240

1188

1136

1084

1032 98

092

887

682

477

272

066

861

656

451

2

Unexposed wool Texp.= 7 days

(168 hrs) Texp. = 21 days (504 hrs)

Wavelength (cm-1)


5.2 FT-Raman Spectroscopy of Irradiated Wool

In this chapter, irradiated undyed and dyed wool were studied using Raman

spectroscopy with the aid of chemometrics.

Briefly, in this project, samples of undyed woollen fabric were weathered using a

Sun-Test instrument. One set of samples was irradiated for seven days, while the other

set was weathered for twenty-one days. A set of swatches was kept as a control set

(texp = 0 days). These samples were kept in dark, under constant temperature and

humidity. FT-Raman spectra were then recorded for each set. The spectra for samples

at texp = 0, 7 and 21days is shown in Figure 5.1.

Figure 5.1 shows the spectra, in the region of 1500-500 cm-1, of samples with

different exposure times. These spectra are very similar to those reported by Lewis et

al. 2. The peaks marked by an arrow are the ones found both in this study and by

Lewis that were affected by exposure to accelerated weathering.

The frequencies and assignments of these peaks are given in Table 5.4. The peak at

977cm-1 reported by Lewis et al.2 was also found to be present in these spectra. They

have not assigned this peak and indicated that they do not know its origin. It may be

that this band is due to the products of photo-oxidation of the wool cystine residues;

dithiocarbamates have a strong Raman band at ca. 970cm-1 and both monothiolic

acids and ionic dithiolates have strong Raman bands in this region14. This is also

evident from the changes in the intensity of S-O vibrational band at 1040cm-1. It may

be that, upon increasing the exposure time, the S-O bond is further oxidised.

Figure 5.2(b) Loading plot for the component 1 of PCA plot in Figure 5.2 (a1)

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

1524

1460

1396

1332

1268

1204

1140

1076

1012 94

8

884

820

756

692

628

564

Loadings Comp. 1

Wavenumber (cm-1)

Figure 5.2 (a) PCA Plot for Undyed Wool Irradiated (texp= 7, 21 days or 168,

504 hrs.)

I) Spectral region: 1524-524 cm-1

Comp. 1 (81.1%)

Comp. 2 (6.1%)

-2.0 -1.0 0.0 1.0 2.0*10

-1 -1.8

-0.8

0.3

1.3

2.3 *10

-1

** * ***

Undyed wool (texp=504hrs)

Undyed wool (texp=168hrs)

II) Spectral region: 800-524 cm-1

Comp. 1 (60.7%)

Comp. 2 (13.1%)

-4.0 -2.0 0.0 2.0 4.0 *10-2-4.0

-2.0

0.0

2.0

4.0 *10 -2

*

** **

*

texp.=504 hrs. texp.=168 hrs.


As seen in Table 5.4, the position of the peaks as well as their intensity changes

recorded in this thesis and those reported in the literature2 are in good agreement.

5.3 Chemometrics

PCA

Irradiated Undyed Wool Fabric

Three FT-Raman spectra of undyed wool samples irradiated for 168hours (7days) and

504hours (21 days) respectively were recorded. The first derivative of each spectrum

was calculated in Grams32 (9 points, 7 degrees). Those spectra were then block

normalised (i.e. the variables for each spectrum is divided by their sum so that the

block normalised variables add to 100 for each of the spectra) in Excel 97.

A matrix consisting of 55 objects consisting of undyed samples (irradiated for 7days

and 21 days) and 251 variables was built for the 1524-524cm-1 region to be analysed

by Principle Component Analysis (PCA). Although the number of objects in the data

matrix was small, the PCA plot (Figure 5.2(a)) clearly shows the differences between

the samples irradiated for different times. PC1 has separated the samples irradiated for

168hours (negative) from the ones irradiated for 504hours (positive). The loading plot

for PC1, Figure 5.2(b) shows the peaks that cause this separation.

The loading plot indicates that peaks in the regions 976-930, 1020-1004, 1380-1344

and 1500-1452cm-1 exhibit the greatest variation with exposure time. These regions

correspond with the previously reported spectral changes on irradiation as shown in

Table 5.4. While Lewis et al.2 reports that the peaks belonging to tryptophan are too

weak to observe any changes in them, the loading plot demonstrates that the peaks

Table 5.4- FT-Raman spectral frequencies and assignments of the bands

(1500-500 cm-1) affected by exposure to stimulated sunlight in this

thesis and by Lewis 2

Note: * = the gradual increase of the peak at 977cm-1 w.r.t. exposure time was more obvious than the increase or decrease observed for the other peaks.

Frequencies (cm-1) found

in literature2

Changes Reported

With Exposure

Frequencies (cm-1) found

in this work

Changes Observed

With Exposure

Peak Assignments

516 (7 days) 513 Decrease 522 (21 days)

Decrease Cystine S-S band

623 621 (s)

Not changed 623

Not changed Phenylalanine

644 643 (s)

Decrease 644

Decrease Tyrosine

664 665 (br, wk) Decrease 664

Decrease Cystine C-S stretching

830: 854 830: 854 (Fermi Doublet)

Decrease in 854 peak intensity w.r.t. 830 peak

830: 854

Decrease in 854 peak intensity w.r.t. 830 peak

Tyrosine residues

936 935 Decrease

934

Decrease C-C skeletal stretching in α-helix

967 977 Increase gradually* 972

Increase gradually

Not assigned

1004 1004 (s) No change

1004

No change Ring vibration of Tryptophan & Phenylalanine

1040 1040 Increase 1040

Increase S-O vibration of cysteic acid

1244 1245 Increase 1244

Minor increase

Random coil structure

1336, 896 1341, 882 Has shifted in position 1336, 896

Too weak in FT-Raman

Tryptophan


belonging to this amino acid residue, namely 1341 and 882cm-1, are influenced by the

exposure time. This agrees with Asquith and Rivett13 who have concluded that photo-

degradation causes a decrease in the tryptophan content of wool. The loading plot also

indicates that there is a change in the C-C skeletal structure of α-helical backbone.

This is demonstrated in the region of 1250-1220cm-1, where there is an indication of a

conformational change.

In previous studies on photo-oxidation of wool fabric15, PCA analysis of Raman

spectra was performed on the 800-400cm-1 region. However, in this study it was

found that the 1524-524cm-1 region offers a better separation and grouping of the

data. See Figure 5.2(a, II).

Irradiated Dyed Wool Fabric

In these studies, wool samples dyed with various ratios of three dyes were exposed to

accelerated sunlight. The fabric swatches were left in the weathering instrument and

then checked for changes in colour at 24 hour intervals. Since the first obvious colour

change was observed after seven days, one set was exposed for seven days. After

twenty-one days the colour-change was visually near to extreme.

The samples were then kept in the dark until their Raman spectra were recorded.

Three spectra were recorded for each sample. Three layers of the same sample were

positioned on the top of each other in the sample holder, with the weathered side

exposed to the laser. The spectral data were transformed to their first derivative and

block-normalised as for the undyed wool spectra.


A comparison of the spectra for the irradiated and non-irradiated samples shows that

the dye peaks are still at approximately the same frequencies but exhibit changes in

their intensity. Figure 5.3 demonstrates this point by showing the spectra for irradiated

sample 073 at the two different exposure times. The arrows identify peaks that change

in intensity with exposure.

It is known16 that, since the perceived colour of an object is associated with the

absorbance of some wavelengths of light and reflectance of the rest by the

chromophore, the alteration — or disappearance — of the colour may be related to

chemical changes in that chromophore.

The exact structures of the Lanasol dyes are not available to the public and hence it is

not easy to readily identify the chemical changes that might have occurred during

photo-oxidation. This was not a significant disadvantage because chemometrics relies

on statistical analysis of the spectra and requires no previous chemical knowledge of

the samples. Even without knowledge of the original chemical structure of the dye,

analysis of the PCA loadings may provide some information on what chemical

changes accompany the weathering process.

Group (i)

Data matrix: Woollen samples (texp = 0, 168, 504 hrs.)

Pre-treatment: 1st der., block normalised, Y-mean centred


A data matrix was constructed containing 141 objects (i.e. all the spectra with

exposure time of 0, 168 and 504 hours) and 251 variables (the dye region

Figure 5.3 Spectra of sample 073 irradiated for texp= 168 and 504 hours)

Note: Arrows show the peaks that have changed intensity by increase in exposure time

-0.004

-0.002

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

1500

1448

1396

1344

1292

1240

1188

1136

1084

1032 98

0

928

876

824

772

720

668

616

564

512

Wavenumber (cm-1)

073 (texp=168hrs)

073 (texp=504hrs)

Figure 5.5(a): PCA plot of PC1 vs PC3 for dyed wool samples irradiated for 504 hours.

Com p. 1 (79.5%)

Com p. 3 (3.8%)

-3.1 -1.4 0.3 2.0 3.6 *10

-1

-3.1

-1.4

0.3

1.9

3.6 *10

-1

* ** ** ** * **** **

217

730 343

073 307

Figure 5.5(b): The Approximate Position of Samples chosen to be irradiated

730 •

073 •

181 •

811 •

343 •

217 •

307 •

Figure 5.4 (a): PCA Plot for Dyed Wool samples (texp = 0, 168 and 504 hrs.)

(Pre-treatment: 1st derivative, block normalised, Y-mean centred)

Note: U7 = texp = 168 hrs. U21 = texp = 504 hrs.

Comp. 1 (71.8%)

Comp. 2 (8.7%)

-0.85 -0.18 0.49 1.16 1.83 *101

-1.20

-0.53

0.14

0.81

1.48 *10

1

019Ra073Ra 073Rd 154Ra 154Rb 154Rc

154Rd154Re 154Rf 127Ra 127Rb 127Rc 127Rd 127Re 127Rf 181Ra 181Rb 181Rc 181Rd

217Ra217Rc 235Ra 235Rb 235Rc 235Rd 235Re 235Rf 244Ra 244Rb 244Rc 244Rd 244Re 244Rf 361Ra 361Rb 361Rc 361Re 208Rb 208Rc 208Rd 208Re208Rf316Ra 316Rb

316Rd 325Ra325Rb 325Rc 325Rf 307Rb307Rc 307Rd 307Re 307Rf

343Ra 343Rb 343Rc 343Rd343Re

343Rf 370Ra 370Rb 370Rc 424Ra 424Rb 424Rc 424Rd 424Rf 433Ra 433Rb 433Rc 433Rd 433Re 433Rf 532Ra 532Rb 532Rc 532Rd 532Re 532Rf

550Ra 550Rc550Rf613Ra 613Rb 613Rc 613Rd 613Re 613Rf

811Ra

811Rb 811Rc 811Rd 811Re811Rf

703Ra 703Rb 703Rc 703Rd 703Rf 730Ra 730Re

073U211IN

073U2122N 073U2123N

073U722N

073U723N 181U2122N 181U2123N

181U723N217U211IN 217U2122N 217U2123N 217U71IN 217U722N 217U723N 307U211IN 307U2122N 307U2123N 307U71IN 307U722N 307U723N 343U212IN 343U2121N 343U2122N 343U2123N

343U71IN343U722N 343U723N 811U211IN 811U2122N 811U2123N

811U71IN

811U722N 811U723N

730U211IN 730U2122N 730U2123N 730U71IN 730U722N 730U723N

Figure 5.4 (b) Score Vs. Objects Plot for PC1 and PC2

Objects

Scores

019 Ra

154 Rb

127 Rf 235

Ra 244Rb 361

Rc 316 Ra 307

Rb 343 Rc 424

Ra 433 Rc 532

Rd 613 Rb 811

Rc 703Rd 073

U7 22N

217U2123N

307U7 1IN

343U7 1IN

811U7 22N

730U7 23N

-1.00

-0.50

0.00

0.50

1.00

1.50 *10

1

Comp. 1

Objects

Scores

019Ra

154Re 127

Rf 235 Ra 244

Rb 361 Rc 316

Ra 307 Rb 343

Rc 424Ra 433

Rc 532 Rd 613

Rb 811 Rc 703

Rd 073 U7 22N

217 U21 23N

307U7 1IN

343U7 1IN

811U7 22N

730U7 23N

-4.0

-2.0

0.0

2.0

4.0

6.0 Comp. 2

texp =168, 504 hrs

texp = 0


1524-524cm-1). This data matrix was then analysed with PCA. The results are

displayed in Figure 5.4(a) and Figure 5.4(b).

Three PCs accounted for 86% of the total variance. PC1 explaining 71.8% separated

all the irradiated samples from the non-irradiated ones. PC2 explaining 8.7% of the

total variance separated non-irradiated samples with lower ratio of the red colour

(PC2>0), (except for 181 and 343 and 370) from those with higher red colour ratio

(PC2<0). PC2 also separated the irradiated samples with lower ratio of red (PC2<0),

i.e. 073,181,217,307, from those with higher ratio of red (PC2>0), i.e. 343,811, 730.

Although difficult to distinguish at the first glance, the spectral repeats for zero

exposure have clustered together on PC1. The same is not true for the irradiated

samples. This indicates that there is some sort of difference between these samples at

various exposure times (texp = 0, 168 and 504 hours). Therefore, the next point to

consider is how well the exposed samples follow the pattern and the position of the

same non-exposed samples in the colour triangle seen in Figure 5.5(b). For example,

if sample 343 when irradiated still clusters nearly in the centre with respect to the

position of all the other samples in the PCA plot. Therefore a matrix consisting of 14

objects: 073, 217, 307, 730 and 343 and 251 variables (dye region: 1524-524 cm-1)

was built and submitted to PCA for analysis. The results are shown in Figure 5.5(a).

Three PC’s explain nearly 95% of the total variance. The PCA plot shows that the

spectra recorded for the same sample cluster together. However, comparing the

position of the samples in this plot with those in Figure 4.5, shows that the samples

(such as sample number 343) in Figure 5.5(a) are not situated according to the ratio of

the three colours on the sample (for more details refer to Chapter 4). This is to be


expected since either the ratios of dyes on the exposed samples are expected to differ

from those of the original samples. The (yet unknown) decomposition products from

the weathering process may also make significant contribution to the spectra. It is

also possible that the ratio of the dyes may be the same after irradiation while the

absolute amount of dye on the fabric is different.

Thus, it may be concluded here that:

• Upon exposure of dyed wool fabric, there are changes observed in the sample, both

physically (colour) and chemically (spectral).

• The spectra are further separated according to the ratio of red dye.

• When the first derivative of the spectra are taken and block normalised, PCA

separates irradiated from nonirradiated sample but does not separate the irradiated

samples according to their exposure time

Previously, Kokot17 et al. have shown that microscopic amount of dyes extracted from

a worn textile garments may be compared and matched even though the DRIFT

spectra used were quite noisy and the absorbance intensities of the dyes were very

low. Therefore, it was decided in these studies to use the resulting substrate-subtracted

spectra, since they were quite noisy and that the intensity of the dye peaks were low as

well.

Group (ii)


Pre-treatment: 1st derivative spectra, subtracted spectra normalised to 100% substrate

and Y-mean centred



In addition to the first-derivative, block-normalising pre-treatment used so far, the

spectra of the irradiated undyed wool was subtracted, in Grams 32, from the spectra of

the corresponding irradiated dyed samples. To a first approximation, this should leave

only the peaks from the dye remaining. The resulting spectra were found to be not as

sharp as the subtraction spectral results for the non-irradiated samples. This is

understandable since the dye peaks would have changed in intensity. The irradiated

dye spectrum was then normalised in Excel 97. This was done, by dividing each

irradiated dye spectrum by the same factor (N) used for that particular subtraction in

order to normalise each spectrum to 100% intensity of wool peaks in the original

spectrum; i.e. for each of these subtractions, the spectrum of irradiated undyed wool

was multiplied by a factor (N), which was not necessarily being the same for each

subtraction:

Irradiated Dyed Wool- [Irradiated Undyed Wool X factor (N)] = Irradiated Dye Spec.

Irradiated Dye Spec. / factor (N) = Normalised Irradiated Spec. to 100% Wool Spec.

The resulting spectra were then submitted to Sirius™ and Y-mean centred for

principal component analysis. It was anticipated that the resulting PCA plot (Figure

5.6) should show a tighter cluster of the un-irradiated data while the irradiated spectral

data were expected to demonstrate a greater scattering. This is possible due to

different sub-structures in the wool give rise to different decomposition products upon

exposure. There may also be variation in the photosensitivity of the dye due to factors

which do not affect the Raman spectrum. Figure 5.6 supports this hypothesis as the

spectral data of the non-irradiated samples are clustered tightly together, separated

from the rest of the data by PC1. On the other hand, in among the irradiated spectra

the samples with 0-28% red are separated from those with 30-67% red by PC2.

Figure 5.6 PCA Plot of the Irradiated Samples for (texp= 168 and 504 hrs.) and

Non-irradiated Samples (texp= 0 hrs.) (Pre-treatment: subtracted spectra, normalised to 100% wool, Y-mean centred)

Note:

• Colour code scheme: 073 pink 181 lavender 343 plum 307 indigo 730 sea green All non-irradiated samples red

• Legend: texp = 168hrs. = 7 texp = 504hrs. = 21

DataSet: SrjD0721nW, Subset: c1, Scores 1 vs 2

Comp. 1 (51.8%)

Comp. 2 (36.8%)

-1.00 0.00 1.00 2.00 3.00*10

1 -2.00

-1.00

0.00

1.00

2.00 *10

1

77 7

7 7

7

7

7

7

77

721 7 7

21

21

21 21

21 21

21 21

21 21

21

21

2121

21

21

s6733f s6733e s6733c s6733b s6733a s3335f s3335e s3335d s3335c s3335b s3335a s2800f s2800e s2800d s2800c s2800b s2800a s1767f s1767e

s1767d s1767c s1767b s1767a s0069f s0069e s0069d s0069c s0069b s0069a

Not irradiated

Red ratios : 0,1,3 Red ratio:3,7


Furthermore, it seems that there is a trend of separation among the data treated for 168

hours and 504 hours. One would expect that the samples irradiated for 168 hours

would be positioned closer to those samples, which have not been irradiated at all.

Although this is true in the case of samples with higher percentage of red dye, it is the

other way around for those with lower percentage of red. Comparing the PCA plots in

Figures 5.4(a) and 5.7, it is clearly shown that the new pre-treatment used has aided in

separating the samples much better.

Earlier work17 has shown that pre-treating the data by normalising the spectra to 100%

substrate and then double centring offers a better separation. Kokot et al.18 have used

the same pre-treatment for the analysis of FT-Raman spectra of oxidised wool fabric.

Therefore, the same data pre-treatment was tried for the subtracted spectra obtained

here as well, with the expectation that double centring the data would separate the

different samples even further.

Group (iii)


Pre-treatment: 1st derivative spectra, subtracted spectra normalised to 100% substrate

and double centred


182 objects were generated from all the irradiated and not irradiated sample spectra by

subtracting the undyed wool spectra, taking the first derivative and double-centring.

These were analysed by PCA. The PCA plot is shown in Figure 5.7. Two PC’s

explained 86% of the total variance. The pattern displayed by these samples is quite

interesting since it clearly shows that ths pre-treatment offers the best separation and

Figure 5.7: PCA plot of the Double Centred Irradiated and Non-Irradiated Samples

(Pre-treatment: subtracted spectra, normalised to 100% wool, double centred) Note: Colour code scheme: 073 pink, 181 lavender, 217 lime, 343 plum, 307 indigo, 730 sea green, 811

rose, All non-irradiated samples red

DataSet: SD0721nwdc, Subset: a1, Scores 1 vs 2

Com p. 1 (63.0%)

Com p. 2 (22.8%)

-3.2 -2.2 -1.1 -0.0 1.0 *101

-2.4

-1.3

-0.3

0.8

1.9 *10

1

u7 u7 u7

u7

u7 u7

u7 u7 u7 u7

s20u723n u7s20u71in u7u7

u7

u7u7u7

u21 u21 u21

u21 u21 u21

u21

u21 u21 u21

u21 u21 u21 u21 u21

s20u21 u21u21 u21

u21u21

u21

s6733f s6733e s6733c s6733b s6733a s6700f s6700e s6700d s6700c s6700b s6700a s6617f s6617es6617ds6617c s6617b s6617as5515f s5515e s5515d s5515c s5515b s5515a

s5050f s5050e s5050d s5050c s5050b s5050a s5030f s5030e s5030d s5030c s5030b s5030a s4232f s4232e s4232d s4232c s4232b s4232a s4124f s4124d s4124c s4124b s4124a s3367f s3367e s3367d

s3367c s3367b s3367a s3335f s3335e s3335d s3335c s3335b s3335a s3300f s3300e s3300d s3300c s3300b s3300a s3019f s3019e s3019d s3019c s3019b s3019a s2914f s2914e s2914d s2914c s2914b s2914a s2800f s2800e s2800d s2800c s2800b s2800a s2760f s2760e s2760d s2760c s2760b s2760a s2044f s2044e s2044d s2044c s2044b s2044a s2034f s2034e s2034d s2034c s2034b s2034a

s2006f

s2006e s2006d s2006c s2006b s2006a s1767f s1767e s1767d s1767c s1767b s1767a s1521f s1521e s1521d s1521c s1521b s1521a s1046f s1046e s1046d s1046c s1046b s1046a s0069f s0069e s0069d s0069c s0069b s0069a

s0038f s0038e s0038d

s0038c s0038b

s0014f

s0014d s0014e s0014c

s0014b

s0014a

texp=0hrs.


grouping in among each sample. Double centring the data versus Y-mean centring,

however, has not made any difference to the manner of separation for those samples

irradiated for different times.

Overall therefore, it may be concluded that the best pre-treatment for pattern

recognition (PCA) in dyed, irradiated woollen samples (texp=0, 168, 504 hours), is:

• Subtraction of the substrate from the dyed wool FT-Raman spectrum

• Normalisation of the resulting spectrum to 100% wool

• Double centring the subtracted spectra

Next, It would be of interest to examine the ability of the PLS model to predict the ratio

of the dyes on irradiated samples, especially in reference to the type of pre-treatments

discussed previously.

PLS

Group (i)

Calibration set: spectra from non-irradiated samples

Unknown set: spectra from the UV-irradiated samples

Pre-treatment: 1st derivative spectra, block normalised, Y-mean centred.



Figure 5.8 (a): Predicted vs. Measured plot for the calibration set for RED (non irradiated samples) (Pre-treatment: 1st derivative, block normalised, Y-mean centred)

Measured (Red)

Predicted (Red)

0.0 2.0 4.0 6.0 8.0-0.50

0.00

0.50

1.00

1.50 *10

1

154Ra 154Rb 154Rc 154Rd 154Re 154Rf 127Ra 127Rb 127Rc 127Rd 127Re 127Rf 181Ra 181Rb 181Rc

181Rd 181Re 181Rf

217Ra 217Rb 217Rc 217Re

217Rf 235Ra 235Rb 235Rc 235Rd 235Re 235Rf 244Ra 244Rb 244Rc 244Rd 244Re 244Rf 361Ra361Rb361Rc

361Rd361Re361Rf

208Ra

208Rb 208Rc 208Rd 208Re 208Rf 316Ra316Rb316Rc316Rd316Re

316Rf

325Ra325Rb325Rc325Rd325Rf307Ra307Rb307Rc307Rd307Re

307Rf343Ra343Rb343Rc343Rd343Re

343Rf370Ra370Rb370Rc370Rd370Re370Rf 424Ra

424Rb424Rc424Rd424Rf433Ra433Rb433Rc433Rd433Re433Rf 532Ra532Rb532Rc532Rd532Re532Rf550Ra550Rb550Rc550Rd

550Rf613Ra613Rb613Rc613Rd613Re613Rf

811Ra



730Ra730Rb730Rc

730Re730RfSlope =

0.807

Interc. =

0.684

Corr. =

0.898

Figure 5.8 (b)- Predicted vs. Measured plot for the calibration set for BLUE

Measured (Blue)

Predicted (Blue)

0.0 2.0 4.0 6.0 8.0-0.50

0.0

0.50

1.00

1.50 *10

1


127Ra 127Rb 127Rc 127Rd 127Re 127Rf

181Ra181Rb181Rc

181Rd

181Re181Rf

217Ra217Rb217Rc217Re217Rf 235Ra 235Rb 235Rc 235Rd 235Re 235Rf 244Ra244Rb244Rc244Rd244Re244Rf 361Ra361Rb361Rc361Rd

361Re361Rf

208Ra 208Rb 208Rc 208Rd 208Re 208Rf


325Ra 325Rb 325Rc 325Rd 325Rf

307Ra 307Rb

307Rc 307Rd 307Re 307Rf


370Ra370Rb370Rc

370Rd370Re370Rf

424Ra 424Rb 424Rc 424Rd 424Rf 433Ra 433Rb 433Rc 433Rd 433Re 433Rf 532Ra 532Rb 532Rc 532Rd 532Re 532Rf 550Ra

550Rb550Rc550Rd550Rf

613Ra613Rb613Rc

613Rd613Re613Rf 811Ra811Rb811Rc811Rd811Re811Rf 703Ra 703Rb 703Rc 703Rd 703Re

703Rf 730Ra 730Rb 730Rc 730Re

730Rf Slope = 0.822

Interc. = 0.519

Corr. = 0.907

Figure 5.8 (c)- Predicted vs. Measured plot for the calibration set for YELLOW

Measured (Yellow)

Predicted (Yellow)

0.0 2.0 4.0 6.0 8.0-0.20

0.00

0.20

0.40

0.60

0.80

1.00 *10

1

154Ra

154Rb154Rc154Rd154Re

154Rf

127Ra127Rb

127Rc127Rd127Re127Rf

181Ra

181Rb181Rc181Rd181Re

181Rf

217Ra

217Rb

217Rc217Re

217Rf

235Ra

235Rb235Rc

235Rd

235Re235Rf244Ra

244Rb244Rc

244Rd244Re244Rf


208Ra

208Rb

208Rc

208Rd

208Re208Rf

316Ra



307Ra

307Rb

307Rc

307Rd

307Re

307Rf

343Ra 343Rb 343Rc 343Rd 343Re 343Rf


433Ra 433Rb 433Rc 433Rd 433Re 433Rf 532Ra 532Rb 532Rc

532Rd 532Re 532Rf 613Ra

613Rb 613Rc 613Rd

613Re 613Rf

811Ra811Rb811Rc

811Rd811Re

811Rf

703Ra 703Rb 703Rc 703Rd 703Re 703Rf 730Ra

730Rb

730Rc

730Re 730Rf

Slope = 0.824

Interc. =

0.690

Corr. = 0.908

Table 5.5 Predicted values for dependent variable RED Calibration set: All non-irradiated samples Unknowns (Val. set): Samples irradiated (texp= 168, 504 hours.)

(Pre-treatment: 1st derivative, block normalised, Y-mean centred)

Name (Unknown Set)

Measured Original Ratio of Red

Predictions For Unknown Set (Closest whole unit)

Predictions For Validation Set (Closest whole unit)

Name (Validation Set)

181U211IN 1 -1.9 (-2) 1.9 (2) 181Ra 181U2122N 1 8.5 (9) 2.2 (2) 181Rb 181U2123N 1 12. (12) 2.4 (2) 181Rc 181U71IN 1 -64. (-64) 1.3 (1) 181Rd 181U722N 1 -8.2 (-8) 2.0 (2) 181Re 181U723N 1 12. (12) 2.1 (2) 181Rf 217U211IN 2 4.6 (5) 4.0 (4) 217Ra 217U2122N 2 4.5 (5) 4.0 (4) 217Rb 217U2123N 2 4.5 (5) 3.4 (3) 217Rc 217U71IN 2 4.6 (5) 0.34 (0) 217Re 217U722N 2 4.5 (5) 1.9 (2) 217Rf 217U723N 2 4.5 (5) 307U211IN 3 4.4 (4) 4.2 (4) 307Ra 307U2122N 3 4.4 (4) 2.8 (3) 307Rb 307U2123N 3 4.4 (4) 3.0 (3) 307Rc 307U71IN 3 4.4 (4) 3.1 (3) 307Rd 307U722N 3 4.4 (4) 3.0 (3) 307Re 307U723N 3 4.4 (4) 2.4 (2) 307Rf 343U212IN 3 4.1 (4) 4.5 (5) 343Ra 343U2121N 3 4.3 (4) 4.8 (5) 343Rb 343U2122N 3 4.3 (4) 5.2 (5) 343Rc 343U2123N 3 4.3 (4) 5.3 (5) 343Rd 343U71IN 3 4.6 (5) 6.7 (7) 343Re 343U722N 3 4.3 (4) 4.5 (5) 343Rf 343U723N 3 4.4 (4) 811U211IN 8 3.6 (4) 11. (11) 811Ra 811U2122N 8 3.2 (3) 8.3 (8) 811Rb 811U2123N 8 3.5 (4) 8.8 (9) 811Rc 811U71IN 8 0.13 (0) 9.3 (9) 811Rd 811U722N 8 3.2 (3) 10. (10) 811Re 811U723N 8 1.9 (2) 9.8 (10) 811Rf 730U211IN 7 4.5 (5) 4.7 (5) 730Ra 730U2122N 7 4.5 (5) 7.6 (8) 730Rb 730U2123N 7 4.5 (5) 4.2 (4) 730Rc 730U71IN 7 4.4 (4)


A calibration set consisting of non-irradiated samples (121 objects and 252 variables)

was built. It was then cross-validated in order to find the most appropriate calibration

set for the model. Five factors explained 80.7% of the total variance. The samples

with no red dye were excluded from this calibration set. The predicted versus

measured values for the calibration set indicate that the calibration set chosen is an

appropriate one. A line of best fit was drawn through the data, with a slope of 0.807,

intercept of 0.684 and correlation coefficient of 0.898, indicating that there is some

correlation between the data set. This is shown in Figure 5.8(a).

In Chapter 4, the calibration set selected for the chosen PLS model was validated.

This was done using the same set of seven samples (073, 181, 217, 307, 343, 730,

811, t exp= 0 hours) as the validation set. Ratios of dyes on each of these samples were

then predicted and the values of SEE and SEP calculated for each dependent variable.

This validation set was next UV treated and then introduced to the PLS model as an

unknown set. This is an unknown set in the sense that it is not known how much of

each dye is still present in the original form after irradiation. Table 5.5 shows the ratio

values for dependent variable red on each sample, before they were irradiated, along

with the values predicted by the PLS model for the irradiated samples (t exp = 168, 504

hours). It must be noted that SEP value cannot be calculated for any of these sets of

data since no residual value could be calculated, which is due to the unknown value of

the dye ratio after irradiation. The results for this model are quite inconsistent with

some of the samples predicted to have more dye after irradiation than before and some

predicted to have negative amounts of dye. From this it is clear that the model cannot

predict the ratio of red after irradiation very well. This is despite the fact that, where

Table 5.7: Predicted values for dependent variable YELLOW

Calibration set: All non-irradiated samples Unknowns (Val. set): Samples irradiated (texp= 168, 504 hrs.) (Pre-treatment: 1st derivative, block normalised, Y-mean centred)

Name (unknown set)

Measured original ratio of yellow

Predictions for unknown set (Closest whole unit)

Predictions for validation set (Closest whole unit)

Name (validation set)

073U211IN 3 1.9 (2) 1.8 (2) 073Ra 073U2122N 3 2.1 (2) 2.0 (2) 073Rb 073U2123N 3 2.1 (2) 1.5 (2) 073Rc 073U71IN 3 1.9 (2) 1.8 (2) 073Rd 073U722N 3 2.3 (2) 1.7 (2) 073Re 073U723N 3 2.1 (2) 181U211IN 1 0.7 (1) 1.4 (2) 181Ra 181U2122N 1 3.0 (3) 1.3 (1) 181Rb 181U2123N 1 4.1 (4) 1.3 (1) 181Rc 181U71IN 1 -20.0 (-20) 1.1 (1) 181Re 181U722N 1 -3.2 (-3) 1.0 (1) 181Rf 181U723N 1 4.8 (5) 217U211IN 7 1.6 (2) 4.5 (5) 217Ra 217U2122N 7 1.8 (2) 7.5 (8) 217Rb 217U2123N 7 1.7 (2) 5.9 (6) 217Rc 217U71IN 7 1.5 (2) 4.2 (4) 217Rf 217U722N 7 1.7 (2) 217U723N 7 1.6 (2) 307U211IN 7 1.9 (2) 5.6 (6) 307Ra 307U2122N 7 1.7 (2) 5.4 (5) 307Rb 307U2123N 7 1.8 (2) 5.0 (5) 307Rc 307U71IN 7 1.6 (2) 6.3 (6) 307Rd 307U722N 7 1.7 (2) 6.1 (6) 307Re 307U723N 7 1.8 (2) 4.9 (5) 307Rf 343U212IN 3 2.1 (2) 2.6 (3) 343Ra 343U2121N 3 2.1 (2) 2.4 (2) 343Rb 343U2122N 3 2.1 (2) 2.4 (2) 343Rc 343U2123N 3 2.1 (2) 2.4 (2) 343Rd 343U71IN 3 2.4 (2) 2.1 (2) 343Re 343U722N 3 2.2 (2) 2.4 (2) 343Rf 343U723N 3 2.2 (2) 811U211IN 1 2.4 (2) 2.0 (2) 811Ra 811U2122N 1 2.5 (3) 2.4 (3) 811Rb 811U2123N 1 2.4 (2) 2.4 (3) 811Rc 811U71IN 1 3.6 (4) 1.9 (2) 811Rd 811U722N 1 2.5 (3) 2.0 (2) 811Re 811U723N 1 3.0 (3) 2.4 (3) 811Rf 730U211IN 0 1.8 (2) 3.7 (4) 730Ra 730U2122N 0 1.8 (2) 5.3 (5) 730Rb 730U2123N 0 1.8 (2) 3.7 (4) 730Rc 730U71IN 0 1.7 (2) 4.7 (5) 730Re 730U722N 0 1.9 (2) 3.5 (4) 730Rf 730U723N 0 1.8 (2)

Table 5.6: Predicted values for dependent variable BLUE Calibration set: All non-irradiated samples Unknowns (Val. set): Samples irradiated (texp= 168, 504 hrs.) (Pre-treatment: 1st derivative, block normalised, Y-mean centred)

Name (unknown set)

Measured original ratio of blue

Predictions unknown set (Closest whole unit)

Predictions validation set (Closest whole Unit)

Name (validation set)

073U211IN 7 -2.9 (-3) 8.9 (9) 073Ra 073U2122N 7 2.4 (2) 8.8 (9) 073Rb 073U2123N 7 2.5 (3) 9.9 (10) 073Rc 073U71IN 7 14.9 (15) 10. (11) 073Rd 073U722N 7 -7.6 (-8) 11.0 (11) 073Re 073U723N 7 -0.6 (-1) 181U211IN 8 10.8 (11) 7.7 (8) 181Ra 181U2122N 8 -2.0 (-2) 7.6 (8) 181Rb 181U2123N 8 -6.9 (-7) 7.5 (8) 181Rc 181U71IN 8 94.7 (95) 11. (12) 181Rd 181U722N 8 20.9 (21) 8.3 (8) 181Re 181U723N 8 -7.8 (-8) 8.1 (8) 181Rf 217U211IN 1 3.1 (3) 1.7 (2) 217Ra 217U2122N 1 3.0 (3) -0.84 (-1) 217Rb 217U2123N 1 3.0 (3) 0.29 (0) 217Rc 217U71IN 1 3.1 (3) 3.4 (4) 217Rf 217U722N 1 3.1 (3) 217U723N 1 3.1 (3) 307U211IN 0 3.1 (3) 0.33 (0) 307Ra 307U2122N 0 3.1 (3) 0.40 (0) 307Rb 307U2123N 0 3.1 (3) 1.3 (1) 307Rc 307U71IN 0 3.2 (3) -0.25 (0) 307Rd 307U722N 0 3.2 (3) -0.05 (0) 307Re 307U723N 0 3.1 (3) 2.5 (3) 307Rf 343U212IN 4 3.2 (3) 3.3 (3) 343Ra 343U2121N 4 2.9 (3) 3.1 (3) 343Rb 343U2122N 4 2.9 (3) 3.2 (3) 343Rc 343U2123N 4 2.9 (3) 2.5 (3) 343Rd 343U71IN 4 2.0 (2) 2.1 (2) 343Re 343U722N 4 2.8 (3) 3.6 (4) 343Rf 343U723N 4 2.8 (3) 811U211IN 1 3.3 (3) -2.8 (-3) 811Ra 811U2122N 1 3.6 (4) -0.58 (-1) 811Rb 811U2123N 1 3.4 (3) -1.1 (-1) 811Rc 811U71IN 1 5.2 (5) -1.0 (-1) 811Rd 811U722N 1 3.5 (4) -1.9 (-2) 811Re 811U723N 1 4.3 (4) -2.2 (-2) 811Rf 730U211IN 3 3.0 (3) 1.6 (2) 730Ra 730U2122N 3 3.0 (3) -2.7 (-3) 730Rb 730U2123N 3 3.0 (3) 1.81 (2) 730Rc 730U71IN 3 3.1 (3) -1.9 (-2) 730Re 730U722N 3 3.1 (3) 730U723N 3 3.1 (3)


the same samples at exposure time of zero were used as the validation set, the values

obtained for those predictions were reasonable.


The same calibration model (121objects and 252 independent variables) was used to

predict the blue dependent variable for the unknown set (43 objects). The model was

again cross-validated and it was concluded that this model still represents the best

PLS model. Five factors were used to explain 82% of the variance in the dependent

variables and 66% of that of the independent variables. The plot of predicted versus

measured dye ratios for this model is shown in Figure 5.8(b) and Table 5.6. The

values for the slope, intercept and the correlation coefficient for the best line fitted to

the data are 0.822, 0.519 and 0.907 respectively. Upon introduction of the unknown

samples, however, the prediction of the model was not satisfactory.


A calibration set consisting of 110 objects and 252 independent variables was built.

This set was then cross-validated. The predicted versus measured plot is displayed in

Figure 5.8(c). Six factors explained a total of 82% of the variance in the dependent

variable. There is a good correlation between the data in the calibration set. That is

shown by the information obtained from the best line fitted through the data. The

unknown samples were then introduced to this model in order to predict the amount of

yellow colour left on them after irradiation. The results obtained are shown in Table

5.7. Although it is difficult to comment on these results since the actual amount of dye

remaining to be predicted is unknown, it seems logical to conclude that the results


predicted for the ratios of 0 and 1 (ratios before irradiation) were not predicted well by

the model. However, for the ratios for higher original amount of dyes the results were

acceptable since the predicted values for the irradiated samples were less than both the

predicted and measured values for the same samples before irradiation.

Therefore it may be concluded that:

• The PLS model has a better ability to predict the ratio of the yellow colour than the

other two colours. The poor predicted value for zero ratio of yellow on sample 730

might be due to the fact that there are only a few samples with zero ratios in the

calibration set.

• The PLS model chosen does not have the ability to differentiate between the two

different treatment times. Therefore, it has predicted the same or very close values

even though the samples irradiated for 504 hours might be expected to have less dye

remaining.

• The predictive ability for the unknown samples is poorer than for the untreated

samples. This may be due to the fact that samples with lower ratio of dye might lose

that ratio altogether after treatment, which in turn would cause the model to perform

poorly.

• Simpson19 has shown that fading of the most of the wool dyes occurs as fast as the

degradation of wool itself. He suggests however that a small number of yellow dyes

with high light-fastness offer a good retention of the appearance of the fabric. This

may also be applied to the results discussed above, where it was found that reactive

dye yellow gives more comprehensive results. This might be due to the fact that the

yellow colour has protected the fabric-dye system much better than the others. This


agrees with the conclusion made by Choi et al.12, where they claim that the order of

degradation efficiency of reactive dyes is yellow, red followed by blue.

Next, the same data were studied using two different pre-treatments; in Group (ii)

wool substrate was first subtracted from the dye-substrate spectrum. This was done to

see if taking away one of the common factors underlying the data would allow better

predictability in PLS. Group (iii) on the other hand was studied to see if double

centring the data instead of Y-mean centring would offer better results.

Group (ii)



Pre-treatment: 1st der., substrate subtracted spectra normalised to 100% wool, Y-mean

centred.



A calibration matrix consisting of 138 objects and 252 independent variables was

built and cross-validated with red as the dependent variable. Eight factors were

significant to explain 90% of the variance in the dependent variable, with the first five

explaining 85% of it. The plot of predicted versus measured values (Figure 5.9)

indicates a very good correlation between the data. The line fitted through the data has

a slope of 0.908, intercept of 0.295 and correlation coefficient of 0.953. These values

also confirm that there is a high correlation between the objects in the calibration set.

The irradiated samples were then introduced to this model as an unknown set of 43

objects. The results of the predictions for the ratio of red dye remaining on the fabric

Figure 5.9: Predicted vs. Measured red values for calibration set

Pre-treatment: Subtracted spectra normalised to 100% wool, Y-mean centred

Measured (Red)

Predicted (Red)

0.0 2.0 4.0 6.0 8.0 -0.20

0.00

0.20

0.40

0.60

0.80

1.00 *10

1

s6733f

s6733e s6733c

s6733b

s6733a

s6700f s6700e s6700d s6700c s6700b s6700a

s6617f

s6617es6617d

s6617c

s6617b

s6617a

s5515f

s5515es5515ds5515c

s5515bs5515a

s5050fs5050es5050ds5050cs5050bs5050as5030f

s5030es5030d

s5030cs5030bs5030a

s4232fs4232es4232ds4232cs4232bs4232as4124fs4124d

s4124cs4124b

s4124as3367fs3367e

s3367d

s3367cs3367bs3367a

s3335fs3335es3335ds3335cs3335bs3335as3300f

s3300e

s3300ds3300cs3300bs3300as3019fs3019es3019ds3019cs3019bs3019a

s2914f

s2914es2914d

s2914cs2914bs2914as2800fs2800es2800ds2800cs2800bs2800as2760fs2760es2760ds2760cs2760b

s2760as2044fs2044es2044ds2044cs2044bs2044a

s2034fs2034es2034d

s2034cs2034bs2034as2006es2006ds2006cs2006b

s2006a

s1767f s1767e s1767d s1767c s1767b s1767a s1521f

s1521e s1521d s1521c s1521b s1521a s1046f s1046e s1046d s1046c s1046b s1046a

s0069f s0069e

s0069d s0069c s0069b s0069a s0038f s0038e s0038d s0038c s0038b s0014d

s0014e

s0014c s0014a

Slope = 0.908

Interc. =

0.295

Corr. = 0.953

Table 5.10 Predicted values for dependent variable YELLOW Calibration set: all non-irradiated samples Unknowns: samples irradiated (texp= 168 and 504 hrs.)

Predicted Yellow Value For Unknown Set Name

(unknown set) Measured value (Original yellow ratio) 1st der.,

block norm. (Closest whole unit)

subtracted spec. norm. to100% wool, d.c. (Closest whole unit)

subtracted spec. norm. to100% wool, Y-m.c. (Closest whole unit)

S730u723n 0 -4.5 (-5) -1.9 (-2) 1.7 (2) S730u722n 0 -3.3 (-3) -0.71 (-1) 1.9 (2) S730u71in 0 -3.7 (-4) -1.2 (-1) 1.8 (2) S811u723n 1 -33. (-33) -46 (-46) 3.6 (4) S811u722n 1 -28. (-28) -36 (-36) 2.5 (3) S811u71in 1 -32. (-32) -49 (-49) 3.0 (3) S343u723n 3 -15. (-15) -19 (-19) 2.4 (2) S343u722n 3 -13. (-13) -17 (-17) 2.2 (2) S343u71in 3 -16. (-16) -25 (-25) 2.2 (2) S307u723n 7 2.2 (2) 3.3 (3) 1.6 (2) S307u722n 7 6.7 (7) 11 (11) 1.7 (2) S307u71in 7 4.7 (5) 6.4 (6) 1.8 (2) S217u723n 7 6.1 (6) 4.1 (4) 1.5 (2) S217u722n 7 5.2 (5) 3.9 (4) 1.7 (2) S217u71in 7 5.3 (5) 4.1 (4) 1.6 (2) S181u723n 1 -31. (-31) -33 (-33) -20.0 (-20) S181u722n 1 -36. (-36) -39 (-33) -3.2 (-3) S181u71in 1 -28. (-28) -31 (-31) 4.8 (5) S073u723n 3 -21. (-21) -23 (-23) 1.9 (2) S073u722n 3 -19. (-19) -25 (-25) 2.3 (2) S073u71in 3 -21. (-21) -0.47 (-1) 2.1 (2) S730u2123n 0 -2.7 (-3) 3.7 (4) 1.8 (2) S730u2122n 0 -2.3 (-2) 1.3 (1) 1.8 (2) S730u211in 0 -2.3 (-2) -30 (-30) 1.8 (2) S811u2123n 1 -20. (-20) -39 (-39) 2.4 (2) S811u2122n 1 -25. (-25) -18 (-18) 2.5 (3) S811u211in 1 -14. (-14) -10 (-10) 2.4 (2) S343u2123n 3 -11. (-11) -3.7 (-4) 2.1 (2) S343u2122n 3 -6.5 (-7) -7.2 (-7) 2.1 (2) S343u2121n 3 -9.0 (-9) -14 (-14) 2.1 (2) S343u211in 3 -8.4 (-8) 5.3 (5) 2.1 (2) S307u2123n 7 0.3 (0) 2.5 (3) 1.9 (2) S307u2122n 7 2.2 (2) 3.7 (4) 1.7 (2) S307u211in 7 1.0 (1) 1.9 (2) 1.8 (2) S217u2123n 7 2.0 (2) 3.1 (3) 1.6 (2) S217u2122n 7 2.1 (2) 1.7 (2) 1.8 (2) S217u211in 7 3.5 (4) -30 (-30) 1.7 (2) S181u2123n 1 -24. (-24) -33 (-33) 3.0 (3) S181u2122n 1 -26. (-26) -32 (-32) 4.1 (4) S181u211in 1 -25. (-25) -24 (-24) 0.7 (1) S073u2123n 3 -15. (-15) -22 (-22) 1.9 (2) S073u2122n 3 -20. (-20) -32 (-32) 2.1 (2) S073u211in 3 -20. (-20) -1.9 (-2) 2.1 (2)

Figure 5.12 Predicted vs. Measured Red (I), Blue (II) and Yellow (III) values for calibration set

(Pre-treatment: 1st der., subtracted spectra normalised to 100% wool, double centred) (I)

Measured (%R)

Predicted (%R)

0.0 2.0 4.0 6.0 8.0 -0.20

0.00

0.20

0.40

0.60

0.80

1.00 *10 1

s6733f

s6733es6733c

s6733b

s6733a

s6700f

s6700es6700ds6700cs6700bs6700a

s6617f

s6617es6617d

s6617cs6617b

s6617a

s5515fs5515e

s5515ds5515cs5515bs5515a

s5050fs5050e

s5050d

s5050c

s5050b

s5050a

s5030fs5030es5030ds5030cs5030bs5030a

s4232f

s4232e

s4232ds4232c

s4232b

s4232a

s4124fs4124ds4124cs4124b

s4124a

s3367f s3367e s3367d s3367c s3367b s3367a s3335f s3335e s3335d s3335c s3335b s3335a s3300f s3300e s3300d s3300c s3300b s3300a s3019f s3019e

s3019d

s3019c s3019b s3019a s2914f s2914e s2914d s2914c s2914b s2914a s2800f s2800e s2800d s2800c s2800b s2800a s2760f s2760e s2760d s2760c s2760b s2760a

s2044f s2044e s2044d s2044c s2044b s2044a s2034f s2034e s2034d s2034c s2034b s2034a s2006f s2006e s2006d s2006c s2006b s2006a

s1767f s1767e s1767d s1767c s1767b s1767a s1521f

s1521e s1521d s1521c s1521b s1521a s1046f s1046e s1046d s1046c s1046b s1046a

s0069f s0069e s0069d s0069c s0069b s0069a s0038f s0038e s0038d s0038c s0038b s0038a s0014f s0014d s0014e s0014c s0014b s0014a

Slope = 0.867

Interc. = 0.389

Corr. = 0.921

(II)

Measured (%B)

Predicted (%B)

0.0 2.0 4.0 6.0 8.0 -0.20

0.00

0.20

0.40

0.60

0.80

1.00

*10 1

s6733f s6733e s6733c

s6733b s6733a

s6700f s6700e s6700d s6700c s6700b s6700a s6617f

s6617es6617ds6617cs6617bs6617a

s5515f

s5515e

s5515ds5515c

s5515bs5515a

s5050fs5050e

s5050d

s5050cs5050b

s5050as5030f s5030e s5030d s5030c s5030b s5030a s4232f s4232e s4232d s4232c s4232b s4232a

s4124f s4124d s4124c s4124b s4124a

s3367fs3367e

s3367d

s3367cs3367bs3367a

s3335fs3335es3335ds3335cs3335b

s3335a

s3300f s3300e s3300d s3300c s3300b s3300a s3019f

s3019e

s3019d

s3019c s3019b s3019a

s2914f s2914es2914ds2914cs2914bs2914a

s2800f s2800e s2800d s2800c s2800b s2800a

s2760fs2760e

s2760d

s2760cs2760bs2760a

s2044fs2044es2044d

s2044cs2044bs2044as2034f

s2034e s2034d s2034c s2034b s2034a

s2006f s2006es2006ds2006cs2006bs2006a

s1767fs1767es1767ds1767cs1767bs1767a

s1521f s1521e s1521d s1521c s1521b s1521a

s1046f

s1046es1046d

s1046c

s1046bs1046a

s0069f

s0069es0069d

s0069c

s0069b

s0069a

s0038fs0038e

s0038ds0038c

s0038b

s0038a

s0014f s0014ds0014es0014c

s0014b

s0014a

Slope = 0.871Interc. = 0.345

Corr. = 0.922

(III)

Measured (%Y)

Predicted (%Y)

0.0 2.0 4.0 6.0 8.0 10.0-0.20

0.00

0.20

0.40

0.60

0.80

1.00 *10

1

s6700f s6700e s6700d s6700c s6700b s6700a

s6617f s6617e s6617d s6617c s6617b s6617a

s5515f s5515e s5515d s5515c s5515b s5515a s5030f s5030e s5030d s5030c s5030b s5030a

s4232f s4232e s4232d s4232c s4232b s4232a

s4124f s4124d s4124c s4124b s4124a s3335f s3335e s3335d s3335c s3335b s3335a

s3300fs3300e

s3300ds3300cs3300bs3300a

s3019f

s3019e

s3019d

s3019cs3019bs3019a s2914fs2914e

s2914ds2914cs2914bs2914a

s2800fs2800es2800ds2800cs2800b

s2800a

s2760f s2760e s2760d s2760c s2760b s2760a s2044f

s2044e s2044d s2044c s2044b s2044a s2034fs2034e

s2034d

s2034cs2034bs2034a

s2006f

s2006e

s2006ds2006cs2006bs2006a

s1767f s1767e s1767d s1767c s1767b s1767a

s1521fs1521es1521d

s1521cs1521bs1521a

s1046f s1046e s1046d s1046c s1046b s1046a

s0069f s0069e s0069d s0069c s0069b s0069a

s0038f

s0038e

s0038d

s0038cs0038bs0038a

s0014fs0014d

s0014es0014c

s0014b

s0014a

Slope = 0.963

Interc. = 0.125

Corr. =

0.963

Table 5.8 Predicted values for dependent variable RED

Calibration set: All non-irradiated samples Unknowns: Samples irradiated (texp= 168, 504 hrs.)

Note: N.I. = Not Included; i.e. it was not included in the validation set (outliers)

Predicted red value for unknown set Name (unknown set)

Original red dye ratio on the fabric



1st der.,block norm., Y-m.c. (Closest whole unit)

S730u723n 7 0.48 (1) 9.7 (10) 4.4 (4) S730u722n 7 -0.08 (0) 9.5 (10) N.I. S730u71in 7 1.8 (2) 8.7 (9) N.I. S811u723n 8 23 (23) 47 (47) 0.13 (0) S811u722n 8 18 (18) 41 (41) 3.2 (3) S811u71in 8 23 (23) 48 (48) 1.9 (2) S343u723n 3 -7.6 (-8) 11 (11) 6.71 (7) S343u722n 3 -7.5 (-8) 10 (10) 4.54 (5) S343u71in 3 -9.3 (-9) 9.8 (10) N.I. S307u723n 3 -3.2 (-4) 6.9 (7) 3.08 (3) S307u722n 3 -6.8 (-7) 2.4 (2) 2.98 (3) S307u71in 3 -4.9 (-5) 4.9 (5) 2.41 (2) S217u723n 2 -5.3 (-5) 2.5 (3) 0.34 (0) S217u722n 2 -4.0 (-4) 4.1 (4) 1.90 (2) S217u71in 2 -6.3 (-6) 3.4 (3) N.I. S181u723n 1 -25 (-25) -2.6 (-3) 1.33 (1) S181u722n 1 -29 (-29) -3.2 (-3) 1.98 (2) S181u71in 1 -24 (-24) -2.4 (-2) 2.07 (2) S073u723n 0 -31 (-31) -12 (-12) N.I. S073u722n 0 -37 (-37) -17 (-17) N.I. S073u71in 0 -42 (-42) -21 (-21) N.I. S730u2123n 7 -8.1 (-8) 6.5 (7) 4.5 (5) S730u2122n 7 -9.7 (-10) 5.6 (6) 4.5 (5) S730u211in 7 -5.9 (-6) 5.5 (6) 4.5 (5) S811u2123n 8 11 (11) 31 (31) 3.6 (4) S811u2122n 8 14 (14) 36 (36) 3.2 (3) S811u211in 8 3.4 (3) 23 (23) 3.5 (4) S343u2123n 3 -12 (-12) 8.9 (9) 4.1 (4) S343u2122n 3 -12 (-12) 7.9 (8) 4.3 (4) S343u2121n 3 -8.7 (-9) 9.7 (10) 4.3 (4) S343u211in 3 -25 (-25) 6.2 (6) 4.1 (4) S307u2123n 3 -5.6 (-6) 4.7 (5) 4.4 (4) S307u2122n 3 -5.3 (-5) 5.9 (6) 4.4 (4) S307u211in 3 -3.5 (-4) 5.2 (5) 4.4 (4) S217u2123n 2 -3.9 (-4) 5.2 (5) 4.6 (5) S217u2122n 2 -2.5 (-3) 5.3 (5) 4.5 (5) S217u211in 2 -4.1 (-4) 4.4 (4) 4.5 (5) S181u2123n 1 -26 (-26) 1.6 (2) -1.9 (-2) S181u2122n 1 -26 (-26) 0.65 (1) 8.5 (9) S181u211in 1 -26 (-26) -0.68 (-1) 12 (12) S073u2123n 0 -34 (-34) -10 (-10) N.I. S073u2122n 0 -31 (-31) -6.6 (-7) N.I. S073u211in 0 -41 (-41) -17 (-17) N.I.

Figure 5.10 Predicted vs. Measured blue values for calibration set (Pre-treatment: 1st der., subtracted spectra normalised to 100% wool, Y-mean centred)

Measured (Blue)

Predicted (Blue)

0.0 2.0 4.0 6.0 8.0 -0.20

0.00

0.20

0.40

0.60

0.80

1.00 *10 1

s6733f

s6733es6733cs6733bs6733a

s6700f s6700e s6700d s6700c s6700b s6700a

s6617f s6617e s6617d s6617c s6617b s6617a

s5515f

s5515e s5515d s5515c

s5515b s5515a

s5050fs5050e

s5050d

s5050cs5050b

s5050as5030fs5030es5030ds5030cs5030bs5030as4232fs4232es4232ds4232c

s4232bs4232a

s4124f s4124d s4124c s4124b s4124a

s3367f s3367e s3367d s3367c s3367b s3367a

s3335fs3335es3335ds3335cs3335bs3335a

s3300f s3300e s3300d s3300c s3300b s3300a

s3019f s3019e s3019d s3019c s3019b s3019a

s2914f s2914e s2914d s2914c s2914b s2914a

s2800f s2800e s2800d s2800c s2800b s2800a

s2760fs2760es2760d

s2760cs2760bs2760a

s2044fs2044es2044d

s2044c

s2044bs2044a

s2034fs2034e

s2034d

s2034cs2034bs2034a

s2006e s2006d s2006c s2006b s2006a

s1767f s1767e s1767d s1767c s1767b s1767a

s1521f s1521e s1521d s1521c s1521b s1521a

s1046fs1046es1046ds1046c

s1046bs1046a

s0069f s0069e s0069d s0069c s0069b s0069a

s0038fs0038es0038d

s0038c

s0038b

s0014d s0014e s0014c s0014a

Slope 0.924 Interc. 0.234 Corr. 0.961

Figure 5.11 Predicted vs. Measured yellow values for calibration set

(Pre-treatment: 1st der., subtracted spectra normalised to 100% wool, Y-mean centred)

Measured (Yellow)

Predicted (Yellow)

0.0 2.0 4.0 6.0 8.0 10.0 -0.50

0.00

0.50

1.00

1.50 *10

1

s6733f

s6733e s6733c s6733b s6733a

s6700fs6700es6700ds6700cs6700bs6700a

s6617f s6617e s6617d s6617c s6617b s6617a

s5515fs5515es5515ds5515cs5515bs5515as5050f s5050e

s5050d s5050c s5050b s5050a s5030f s5030e s5030d s5030c s5030b s5030a

s4232fs4232es4232ds4232cs4232bs4232a

s4124fs4124ds4124cs4124b

s4124a

s3367f s3367e s3367d s3367c s3367b s3367a

s3335fs3335es3335ds3335cs3335bs3335a

s3300f

s3300e

s3300ds3300cs3300bs3300a

s3019fs3019es3019ds3019cs3019bs3019a

s2914f

s2914es2914ds2914cs2914bs2914a s2800f

s2800es2800ds2800cs2800bs2800a

s2760f s2760e s2760d s2760c s2760b s2760a

s2044fs2044es2044ds2044c

s2044bs2044a s2034fs2034e

s2034d

s2034cs2034bs2034a

s2006es2006ds2006cs2006bs2006a

s1767f s1767e s1767d s1767c s1767b s1767a

s1521f

s1521es1521ds1521cs1521bs1521a

s1046fs1046es1046ds1046cs1046bs1046a

s0069f

s0069es0069ds0069cs0069b

s0069a

s0038fs0038es0038d

s0038cs0038b

s0014d

s0014e s0014c s0014a

Slope =

0.871

Interc. = 0.477

Corr. =

0.933


after irradiation are displayed in Table 5.8. As it can be seen the results obtained are

not very favourable. Indeed, the pre-treatment used here does not improve the

prediction ability of the PLS model at all.


A calibration set with the same objects and independent variables was built and

cross-validated for the dependent variable blue. Five factors explained 78% of the

total variance of the dependent and independent variables. The plot of predicted

versus measured is shown in Figure 5.10 indicates that there is good correlation

between the objects in the model. The values for slope, intercept and the correlation

coefficient for the best line fitted are as shown in Figure 5.10.

The same unknown set was then introduced to the model. The results of the

predictions for these 43 objects are displayed in Table 5.9. The predictions for the

dependent variable blue also show that the pre-treatment used here, reduces the

prediction ability of the model.


A matrix was built consisting of the same objects in the calibration set as in the

previous models. It was then cross-validated and the best model was chosen. Eight

factors explained 87% of the total variance for the dependent variables. The first six

factors explained 80% of the variances. The plot of predicted versus measured values

for this set is shown in Figure 5.11. The values for the slope and correlation

coefficient for the best line fitted shows that there is a good correlation between the

data. The unknown set consisting of 43 objects was then introduced to the model and

Table 5.9 Predicted values for dependent variable BLUE

Calibration set: all non-irradiated samples Unknowns: samples irradiated (texp=168, 504 hrs.)

Predicted blue value for unknown set Name (unknown set)

Measured value (Original blue ratio) 1st der.,

block norm. (Closest whole unit)



S730u723n 3 4.7 (5) -4.3 (-4) 3.1 (3) S730u722n 3 3.6 (4) -5.1 (-5) 3.1 (3) S730u71in 3 4.9 (5) -3.9 (-4) 3.1 (3) S811u723n 1 -3.6 (-4) -37 (-37) 5.2 (5) S811u722n 1 -2.0 (-2) -27 (-27) 3.5 (4) S811u71in 1 -6.0 (-6) -43 (-43) 4.3 (4) S343u723n 4 13. (13) -11 (-11) 2.0 (2) S343u722n 4 12. (12) -9 (-9) 2.8 (3) S343u71in 4 16. (16) -15 (-15) 2.8 (3) S307u723n 0 0.59 (1) -9 (-9) 3.2 (3) S307u722n 0 0.21 (0) -12 (-12) 3.2 (3) S307u71in 0 -0.04 (0) -14 (-14) 3.1 (3) S217u723n 1 1.1 1) -12 (-12) 1.5 (2) S217u722n 1 0.55 (1) -11 (-11) 1.7 (2) S217u71in 1 1.1 (1) -13 (-13) 1.6 (2) S181u723n 8 50. (50) 7.7 (8) 94.7 (95) S181u722n 8 41. (41) 7.4 (7) 20.9 (21) S181u71in 8 43. (43) 7.7 (8) -7.8 (-8) S073u723n 7 47. (47) 12 (12) 14.9 (15) S073u722n 7 53. (53) 13 (13) -7.6 (-8) S073u71in 7 6.1 (6) -12 (-12) -0.6 (-1) S730u2123n 3 6.5 (7) -12 (-12) 3.0 (3) S730u2122n 3 6.7 (7) -9.2 (-9) 3.0 (3) S730u211in 3 -0.26 (0) -24 (-24) 3.0 (3) S811u2123n 1 -0.37 (0) -31 (-31) 3.3 (3) S811u2122n 1 0.41 (0) -18 (-18) 3.6 (4) S811u211in 1 50. (50) -16 (-16) 3.4 (3) S343u2123n 4 12. (12) -12 (-12) 3.2 (3) S343u2122n 4 8.4 (8) -9.6 (-10) 2.9 (3) S343u2121n 4 9.3 (9) -28 (-28) 2.9 (3) S343u211in 4 12. (12) -13 (-13) 2.9 (3) S307u2123n 0 4.8 (5) -14 (-14) 3.1 (3) S307u2122n 0 1.7 (2) -12 (-12) 3.1 (3) S307u211in 0 3.7 (4) -10 (-10) 3.1 (3) S217u2123n 1 2.8 (3) -8.8 (-9) 3.1 (3) S217u2122n 1 2.5 (3) -10 (-10) 3.0 (3) S217u211in 1 2.2 (2) -3.2 (-3) 3.0 (3) S181u2123n 8 33. (33) -2.4 (-2) 10.8 (11) S181u2122n 8 36. (36) -1.8 (-2) -2.0 (-2) S181u211in 8 36. (36) -0.25 (0) -6.9 (-7) S073u2123n 7 36. (36) 0.90 (1) -2.9 (-3) S073u2122n 7 37. (37) 3.9 (4) 2.4 (2) S073u211in 7 48. (48) -4.3 (-4) 2.5 (3)


the values for the dependent variable yellow predicted. The results are shown in Table

5.10. Again it is shown that the results obtained here are not acceptable.

Group (iii)



Pre-treatment: 1st der., substrate subtracted spectra normalised to 100% wool, double

centred.



A calibration set consisting of 142 objects was built. The set was then cross validated.

The plot of predicted versus measured (Figure 5.12(I)) shows that there is a

reasonable correlation between the data in this set. The irradiated samples were then

introduced to this set as the unknown set. The results are shown in Table 5.8.

Dependent Variable: blue

The same calibration set was used here as well. The data are again spread across the

best fitted line. See Figure 5.12 (II). The unknown set was then introduced to this

model. Predicted values for this set are shown in Table 5.9.


The same calibration set as described previously was chosen. See Figure 5.12(III).

125 objects were introduced as the unknown set to this model. Samples in the

unknown set with zero ratios of yellow dye were not considered since the


corresponding samples in the calibration set were not predicted very well. The results

of the prediction are displayed in Table 5.10.

Therefore, overall it is concluded that:

• The pre-treatment of the spectral data used here, does not enhance the ability of

the PLS model to predict the ratio of dyes.

• Considering all the results obtained so far, it may be concluded that the best

pre-treatment to be used for PLS1 model here is normalising the spectral data to

100% substrate of the first derivative FT-Raman spectra of the samples, followed

by Y-mean centring of the matrix.

• The PLS model used to predict the dye ratios of the irradiated samples indicates

that the model shows a small ability of the system for prediction, but the

calibration set for the model has to be reviewed.

It has been suggested20,21 that, the correlation and error parameters may be improved

by including data from the same statistical population as the predicting set. This

strategy defines how “similar” a sample is to the rest of samples contained in the

calibration set. A sample is considered to be similar to the one in the calibration set if

the model is able to predict the properties of a sample. If the sample is found to be

dissimilar to the rest of the samples, then it is assumed that there is new information

in that sample unknown to the calibration set and the new sample is then added to the

calibration set automatically in order to improve the model.


It is anticipated that this procedure would indeed improve the predictive ability of the

PLS model. It was, however, not possible here to do so since the ratio of the dyes

remaining on the irradiated samples are not known so it is suggested that a further

study should be conducted in which a larger matrix could be built and the ratios of

the irradiated dyes would be estimated by other means. In this way a calibration set

containing some of the new properties introduced to the model by the irradiation

could be constructed.

Another approach may be to look at the faded dyes in colour-space — that is the shift

colour from the original colour — or in colour-space coordinates. In this way, all of

the data for the original and faded fabric may be used in the calculations rather than

the amount of dye exhausted onto the fabric.


5.4 References 1 P. Brennan, C. Fedor, “Sunlight, UV, & Accelerated Weathering”, (The Q Panel Company,

26200 First St., Cleveland, Ohio 44145), p.1 2 D.C. Jones, C.M. Carr, W.D. Cooke, D.M. Lewis, Textile Res. J., 68, 1998, 739-748. 3 W.S. Simpson, C.T. Page, ‘The Effect of Light on Wool and the Inhibition of Light

Tendering’, Wool Research Organisation of New Zealand Inc., Report No. 60 (1979). 4 S. Collins, R.S. Davidson, J. Photochem. Photobiol. A; 77, 1994, 277-282. 5 E. Wojciechowska, A. Pielesz, A. Wlochowicz, Textile Res. J., 62, 1992, 580-585. 6 I.J. Miller, G.J. Smith, J. Soc. Dyer Color., 111, 1995, 103-106 7 J.S. Church, K.R. Millington, Biospectroscopy, 2, 1996, 249-258. 8 K. Schäfer, D. Goddinger, H. Höcker, J. Soc. Dyer Color., 113, 1998, 350-355. 9 I.H. Leaver, R.E. Hester, R.B. Girling, Textile Research Institute, 1998, 182-184 10 T.J. Racey, P.L. Rochon, Canadian Journal of Applied Spectroscopy, 39, 1994, 38-42. 11 I.Rusznàk, J. Frankl, J. Gombkötő, JSDC, 101 (1985), p.p. 130-136. 12 B. Neppolian, H.C. Choi, S. Sakthivel, B. Arabindoo, V. Murugesan, Journal of Hazardous

Materials, B89, 2002, 303-317. 13 R.S. Asquith, D.E. Rivett, Appl. Polym. Symp., 18, 1971, 333. 14 G. Socrates, “Infrared and Raman Characteristic Group Frequencies, Tables and Charts” 3rd

ed., (John Wiley & Sons, 2001) 15 V. Fredline, S. Kokot, C. Gilbert, , Mikrochim. Acta [Suppl.] 14, 1997, 183-184. 16 Private communication with Dr. S. Wallis (8 Feb. 2002); Dept. of Medicine, University of

Queensland. 17 S. Kokot, S. Carswell, D.L. Massart, , Appl. Spec., 46, 1992, 1393-1399. 18 C. Gilbert, S. Kokot, ‘An Analysis of Oxidised Wool Fabric Using FT-Raman Spectroscopy

and Chemometrics’, Proceeding of 13th Australian Symposium on Analytical Chemistry, Darwin, Australia (1995), AS 40-1.

19 W.S. Simpson, ‘Photo-protection of Wool Fabrics by Dyestuffs’, Wool Research Organisation of New Zealand Inc., Communication No. C77 (1982).

20 D.R. Tallant, R.L. Simpson, ‘The Thermal History of Charred Materials by Raman Spectroscopy’, Sandia Report, SAND2001-0131, Unlimited Release, Printed February 2001, p.p.3-19.

21 S.K. Setarehdan, J.J. Soraghan, D. Littejohn, D.A. Sadler, Analytica Chimica Acta, 452, 2002, 35-45.

Chapter 6 Conclusion 145

Chapter 6 Conclusion

“We yearn for the day when we can totally rely on colour

instrumentation to decide when two colours match, and remove the

responsibility from that colour matcher working third shift.” Bruce Mulholland, Hoeechst-Celanese Engineering Plastics Div., Florence, Ky.1

6.1 Conclusion

To date, the procedure used by the textile industry referred to as “Colour Matching”

involves studying the colour using spectrophotometry of the dye and examining the

reflectance properties of the dye on the substrate. The studies conducted in this thesis

were concerned with analysing dyed wool and dyed wool blend fabrics using

vibrational spectroscopy, viz. FT-IR PAS and FT-Raman carrying on from the work

of Lee-Son et al.2. In this thesis, three dyes on woollen fabrics were examined with

two of the dyes being the same as those studied by Lee-Son et al. FT-Raman spectra

for the dyes were found to be identical with those recorded by Lee-Son et al. The

peaks due to wool keratin are also easily identified. However, unlike the FT-Raman

spectra, the PA spectra do not display well-resolved peaks that are solely due to the

dye molecule. This may be because the backbone vibrations of the dyes are

intrinsically active Raman scatterers and hence weak IR absorbers while the

functional groups in the wool fibre are weak Raman scatterers and hence strong IR

absorbers.


FT- Raman and FT-IR PA spectra of undyed wool and undyed wool/ polyester

samples were also recorded and compared to the ones in the literature3,4. It was found

that they were very similar apart from some minor differences. For example, it was

found that the surface of the undyed wool sample studied in this project was oxidised.

Some variations in the position of the peaks reported in different literature sited were

also observed. Examining the FT-Raman spectrum of the wool sample studied in this

project, it was then possible to draw conclusions to which peak positions reported in

various literatures could be more correct. An undyed wool/ polyester blend sample

was also examined using FT-Raman spectroscopy. All the peaks could be assigned

according to the literature.

One of the main objectives of this project was to investigate the possibility of colour

matching and prediction of dye mixture ratio in dyed woollen fabrics using vibrational

spectroscopy and chemometrics, both qualitatively and quantitatively. For this

purpose PCA and PLS methods of analysis were used respectively.

PCA plots of the FT-IR PA spectra for the woollen fabrics dyed with Lanasol dyes

indicate that the samples of the same colour do cluster loosely together. They do not

position themselves in the same manner as they are positioned on the colour card.

PCA plots for the PA spectra of wool/ polyester blend fabrics displayed the same

conclusion as above. In these plots the spectral data are too scattered to even display

clusters for the repeat spectra of the same sample.

The FT-IR PA spectral data for the woollen fabrics were then submitted to PLS. The

results show that the prediction ability of the PLS1 model studied is quite reasonable


(within ±10% of the correct value) as long as the calibration set chosen demonstrates

satisfactory prediction ability as well.

FT-Raman spectra of woollen fabrics and wool/ polyester blends were also submitted

to PCA and PLS for further analysis. PCA plots of both types of samples indicate that

not only spectral repeats of each sample cluster together but also the arrangement of

the clusters is in accordance with that of the colour card. This is particularly obvious

in the case of wool/ polyester blend samples.

Therefore, overall it may be concluded that the combination of FT-Raman

spectroscopy and PCA offers a better colour recognition than FT-IR PA spectroscopy

and PCA method of analysis for both of these types of samples.

The spectral data were also studied quantitatively by PLS. In these studies it was

found that PLS1 model was the preferred model and that PLS2 model did not work

well at all for this set of data. Unlike the results obtained for the PLS1 model used to

study PA spectra of woollen fabrics, it was found that this method of analysis did not

work as well for the FT-Raman spectral data. On the other hand, when PLS was

applied to the data for the wool/ polyester blend samples, the prediction ability was

satisfactory. Again it was found that the prediction ability of the model depends

heavily on the calibration set to begin with.

The effect of the dye on the Raman spectra was observable without the aid of

chemometric and yet the PLS analysis of the Raman spectra failed to calculate the


correct amount of dye. This strongly indicates that the measured values do not reflect

the true amount of dye on the fabric.

Therefore, overall it was concluded that PLS method used along with FT-Raman data

works well for wool blend, while for pure wool samples it is advisable to use FT-IR

spectra in combination with PLS.

The last objective of the project was to study the fading characteristics of the wool

fabrics with mixed dyes.

A selection out of the woollen samples were UV-irradiated for 7 and 21 days.

FT-Raman spectra of the undyed irradiated wool fabric agreed well with those found

in literature5. In this thesis, the peak at 997cm-1 mentioned by Lewis et al.5 was also

observed. It was suggested that this peak appears and gradually increase due to further

oxidation of –S-O-S- bond in the wool fibre.

A number of dyed woollen samples were also UV-A irradiated and studied by FT-

Raman spectroscopy. Minor changes were observed in the intensities of some of the

peaks with respect to the exposure time. The data were then submitted to PCA and

PLS for pattern recognition and prediction of the ratio of the dye remaining intact,

respectively. The plot for PCA for samples at exposure times zero, 7 and 21 days

indicated that PCA separates non-irradiated samples from the rest. The spectral data

for the exposed samples were also studied in order to find out if these samples would

position themselves according to the colour card, i.e. the way they did before being

irradiated. The results showed that although spectral repeats of the same sample still


clustered together they did not follow the pattern shown by the same ones at

texp= 0 hrs. One possible explanation for this is that the ratio of dyes on the exposed

samples differed from those of the original set due to differential degradation of the

three dyes.

The data were then studied using PLS. It was found that the ability of this method for

predicting the ratio of all the three colours was poor. It was however found that the

ability of prediction for dependent variable yellow was better than for the colours red

and blue. According to the literature6,7 the poor results obtained may be justified since

the irradiated samples may posses properties that are not accounted for in the

calibration. For example, the degradation of the underlying wool in addition to the

dye molecule has not been considered in modelling of the calibration set. Also, the

ratio of the dyes might have not changed but the absolute amount of dye could have

become different. Any combination of these reasons could have in turn reduced the

ability of the model in correctly predicting the ratio of the colours.

This project has therefore shown that:

FT-Raman spectroscopy of dyed wool along with application of

chemometrics (namely, PCA and PLS) is a feasible method of studying

woollen samples both qualitatively and quantitatively; and that the ratio of

colours on the samples may be predicted quite successfully.

The above procedure when applied to UVA exposed samples of the same

origin, does not offer the same success rate.


6.2 Future Work

It is therefore suggested that further studies of the same nature containing a larger data

matrix and for more colours should be conducted in order to make it applicable for the

dyeing industry. As it stands, vibrational spectroscopy techniques are not well suited

to industrial analysis of dyed fabrics since they involve complicated procedures using

expensive equipment and produce results of limited accuracy. With further

development , however, these technique may find useful application in forensics or

other specialized analysis.

The same suggestion applies to the irradiated samples. There, a much larger data

matrix should be considered. The ratio of the dye still intact should also be measured

by some other means. A correlation between the irradiated and non-irradiated samples

should be realised. Followed by building an appropriate calibration set containing

some information about the irradiated samples. Only then the unknown samples

should be introduced to the PLS model and predicted.


6.3 References

1 B. Mulholland, Plastics Compounding, 12, 1989, 33. 2 G. Lee-Son, R.E. Hester, J. Soc. Dyer Color., 106, 1990, 59-63. 3 E. Carter, ‘Vibrational spectroscopic Studies of Wool’, PhD thesis, Queensland University of

Technology, 1998. 4 M.A. Moharram, T.Z. Abdel-Rehim, S.M. Rabie, J. App. Polym. Sci., 26, 1981, 921-932 5 D.C. Jones, C.M. Carr, W.D. Cooke, D.M. Lewis, Textile Res. J., 68, 1998, 739-748. 6 D.R. Tallant, R.L. Simpson, ‘The thermal History of Charred Materials by Raman

Spectroscopy’, Sandia Report, SAND2001-0131, Unlimited Release, Printed February 2001, pp.3-19.

7 S.K. Setarehdan, J.J. Soraghan, D. Littejohn, D.A. Sadler, Analytica Chimica Acta, 452, 2002, 35-45.

Appendix 1 The Forosyn® Colour Diamonds

® Trademark registered by Sandoz Ltd.

Figure A1-1: Diamond 1

halla



halla



halla


Appendix 2 page A2/1

Appendix 2


Table A2-0 FT-Raman Peak Positions and their Assignments for Undyed Wool and

Wool Dyed with Two Lanasol Dyes Note: The three-digit dye code numbers refer to the ratio of red, blue and yellow dyes on the wool (e.g. sample 073 has red:blue:yellow in the ratio 0:7:3).

Bands Assigned

Peak Positn. (cm-1) 019* Dyed

Fabric


Fabric


Fabric

Peak Positn. (cm-1)

208 Dyed

Fabric

Peak Positn. (cm-1)

307 Dyed Fabric

Peak Positn. (cm-1)

703 Dyed Fabric

Peak Positn. (cm-1)

370 Dyed

Fabric

Peak Positn. (cm-1)

550 Dyed Fabric

Peak Positn. (cm-1)

730 Dyed Fabric

Peak Positn. (cm-1)

000 Undyed Fabric

Peak Positn. (cm-1) Lee-Son et. al. 1

Peak Positn. (cm-1) Carter et al.2

Amide I 1653 (vs, br)

1652 (vs, br)

1650 (vs)

1652 (vs, br)

1651 (s, br)

1653 (s, br, shp)

1649 (s, v br)

1649 (s)

1653 (s)

1653 (vs)

1653

Tyr & Trp

1604 (m)

1605 (vs)

1605 (vs, shp)

1611 (m, sh)

1617 (w, sh)

1613 (w, sh)

1604 (vs, sh)

1603 (w)

1613 (s)

1613 (s)

1614

Trp 1553 (w)

1555 (w)

1549 (w)

1551 (w, br)

1553 (vw, sh)

1554 (w, br)

1553 (vw)

1551 (w)

1548 (s)

1552 (vw,sh)

1553

Dye On Fibre

N.O. N.O. N.O. N.O. N.O. N.O. N.O. N.O. N.O. N/A 1475 CH2 & CH3

bending mode

1448 (vs, br)

1449 (vs, br)

1449 (vs, shp)

1447 (vs, shp)

1447 (vs, br)

1447 (vs, shp)

1448 (vs, br)

1449 (vs, br)

1446 (s)

1447 (vs)

1448

Dye On Fibre

N.O. N.O. N.O. N.O. N.O. N.O. N.O. N.O. N.O. N/A 1430

Dye On Fibre

1369 (vw, sh)

1377 (vw, br)

N.O. 1369 (vw)

1382 (vw, sh)

1369 (w, sh)

N.O. N.O. N.O. N/A 1375

Dye On Fibre

N.O. 1364 (w, v br)

1359 (w, sh)

N.O. 1362 (w, sh)

N.O. 1362 (m, br)

N.O. 1365 (vw, sh)

N/A 1362 CH2 bend, Trp

1340 (vw, sh)

1335 (w)

1338 (vw, sh)

1338 (vs, shp)

1338 (m, br)

1340 (vs, shp)

1338 (m)

1331 (w, v br)

1341 (s)

1337 (s)

1340 1338

Cα-H bend

1315 (vw, sh)

1317 (w, br)

1318 (vw)

1314 (s, br)

1320 (w, sh)

1318 (w, sh)

1323 (w, sh)

N.O. 1319 (s,sh)

1314 (s)

1318

Amide III (α)

1273 (w)

1278 (w)

N.O. 1271 (vw)

1284 (vw)

1282 (vw, sh)

1265 (vw, sh)

1282 (vw, sh)

1270 (s)

1272 (vw)

1281

Amide III (α)

1263 (vw, sh)

1268 (vw)

1263 (vs, br)

1252 (vw, sh)

1264 (vw)

1266 (w, sh)

1256 (vw, sh)

N.O. 1257 (s, sh)

1260 (vw)

1260 1259

Amide III

(unordered)

1243 (w, sh)

1253 (vw, sh)

1248 (vw, sh)

N.O. N.O. N.O. 1238 (w)

1243 (w)

N.O. 1246 (vw)

1244

Tyr & Phe

1208 (w, sh)

1207 (vw, sh)

1207 (vw, sh)

1206 (vw, br)

1207 (vw, sh)

N.O. N.O. N.O. 1205 (s)

1203 (w)

1207

Tyr 1177 (w)

1175 (w, sh)

1178 (w)

1173 (m, shp)

1174 (w)

1172 (m)

1175 (w)

1175 (w)

1175 (s)

1175 (w)

1176

C-N stretch

1155 (w, sh)

1157 (w, sh)

1155 (vw, sh)

1153 (vw, sh)

1158 (w)

1155 (w, sh)

1154 (vw, sh)

N.O. 1155 (s,sh)

1155 (w)

1155

C-N stretch

1124 (w, br)

1123 (w, br)

1124 (m, shp)

1126 (s, shp)

1123 (m, shp)

1125 (s, shp)

1124 (vs, shp)

1122 (w)

1124 (s)

1124 (w)

1127 1126

C-N stretch

1099 (vw)

1107 (vw, sh)

1094 (vw)

1090 (vw)

1094 (vw)

N.O. 1096 (vw, sh)

N.O. N.O. 1096 (vw, sh)

1096

C-N stretch

1079 (vw)

1075 (vw, sh)

1079 (vw)

1080 (vw, sh)

1083 (vw, sh)

1079 (w)

1078 (vw)

N.O. 1084 (s)

1079 (vw, sh)

1079

Phe

1033 (w, br)

1029 (vw, sh)

1034 (s, shp)

1029 (m, br)

1031 (w, br)

1032 (w, br)

1034 (m, shp)

1031 (w,br)

1032 (s,br)

1031 (m)

1030 1031

Phe & Trp

1001 (m, sh)

1001 (w, sh)

1001 (s, shp)

1001 (s, shp)

1001 (s, shp)

1001 (s, shp)

1001 (s, shp)

1001 (w, shp)

1000 (s,shp)

1001 (s)

1002

CH2 rock

956 (vw, sh)

945 (vw, sh)

952 (vw, sh)

953 (vw, sh)

958 (vw)

956 (vw, sh)

955 (w, sh)

N.O. 956 (s)

955 (m)

952

Skeletal C-C

stretch (α)

936 (w, br)

936 (vw, sh)

934 (vw, sh)

934 (m, br)

935 (m, br)

931 (w, br)

926 (w, br)

934 (w, br)

930 (s)

931 (m)

934

Dye On Fibre

922 (vw, sh)

912 (w, sh)

N.O. N.O. N.O. N.O. 925 (w, br)

N.O. N.O. N/A 917

Skeletal C-C

stretch (α)

902 (vw)

910 (vw)

N.O. N.O. N.O. N.O.

N.O. N.O. 910 (s)

N.O. 905

Skeletal C-C

N.O. 898 896 895 898 894 897 895 896 897 897


Table A2-0 FT-Raman Peak Positions and their Assignments for Undyed Wool and Wool Dyed with Two Lanasol Dyes

Note: The three-digit dye code numbers refer to the ratio of red, blue and yellow dyes on the wool (e.g. sample 073 has red:blue:yellow in the ratio 0:7:3).

Bands Assigned


Fabric


Fabric


Fabric

Peak Positn. (cm-1)

208 Dyed

Fabric

Peak Positn. (cm-1)

307 Dyed Fabric

Peak Positn. (cm-1)

703 Dyed Fabric

Peak Positn. (cm-1)

370 Dyed

Fabric

Peak Positn. (cm-1)

550 Dyed Fabric

Peak Positn. (cm-1)

730 Dyed Fabric

Peak Positn. (cm-1)

000 Undyed Fabric

Peak Positn. (cm-1) Lee-Son et. al. 1

Peak Positn. (cm-1) Carter et al.2

stretch (α) (w) (m,shp) (w, br) (vw, sh) (w,br) (w, br) (vw) (s) (m) Trp 875

(vw, sh) 877 (vw, sh)

878 (vw,sh)

878 (vw, sh)

877 (vw)

879 (vw)

877 (vw, sh)

N.O. 875 (s)

876 (w, sh)

881

Tyr 850 (w, br)

848 (w)

850 (m, shp)

851 (m, shp)

851 (w, br)

850 (w, shp)

850 (w)

850 (w)

851 (s)

851 (s)

851

Tyr 826 (w, br)

830 (w)

826 (vw)

827 (w, shp)

829 (w, v br)

826 (w)

828 (w, v br)

827 (w)

823 (s,sh)

826 (m, vbr)

828

Dye On Fibre

806 (w, sh)

796 (w, br)

N.O. 795 (vw)

795 (vw)

791 (w)

N.O. N.O. N.O. N/A 796

Dye On Fibre

694 (w)

694 (w, br)

N.O. 695 (vw)

701 (vw)

690 (vw)

N.O. N.O. N.O. N/A 690 (and may be 695)

1. G. Lee-Son, R.E. Hester, J. Soc. Dyer Color., 106, 1990, 59. 2. E. Carter, P.E. Fredericks, J.S. Church, R.J. Denning, Spectrochimica Acta, 50A (11), 1994,

1927-1936


Group: (i) Model:2 Cal. Set:84 Val. Set:39 Dep. Var.: Red Figure A2-1 Plot of predicted vs. measured values for calibration (I) and validation (II) sets, group (i), model 2, dep. Var. Red I)Calibration Set:

Pred. vs Measured, Var %R,(5 Comp), Avg. Pred. Err.= 0.950, Date 5/1/2002

Measured (%R)

Predicted (%R)

0.0 2.0 4.0 6.0 8.0 -0.20

0.00

0.20

0.40

0.60

0.80

1.00 *10

1

046Ra

046Rb

046Rc 046Re

154Ra 154Rb 154Rc 154Rd 154Re 154Rf 127Ra 127Rb 127Rc 127Rd 127Re 127Rf

235Ra 235Rb 235Rc 235Rd 235Re 235Rf 244Ra 244Rb 244Rc 244Rd 244Re 244Rf 361Ra361Rb361Rc361Rd

361Re

361Rf208Rb 208Rc 208Rd 208Re 208Rf

316Ra316Rb316Rc316Rd316Re316Rf325Ra325Rb325Rc325Rf

370Ra370Rb370Rc

370Rd

370Re

370Rf


424Rf433Ra433Rb433Rc433Rd433Re433Rf

532Ra


613Ra613Rb613Rc

613Rd613Re

613Rf

703Ra 703Rb 703Rc 703Rd 703Re 703Rf Slope =

0.827

Interc. = 0.542

Corr. = 0.910

II)Validation Set:

Predicted vs Measured for Variable %R

Measured (%R)

Predicted (%R)

0.0 2.0 4.0 6.0 8.0 -2.00

-1.00

0.00

1.00

2.00 *10

1

073Ra 073Rb 073Rc 073Rd 073Re

073Rf

181Ra 181Rb 181Rc 181Rd 181Re 181Rf 217Ra

217Rb 217Rc 217Rf 307Ra307Rb307Rc307Rd307Re307Rf


811Ra 811Rb 811Rc 811Rd 811Re 811Rf

730Ra 730Rb 730Rc 730Re 730Rf Slope =

1.512

Interc. = -1.797

Corr. = 0.771


Table A2-1-Results of PLS model 2 applied to Group (i), dependent variable red Note: The number in the parenthesis indicates the predicted value rounded to the nearest digit for comparison with the measured value Name Predicted Measured 073Ra -0.89 (-1) 0073Rb -0.72 (-1) 0073Rc -1.8 (-2) 0073Rd -2.4 (-2) 0073Re -2.8 (-3) 0073Rf -19 0181Ra 1.3 (1) 1181Rb 1.8 (2) 1181Rc 1.7 (2) 1181Rd -0.60 (-1) 1181Re 1.4 (1) 1181Rf 1.6 (2) 1217Ra 3.1 (3) 2217Rb 1.9 (2) 2217Rc 2.7 (3) 2217Rf 3.0 2307Ra 2.5 (3) 3307Rb 1.9 (2) 3307Rc 2.2 (2) 3307Rd 1.3 (1) 3307Re 0.50 (1) 3307Rf 2.0 3343Ra 5.0 3343Rb 5.4 (5) 3343Rc 5.8 (6) 3343Rd 5.9 (6) 3343Re 6.7 (7) 3343Rf 5.0 3811Ra 13 8811Rb 10 8811Rc 11 8811Rd 11 8811Re 13 8811Rf 12 8730Ra 4.4 (4) 7730Rb 7.6 (8) 7730Rc 4.3 (4) 7


Group: (ii) Cal. Set: 86 Val. Set: 47 Dep. Var.: Blue Figure A2-2 (a)- Plot of predicted vs. measured values for calibration (I) and the validation (II) sets, group (ii) (dep. Var. Blue) I)Calibration Set:

Pred. vs Measured, Var %BL,(6 Comp), Avg. Pred. Err.= 1.264, Date 2/7/2002

Measured (%BL)

Predicted (%BL)

0.0 2.0 4.0 6.0 8.0 -0.50

0.00

0.50

1.00

1.50 *10

1

019Ra 019Rb 019Rc 019Re 019Rf

073Ra 073Rb 073Rc 073Rd 073Re


181Ra 181Rb 181Rc

181Rd

181Re 181Rf

217Ra 217Rb 217Rc 217Re 217Rf

244Ra244Rb244Rc244Rd244Re244Rf 361Ra361Rb361Rc361Rd361Re361Rf

208Ra 208Rb 208Rc 208Rd 208Re 208Rf

316Ra 316Rb 316Rc 316Rd 316Re 316Rf 325Ra 325Rb 325Rc 325Rd

325Re 325Rf

370Ra 370Rb 370Rc 370Rd 370Re 370Rf

424Ra 424Rb 424Rc 424Rd 424Rf 613Ra 613Rb 613Rc 613Rd 613Re 613Rf

703Ra 703Rb 703Rc 703Rd 703Re 703Rf

730Ra

730Rb

730Rc

730Re

730Rf

Slope = 0.819 Interc. = 0.582 Corr. = 0.905

II) Validation Set:

Predicted vs Measured for Variable %BL

Measured (%BL)

Predicted (%BL)

0.0 1.0 2.0 3.0 4.00.00

1.00

2.00

3.00

4.00

5.00


127Re

127Rf

235Ra

235Rb235Rc235Rd

235Re235Rf

307Ra 307Rb

307Rc

307Rd

307Re

307Rf

343Ra 343Rb 343Rc 343Rd 343Re 343Rf

433Ra433Rb433Rc433Rd433Re433Rf532Ra532Rb

532Rc532Rd

532Re532Rf

811Ra

811Rb 811Rc 811Rd 811Re 811Rf

Slope = 0.714 Interc. = 0.830

Corr. = 0.889


Table A2-2- Results of PLS model applied to Group (ii), (for dep. Var. blue) Name Predicted Measured 127Ra 2.6 (3) 2127Rb 2.8 (3) 2127Rc 2.8 (3) 2127Rd 2.5 (3) 2127Re 3.4 (3) 2127Rf 2.6 (3) 2235Ra 3.2 (3) 3235Rb 2.9 (3) 3235Rc 2.9 (3) 3235Rd 2.9 (3) 3235Re 3.3 (3) 3235Rf 3.2 (3) 3307Ra 0.86 (1) 0307Rb 0.67 (1) 0307Rc 1.8 (2) 0307Rd 0.22 (0) 0307Re 1.1 (1) 0307Rf 1.9 (2) 0343Ra 3.6 (4) 4343Rb 3.7 (4) 4343Rc 3.8 (4) 4343Rd 3.6 (4) 4343Re 4.1 (4) 4343Rf 3.8 (4) 4433Ra 3.0 (3) 3433Rb 3.0 (3) 3433Rc 3.0 (3) 3433Rd 3.0 (3) 3433Re 3.0 (3) 3433Rf 2.7 (3) 3532Ra 2.7 (3) 3532Rb 2.4 (2) 3532Rc 2.7 (3) 3532Rd 2.8 (3) 3532Re 2.5 (3) 3532Rf 2.6 (3) 3811Ra 0.3 (0) 1811Rb 1.5 (2) 1811Rc 1.4 (1) 1811Rd 1.1 (1) 1811Re 0.8 (1) 1811Rf 0.56 (1) 1


Group: (ii) Cal. Set:86 Val. Set: 47 Dep. Var.: Yellow Figure A2-3- Plot of predicted vs. measured values for calibration (I) and validation (II) sets, group (ii), (dep. Var. Yellow)

I)Calibration Set:

Pred. vs Measured, Var %Y,(8 Comp), Avg. Pred. Err.= 1.069, Date 2/7/2002

Measured (%Y)

Predicted (%Y)

0.0 2.0 4.0 6.0 8.0 10.0 -0.50

0.00

0.5

1.00

1.50 *10

1

019Ra 019Rb 019Rc 019Re 019Rf


073Re 154Ra154Rb154Rc154Rd154Re

154Rf

181Ra 181Rb 181Rc 181Rd 181Re 181Rf

217Ra217Rb217Rc

217Re

217Rf


361Ra 361Rb 361Rc 361Rd 361Re 361Rf

208Ra

208Rb

208Rc

208Rd208Re208Rf

316Ra316Rb316Rc

316Rd316Re

316Rf


370Ra 370Rb 370Rc 370Rd 370Re 370Rf


613Ra613Rb613Rc613Rd613Re613Rf703Ra703Rb703Rc703Rd703Re703Rf730Ra

730Rb

730Rc

730Re 730Rf

Slope =

0.868

Interc. = 0.504

Corr. =

0.932

II)Validation Set:

Predicted vs Measured for Variable %Y

Measured (%Y)

Predicted (%Y)

0.0 2.0 4.0 6.0 8.0 -2.0

0.0

2.0

4.0

6.0

8.0

127Ra 127Rb 127Rc 127Rd

127Re 127Rf

235Ra

235Rb

235Rc

235Rd

235Re

235Rf

307Ra 307Rb 307Rc

307Rd 307Re

307Rf

343Ra343Rb

343Rc

343Rd

343Re

343Rf

433Ra433Rb433Rc

433Rd433Re433Rf532Ra

532Rb 532Rc 532Rd 532Re 532Rf

811Ra 811Rb 811Rc 811Rd 811Re 811Rf

Slope =

0.644

Interc. = 0.599

Corr. = 0.816


Table A2-3- Results of PLS model applied to Group (ii) (for dep. var. yellow) Name Predicted Measured 127Ra 4.2 (4) 7127Rb 4.2 (4) 7127Rc 4.9 (5) 7127Rd 4.4 (4) 7127Re 3.1 (3) 7127Rf 4.1 (4) 7235Ra 3.8 (4) 5235Rb 4.7 (5) 5235Rc 3.8 (4) 5235Rd 4.8 (5) 5235Re 4.1 (4) 5235Rf 3.3 (3) 5307Ra 4.6 (5) 7307Rb 5.3 (5) 7307Rc 5.6 (6) 7307Rd 7.21 (7) 7307Re 6.9 (7) 7307Rf 5.2 (5) 7343Ra 2.0 (2) 3343Rb 2.0 (2) 3343Rc 1.0 (1) 3343Rd 1.7 3343Re 0.7 (1) 3343Rf 1.9 (2) 3433Ra 3.3 (3) 3433Rb 3.6 (4) 3433Rc 3.5 (4) 3433Rd 2.6 (3) 3433Re 3.1 (3) 3433Rf 3.1 (3) 3532Ra 3.0 (3) 2532Rb 3.6 (4) 2532Rc 3.3 (3) 2532Rd 2.5 (3) 2532Re 3.1 (3) 2532Rf 3.1 (3) 2811Ra 0.08 (0) 1811Rb 0.67 (1) 1811Rc 0.70 (1) 1811Rd 0.097 (0) 1811Re 0.048 (0) 1811Rf 0.67 (1) 1


Figure A2-4 Plot of predicted vs. measured for sides 1,4 and 5 in Diamond 2, Calibration set (I) and Validation set (larger set) (II), dep. Var.= brown, indep. Var. 1800-800 cm-1

I) Calibration Set:

Pred. vs Measured, Var Brown,(8 Comp), Avg. Pred. Err.= 0.212, Date 10/7/2002

Measured (Brown)

Predicted (Brown)

0.0 1.0 2.0 3.0 4.0 5.0-2.0

0.0

2.0

4.0

6.0

501B216 501B212 501B213 501B214 501B215 501B211

402B216402B212402B213402B214402B215402B211

204B216204B212204B213204B214204B215204B211

105B216 105B212 105B213 105B214 105B215 105B211

015B221 015B222 015B223 015B224 015B225 015B226 024B221 024B222 024B223 024B224 024B225 024B226 042B221 042B222 042B223 042B224 042B225 042B226 051B221 051B222 051B223 051B224 051B225 051B226

150Y11 150Y12 150Y13 150Y14 150Y15 150Y16

240Y11240Y12240Y13240Y14240Y15240Y16

420Y11420Y12420Y13420Y14420Y15420Y16

510Y11 510Y12 510Y13 510Y14 510Y15 510Y16

Slope = 0.990

Interc. = 0.019

Corr. = 0.995

I) Validation Set:

Predicted vs Measured for Variable Brown

Measured (Brown)

Predicted (Brown)

0.0 2.0 4.0 6.0 -2.0

0.0

2.0

4.0

6.0

8.0

600B26 600B211 600B212 600B213

600B214 600B215 600B216 600B22 600B23 600B24 600B25 600B21

303B216303B212303B213303B214303B215303B211

060B26 006B211 006B212 006B213 006B214 006B215 006B216 006B22 006B221 006B222 006B223 006B224 006B225 006B226 006B23 006B24 006B25 006B26 033B221 033B222 033B223 033B224 033B225 033B226 060B21 060B22 060B221 060B222 060B223 060B224 060B225 060B226 060B23 060B24 060B25 006B21 600Y316

060Y111 060Y112 060Y113 060Y114 060Y115 060Y116 060Y12 060Y13 060Y14 060Y15 060Y16 060Y211 060Y212 060Y213 060Y214 060Y215 060Y216 060Y311 060Y312 060Y313 060Y314 060Y315 060Y316

330Y11330Y12330Y13330Y14330Y15330Y16


060Y11

Slope = 0.784

Interc. = 0.161

Corr. = 0.973


Figure A2-5 Plot of predicted vs. measured for sides 1,4 and 5 in Diamond 2, Calibration set (I) and Validation set (larger set) (II), dep. Var.= yellow, indep. Var. 1800-800 cm-1

I) Calibration Set:

Pred. vs Measured, Var Yellow,(5 Comp), Avg. Pred. Err.= 0.180, Date 10/7/2002

Measured (Yellow)

Predicted (Yellow)

0.0 1.0 2.0 3.0 4.0 5.0-2.0

0.0

2.0

4.0

6.0

501B216 501B212 501B213 501B214 501B215 501B211 402B216 402B212 402B213 402B214 402B215 402B211 204B216 204B212 204B213 204B214 204B215 204B211 105B216 105B212 105B213 105B214 105B215 105B211

015B221 015B222 015B223 015B224 015B225 015B226

024B221024B222024B223024B224024B225024B226

042B221042B222042B223042B224042B225042B226

051B221 051B222 051B223 051B224 051B225 051B226

150Y11 150Y12 150Y13 150Y14 150Y15 150Y16

240Y11240Y12240Y13240Y14240Y15240Y16

420Y11420Y12420Y13420Y14420Y15420Y16

510Y11 510Y12 510Y13 510Y14 510Y15 510Y16

Slope =

0.992

Interc. = 0.015

Corr. =

0.996

I) Validation Set:

Predicted vs Measured for Variable Yellow

Measured (Yellow)

Predicted (Yellow)

0.0 2.0 4.0 6.0 -2.0

0.0

2.0

4.0

6.0

8.0

600B26 600B211 600B212 600B213 600B214 600B215 600B216 600B22 600B23 600B24 600B25 600B21 303B216 303B212 303B213 303B214 303B215 303B211

060B26

006B211 006B212 006B213 006B214 006B215 006B216 006B22 006B221 006B222 006B223 006B224 006B225 006B226 006B23 006B24 006B25 006B26

033B221033B222033B223033B224033B225033B226

060B21 060B22 060B221 060B222 060B223 060B224 060B225 060B226 060B23 060B24 060B25

006B21

600Y316

060Y111 060Y112 060Y113 060Y114 060Y115 060Y116 060Y12 060Y13 060Y14 060Y15 060Y16 060Y211 060Y212 060Y213 060Y214 060Y215 060Y216 060Y311 060Y312 060Y313 060Y314 060Y315 060Y316

330Y11330Y12330Y13330Y14330Y15330Y16


060Y11

Slope = 0.859

Interc. = 0.566

Corr. = 0.975


Figure A2-6 Plot of predicted vs. measured for sides 1,4 and 5 in Diamond 2, Calibration set (I) and Validation set (larger set) (II), dep. Var.= red, indep. Var. 1800-800 cm-1

I) Calibration Set:

Pred. vs Measured, Var Red,(7 Comp), Avg. Pred. Err.= 0.180, Date 10/7/2002

Measured (Red)

Predicted (Red)

0.0 1.0 2.0 3.0 4.0 5.0-2.0

0.0

2.0

4.0

6.0

501B216 501B212 501B213 501B214 501B215 501B211

402B216402B212402B213402B214402B215402B211

204B216204B212204B213204B214204B215204B211

105B216 105B212 105B213 105B214 105B215 105B211 015B221 015B222 015B223 015B224 015B225 015B226

024B221024B222024B223024B224024B225024B226

042B221042B222042B223042B224042B225042B226

051B221 051B222 051B223 051B224 051B225 051B226


Slope =

0.993

Interc. = 0.014

Corr. = 0.997

I) Validation Set:

Predicted vs Measured for Variable Red

Measured (Red)

Predicted (Red)

0.0 2.0 4.0 6.0 -2.0

0.0

2.0

4.0

6.0

8.0

600B26 600B211 600B212 600B213 600B214 600B215 600B216 600B22 600B23 600B24 600B25 600B21

303B216303B212303B213303B214303B215303B211

060B26

006B211 006B212 006B213 006B214 006B215 006B216 006B22 006B221 006B222 006B223 006B224 006B225 006B226 006B23 006B24 006B25 006B26

033B221033B222033B223033B224033B225033B226

060B21 060B22 060B221 060B222 060B223 060B224 060B225 060B226 060B23 060B24 060B25

006B21

600Y316 060Y111 060Y112 060Y113 060Y114 060Y115 060Y116 060Y12 060Y13 060Y14 060Y15 060Y16 060Y211 060Y212 060Y213 060Y214 060Y215 060Y216 060Y311 060Y312 060Y313 060Y314 060Y315 060Y316 330Y11 330Y12 330Y13 330Y14 330Y15 330Y16 600Y11 600Y12 600Y13 600Y14 600Y141 600Y142 600Y143 600Y144 600Y145 600Y146 600Y15 600Y16 600Y311 600Y312 600Y313 600Y314 600Y315 060Y11

Slope =

1.055

Interc. =

0.004

Corr. = 0.993


Figure A2-7 Plot of predicted vs. measured for sides 1,4 and 5 in Diamond 1, Calibration set (I) and Validation set (II), dep. Var.= brown, indep. Var. 1800-800 cm-1

I) Calibration Set:


Measured (Brown)

Predicted (Brown)

0.0 1.0 2.0 3.0 4.0 5.0-2.0

0.0

2.0

4.0

6.0

015Y211 015Y212 015Y213 015Y214 015Y215 015Y216

024Y211

024Y212

024Y213024Y214024Y215024Y216

042Y211042Y212042Y213042Y214042Y215042Y216

051Y211 051Y212 051Y213 051Y214 051Y215 051Y216

105Y141 105Y142 105Y143 105Y144 105Y145 105Y146

150Y11 150Y12 150Y13 150Y14 150Y15 150Y16

204Y141 204Y142 204Y143 204Y144 204Y145 204Y146

240Y11240Y12240Y13240Y14240Y15240Y16

402Y141 402Y142 402Y143 402Y144 402Y145 402Y146

420Y11420Y12420Y13420Y14420Y15420Y16

501Y141 501Y142 501Y143 501Y144 501Y145 501Y146

510Y11 510Y12 510Y13 510Y14 510Y15 510Y16

Slope =

0.986

Interc. = 0.028

Corr. = 0.993

II) Validation Set:


Measured (Brown)

Predicted (Brown)

0.0 2.0 4.0 6.0 -2.0

0.0

2.0

4.0

6.0


033Y211033Y212033Y213033Y214033Y215

033Y216

060Y11 060Y111

060Y112 060Y113 060Y114

060Y115 060Y116 060Y12

060Y13

060Y14 060Y15 060Y16 060Y211

060Y212

060Y213 060Y214 060Y215 060Y216 060Y311

060Y312

060Y313 060Y314 060Y315 060Y316

303Y141 303Y142 303Y143 303Y144 303Y145 303Y146

330Y11330Y12330Y13330Y14330Y15330Y16


Slope = 0.769

Interc. = 0.140

Corr. = 0.985


Table A2-4- Results of PLS model applied to sides 1, 4 and 5 for Diamond 1 (for dep. var. brown)

Name Predicted Measured 006Y112 0.22 (0) 0.0 006Y111 0.32 (0) 0.0 006Y213 0.46 (0) 0.0 006Y214 0.45 (0) 0.0 006Y215 0.15 (0) 0.0 006Y216 -0.093 (0) 0.0 006Y241 0.45 (0) 0.0 006Y242 0.18 (0) 0.0 006Y243 0.36 (0) 0.0 006Y244 0.16 (0) 0.0 006Y245 -0.40 (0) 0.0 006Y246 0.003 (0) 0.0 006Y311 0.20 (0) 0.0 006Y312 0.006 (0) 0.0 006Y313 0.24 (0) 0.0 006Y314 0.18 (0) 0.0 006Y315 0.12 (0) 0.0 006Y316 -0.058 (0) 0.0 033Y211 3.03 (3) 3.0 033Y212 2.8 (3) 3.0 033Y213 2.8 (3) 3.0 033Y214 2.9 (3) 3.0 033Y215 2.9 (3) 3.0 033Y216 2.4 (2) 3.0 060Y11 4.3 (4) 6.0 060Y111 4.4 (4) 6.0 060Y112 5.6 (6) 6.0 060Y113 4.6 (5) 6.0 060Y114 5.2 (5) 6.0 060Y115 4.1 (4) 6.0 060Y116 4.3 (4) 6.0 060Y12 4.3 (4) 6.0 060Y13 5.5 (6) 6.0 060Y14 4.2 (4) 6.0 060Y15 5.1 (5) 6.0 060Y16 4.2 (4) 6.0 060Y211 4.1 (4) 6.0 060Y212 5.5 (6) 6.0 060Y213 4.4 (4) 6.0 060Y214 5.3 (5) 6.0 060Y215 4.4 (4) 6.0 060Y216 4.5 (5) 6.0 060Y311 4.3 (4) 6.0 060Y312 5.5 (6) 6.0


Name Predicted Measured 060Y313 4.1 (4) 6.0 060Y314 4.6 (5) 6.0 060Y315 5.4 (5) 6.0 060Y316 4.6 (5) 6.0 303Y141 0.028 (0) 0.0 303Y142 0.017 (0) 0.0 303Y143 0.24 (0) 0.0 303Y144 0.003 (0) 0.0 303Y145 -0.064 (0) 0.0 303Y146 0.30 (0) 0.0 330Y11 2.6 (3) 3.0 330Y12 2.6 (3) 3.0 330Y13 2.5 (3) 3.0 330Y14 2.6 (3) 3.0 330Y15 2.8 (3) 3.0 330Y16 2.6 (3) 3.0 600Y11 -0.026 (0) 0.0 600Y12 -0.41 (0) 0.0 600Y13 -0.14 (0) 0.0 600Y14 0.14 (0) 0.0 600Y141 -0.39 (0) 0.0 600Y142 0.35 (0) 0.0 600Y143 0.016 (0) 0.0 600Y144 -0.002 (0) 0.0 600Y145 0.28 (0) 0.0 600Y146 -0.019 (0) 0.0 600Y15 0.30 (0) 0.0 600Y16 -0.27 (0) 0.0 600Y311 0.24 (0) 0.0 600Y312 0.21 (0) 0.0 600Y313 -0.13 (0) 0.0 600Y314 0.23 (0) 0.0 600Y315 0.093 (0) 0.0 600Y316 0.31 (0) 0.0


Figure A2-8 Plot of predicted vs. measured for sides 1, 4 and 5 in Diamond 1, Calibration set (I) and Validation set (II), dep. Var.= grey, indep. Var. 1800-800 cm-1

I) Cal. Set:

Pred. vs Measured, Var Grey,(6 Comp), Avg. Pred. Err.= 0.178, Date 4/7/2002

Measured (Grey)

Predicted (Grey)

0.0 1.0 2.0 3.0 4.0 5.0-2.0

0.0

2.0

4.0

6.0

015Y211 015Y212 015Y213 015Y214 015Y215 015Y216

024Y211024Y212024Y213024Y214024Y215024Y216

042Y211042Y212042Y213042Y214042Y215042Y216

051Y211 051Y212 051Y213 051Y214 051Y215 051Y216

105Y141 105Y142 105Y143 105Y144 105Y145 105Y146

150Y11 150Y12 150Y13 150Y14 150Y15 150Y16

204Y141204Y142204Y143204Y144204Y145

204Y146

240Y11 240Y12 240Y13 240Y14 240Y15 240Y16

402Y141402Y142402Y143

402Y144402Y145402Y146

420Y11 420Y12 420Y13 420Y14 420Y15 420Y16 501Y141 501Y142 501Y143 501Y144 501Y145 501Y146

510Y11 510Y12 510Y13 510Y14 510Y15 510Y16

Slope =

0.994

Interc. =

0.013

Corr. = 0.997

II) Val Set:

Predicted vs Measured for Variable Grey

Measured (Grey)

Predicted (Grey)

0.0 2.0 4.0 6.0 -2.0

0.0

2.0

4.0

6.0

8.0


033Y211033Y212033Y213033Y214033Y215033Y216


303Y141303Y142303Y143303Y144303Y145303Y146


Slope =

0.976

Interc. =

0.080

Corr. = 0.997


Table A2-5 Results of PLS model for the prediction of dep. Var. grey in the region 1800-800 cm-1

Name Predicted Measured 006Y112 6.1 (6) 6.00 006Y111 5.8 (6) 6.00 006Y213 5.4 (5) 6.00 006Y214 5.7 (6) 6.00 006Y215 5.8 (6) 6.00 006Y216 6.1 (6) 6.00 006Y241 5.6 (6) 6.00 006Y242 5.9 (6) 6.00 006Y243 5.9 (6) 6.00 006Y244 5.9 (6) 6.00 006Y245 6.2 (6) 6.00 006Y246 6.0 (6) 6.00 006Y311 6.0 (6) 6.00 006Y312 6.1 (6) 6.00 006Y313 5.8 (6) 6.00 006Y314 5.9 (6) 6.00 006Y315 6.0 (6) 6.00 006Y316 6.1 (6) 6.00 033Y211 3.1 (3) 3.00 033Y212 3.3 (3) 3.00 033Y213 3.31 (3) 3.00 033Y214 3.0 (3) 3.00 033Y215 3.2 (3) 3.00 033Y216 3.5 (4) 3.00 060Y11 0.09 (0) 0.00 060Y111 0.12 (0) 0.00 060Y112 -0.16 (0) 0.00 060Y113 0.03 (0) 0.00 060Y114 0.08 (0) 0.00 060Y115 0.32 (0) 0.00 060Y116 0.17 (0) 0.00 060Y12 0.11 (0) 0.00 060Y13 -0.0 (0) 0.00 060Y14 0.13 (0) 0.00 060Y15 0.18 (0) 0.00 060Y16 0.12 (0) 0.00 060Y211 0.24 (0) 0.00 060Y212 -0.15 (0) 0.00 060Y213 0.051 (0) 0.00 060Y214 0.11 (0) 0.00 060Y215 0.10 (0) 0.00 060Y216 0.18 (0) 0.00 060Y311 0.19 (0) 0.00


Name Predicted Measured 060Y312 -0.099 (0) 0.00 060Y313 0.32 (0) 0.00 060Y314 0.15 (0) 0.00 060Y315 0.03 (0) 0.00 060Y316 -0.059 (0) 0.00 303Y141 3.0 (3) 3.00 303Y142 3.0 (3) 3.00 303Y143 2.9 (3) 3.00 303Y144 2.9 (3) 3.00 303Y145 3.1 (3) 3.00 303Y146 2.8 (3) 3.00 330Y11 0.40 (0) 0.00 330Y12 0.32 (0) 0.00 330Y13 0.43 (0) 0.00 330Y14 0.35 (0) 0.00 330Y15 0.17 (0) 0.00 330Y16 0.31 (0) 0.00 600Y11 -0.045 (0) 0.00 600Y12 0.41 (0) 0.00 600Y13 -0.10 (0) 0.00 600Y14 -0.13 (0) 0.00 600Y141 0.077 (0) 0.00 600Y142 -0.34 (0) 0.00 600Y143 -0.16 (0) 0.00 600Y144 -0.14 (0) 0.00 600Y145 -0.16 (0) 0.00 600Y146 -0.21 (0) 0.00 600Y15 -0.10 (0) 0.00 600Y16 0.077 (0) 0.00 600Y311 -0.079 (0) 0.00 600Y312 -0.055 (0) 0.00 600Y313 0.23 (0) 0.00 600Y314 -0.049 (0) 0.00 600Y315 -0.074 (0) 0.00 600Y316 -0.046 (0) 0.00


Figure A2-9 Plot of predicted vs. measured for top triangle of Diamond 1 for Calibration set (I) and Validation (II,III) sets, dep. Var.= Brown, indep. Var. 1800-800 cm-1

I) Cal. Set:


Measured (Brown)

Predicted (Brown)

0.0 2.0 4.0 6.0 -2.0

0.0

2.0

4.0

6.0

8.0


015Y211 015Y212 015Y213 015Y214 015Y215 015Y216

024Y211 024Y212 024Y213 024Y214 024Y215 024Y216

033Y211033Y212033Y213033Y214033Y215033Y216

042Y211042Y212042Y213042Y214042Y215042Y216

051Y211

051Y212051Y213051Y214051Y215051Y216

060Y11060Y111

060Y112

060Y113

060Y114

060Y115060Y116060Y12

060Y13

060Y14

060Y15

060Y16060Y211

060Y212

060Y213

060Y214

060Y215060Y216060Y311

060Y312

060Y313060Y314

060Y315

060Y316

105Y141 105Y142 105Y143 105Y144 105Y145 105Y146

150Y11150Y12

150Y13150Y14150Y15150Y16

204Y141 204Y142 204Y143 204Y144 204Y145 204Y146 213Y311 213Y312

213Y313

213Y314 213Y315 213Y316 231Y311231Y312

231Y313231Y314231Y315231Y316

240Y11240Y12240Y13240Y14240Y15240Y16

303Y141 303Y142 303Y143 303Y144 303Y145 303Y146 312Y311 312Y312 312Y313 312Y314 312Y315 312Y316

330Y11330Y12330Y13330Y14330Y15330Y16

402Y141 402Y142 402Y143 402Y144 402Y145 402Y146

420Y11 420Y12 420Y13 420Y14 420Y15 420Y16

501Y141 501Y142 501Y143 501Y144 501Y145 501Y146 510Y11 510Y12 510Y13 510Y14 510Y15 510Y16


Slope = 0.940 Interc. =

0.127

Corr. = 0.969

II) Val. Set:


Measured (Brown)

Predicted (Brown)

1.00

1.2 1.4 1.6 1.8 2.00.00

1.00

2.00

3.00

4.00

222Y311

222Y312

222Y313

222Y314222Y315222Y316321Y311321Y312

321Y313

321Y314321Y315321Y316

411Y311 411Y312 411Y313 411Y314 411Y315 411Y316

Slope = 1.156

Interc. = -0.094

Corr. =

0.706

III) Val. Set (without suspected outliers):


Measured (Brown)

Predicted (Brown)

1.0 1.2 1.4 1.6 1.8 2.0 0.50

1.00

1.50

2.00

2.50

222Y314222Y315

222Y316321Y311321Y312

321Y313

321Y314321Y315321Y316

411Y311

411Y312 411Y313 411Y314 411Y315 411Y316

Slope = 0.784

Interc. = 0.278

Corr. = 0.929


Table A2-6 Results of PLS model for the top triangle of Diamond 1 for prediction of dep. Var. brown in the region 1800-800 cm-1

Name Predicted

(no outlier) Predicted (with outlier)

Measured

222Y311 N/A 3.3 (3) 2.0 222Y312 N/A 3.5 (4) 2.0 222Y313 N/A 3.2 (3) 2.0 222Y314 1.7 (2) 1.7 (2) 2.0 222Y315 1.8 (2) 1.8 (2) 2.0 222Y316 1.9 (2) 1.9 (2) 2.0 321Y311 2.0 (2) 2.0 (2) 2.0 321Y312 2.0 (2) 2.0 (2) 2.0 321Y313 1.7 (2) 1.7 (2) 2.0 321Y314 1.9 (2) 1.9 (2) 2.0 321Y315 1.9 (2) 1.9 (2) 2.0 321Y316 1.9 (2) 1.8 (2) 2.0 411Y311 1.5 (1) 1.5 (2) 1.0 411Y312 1.1 (1) 1.1 (1) 1.0 411Y313 0.9 (1) 0.87 (1) 1.0 411Y314 0.9 (1) 0.94 (1) 1.0 411Y315 0.9 (1) 0.95 (1) 1.0 411Y316 1.0 (1) 1.0 (1) 1.0


Figure A2-10 Plot of predicted vs. measured for top triangle of Diamond 1 for Calibration set (I) and Validation (II) set, dep. Var.= Grey, indep. Var. 1800-800 cm-1

I) Cal. Set:

Pred. vs Measured, Var Grey,(6 Comp), Avg. Pred. Err.= 0.330, Date 5/7/2002

Measured (Grey)

Predicted (Grey)

0.0 2.0 4.0 6.0 -2.0

0.0

2.0

4.0

6.0

8.0


015Y211

015Y212015Y213015Y214015Y215015Y216

024Y211024Y212

024Y213024Y214024Y215024Y216

033Y211033Y212033Y213033Y214033Y215033Y216

042Y211042Y212042Y213042Y214042Y215042Y216

051Y211 051Y212 051Y213 051Y214 051Y215 051Y216 060Y11 060Y111 060Y112 060Y113 060Y114 060Y115 060Y116 060Y12 060Y13 060Y14 060Y15 060Y16 060Y211 060Y212 060Y213 060Y214 060Y215 060Y216 060Y311 060Y312 060Y313 060Y314 060Y315 060Y316

105Y141105Y142105Y143105Y144105Y145105Y146

150Y11 150Y12 150Y13 150Y14 150Y15 150Y16

204Y141204Y142204Y143204Y144204Y145

204Y146213Y311213Y312213Y313

213Y314213Y315213Y316231Y311 231Y312 231Y313 231Y314 231Y315 231Y316 240Y11 240Y12 240Y13 240Y14 240Y15 240Y16

303Y141303Y142303Y143303Y144303Y145303Y146

312Y311312Y312312Y313312Y314312Y315312Y316

330Y11 330Y12 330Y13 330Y14 330Y15 330Y16

402Y141402Y142402Y143402Y144402Y145402Y146

420Y11 420Y12 420Y13 420Y14 420Y15 420Y16

501Y141 501Y142 501Y143 501Y144 501Y145 501Y146 510Y11 510Y12 510Y13 510Y14 510Y15 510Y16 600Y11 600Y12 600Y13 600Y14 600Y141 600Y142 600Y143 600Y144 600Y145 600Y146 600Y15 600Y16 600Y311 600Y312 600Y313 600Y314 600Y315 600Y316

Slope = 0.977

Interc. = 0.043

Corr. =

0.989

II) Val. Set:

Predicted vs Measured for Variable Grey

Measured (Grey)

Predicted (Grey)

1.0 1.2 1.4 1.6 1.8 2.00.50

1.00

1.50

2.00

2.50

222Y311 222Y312 222Y313

222Y314 222Y315 222Y316

321Y311 321Y312 321Y313 321Y314 321Y315 321Y316 411Y311 411Y312 411Y313 411Y314 411Y315 411Y316

Slope =

0.517

Interc. = 0.655

Corr. = 0.562


Table A2-7 Results of PLS model for top triangle of Diamond 1 the prediction of dep. Var. Grey in the region 1800-800 cm-1

Name Predicted Measured 222Y314 2.4 (2) 2.00 222Y315 2.3 (2) 2.00 222Y316 2.2 (2) 2.00 321Y311 1.2 (1) 1.00 321Y312 1.1 (1) 1.00 321Y313 1.2 (1) 1.00 321Y314 1.1 (1) 1.00 321Y315 1.2 (1) 1.00 321Y316 1.1 (1) 1.00 411Y311 0.95 (1) 1.00 411Y312 1.0 (1) 1.00 411Y313 1.2 (1) 1.00 411Y314 1.4 (1) 1.00 411Y315 1.3 (1) 1.00 411Y316 1.2 (1) 1.00

Documents

Colour Matching of Dyed Wool by Vibrational Spectroscopyeprints.qut.edu.au/15867/1/Mandana_Mozaffari-Medley_Thesis.pdf · known as keratins (from the Greek word 'κερας' meaning