A Forensic Investigation of Single Human Hair Fibres Using FTIR-ATR Spectroscopy and Chemometrics
A Forensic Investigation of Single Human Hair Fibres using FTIR-ATR Spectroscopy and Chemometrics
A thesis submitted as partial fulfilment
of the requirements
for the degree of
Doctor of Philosophy (PhD)
By
Paul M.J. Barton
BAppSc (Hons)
Based on research carried out in the
School of Physical and Chemical Sciences/Discipline of Chemistry
Queensland University of Technology
Under the supervision of
Adjunct Associate Professor Serge Kokot
Associate Professor Godwin Ayoko
Queensland University of Technology, Brisbane February 2011
i STATEMENT OF ORIGINAL AUTHORSHIP
The work contained in this thesis has not been submitted for a degree of 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.
______
Paul M.J Barton
ii ACKNOWLEDGEMENTS
First and foremost I would like to dedicate this work to and acknowledge my late Grandfather, Earnest Benjamin Moya (1918-2006), a proud Cherokee Native American. I strongly believe I inherited my determination and intellect from him. I also would like to thank my mother, Linda Maureen Moya, who has always supported and protected me from harm‟s way and life‟s misfortunes. I would like to thank my high school teachers, Mr Ewan Toombes (Year 9 Science Teacher) who triggered my love of Science and Mrs Sarah Howes (Year 11 and 12 Chemistry Teacher) who furthered my motivation in Chemistry and guided me to University. I strongly believe in my High School‟s Motto (Glenala State High School) “Believe and Achieve”. However, without the mention of the next two mentors, I believe that I may not have reached the pinnacle of education that I have accomplished. Dr Serge Kokot, my principal supervisor, a man who I hold up in the highest respect, has always believed in and supported me through to the completion of my education. Dr Godwin Ayoko, my associate supervisor, another man who I highly regard as a mentor and motivator, also believed that I could complete my PhD candidature. I sincerely thank my fellow undergraduate and postgraduate colleagues, Adrian Fuchs, Adrian Friend, Ben Morrow, Dylan Nagle, and Kenneth Nuttall. The King of Science, Albert Einstein, gave me the inspiration to study Science in general.
I would also like to acknowledge the funding that I have received from the University namely the QUT BLUPRINT Award Scholarship and the Write-Up Scholarship, without which the completion of the PhD candidature would have been extremely difficult. Honourable mentions should also extend to Associate Professor Fredericks and Dr Llew Rintoul for teaching and sharing their knowledge of Fourier Transform Infrared Spectroscopy. Lastly, I would thank all the people that donated their hair fibres for this research project, especially those from Sugarland, Texas, U.S.A. These fibres have allowed the continued research into the forensic analysis of hair for matching and discrimination. I hope and envisage this dissertation will be an important and novel contribution to the field of forensic science, inspiring others from disadvantaged backgrounds as did the former Inala High student, now the former Premier of QLD, Mr Wayne Goss.
iii ABSTRACT Human hair fibres are ubiquitous in nature and are found frequently at crime scenes often as a result of exchange between the perpetrator, victim and/or the surroundings according to Locard‟s Principle. Therefore, hair fibre evidence can provide important information for crime investigation. For human hair evidence, the current forensic methods of analysis rely on comparisons of either hair morphology by microscopic examination or nuclear and mitochondrial DNA analyses. Unfortunately in some instances the utilisation of microscopy and DNA analyses are difficult and often not feasible. This dissertation is arguably the first comprehensive investigation aimed to compare, classify and identify the single human scalp hair fibres with the aid of FTIR- ATR spectroscopy in a forensic context.
Spectra were collected from the hair of 66 subjects of Asian, Caucasian and African (i.e. African-type). The fibres ranged from untreated to variously mildly and heavily cosmetically treated hairs. The collected spectra reflected the physical and chemical nature of a hair from the near-surface particularly, the cuticle layer. In total, 550 spectra were acquired and processed to construct a relatively large database. To assist with the interpretation of the complex spectra from various types of human hair, Derivative Spectroscopy and Chemometric methods such as Principal Component Analysis (PCA), Fuzzy Clustering (FC) and Multi-Criteria Decision Making (MCDM) program; Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) and Geometrical Analysis for Interactive Aid (GAIA); were utilised.
FTIR-ATR spectroscopy had two important advantages over to previous methods: (i) sample throughput and spectral collection were significantly improved (no physical flattening or microscope manipulations), and (ii) given the recent advances in FTIR- ATR instrument portability, there is real potential to transfer this work‟s findings seamlessly to on-field applications.
The “raw” spectra, spectral subtractions and second derivative spectra were compared to demonstrate the subtle differences in human hair. SEM images were used as corroborative evidence to demonstrate the surface topography of hair. It indicated that the condition of the cuticle surface could be of three types: untreated, mildly treated and
iv treated hair. Extensive studies of potential spectral band regions responsible for matching and discrimination of various types of hair samples suggested the 1690-1500 cm-1 IR spectral region was to be preferred in comparison with the commonly used 1750-800 cm-1. The principal reason was the presence of the highly variable spectral profiles of cystine oxidation products (1200-1000 cm-1), which contributed significantly to spectral scatter and hence, poor hair sample matching. In the preferred 1690-1500 cm-1 region, conformational changes in the keratin protein attributed to the α-helical to β-sheet transitions in the Amide I and Amide II vibrations and played a significant role in matching and discrimination of the spectra and hence, the hair fibre samples.
For gender comparison, the Amide II band is significant for differentiation. The results illustrated that the male hair spectra exhibit a more intense β-sheet vibration in the Amide II band at approximately 1511 cm-1 whilst the female hair spectra displayed more intense α-helical vibration at 1520-1515cm-1. In terms of chemical composition, female hair spectra exhibit greater intensity of the amino acid tryptophan (1554 cm-1), aspartic and glutamic acid (1577 cm-1). It was also observed that for the separation of samples based on racial differences, untreated Caucasian hair was discriminated from Asian hair as a result of having higher levels of the amino acid cystine and cysteic acid. However, when mildly or chemically treated, Asian and Caucasian hair fibres are similar, whereas African-type hair fibres are different.
In terms of the investigation‟s novel contribution to the field of forensic science, it has allowed for the development of a novel, multifaceted, methodical protocol where previously none had existed. The protocol is a systematic method to rapidly investigate unknown or questioned single human hair FTIR-ATR spectra from different genders and racial origin, including fibres of different cosmetic treatments. Unknown or questioned spectra are first separated on the basis of chemical treatment i.e. untreated, mildly treated or chemically treated, genders, and racial origin i.e. Asian, Caucasian and African-type. The methodology has the potential to complement the current forensic analysis methods of fibre evidence (i.e. Microscopy and DNA), providing information on the morphological, genetic and structural levels.
v TABLE OF CONTENTS
STATEMENT OF ORIGINAL AUTHORSHIP ...... ii
ACKNOWLEDGEMENTS ...... iii
ABSTRACT ...... iv
TABLE OF CONTENTS ...... vi
LIST OF FIGURES ...... xii
LIST OF TABLES ...... xxiv
ABBREVIATIONS ...... xxvii
1.0 INTRODUCTION ...... 1 1.1 Prologue to the Investigation ...... 1 1.2 Human Hair Fibres ...... 6 1.2.1 The Morphology of Human Hair Fibres ...... 7 1.2.1.1 The Cuticle ...... 7 1.2.1.2 The Cortex ...... 9 1.2.1.3 The Medulla ...... 10 1.2.1.4 Melanin Pigment and Greying of Hair ...... 10 1.2.2 The Chemical Structure of Human Hair Fibres ...... 11 1.2.2.1 α- Keratin Proteins ...... 11 1.2.2.2 Bonding Mechanisms in Keratin – Covalent and Non-covalent Forces . 13 1.2.3 The Chemical Process of Bleaching Human Hair Fibres ...... 14 1.2.3.1 The Mechanism of Bleaching ...... 15 1.2.3.2 The Disulphide (S-S) Cleavage Mechanism ...... 15 1.2.4 Chemical Process of Hair Dyeing and Colouring ...... 16 1.2.4.1 Temporary Colourants ...... 16 1.2.4.2 Semi-Permanent Colourants ...... 16 1.2.4.3 Permanent or Oxidative Dyeing ...... 17 1.2.5 Permanent Waving and Straightening of Human Hair Fibres ...... 17 1.2.5.1 Chemical Process of Permanent Waving ...... 18 1.2.6 Hair Straightening ...... 19 1.2.7 Photo-oxidative Bleaching ...... 19
vi 1.2.8 Oxidation of Hair with Chlorine ...... 20 1.2.9 Physical Properties of the α-Keratin Fibre ...... 20 1.2.9.1 Mechanical Properties of the Keratin Fibre ...... 20 1.2.9.2 The Keratin-Water System ...... 21 1.2.10 The Effects of Mechanical or Physical Processes on Human Hair Fibres ... 22 1.2.10.1 Effects of Shampooing, Conditioning, Combing, Grooming and Towel Drying ...... 22 1.2.10.2 Effects of Thermal Treatments on Human Hair Fibres ...... 23
1.3 Forensic Science: Trace Physical Evidence ...... 24 1.3.1 Forensic Fibre Evidence ...... 25 1.4 Current Methods of Forensic Fibre Analysis with the use of Microscopy and DNA Analysis ...... 27 1.4.1 Macroscopic Analysis...... 27 1.4.1 Microscopy ...... 27 1.4.1.1 Optical Light Microscopy and Stereomicroscopy ...... 27 1.4.1.2 Scanning Electron Microscopy ...... 28 1.4.2 Fibre Evidence from Burial Scenes ...... 29 1.4.2.1 Burial of Hair Fibres ...... 29 1.4.2.2 Environmental Weathering of Fibre Evidence ...... 30 1.4.3 DNA Analysis ...... 31 1.4.3.1 DNA Analysis of Human Hair Fibres ...... 31 1.4.3.2 Mitochondrial DNA ...... 32
1.5 Vibrational Spectroscopy ...... 34 1.5.1 Infrared Spectroscopy ...... 37 1.5.1.1 Infrared Absorptions ...... 37 1.5.1.2 Infrared Modes of Vibration ...... 38 1.5.2 The Fourier Transform Infrared Spectrometer ...... 40 1.5.2.1 Fourier-Transformation...... 42 1.5.2.2 Advantages ...... 42 1.5.3 Forensic Investigations of Human Hair Fibres using FT-IR Spectroscopy ... 43 1.5.3.1 Applications of Chemometrics to Forensic Science ...... 45 1.5.3.2 Previous Investigations using FT-IR Spectroscopy and Chemometrics .. 47 1.5.3.3 Limitations to the Previous Investigations...... 47 1.5.4 Fourier Transform Infrared Spectroscopy - Attenuated Total Reflectance ... 49 1.5.4.1 Previous Investigations of Human Hair Fibres Utilising FTIR-ATR Spectroscopy with the aid of Chemometrics and SEM ...... 53 1.5.5 Alternative FT-IR Sampling Techniques for Analysing α-Keratin Fibres .... 55 1.5.5.1 FT-IR Photoacoustic Spectroscopy (PAS) of Human Hair Fibres ...... 55 1.5.5.2 FT-Raman Spectroscopy of Human Hair Fibres ...... 56
vii 1.5.6 Derivative Spectroscopy ...... 57 1.5.6.1 Properties of Derivative Profiles ...... 59 1.5.6.2 Generating Derivative Spectra: The Savitzky-Golay Method ...... 62
1.6 Aims and Objectives ...... 64
2.0 EXPERIMENTAL: MATERIALS AND METHODS ...... 66 2.1 Collection of Fibre Samples ...... 66 2.2 SEM Analysis ...... 66 2.3 Cleaning Methodology ...... 67 2.3.1 Revised IAEA Method for Cleaning Hair Fibres ...... 67 2.4 FTIR-ATR Spectroscopy ...... 68 2.5 Spectral Processing ...... 69 2.5.1 Derivative Spectroscopy ...... 70 2.6 Pre-processing of the Raw Data Matrix and Chemometric Analysis ...... 70 2.6.1 Variance Scaling ...... 71 2.6.1.1 Double Centring ...... 71 2.6.1.2 Standardisation ...... 72 2.6.1.3 Autoscaling ...... 72 2.6.2 Chemometric Analysis ...... 73 2.6.3 Multi-criteria Decision Making (MCDM) ...... 73 2.7 Chemometrics ...... 73 2.7.1 Chemometrics and Forensic Science ...... 74 2.7.2 Principal Component Analysis (PCA) ...... 75 2.7.3 Classification ...... 76 2.7.3.2 Fuzzy Clustering (FC) ...... 78 2.7.4 Multi-criteria Decision Making Techniques (MCDM) ...... 79 2.7.4.1 PROMETHEE I and II Multivariate Techniques ...... 80 2.7.4.2 GAIA ...... 88
3.0 CUTICLE SURFACE TOPOGRAPHY AND FTIR-ATR SPECTRAL CHARACTERISTICS OF THE MORPHOLOGICAL-CHEMICAL STRUCTURE OF HUMAN HAIR FIBRES ...... 90 3.1 Morphological Characteristics of the Cuticle Surface Topography of Human Hair Fibres Involving SEM ...... 94 3.1.1 Comparison of Chemically Untreated and Cosmetically Treated Human Hair Fibres ...... 94 3.1.1.1 SEM Analysis of Non-Treated Hair Fibres ...... 95 3.1.1.2 SEM Analysis of Different Cosmetically Treated Hair Fibres ...... 97
3.2 Structural Elucidation of -Keratin Hair Fibres using FTIR-ATR Spectroscopy ...... 102
viii 3.2.1 Comparison of Chemically Untreated and Cosmetically Treated Fibres .... 102 3.2.1.1 Secondary Structure Conformations and Vibrational Modes of the Peptide Bond ...... 102 3.2.1.2 FTIR-ATR Spectral Analysis of Untreated Hair Fibres ...... 103 3.2.1.3 Spectral Analysis of Cosmetically Treated Hair Fibres ...... 108 3.2.2 Analysis of Difference FTIR-ATR Spectra of Human Hair Fibres between Gender ...... 120 3.2.2.1 Spectral Differences between Genders of each Race ...... 120
3.3 The Application of Derivative Spectroscopy for Interpretation of FTIR-ATR Spectra of Single Hair Fibres ...... 125 3.3.1. Optimisation of the Savitzky-Golay Method for Second Derivative Analysis ...... 125 3.3.2. Assessment of Typical Second Derivative FTIR-ATR Spectra of Untreated α-Keratin Fibres ...... 129 3.2.3. Assessment of Typical Second Derivative FTIR-ATR Chemically Treated α- Keratin Spectra ...... 136 3.3 Chapter Conclusions ...... 146
4.0 FORENSIC PROTOCOL FOR ANALYSING HUMAN HAIR FIBRES USING FTIR-ATR SPECTROSCOPY WITH THE AID OF CHEMOMETRICS AND MCDM ...... 147 4.1 The Protocol – A Systematic Approach to Hair Fibre Analysis ...... 148 4.2 Optimisation of the Proposed Forensic Protocol for Spectroscopic Analysis of Human Hair Fibres with the aid of Chemometrics ...... 152 4.2.1 Spectral Regions and Fibre Discrimination ...... 153 4.2.1.1 Spectral Range 1750-800 cm-1 ...... 153 4.2.1.2 PROMETHEE and GAIA Analysis: 1750-800 cm-1 Spectral Range .... 169 4.2.1.3 Conclusions: 1750-800 cm-1 Database ...... 178 4.2.2 Investigation of the Alternative Spectral Regions ...... 179 4.2.2.1 Spectral Range - 1690-1200 cm-1 ...... 179 4.2.2.2 Chemometric Analysis of Single Human Hair Fibres using Alternative Spectral Regions - 1690-1500 cm-1 ...... 189 4.2.3 Chemometric Analysis of Further Alternative Spectral Regions of Keratin FTIR-ATR and Second Derivative Spectra ...... 197
4.3 Chapter Conclusions...... 197
5.0 APPLICATIONS OF THE FORENSIC PROTOCOL AS AN IDENTIFICATION PROCEDURE FOR SINGLE HUMAN HAIR FIBRES ..... 201 5.1 Principles of the Forensic Protocol ...... 201
ix 5.2 African-type Hair Fibres ...... 204 5.2.1 Physical and Chemical characteristics of African-type hair fibres: ...... 204 5.2.2 FTIR-ATR Spectroscopic-Chemometric Analysis of African-type Hair Fibres ...... 205 5.2.2.1 Comparison of the 1750-800 cm-1 and 1690-1500 cm-1 regions ...... 206 5.2.2.2 MCDM Analysis of African-type Hair Fibres ...... 213 5.3.1 Incorporation of the African-type Hair IR Spectra to the Protocol ...... 220 5.3.1.1 Chemometric Analysis of the Entire (3 Races) Database ...... 220
5.3 Gender: Male vs. Female Hair Fibres ...... 229 5.3.1 Gender Differences between Untreated, Mildly Treated and Chemically Treated Fibres ...... 229 5.3.1.1 Untreated Hair Fibres ...... 229 5.3.1.2 Mildly Treated Hair Fibres ...... 233 5.3.1.3 Chemically Treated Hair Fibres ...... 242
5.4 Race: Asian, Caucasian and African-type Hair Fibres ...... 247 5.4.1 Racial Spectral differences between Female Hair Fibres ...... 249 5.4.1.1 Untreated Female Hair Fibres ...... 249 5.4.1.2 Chemically Treated Female Hair Fibres ...... 253 5.4.2 Racial spectral differences between Male Hair Fibre Spectra ...... 258 5.4.2.1. Mildly Treated Male Hair Fibres ...... 260 5.4.2.2. Chemically Treated Male Hair Fibres ...... 265
5.5 Potential Extension of the Forensic Protocol ...... 270 5.6 Chapter Conclusions...... 271
6.0 CONCLUSIONS AND FUTURE INVESTIGATIONS ...... 274 6.1 Concluding Remarks ...... 274 6.1.1 Conclusions of Chapter 3 ...... 274 6.1.2 Conclusions of Chapter 4 ...... 276 6.1.3 Conclusions to Chapter 5 ...... 277 6.2 Future Investigations ...... 279
7.0 REFERENCES ...... 282
Appendix I – Data on Subjects - Forensic Protocol ...... 299
Appendix I (Continued) - Hair Profile Survey for Forensic Investigation ...... 302
x Appendix II – Fuzzy Clustering (p = 1.2) 3-cluster model 1750-800 cm-1 ...... 303
Appendix III–Fuzzy Clustering (p = 1.2) 4-cluster Model 1750-800 cm-1 ...... 305
Appendix IV–Fuzzy Clustering (p = 1.2) 3 Cluster 1690-1200 cm-1 ...... 309
Appendix V–Fuzzy Clustering (p = 1.2) 3-cluster Model 1690-1500 cm-1 ...... 311
Appendix VI – FC (p=1.2) African-type Hair Fibres 1750-800 cm-1...... 314
Appendix VII – FC (p = 1.2) African-type Hair Fibres 1690-1500 cm-1 ...... 317
Appendix VIII – FC (p = 1.2) Mildly Treated Database 1690-1500 cm-1 ...... 319
Appendix IX – FC (p =1.2) Treated Hair Database 1690-1500 cm-1 ...... 322
Appendix X – Alternative Spectral Regions for the Proposed Forensic Protocol (Continued from Chapter 4) ...... 324 4.2.3.1 Chemometric Analysis of Single Human Hair Fibres using Alternative Spectral Regions - 1690-1360 cm-1 ...... 324 4.2.3.2 Second Derivative Keratin FTIR-ATR Spectra 1750-800 cm-1 Region ...... 329 4.2.3.3 Second Derivative Keratin FTIR-ATR Spectra 1690-1500 cm-1 Region ...... 333
xi LIST OF FIGURES
Figure 1.1: A schematic diagram of a human hair fibre illustrating 7 the morphological features starting from the external Cuticle, Cortex, Macrofibril, Microfibril down to the - Helical Protein. Figure 1.2: An illustration of the cross-section of a developed 8 cuticle cell. Figure 1.3: The condensation reaction of amino acids. 11
Figure 1.4: Molecular structure of the amino acid Cystine. 13 Figure 1.5: Hydrogen bonding between the amide and carbonyl 14 groups in the -keratin structure. Figure 1.6: Scheme of the S-S cleavage mechanism for the 15 bleaching process. Figure 1.7: Reaction scheme between the disulphide bond and a 18 mercaptan where K represents the Keratin chain and R represents the amino R-group side chains Figure 1.8: C-S fission mechanism of -keratin by photo-oxidative 19 bleaching. Figure 1.9: A histograph indicating the relationship between the 27 frequency with which different types of trace evidence occurs in criminal cases. Figure 1.10 Absorption of energy for a vibration where the 36
molecule is promoted from state E0 to state E1 and the
molecule in the higher vibrational state (E1) dropping to
the lower vibrational state (E0) emitting radiation of ΔE. Figure 1.11 : Localised vibrations of the methylene group 38 highlighting the symmetric and anti-symmetric
stretches, and the bending/scissoring, rocking, twisting
and wagging vibrations respectively.
xii Figure 1.12: Modes of Vibrations for the Amide I, Amide II and 39 Amide III bands respectively for -keratin protein. Figure 1.13 A schematic diagram of the Michelson Interferometer. 41
Figure 1.14: Total Internal Reflection in Attenuated Total 50 Reflectance Spectroscopy.
Figure 1.15: An evanescent wave that is produced upon Total 50 Internal Reflection that eventually penetrates the sample. Figure 1.16: A spectral comparison of -keratin spectra using FTIR 54 Micro-spectroscopy (blue line) and FTIR-ATR Spectroscopy (pink line). Figure 2.1: A photograph of the MEGANSON Ultrasonic 67 Disintegrator that was used to sonicate the fibres for this
study.
Figure 2.2: A photograph of the NEXUS 870 FT-IR E.S.P 68
Spectrometer fitted with a Diamond-ATR Smart
Accessory. The arrows indicate the positions of the
pressure tower and the diamond crystal.
81 Figure 2.3: A preference function P(d).
Figure 2.4: Function H(d). 82
Figure 3.1: SEM image of an untreated Asian female hair fibre. 95
Figure 3.2: SEM image of an untreated Caucasian male hair fibre. 96
Figure 3.3: SEM image of an untreated African hair fibre. 97
Figure 3.4: SEM image of the tip end of a treated African male hair 98
fibre that has formed a knot possibly caused due by the
effects of grooming.
99 Figure 3.5: SEM image of the same treated African male hair fibre
(Figure 3.4) which has been subject to a “pink”
moisturising lotion. This image illustrates lifting and
chipping of the cuticle scales.
xiii Figure 3.6: SEM image of a permanently dyed Asian female hair 100
fibre.
Figure 3.7: SEM image of a bleached and semi-permanently dyed 101
Caucasian female hair fibre that receives constant sun
exposure.
A selection of 12 typical untreated FTIR-ATR spectra Figure 3.8: 104 of human hair fibres from male (M) and female (F)
donors of the major races: Caucasian (C), Asian (A) and
African-type (N). (Note: The vertical lines designate
the vibrational assignment and peak position of each
functional group/molecular fragment. The arrows
indicate the direction of the vibration).
Figure 3.9: A selection of 10 typical and 2 atypical chemically 109
treated FTIR-ATR spectra of human hair fibres from
male (M) and female (F) donors of the major races:
Caucasian (C), Asian (A) and African-type (N).
Figure 3.10: (a) FTIR-ATR spectrum of NF5 suspected to contain a 115
hair activator on the surface, (b) FTIR-ATR spectrum of
NF5 after cleaning of the surface and (c) the subtraction
of (b) - (a) yielding the IR spectrum of the suspicious
material.
Resultant FTIR-ATR spectral subtraction of the Figure 3.11: 119 chemically treated NM7 spectrum minus the cleaned
version of the fibre revealing the characteristic bands of
a long-chain silo-oxane resin used in hair gel and
hairspray formulations.
Figure 3.12: A subtraction FTIR-ATR spectrum of the average of 121
untreated Caucasian female No. 1 (peak maxima) minus
the average of untreated Caucasian male No. 3(peak
minima).
xiv Figure 3.13: A subtraction FTIR-ATR spectrum of the average of 123
untreated Asian female No. 17 (peak maxima) minus
the average of untreated Asian male No. 20 (peak
minima).
Figure 3.14: A subtraction FTIR-ATR spectrum of the average of 124
untreated African-type female No. 21 (peak maxima)
minus the average of untreated African-type male No. 1
(peak minima).
Figure 3.15: Second derivative FTIR-ATR spectra of an untreated 128
Caucasian female fibre using a two degree polynomial
and comparing different number of smoothing points (5,
7, 9 and 11). Increase in smoothing points shows that
resolution between the bands decreases. Thus a 2o
polynomial with 5-points was selected.
Figure 3.16: Typical second derivative FTIR-ATR spectrum of hair 130
from a Caucasian female untreated No. 1(CFUN1).
Figure 3.17: A comparison of six typical (alleged according to hair 131
history) untreated second, derivative FTIR-ATR spectra
of hair from both male (M) and female (F) of the
Caucasian (C), Asian (A) and African-type (N) races.
Figure 3.18: A comparison of four typical mildly treated, second 139
derivative FTIR-ATR spectra of hair from both male
(M) and female (F) of the Caucasian (C), Asian (A) and
African-type (N) races.
Figure 3.19: A comparison of seven typical chemically treated, 142
second derivative FTIR-ATR spectra of hair from both
male (M) and female (F) of the Caucasian (C), Asian
(A) and African-type (N) races.
Figure 4.1: The proposed forensic protocol for the analysis of 149
unknown hair fibres using FTIR spectroscopy and
Chemometrics with the inclusion of the novel African-
type group (green).
xv Figure 4.2: PCA scores plot of PC1 (75.7 %) vs. PC2 (10.8 %) of 155
the untreated fibres (blue), the chemically treated fibres
(pink) and the entire African-type fibre database (green)
using the traditional spectral region between
1750-800 cm-1.
Figure 4.3: PCA scores plot of PC1 (74.8 %) vs. PC2 (14.4 %) of 156
the untreated fibres (blue) and the chemically treated
fibres (pink) of Caucasian and Asian fibres between
1750-800 cm-1.
Figure 4.4: Re-classified PCA scores plot of PC1 (74.8 %) vs. PC2 162
(14.4 %) of the untreated fibres (blue), the chemically
treated fibres (pink), the mild treated fibres (green) and
the „fuzzy‟ samples (black) of the Caucasian and Asian
fibres.
Re-classified PCA scores plot of PC1 (74.8 %) vs. PC2 Figure 4.5: 163 (14.4 %) of the untreated fibres (blue), the chemically
treated fibres (pink) and the mildly treated fibres
(green) of the Caucasian and Asian hair fibres between
1750-800 cm-1.
Figure 4.6: PCA scores plot of PC1 (74.8 %) vs. PC2 (14.4 %) of 164
the untreated fibres (blue), the chemically treated fibres
(pink), the mildly physically treated fibres (turquoise),
and the mild chemically treated fibres (light green) of
the Caucasian and Asian hair fibres between
1750-800 cm-1based on a four class FC model.
Figure 4.7: PC1 Loadings plot of the chemically treated and mildly 166
treated fibres (positive loadings), and the untreated and
mildly treated fibres (negative loadings) between
1750-800 cm-1 region.
Figure 4.8: PC2 Loadings plot of the mildly treated hair fibres 168
(positive loadings), and the untreated and chemically
treated fibres (negative loadings) 1750-800 cm-1.
xvi Figure 4.9: GAIA analysis of the 176 spectra for the Caucasian and 173
Asian hair fibre database between 1750-800 cm-1; ■
untreated fibres, ■ chemically treated fibres, ■ mildly
treated hair fibres, ● pi (Π) decision-making axis, and ■
Original PC1 and PC2 criteria using a Gaussian
preference function.
Figure 4.10: GAIA analysis of the 164 spectra for the Caucasian and 177
Asian hair fibre database between 1750-800 cm-1 using
a 4-cluster model; ▲untreated fibres, ■ chemically
treated fibres, ■ mild chemical treatment hair fibres, ■
mild physical treatment hair fibres, ● pi (Π) decision-
making axis, and ■ Original PC1, PC2 and PC3 criteria
using a Gaussian preference function.
Figure 4.11: PCA scores plot of PC1 (79.5 %) vs. PC2 (8.3 %) of the 180
untreated fibres (blue), chemically treated fibres (pink),
mildly treated fibres (green) using the alternate spectral
region between 1690-1200 cm-1.
Figure 4.12: PC1 Loadings plot of the chemically treated fibres 182
(positive loadings) and the untreated and mildly treated
fibres (negative loadings) between
1690-1200 cm-1.
Figure 4.13: PC2 Loadings of the untreated and chemically treated 183
fibres (positive loadings) and mildly treated fibres
(negative loadings) between 1690 -1200 cm-1.
Figure 4.14: GAIA analysis of the 212 spectra for the 188
1690-1200 cm-1 hair fibre database; ▲ untreated fibres,
■ chemically treated fibres, ■ mildly treated hair fibres,
● pi (Π) decision-making axis, and ■ Original PC1 and
PC2 criterion variables using a Gaussian preference
function.
xvii Figure 4.15: PCA scores plot of PC1 (72.3 %) vs. PC2 (16.6 %) of 190
the untreated fibres (blue), mildly treated fibres (green)
and the chemically treated fibres (pink) using the
alternate spectral region between 1690-1500 cm-1.
Figure 4.16: PC1 Loadings plot of the untreated and mildly treated 191
fibres (positive loadings) and the chemically treated
fibres (negative loadings) between
1690-1500 cm-1.
Figure 4.17: PC2 Loadings plot of the untreated and chemically 191
treated fibres (positive loadings) and the mildly treated
fibres (negative loadings) between 1690-1500 cm-1.
Figure 4.18: GAIA analysis of the 209 spectra for the 196
1690-1500 cm-1 hair fibre database; ▲ untreated fibres,
■ chemically treated fibres, ■ mildly treated hair fibres,
● pi (Π) decision-making axis, and ■ Original PC1 and
PC2 criterion variables using a Gaussian preference
function.
Figure 5.1: PC1 vs. PC2 scores plot of untreated♦, mildly treated▲ 207
and chemically treated fibres■ for the African-type hair
fibres between 1750 - 800 cm-1.
Figure 5.2: PC1 vs. PC2 scores plot of untreated♦, mildly treated▲ 207
and chemically treated fibres■ for the African-type hair
fibres between 1690-1500 cm-1. Figure 5.3: 209 PC1 vs. PC2 scores plot of the African-type 1750-800
cm-1 spectral database based on a 4-cluster FC model
illustrating the untreated♦, mild physical treatment▲,
mild chemical treatment■ and chemically treated■
spectral objects.
xviii Figure 5.4: PC1 vs. PC2 scores plot of the African-type 210
1690-1500 cm-1 spectral database based on a 4-cluster
FC model illustrating the untreated■, mild physical
treatment▲, mild chemical treatment and chemically
treated♦ spectral objects.
Figure 5.5: PC1 Loadings plot of the chemically treated and mildly 211
treated African-type hair fibres (positive loadings), and
the untreated and mildly treated African-type fibres
(negative loadings) between 1750-800 cm-1 IR region.
Figure 5.6: PC1 Loadings plot of the untreated and mildly treated 212
African-type hair fibres (positive loadings) and the
chemically treated African-type hair fibres (negative
loadings) between 1690-1500 cm-1 IR region.
Figure 5.7: GAIA analysis of the 111 spectra for the African-type 218
hair fibre database between 1750-800 cm-1; ▲untreated
fibres, ■ chemically treated fibres, ■ mildly treated hair
fibres, ● pi (Π) decision-making axis, and ■ Original
PC1 and PC2 criteria using a Gaussian preference
function.
Figure 5.8: GAIA analysis of the 124 spectra for the African-type 219
hair fibre database between 1690-1500 cm-1; ■untreated
fibres, ■ chemically treated fibres, ■ mildly treated hair
fibres, ● pi (Π) decision-making axis, and ■ PC1 and
PC2 criteria using a Gaussian preference function.
Figure 5.9 PCA scores plot of the 1690 -1500 cm-1IR Database; 221
Caucasian and Asian untreated fibres●, chemically
treated fibres■, with the inclusion of the untreated
African-type untreated♦ and chemically treated■
African-type spectral objects.
xix Figure 5.10: PCA scores plot of PC1 vs. PC2 of the 222
1690 -1500 cm-1IR Database. Caucasian and Asian
untreated fibres●, chemically treated fibres■, mildly
treated fibres▲ and African-type untreated♦, mildly
treated▲ and chemically treated■ hair fibres.
Figure 5.11: GAIA analysis of the 257 spectra for the Entire (3 226
Race) IR database between 1690-1500 cm-1; ■untreated
fibres, ■ untreated African-type fibres, ■ chemically
treated fibres, ■ chemically treated African-type fibres,
■ mildly treated hair fibres, ■mildly treated African-
type fibres, ● pi (Π) decision-making axis, and ■
Original PC1, PC2 and PC3criteria using a Gaussian
preference function.
Figure 5.12: PCA scores plot of PC1 vs. PC2 of the 1750-800 cm-1 227
IR Database. Caucasian and Asian untreated fibres●,
chemically treated fibre■, mildly treated fibres▲, and
African-type untreated♦, mildly treated▲ and
chemically treated■ spectral objects.
Figure 5.13: PCA scores plot of PC1 vs. PC2 of the Untreated Hair 230
Fibre Spectral Database illustrating the separation of
untreated African-type Male No.1♦ from untreated
Female■ spectral objects along the PC2 axis.
Figure 5.14: PC2 Loadings plot of the untreated African-type Male 231
No. 1 fibres (positive loadings) and the untreated
Female fibres (negative loadings). 234 GAIA analysis of the 39 spectra for the Untreated hair Figure 5.15: fibre database; ■ Male untreated fibres, ■ Female
untreated fibres, ● pi (Π) decision-making axis, and
Original ■ PC1 and PC2 criteria using a Gaussian
preference function.
xx Figure 5.16: PCA scores plot of PC1 vs. PC2 of the Mildly Treated 236
Hair Fibre Spectral Database illustrating the separation
of mildly treated male♦ from mildly treated female♦
spectral objects.
Figure 5.17: PCA scores plot of PC1 vs. PC2 of the Mildly Treated 236
Hair Fibre Spectral Database illustrating the separation
of mildly treated male♦ from mildly treated female♦ and
male mild physical■ and female mild physical ■ from
female mild chemical▲ and male mild chemical▲.
Figure 5.18: PC2 Loadings plot of the Mildly Treated spectral 237
database showing the separation of male-female mild
physical-chemical from mildly treated female and male
fibres on the PC2 axis.
Figure 5.19: GAIA analysis of the 121 spectra for the Mildly Treated 241
hair fibre database; ■ Male mildly treated fibres, ■
Female mildly treated fibres,■ Male mild physical,
■Female mild physical, ■Male mild chemical, ■
Female mild chemical, ● pi (Π) decision-making axis,
and ■ PC1 and PC2 criteria.
Figure 5.20: PCA scores plot of PC1 vs. PC2 of the Chemically 243
Treated Hair Fibre Spectral Database illustrating the
separation of treated male■, African-type male treated■
African-type female treated▲ from treated female♦ on
the PC2 axis.
Figure 5.21: GAIA analysis of the 109 spectra for the Chemically 246
Treated hair fibre database; ■ Male mildly treated
fibres, ■ Female mildly treated fibres,■ African-type
male, ■ African-type female,, ● pi (Π) decision-making
axis, and ■ PC1, PC2 and PC3 criteria.
xxi Figure 5.22: PCA scores plot of PC1 vs. PC2 of the Untreated 250
Female spectral database which illustrates the
separation of untreated Caucasian female♦ spectra from
untreated Asian female■ spectra on the PC1 axis.
Figure 5.23: PC1 Loadings plot of the Untreated Female spectral 251
database. The Amide I and II vibrational bands
(positive loadings) correlate to the untreated Asian
female spectral objects whilst the β-sheet, (CO ) and υa 2 Tryptophan bands (negative loadings) are associated
with the untreated Caucasian female spectral objects.
Figure 5.24: GAIA analysis of the 29 spectra for the Untreated 254
Female hair fibre database; ■ Caucasian Female
untreated spectral objects, ■ Asian Female untreated
spectral objects, ● pi (Π) decision-making axis, and ■
Original PC1, PC2 and PC3 criteria using a Gaussian
preference function.
Figure 5.25: PCA scores plot of PC1 vs. PC2 of the Female Treated 255
spectral database illustrating the segregation of Asian■,
Caucasian♦ and African-type▲ spectral objects.
Figure 5.26: PC2 Loadings plot of the FemaleTreated database 256
where the treated Asian spectral objects (positive
loadings) are separated from the treated Caucasian and
African-type spectral objects (negative loadings).
Figure 5.27: GAIA analysis of the 35 spectra for the Chemically 259
Treated Female hair fibre database; ▲ Caucasian
female treated objects, ■ Asian female treated objects,
African-type female objects■, ● pi (Π) decision-making
axis, and ■ Original PC1 and PC2 criteria using a
Gaussian preference function.
xxii Figure 5.28: PCA scores plot of PC1 vs. PC2 of the Male Mildly 260
Treated spectral database illustrating the separation of
African-type male objects▲ from Asian■ and
Caucasian♦ objects on the PC2 axis.
Figure 5.29: PC2 Loadings plot of the Male Mildly treated database 261
which illustrates spectral variables that separate
African-type male mildly treated (positive loadings)
from Asian and Caucasian (negative loadings) mildly
treated fibres.
Figure 5.30: GAIA analysis of the 92 spectra for the Male Mildly 264
Treated hair fibre database; ■ Caucasian male mildly
treated objects, ■ Asian male mildly treated objects,
African-type male mildly treated objects■, ● pi (Π)
decision-making axis, and ■ Original PC1, PC2 and
PC3 criteria using a Gaussian preference function.
Figure 5.31: PCA scores plot of PC1 vs. PC2 of the Male 265
Chemically Treated Database which illustrates the
separation of Asian■ and Caucasian♦ from African-
type▲ spectral objects on the PC2 axis.
Figure 5.32: PC2 Loadings plot of the male treated spectral database 266
illustrating the variables which separate the Asian and
Caucasian (positive loadings) from the African-type
(negative loadings) spectral objects.
Figure 5.33: GAIA analysis of the 41 spectra for the Male 268
Chemically Treated hair fibre database; ■ Caucasian
male treated objects, ■ Asian male treated objects,
African-type male treated objects■, ● pi (Π) decision-
making axis, and ■ Original PC1, PC2 and PC3 criteria
using a Gaussian preference function.
Figure 5.34: Preliminary Forensic Protocol for Analysis of Single 266
Human Hair Fibres by FTIR-ATR Spectroscopy with
the aid of Chemometrics.
xxiii LIST OF TABLES
Table 1.1: Amino acid Composition of Human Hair Fibres from 12 the Major Races (µmol/g) Table 2.1: Specifications and Operating Parameters for the FTIR – 69 ATR Analysis Table 2.2: List of Preference Functions 85 Table 3.1: Major Vibrational Band Assignments of Human Hair 145 Keratin Table 4.1: Data matrix for ranking of Untreated, Mildly Treated 170 and Chemically Treated Hair Fibre Spectra by PROMETHEE (3-Class Model) Table 4.2: PROMETHEE II Net Flows of the 1750 – 800 cm-1 171 Database Table 4.3: Data matrix for ranking of Untreated, Mildly Treated 174 and Chemically Treated Hair Fibre Spectra (4-Class Model) Table 4.4: PROMETHEE II Net Flows of the 1750 – 800 cm-1 175 Database (4 Class Model) Table 4.5: 1690-1200 cm-1 Data matrix for ranking of Untreated, 184 Mildly Treated and Chemically Treated Hair Fibre Spectra by PROMETHEE II Table 4.6: PROMETHEE II Net Flows of the 1690 – 1200 cm-1 185 Database Table 4.7: 1690-1500 cm-1 Data matrix required for ranking of 192 Untreated, Mildly Treated and Chemically Treated Hair Fibre Spectra by PROMETHEE (3-Class) Table 4.8: PROMETHEE II Net Flows of the 1690 – 1500 cm-1 194 Database Table 4.9: Summary of Chemometric Results for Current and 199 Alternative Spectral Regions of Raw and Second Derivative Spectra
xxiv Table 5.1: PROMETHEE Model for African-type Untreated, 213 Mildly Treated and Chemically Treated Hair Spectra (1750-800 cm-1) Table 5.2: PROMETHEE Model for ranking of African-type 214 Untreated, Mildly Treated and Chemically Treated Hair Spectra (1690-1500 cm-1) Table 5.3: PROMETHEE II Net φ Ranking of the African-type 215 1750-800 cm-1 Spectral Database Table 5.4: PROMETHEE II Net φ Ranking of the African-type 216 1690-1500 cm-1 Spectral Database Table 5.5: PROMETHEE II Model of the Entire Spectral Database 222 (257 spectra x 3PC Criteria) within the 1690-1500 cm-1 Spectral Region Table 5.6: PROMETHEE II Net φ Ranking of the 3 Race IR 224 Spectral Database 1690-1500 cm-1 Table 5.7: PROMETHEE II Model of Untreated African Male 231 (NMUN 1) and Untreated Female Hair Spectra Table 5.8: PROMETHEE II Net φ Ranking of the Untreated 233 Spectral Database Table 5.9: PROMETHEE II Model of Male and Female Mildly 238 Treated Hair Spectra Table 5.10: PROMETHEE II Net φ Ranking of the Mildly Treated 239 Spectral Database Table 5.11: PROMETHEE II Model of Male and Female 244 Chemically Treated Hair Spectra Table 5.12: PROMETHEE II Net φ Ranking of the Chemically 245 Treated Spectral Database Table 5.13: PROMETHEE II Model of the Untreated Female 251 Spectral Database Table 5.14: PROMETHEE II Net φ Ranking of the Female 253 Untreated Hair Database Table 5.15: PROMETHEE II Model of the Chemically Treated 256 Female Spectral Database
xxv Table 5.16: PROMETHEE II Net φ Ranking of the Female 258 Chemically Treated Hair Database Table 5.17: PROMETHEE II Model of the Mildly Treated Male 262 Spectral Database Table 5.18: PROMETHEE II Net φ Ranking of the Male Mildly 263 Treated Hair Database Table 5.19: PROMETHEE II Model of the Chemically Treated 266 Male Spectral Database Table 5.20: PROMETHEE II Net φ Ranking of the Male 267 Chemically Treated Hair Database
xxvi ABBREVIATIONS
A Asian AFM Atomic Force Microscopy ATR Attenuated Total Reflectance a.u. Arbitrary Units C Caucasian CMM Cell Membrane Matrix cm-1 1/Wavelength DAP 2-diamino-2,4-phenoxyethanol DNA Deoxyribonucleic Acid DRIFTS Diffuse Reflectance Infrared Fourier Transform Spectroscopy ESEM Environmental Scanning Electron Microscopy F Female FC Fuzzy Clustering FT-IR Fourier Transform Infrared GAIA Geometrical Analysis for Interactive Aid GC/MS Gas Chromatography/Mass Spectroscopy GSR Gun Shot Residue HPLC High Performance Liquid Chromatography IAEA International Atomic Energy Authority IR Infrared IRE Internal Reflection Element IRS Internal Reflection Spectroscopy Kb Kilo bases M Male MEA Methyleicosanoic acid MCDM Multi-Criteria Decision Making (Techniques) MT Mildly Treated mt Mitochondrial N African-type nm Nano-metres NMR Nuclear Magnetic Resonance
xxvii No. Number NTR African-type Treated
Nuc Nuclear (p) Weighting Exponent for Fuzzy Clustering PAP Paraaminophenol PAS Photo-Acoustic Spectroscopy PC Principal Component PCA Principal Component Analysis PCR Polymerase Chain Reaction PNG Papua New Guinea PPD Paraphenylenediamine PROMETHEE Preference Ranking Organisation Method for Enrichment Evaluation % RH Relative Humidity (Percent) RNA Ribonucleic Acid SEM Scanning Electron Microscopy SIMCA Soft Independent Modelling of Class Analogy SNR Signal to Noise Ratio STR Short Tandem Repeats TIR Total Internal Reflection TR Treated UN Untreated UV/Vis Ultra-Violet/Visible Light µm Micrometers α Alpha, keratin proteins Beta, pleated sheet proteins Δ Delta, Energy (kJmol-1) or GAIA Δ % δ delta h Planck‟s constant, 6.625 x 10-22 kJsec Lambda, wavelength of electromagnetic wave (cm) ν Nu, frequency of light Hertz (Hz)
xxviii 1.0 INTRODUCTION
1.1 Prologue to the Investigation
Naturally occurring fibres such as human and animal hair are -keratin proteins.1 Such fibres together with any plant, mineral, or synthetic fibres, are often found on victims of crime, suspects or associated animals. They are frequently collected as trace physical evidence in a wide variety of crimes for subsequent forensic analysis by crime scene investigators.2-5 Fibre evidence such as hair which is associated with a crime scene is of significant forensic value, because it can provide important information which may assist in the investigation and prosecution of criminal cases.6-8
The detection or discovery of most classes of fibres at crime scenes are a regular occurrence due to their ubiquity in nature.9 At any given time, we are constantly surrounded by fibres in our daily lives, from the hairs that cover our body for protection and insulation, to the textile fibres that comprise our clothing, furniture, vehicles and floors.9-12 Furthermore, unless they are destroyed by fire, or degraded under strongly acidic or alkaline conditions, the fibres maintain structural integrity for a longer period of time than most other tissue types.13 14 This is due to the fact that they are encapsulated by a fairly resistant external layer (i.e. the cuticle) which serves to protect the fibre from adverse environmental conditions.10
Presence of trace evidence such as fibres at crime scenes is often the result of some form of physical contact and exchange between the perpetrator and the victim and/or the surroundings during the commission of a crime.13 15 This phenomenon of „exchange evidence‟, is governed by a fundamental principle known as the „Locards Principle of Exchange‟ which states that “every contact leaves a trace”.3 This principle is one of the foundations of modern forensic science, and the detection of trace evidence is the crucial key to the solution of crime.16
For human hair evidence, the current forensic methods of analysis rely on comparisons of either hair morphology by microscopic examination or nuclear and mitochondrial DNA analyses.17 Microscopic examinations of the morphological characteristics of human hairs indicate the colour, thickness, shape, race, body area (e.g. scalp or pubic,
1 auxiliary (armpit, chest and limb regions)) and method of removal (e.g. naturally shed or forcibly removed).5 17 Two problems have confronted researchers and examiners in the forensic examination, comparison, and identification of human hair. First, the ability among workers in different geographical areas have been frustrated owing to the lack of an atlas that all workers could reference when describing a particular characteristic or one of the hair variates.18 Second, the ability of the researcher to develop frequency data for the variates of each characteristic have been hindered owing to a lack of a uniform reference for identifying the specific microscopic characteristic seen in a study hair.18
Complementary to microscopic analysis, nuclear and mitochondrial DNA analyses may provide genetic profiles from an unknown source.17 19 DNA is unique to the individual, and when compared, can form highly significant associations between known and unknown hair samples.17 Unfortunately in some instances the utilisation of microscopy and DNA analyses are difficult and often not feasible. For example in homicide and sexual assault investigations, hair and synthetic fibres have often been influenced by their immediate surroundings such as blood, grease and oil (i.e. hit and run cases), smoke and fire, bodily fluids (e.g. seminal or vaginal fluid) or the broader environment through burial, water immersion and wear.20 Hence, subsequent analysis and comparison of such fibres is complex. Rendle affirms “In the absence of material leading to recovery of DNA, the forensic scientist has to rely upon chemical analysis of fibres in order to establish or eliminate links between suspect and victim and/or scene”.21
In previous investigations22-27, research has been dedicated to the study of the keratin protein structure of single human hair fibres employing the structural elucidation technique known as Fourier Transform Infrared (FT-IR) Spectroscopy. This approach facilitates the characterisation of single hair fibres on a chemical/molecular level, and thus has the potential to complement current forensic microscopic and genetic examinations. However, in earlier or initial spectroscopic investigations, there were restrictions or limitations to the quality of the spectra obtained by the specific technique (FTIR-Microspectroscopy – Transmittance), and also because the sampling populations were small.
2 In more recent studies it has been discovered that FTIR-Attenuated Total Reflectance Spectroscopy (FTIR-ATR) produces spectra of high quality, avoiding high absorbance of IR radiation and eliminating saturation or “peak saturation”.23 Sample preparation is easier and although the technique requires a small area of the fibre to be compressed, it is relatively less destructive, when compared to the rolling technique that had been utilised by previous investigations which is known to change the conformation of the protein.23 Finally, FTIR-ATR spectroscopy is economical on time.
To compare and discriminate the minute differences between spectra from different individuals with varied levels of cosmetic chemical treatment (i.e. from no treatment to bleached and dyed), Panayiotou,24 Paris25, Barton23, McCarthy26 and Brandes27 (NIR spectroscopy), analysed and interpreted the results with the aid of Chemometrics. Chemometrics is primarily concerned with the extraction of significant information from large data sets.28 29 From the various multivariate data analysis techniques that exist to solve chemical problems, exploratory Principal Component Analysis (PCA), Classification techniques such as Fuzzy Clustering (FC) and Soft Independent Modelling of Class Analogy (SIMCA), and Multi-criteria Decision Making (MCDM) techniques were amongst those most heavily used to aid spectral analyses.
As a result of several investigations, at this stage a single human hair fibre can be discriminated from other human hair fibres on the basis of cosmetic chemical treatment, gender and race using a small to medium population size, focusing on of shaft (i.e. middle to root section) spectra only.23 However, these separations have not yet been fully justified, for example the discrimination of male and female hair fibres and the relationship between untreated and treated African-type fibres.
Hence, a further insight into the structural chemistry is necessary as it provides information of hair from all human races.
The global perspective of continuous research and development into this specific field of science seeks to provide forensic authorities with a rapid methodology for discrimination of single unknown human hair fibres via FTIR-ATR Spectroscopy coupled with Chemometrics. The procedure should offer critical evidence or information pertaining to the chemical nature of the fibre including the cosmetic
3 treatment, gender, and major race of the suspect/perpetrator from only a single human hair fibre. It is envisaged that such a study will provide a comprehensive database of IR spectra of fibres originating from individuals of different race and also different cosmetic chemical treatments.
Thus, within the scope of this project, the principal aim involves investigating the provisional, unverified protocol suggested by Panayiotou.24 This will be achieved by detailed examination of the FTIR-ATR spectra of single hair fibres with the aid of novel approaches in this topic such as:
a) Spectral subtraction to determine the key spectral differences between various types of fibre i.e. gender and race (Chapter 3).
b) Derivative spectroscopy i.e. second derivative spectra to unravel the complexity of the keratin spectra and illustrate the underlying principles for the separations (Chapter 3). The objective here is to gain an understanding of any spectral differences based on the above classifications and to assist the information gained from (a) (Chapter 3).
c) On the basis of (a) and (b), a novel investigation of potential classification of hair spectra with the aid of various chemometrics methods such as Fuzzy Clustering (FC), PROMETHEE and GAIA over alternate wavenumber ranges (i.e. between 1750-800 cm-1) selected on the basis of the detailed studies in Parts (a) and (b) (Chapter 4).
The development of a protocol based on the conditions above has the potential to facilitate the discrimination of male and female hair fibres, as well the more complex separation of untreated and chemically treated African-type hair fibres.
From the forensic perspective, this information will significantly narrow down the population of potential suspects to a given race (Chapter 5).
4 Investigation of chemically treated hair fibres is also warranted using the proposed protocol. Such fibres are arguably more common in our society than the untreated ones. This work will add an important dimension to the protocol which has only been addressed briefly by the previous investigations as at this stage the majority of the work was concerned with non-treated hair fibres only.
In addition to the above aim is to explore the possibility of sub-dividing treated hair fibres into different classes as previous studies suggest ambiguity between an untreated/virgin hair and a physical-chemical treated hair (Chapter 4 and 5).
Multi-criteria decision making (MCDM) techniques such as PROMETHEE ranking supported by the GAIA interpretation of these results, has been shown to be useful in a number of studies in which the relative ranking order provided an alternative method for classification of objects and their comparison to selected references.24 This methodology will be applied for comparison of single hair fibres (Chapter 4 and 5).
The remainder of this chapter focuses on the morphology, chemical structure and physical properties of human hair keratin. Attention is especially given to the cosmetic chemical treatments that are applied to hair fibres for personal and social purposes, as well as the mechanical processes that can also have an effect on the hair structure. The significance of forensic hair fibre evidence to a criminal investigation is also discussed. The chapter will conclude by incorporating an essential examination of the current methods employed to characterise hair fibres, highlighting the need to introduce and explore other complementary instrumental techniques such as FT-IR spectroscopy.
The second chapter is concerned with the samples, instrumentation, procedure and statistical software used to analyse the spectra for the investigation. The remainder of this chapter focuses on the theory and applications of Chemometrics and Multi-Criteria Decision Making techniques.
5 The third chapter focuses on the critical examination and comparison of the structural chemistry of keratin and its corresponding FTIR-ATR spectra. The spectra were collected from fibres from a broad number of individuals of both genders encompassing the major human races (i.e. Caucasian, Asian, and African-type). The similarities and differences of raw, subtracted spectra and second derivative spectra of the above types of fibre are discussed. To support the conclusions of the spectral examinations, morphological analysis of the cuticle surface topography of the various fibre types will be conducted through SEM.
The fourth chapter deals with the continued development and inspection of the current proposed forensic protocol24 for analysing human hair fibres through FTIR-ATR spectroscopy aided by Chemometrics. This was achieved through an investigation of various spectral regions to match and discriminate single hair fibres.
The fifth chapter is concerned with the robustness and applications of the optimised protocol (i.e. Chapter four) to investigate specific scenarios such as the analysis of African-type hair fibres; the structural differences of spectra between male and female fibres; the structural differences between races; the separation of single/multiple treated hair fibres.
The sixth chapter summarises the key findings of the investigation in relation to the aims and objectives (Section 1.6) and concludes by suggesting ideas for further or future studies in this field.
1.2 Human Hair Fibres
Hair (the stratified epithelium) is an appendage of the skin that proliferates from large cavities or sacs called follicles.11 12 The length of the hair extends from its root or bulb embedded in the follicle, through the dermis, epidermis, stratum corneum, skin, then continues into a shaft and terminates at the tip end.11
Hair fibres constitute the characteristic outer-covering of all mammalian skin and serve a number of specific purposes, principally protection.11 30 31 Human scalp hair creates a physical barrier from the immediate surroundings, protecting the surface of the scalp
6 and the body respectively during exposure to a wide range of harsh environmental conditions.10 30 31
1.2.1 The Morphology of Human Hair Fibres
Morphologically, three distinct varieties of cells or units are produced in the follicle which ultimately results in the formation of the three basic structural layers of any human hair fibre.10 11 The three layers are: the external Cuticle layer, the Cortex, and the Medulla (not illustrated). A schematic diagram of a typical human hair fibre is presented in Figure 1.1.
1.2.1.1 The Cuticle The outermost or external layer of the fibre consists of flattened overlapping scales known as the cuticle (Figure 1.1), which is responsible for much of the resistance and stability of the hair.10-12
-Helical Protein
Cortex
Microfibril
Macrofibril
Cuticle Layers
Figure 1.1 – A schematic diagram of a human hair fibre illustrating the morphological features starting from the external Cuticle, Cortex, Macrofibril, Microfibril down to the -Helical Protein. (Hand Illustrated and Adapted from10-12 31).
7 In developed hair, the cuticle cells are square sheets approximately 0.5 µm-1.0 µm thick and 50 µm in length, with an overall thickness of approximately 5-10 scales.11 32 The proximal ends are strongly attached to the cortex whilst the distal free edges protrude toward the tip end of the fibre. As a consequence of the extensive overlapping (which is approximately 80 % of their length), the cells slightly tilt away from the fibre axis giving the hair surface a “tiled roof” appearance which in turn allows follicular anchorage of the growing hair. The architecture of the surface also facilitates the removal of trapped or adhered dirt particles and detached cuticle cells.32
A schematic cross-section of a developed cuticle cell is illustrated in Figure 1.2. Each cuticle cell is enclosed and separated by a strongly adhesive layer known as the cell membrane matrix (CMM). The CMM is made up of a central, polysaccharidic δ-layer enclosed by two lipid-rich β-layers. An important lipid constituent of the CMM is 18- methyleicosanoic acid (18-MEA), which is covalently connected to its protein components. It has also been established that a thin layer of 18-MEA is grafted onto the outer surface of each cuticle (upper layer).33 The lipid film attributes to the surface having low friction with concomitant hydrophobic character.
Fibre Surface Outer β-layer
Epicuticle
A-layer (Cystine rich)
Exocuticle
Endocuticle (Cystine- Inner layer deficient) Inner β-layer δ-layer Outer β-layer Epicuticle Figure 1.2 – An illustration of the cross-section of a developed cuticle cell. (Adapted from10 11 31 32)
8 The mature cuticle cell is comprised of a number of distinct layers namely the epicuticle, A-layer, exocuticle and endocuticle which have different levels of proteins, lipids and carbohydrates.
The epicuticle is a thin membrane that is a by-product of the reactive modification of other sub-components of the cuticle.34 AFM studies have illustrated that the epicuticle is a continuous layer 13 nm thick, covering the entire outwardly facing intracellular surface of every cuticle cell.35 The epicuticle is approximately 80 % protein and about 5 % lipid with no evidence of carbohydrate.36 It is a membrane which is an integral part of the individual cuticle cells and is chemically resistant.
The A-layer is cystine-rich (30%), and is characterised as a biochemically stable layer, which strongly resists physical and chemical forces.11 This layer adjoins the major component of the cuticle, the exocuticle, which represents two-thirds of the cuticle structure. The proteins of the exocuticle are densely cross-linked by disulphide bonds of cystine (15% cystine-rich), but not as extensively as the proteins of the A-layer. The next adjacent layer is the endocuticle and is cystine-deficient (~ 3%), containing much of the non-keratinous cellular debris and a high content of basic and acidic proteins.32
1.2.1.2 The Cortex Surrounded within the protective layer of the cuticle is the cortex which constitutes the central core and the main bulk (90 % by weight) of the hair shaft.10 11 30 31,37 The cortex is largely responsible for the mechanical properties of the fibre and is composed of elongated, spindle-shaped cortical cells packed tightly together which are oriented parallel to the axis of the fibre.11 10 30 31
The cortical cells are approximately 100 µm long and 5 µm across at the maximum width aligned along the axis of the fibre.37 Each cell is made up of fine microfibrils which are furthermore comprised of -helical proteins. Microfibrils are approximately 7 nm in diameter and are grouped into larger bundles of rods called Macrofibrils (≈100- 400 nm in diameter) which represent up to 60% of the cortex material by mass.32 These macrofibrils are embedded in an amorphous protein matrix.37
9 The macrofibrils exhibit different variations in packing dispositions within the cortex, and have been designated as paracortex and orthocortex. They are readily discerned in fibre cross-sections in TEM images. The ortho- and para-cortices are approximately hemi-cylinders wound round each other helically in phase with the crimp of the fibre so that the paracortex is always placed on the inside and the orthocortex on the outside of the crimp curvature.36
1.2.1.3 The Medulla The inner structure of the hair fibre (not illustrated in Figure 1.1), with a diameter of about 5-10 µm is the medulla.37 This layer essentially represents a group of specialised cells which are vacuolated and are aligned either continuously or discontinuously along the central axis of the fibre. The medulla may also be either completely absent or in some instances a double medulla may be observed.11
The medulla has high lipid content compared to the rest of the fibre which is deficient in cystine however its rich in citrulline.38 39 Morphologically, the medulla has a porous structure formed by sponge-like keratin and some vacuoles filled with air resulting from the differentiation process.40 41 A layer of CMM separates the medulla from the cortex.42
1.2.1.4 Melanin Pigment and Greying of Hair Another important component of human hair is melanin. This refers to the pigment granules (≈200-800 nm in size) that impart the characteristic natural colours to the fibre.37 Melanin forms dense ovoid or rod-shaped granules and these are of two basic colour varieties interspersed throughout the medulla, cortex and in greater concentration towards the peripheral portion of the cortex.11 10 30 The two types of melanocytes are eumelanin, which produces the dark shades such as brown and black; and pheomelanin, which is responsible for the lighter colours such as red and yellow.31 Both melanocytes originate from the oxidation of the amino acid tyrosine with the aid of the enzyme tyrosinase.11,30,31 The proposed mechanism involves the oxidation of tyrosine to dopaquinone, then depending on the amount of cysteine present, it forms indole intermediates then eumelanins, or 5-S-cysteinyldopa and pheomelanins.11
10 However, when the melanin granule cells cease to produce pigment, the hair fibre turns grey and white. Hair greying is a natural age-associated feature in humans. While the normal incidence of hair greying is 34 ± 9.6 years in Caucasians and 43.9 ± 10.3 in Africans43, on average 50 % of people have at least 50 % ± 5 grey hair at age 50, in a cohort of Caucasians.43 This is irrespective of sex and initial hair colour. Global greying of the scalp has been described as a gradual and progressive process occurring over more than 15 years in humans.43 The cellular and molecular origins of greying are poorly understood, however, the decrease in melanin synthesis appears to be associated with a decrease in tyrosinase activity.43
1.2.2 The Chemical Structure of Human Hair Fibres
1.2.2.1 α- Keratin Proteins Human hair and all other mammalian hair fibres belong to a group of fibrous proteins known as -keratin.1 10 30 44 The keratin family of fibrous proteins are found in the higher vertebrates (reptiles, birds, and mammals). Keratins are the principal constituents of ectodermal tissues such as hair, wool, furs and epidermis. They also make up a majority of the appendages derived from the skin, which includes nails, claws, scales, hooves and feathers.30 36 44 45 Keratin constitutes roughly 85% of the mass of a single fibre and contributes to a range of essential functions which include physical and chemical protection against the influences of the environment (e.g. temperature control, rain, ultra-violet radiation emitted from the sun, etc.) and also provides mechanical strength to the fibre.36 Keratin is a high molecular weight polymer containing polypeptide chains formed by the condensation of L-amino acids as shown by Figure 1.3:
R R - H O H 2 HOOC NH2 2 HOOC N + 2 NH2 HOOC NH R 2 R O 1 1
Figure 1.3 – The condensation reaction of amino acids.44
11 The bond that forms upon condensation which links the amino acids is called the peptide bond.36 A number of these condensation reactions will ultimately produce a polypeptide chain. The polypeptide chain becomes the backbone of the -keratin fibre.
The R1 and R2 group signifies the side chains of the amino acid residues for -keratin corresponding to 18 different compositions from the major races (Table 1.1).
Table 1.1 – Amino acid Composition of Human Hair Fibres from the Major Races (µmol/g)32 Amino Acid African Brown Caucasian Asian Alanine 370-509 345-475 370-415 Arginine 482-540 466-534 492-510 Aspartic acid 436-452 407-455 456-500 Cysteic acid 10-30 22-58 35-41 Glutamic acid 915-1017 868-1063 1026-1082 Glycine 467-542 450-544 454-498 Histidine 60-85 56-70 57-63 Isoleucine 224-282 188-255 205-244 Leucine 484-573 442-558 515-546 Lysine 198-236 178-220 182-196 Methionine 6-42 8-54 21-37 Phenylalanine 139-181 124-150 129-143 Proline 642-697 588-753 615-683 Serine 672-1130 851-1076 986-1101 Threonine 580-618 542-654 568-593 Tyrosine 179-202 126-194 131-170 Valine 442-573 405-542 421-493 ½ Cystine 1310-1420 1268-1608 1175-1357
12 Besides serine and glycine, hair fibres exhibit the presence of a large concentration of the sulphur-containing diamino acid cystine that largely contributes to the stability of the fibre (Figure 1.4).11 30 31
O O
+ + S NH3 H3N S
O O
Figure 1.4 – Molecular structure of the amino acid Cystine.
1.2.2.2 Bonding Mechanisms in Keratin – Covalent and Non-covalent Forces
In the -keratin arrangement, cohesion or structural stability of the hair fibre is provided by a variety of bonding mechanisms. These range from networks of covalent cystine cross-linkages to weaker secondary interactions such as coulombic interactions between side chain groups, hydrogen bonds between neighbouring groups, van der Waals interactions and, in the presence of water, hydrophobic bonds.10 30 31
The covalent cystine linkages or disulphide (-S-S-) cross-links are the strongest type of bonds or associations present, and contributes significantly to the physical and chemical properties of hair keratin.10 30 45 46 The disulphide linkages in hair keratin are the result of an oxidation reaction between adjacent thiol (-S-H) groups of opposing cysteine molecules in the polypeptide chain, consequently forming a molecule of cystine.31 45
Coulombic interactions, occasionally referred to as salt links, are electrostatic forces acting between ionised acidic and basic side chain residues, i.e. the negatively charged - + 10 30 31 carboxylic acid groups (-COO ) and positively charged amino groups (NH3 ).
13 Two types of hydrogen bonding exist in the -keratin structure.10 One type is present between water molecules and hydroxyl groups (-O…H-O-) and the second type is between the amide and the carbonyl group (Figure 1.5) and the amide C=O of side chains.
N H...... O
Figure 1.5 – Hydrogen bonding between the amide and carbonyl groups in the - keratin structure.
Van der Waals interactions play a non-specific role in the cohesive binding of the chains and side chains of -keratin fibres. Finally, hydrophobic bonds (only in the presence of water) have a specialised task of binding the single -helices into double - helical ropes which form intermediate filaments.10
Cosmetic chemical treatment processes such as bleaching, permanent dyeing, permanent waving, straightening, photo-oxidative bleaching (sun exposure) and chlorine oxidation (through swimming), all affect the structural chemistry of the - keratin fibre. These processes target the bonds that provide stability to the fibre. From a forensic perspective, a fibre that has been chemically altered can be of great importance as it can be discriminated from untreated fibres.47
1.2.3 The Chemical Process of Bleaching Human Hair Fibres
The primary objective of cosmetic bleaching is to lighten the natural colour of hair and this is most readily accomplished by oxidation.31 48 This is achieved through partial or total decolourisation of the hair‟s natural melanin pigment by the reaction with an oxidising agent.31 48 Hair bleaching formulations consist of solutions of up to 12% hydrogen peroxide and ammonia to give a final pH around 10, and thickeners. If extensive bleaching is required a “bleach booster” (usually ammonium and potassium persulphates) are added to the peroxide.
14 1.2.3.1 The Mechanism of Bleaching The bleaching process follows two steps. Firstly a fast dissolution step occurs in which the pigment granules disperse and dissolve. This is then followed by a much slower, decolouration step.31 As a consequence of the decolourisation of the melanin, a secondary effect also takes place whereby side reactions alter the properties of hair keratin producing oxidative or “bleaching” damage. The damage is caused by the oxidative cleavage of the disulphide bonds or cross-links to form cysteic acid.31 48 Severe bleaching also reduces the concentration of free sulphydryl groups and to a small degree degrades other amino acid residues such as tyrosine, threonine, and methionine.31 48 49 As a result, the fibre structure is weakened with a lower cross-link density and overall its hydrophilic nature is increased, due to anionic site formation e.g. cysteic acid residues.31 48 In particular the fibre feels more brittle, is more susceptible to breakage, becomes more porous and hence will absorb larger amounts of water. 31
1.2.3.2 The Disulphide (S-S) Cleavage Mechanism The mechanism for the oxidative cleavage of the disulphide bond during the chemical bleaching of human hair is predominantly through an S-S cleavage process (Figure 1.6).50 It is understood from this mechanism that oxidation of cystine principally produces cysteic or sulphonic acid (-SO3H). Accompanying this, there is also the formation of oxidative intermediates such as the cystine monoxide (-SO-S-) and cystine dioxide (-SO2-S-).
O O
S R S R S R R SO H R S R S R S 3 O
Figure 1.6 – Scheme of the S-S cleavage mechanism for the bleaching process.
Evidence of oxidative cleavage is provided with the use of characterisation tools such as infrared (IR) spectroscopy. IR studies have shown that absorbance bands at 1044 cm-1, 1071 cm-1 and 1121 cm-1 can be correlated to the characteristic stretches of the cysteic acid, cystine monoxide and cystine dioxide bonds respectively.47 51-53
15 1.2.4 Chemical Process of Hair Dyeing and Colouring
Hair colouring can be indexed and classified into three major categories: temporary (surface dyeing), semi-permanent and permanent (oxidative) hair dyeing.31 48 54 55
1.2.4.1 Temporary Colourants Temporary colourants are used for single events only and are readily removed by shampooing and to a lesser extent by rinsing with water.31 54 55
Colouration occurs by deposition of acid dyes on the surface of the hair. The dyes contain cationic surfactants or cationic polymers to allow the dye to complex to the anionic surface.31
1.2.4.2 Semi-Permanent Colourants This class of dye will remain for four to six weeks before needing reapplication. 31 54 55 Major uses have been for grey coverage or blending, highlights or brightening of one‟s own hair colour.31 The mechanism does not involve covalent bonding rather it relies on the diffusion of the coloured molecules from solution into the hair cortex. The product contains a number of dyes blended to give the desired shade. The dyes are dissolved or dispersed into a detergent base. As the dyes differ in molecular size, the tip end of the hair fibre retains larger molecules and smaller molecules are retained by the root end but diffuse freely in and out of the tip end.
Typical dye components comprise: • yellow and orange ortho- and para-nitroanilines and nitrodiphenylamines, • yellow to violet nitrophenyldiamines and nitroaminophenolic ethers • violet to blue amino and hydroxyanthraquinones. Semi-permanent dyes are also formulated with solvents, surfactants, foam stabilisers/thickeners, and an alkalising agent.
16 1.2.4.3 Permanent or Oxidative Dyeing Permanent hair colouring involves the migration of colourless/light coloured and low molecular weight precursors (a base dye intermediate and a coupler) into hair with subsequent oxidation with hydrogen peroxide and concurrent bleaching of the natural melanin pigment by one or two shades. The oxidative polymerisation of monomer dyes results in the in-fibre formation of indo-dyes, thus imparting colour to the hair fibre. 31 55 56 57 Therefore commercial oxidative hair dyes consist of three major components for the dyeing process:
• Primary intermediates such as amino (e.g. Paraphenylenediamine (PPD)) and hydroxy (paraaminophenol (PAP)) aromatic compounds that form colour upon oxidation. • Couplers (modifiers), which react with the products from oxidation of the primary intermediates to form dyes (e.g. Phenols, 2-diamino-2,4-phenoxyethanol (DAP), and meta-diaminobenzenes). • An Oxidant, which is commonly hydrogen peroxide, although urea peroxide and peroxide generators such as perborate have been used.
Other components include an alkaliser (e.g. ammonia), surfactants (oleic acid derivatives or non-ionic ethoxylated phenols), antioxidants (sodium sulphite) and metal chelating agents (ethylenediaminetetracetic acid).
1.2.5 Permanent Waving and Straightening of Human Hair Fibres
Chemical or permanent waving and also straightening are two important hair-care treatments that involve association of almost every aspect of hair structure manipulation to accomplish their objectives.32 Both processes endeavour to construct a durable configuration that is different from what an individual‟s hair exhibits in its native form.32 Wolfram states “The hair has a geometry that is the result of the processes of keratinisation and follicular extrusion, transforming a viscous mixture of proteins into strong, resilient, and rigid fibre”.32 Essentially, waving and straightening can be perceived as a combination of reversal and stepwise restaging of these processes,
17 involving the softening of the keratin and molding and annealing the newly conferred hair geometry.
1.2.5.1 Chemical Process of Permanent Waving Permanent hair waving is regarded as a complex proces.31 58 The waving of hair is accomplished by the fission or the reduction of the disulphide bonds by mercaptans such as thioglycolic acid (Figure 1.7)11:
S K R-SH R-S-S-R 2 K-SH K S + 2 +
Figure 1.7 – Reaction scheme between the disulphide bond and a mercaptan where K represents the Keratin chain and R represents the amino R-group side chains.
Different types of perms are available however the chemical principle is similar in all perming solutions and the key steps are summarised as follows58:
1. The hair is initially washed and then placed on curlers dependent on the degree of curl desired.
2. After setting the hair, alkaline agents such as ammonia and ammonium hydroxide (pH 9), are applied to the hair to lift the scales of the cuticle so as to allow the perming solution to reach the cortex.
3. The reducing agent (thioglgycolates or bisulphites) cleaves some of the disulphide bonds in an equilibrium process as depicted in Figure 1.6. The thiol groups can be easily oxidised by atmospheric oxygen, and thus the stabilisation of the reduced species involves blocking the thiol group with iodoacetic acid or cross-linking with dihalogenoalkanes (e.g. dibromomethane).
4. With the bonds broken, a molecular rearrangement can take place where new bonds will be created according to the new shape of the hair.
18 5. The disulphide cross-links are reformed using an oxidising agent such as sodium bromate, hydrogen peroxide.48 The cuticle scales return to their original position.
1.2.6 Hair Straightening
Hair straightening formulations designed for most African-type hair employ strong bases such as sodium hydroxide as the active ingredient. The process involves fission of the disulphide bond by hydrolysis or nucleophilic substitution of sulphur by the hydroxide ion. Straightening can also cause damage to the stable peptide bond. SEM studies on relaxed hair revealed that the cuticle cells are removed causing extensive damage to the cortex.59 The decreased cross-link density leads to increased swelling, which makes the fibre more susceptible to surface damage during normal handling procedures.48
1.2.7 Photo-oxidative Bleaching
Prolonged exposure of keratin to sunlight which contains UV irradiation leads to destructive changes in the keratin structure.48 The primary reaction in the weathering of human hair involves the oxidative cleavage of the disulphide bond in keratin to cysteic acid.11 52 Exposure to sunlight can also lead to bleaching of the melanin pigments as well as degradation of the keratin fibre.60 The mechanism for photo-oxidative bleaching follows a C-S fission route (Figure 1.8)11:
h S R S OH S S R S R S R R SO2H SO3H + R-OH
R-SO3H +
H2SO4
Figure 1.8 – C-S fission (E = hν) mechanism of -keratin by photo-oxidative bleaching.
19 1.2.8 Oxidation of Hair with Chlorine
When hair is treated with chlorine water, bubbles or sacs form at the surface of the 11 fibre. Depending on the pH of the water, the oxidising species Cl2 or HOCl cleave the disulphide bond and the peptide bond. The bubbles diffuse across the cuticle producing smaller, water soluble species too large to migrate out of the hair. As a result, the fibre swells.11 Studies concerning the effects of chlorine in swimming pools on hair, concluded that it increased fibre friction on the surface.61
The composition of the keratin fibre therefore, has an influence upon its reaction to various chemicals. Its physical structure has an influence upon its mechanical properties.
1.2.9 Physical Properties of the α-Keratin Fibre The physical properties of hair include mechanical properties (i.e. tensile properties, strength and elasticity), thermal, electrical, frictional, adsorption and behaviour with water (i.e. the keratin-water system).10 11 31
1.2.9.1 Mechanical Properties of the Keratin Fibre The physical properties of human hair fibres are dependent on moisture content and temperature. Under conditions of low temperature or short times for which no structural mobility can occur in an -keratin fibre, the mechanical properties of the fibre will depend primarily on the whole cohesive bond network.10 In the presence of water, certain cohesive bonds permit the structure to flow. However, other components are unaffected by water. Speakman62 demonstrated that the longitudinal stress-strain relationship for a fibre equilibrated at a fixed relative humidity and at a fixed temperature could be represented by three distinct regions of extension, known as the stress-strain curve. When the fibre is initially extended, the fibre has a near linear stress-strain relationship (up to a few percent extension). At approximately 0.2 % strain, the crimps are removed from the fibre by unbending. Beyond 1 % strain, the relationship is linear and is referred to as the Hookean region. An extension up to 25-30 % results in a small increase in stress to the fibre and is termed the Yield region.
20 Further extension beyond the yield region increases the strain and the fibre stiffens and eventually breaks (i.e. the post-yield region).
1.2.9.2 The Keratin-Water System Water is an important variable component of keratin fibres. At fixed temperatures, the relationship between the equilibrium moisture content (% water regain) and the % relative humidity of -keratin fibres shows a sigmoidal hysterisis curve.10
Water enters the fibre keratin structure via a diffusion process.10 As water is a highly polar molecule, it interacts with the hydrogen bonds and other polar groups in the - keratin chains.10 Amino acid residues with hydrophilic side chains lead to water attachment equivalent to that of water hydration in a salt at low humidities. At higher humidities, water enters the fibre as „solution water‟ not attached to specific sites but with absorption resulting from the free energy difference arising from the entropy of mixing keratin with water. Nuclear magnetic resonance (NMR), has facilitated the determination of the amount and nature of the water in the keratin-water system.10 The results suggest that the system consists of an interpenetrating polymer network made up of a continuous hydrogen bonded water system with the matrix protein as well as with the microfibril protein.
Experimental data concerning moisture binding by hair of different racial background illustrates that no significant differences exist in the water uptake, regardless of the relative humidity.32
Results are also available on the effect of cosmetic chemical treatment on moisture uptake by hair fibres. At ambient humidities (65% RH), there is negligible water absorption compared to the untreated or intact hair fibre. However, drastic increases in fibre swelling or liquid retention can be observed upon wetting.32
21 1.2.10 The Effects of Mechanical or Physical Processes on Human Hair Fibres In conjunction with our day-to-day habits, human hair is under regular abrasion or weathering associated with hair grooming. SEM studies by Swift and Brown63, Garcia et al.64 and Robinson65 have shown that normal hair care treatments such as brushing, combing, shampooing, towel drying and weathering by exposure to rain, sunlight (UV radiation) and dirt all result in physical damage to the surface of the fibre.
1.2.10.1 Effects of Shampooing, Conditioning, Combing, Grooming and Towel Drying Shampoo is used to clean hair and conditioner is used to coat the hair with a thin film in order to protect it.66 Shampoo and conditioner can keep hair smooth, strong and easier to comb.66 Friction is experienced when combing as a result of interactions between hair and the comb material and needs to be low in order to facilitate the maintenance and sculpting of the hair.66 To minimise entanglement, adhesive force needs to be low. For complex and curly hair styles, higher adhesion between fibres is needed.66 This is known as the hairs‟ Tribological (surface roughness, friction, adhesion) properties.66
Experiments have been performed to mimic the actions of shampooing and towel drying. It was concluded that sections of the fibre closest to the root exhibited scales with free edges of relatively smooth contour. However, at increasing distance from the scalp, the scales became more damaged with jagged-like edges, causing them to be lifted away and ultimately completely removed.
Conditioner consists of cationic surfactants, fatty alcohols, silicones and water which thinly coat the hair, primarily through Van der Waals attractions.66 Beard et al.67 showed that conditioner treated hair fibres resulted in dramatic changes to the surface composition with increasing amounts of silicon due to the dimenthicone in the formulation and long chain fatty acid esters in the di-ester quat molecules.
Atomic Force Microscopy (AFM) topography studies66 68-71 have demonstrated variation in the cuticle structure with cracking and miscellaneous damage occurs at the cuticle edges in virgin hair. It was suggested that this damage was caused by mechanical abrasions resulting from daily activities such as washing, drying and combing.
22 Frictional forces are seen to be higher on damaged hair than on virgin hair, due to the increased roughness and a change in surface properties resulting from exposure to chemical damage.
1.2.10.2 Effects of Thermal Treatments on Human Hair Fibres In hair styling and grooming, temporary curling is often achieved with the application of heat from a curling iron. Ruetsch et al.72 carried out an SEM investigation on untreated hair to investigate the damage caused to the cuticular structure with the use of a curling iron. Short and long-term curling of dry and wet hair were considered.
Dry hair fibres, were minimally damaged with the use of the curling iron for short periods (10 seconds) and with normal applied tension; on the other hand, prolonged contact times (10 minutes) combined with increased tension (10-30 g) lead to compression, disintegration, radial cracking, and scale edge fusion of the surface cuticle cell.72
With wet hair fibres, repeated short-term curling resulted in less damage to the cuticle than with short-term use on dry hair. However, repeated long-term use led to the distortion of the cuticle cell an effect which was attributed to trapped moisture expanding in the form of steam, creating bulges in the scale faces and ripples at the scale edges in the fibre.72 Ten minute contact under increased tension produced damage which was significantly different from that observed with the dry hair fibre. In addition to compression, disintegration, radial cuticular cracking, and scale edge fusion, fine-line cracking was observed to be scalloped around the fused scale edges.72 The high temperature flow of water-plasticised cell proteins created mutilated and distorted cuticle cells.
In regards to the change in mechanical properties of the hair fibre, SEM images illustrated that repeated short-term curling-cooling increased the post-yield modulus of the hair fibres, possibly due to thermally induced cross-linking of components of the cortical domains.72 Finally, in relation to the fibre‟s fatigue resistance, the results showed that if the fibre was conditioned, the fatigue resistance increased. It was suggested that this was a result of the conditioning compounds enhancing the heat-
23 induced cross-linking in the form of salt linkages and hydrophobic bonds, which led to significant increases in fatigue resistance.72
Therefore, potentially, a human hair fibre can be recovered from a crime scene that has undergone various chemical treatments or physical-mechanical processes. From a forensic perspective, understanding how the processes operate allows the investigator to have a greater appreciation to the grooming habits or routines of the suspect/victim. Hence having covered the various treatments, it is important to focus on hair in the context of forensic fibre evidence, which is the principal purpose of this investigation.
1.3 Forensic Science: Trace Physical Evidence
Forensic, from the Latin word forensis (forum) as “of or used in courts of law”.73 Forensic science refers to “the application of matters of law”74 with specialised fields which includes the analysis of trace evidence. Trace evidence may be defined as physical evidence of minute size in the form of human hairs, textile fibres, soil, glass and paint fragments, arson accelerants, explosive residues, blood, bullet fragments, fingerprints, plant debris, cosmetics and numerous other forms that require microscopic comparison.20 75 76 Prior to the advent of DNA profiling, these materials constituted the main types of supposed trace evidence.21 Trace physical evidence is readily exchanged between the crime scene, the victim and the perpetrator of the crime.13 20 In the absence of DNA evidence and fingerprints, trace evidence of this nature may be the only means of solving of a crime.21 DeForest states “trace evidence has an important role to play in both the investigative and adjudicative phases of a case”.77 For forensic scientists, detectives and prosecutors, the presence, detection and recovery of trace evidence is crucial and highly significant to an investigation.
DeForest et al. state “The use of trace evidence in criminal investigations and subsequent prosecutions depends on its recognition and preservation at the scene of the crime and its identification and comparison with exemplars in the forensic science laboratory”.78 Therefore in the investigation of crime, hair or textile fibres from questioned or unknown origins that are located on the victim and/or the immediate surroundings are taken as corroborating evidence to link a suspect to a crime scene.4 When properly examined and interpreted, a common origin or connection between
24 evidential and known hairs can be established. This enables a suspect to be connected to a crime, or alternatively, exonerated from a crime.4 79-82 However depending on the nature or condition of the fibre, the association may have great probative value, or very little, or even none at all.82
How trace evidence arises during the act of a crime is very simple. It is governed by the fundamental principle or theory in forensic science, known as “Locards Principle of Exchange” or the “Exchange Principle” formulated in 1910 by the French criminologist Edmund Locard.75 83 82 84 He postulated that “every contact leaves a trace”, essentially meaning that during the commission of a crime involving some form of physical contact between two bodies or surfaces, a cross-transfer of evidence results.83 75 82 Small amounts of materials from each object are transferred to their opposing surface. Locard maintained that “the criminologist re-creates the criminal from traces left behind, just as an archaeologist reconstructs prehistoric beings from his finds”.85
Prime examples of this phenomenon can be witnessed with fingerprints on various surfaces, shoe sole impressions in soil, and more importantly with the transfer of fibres both hair and textile between individuals and the surrounding environment during a crime.2 75 80 82
1.3.1 Forensic Fibre Evidence
Fibre evidence is an important asset, which can provide valuable evidence in the investigation and prosecution of criminal cases.6 86 87 Fibres are classified in broad terms as either natural or man-made. Further subdivision of natural fibres leads to animal (e.g. keratin fibres), vegetable (e.g. cellulose) and mineral fibres.
The transfer of hair and textile fibres can be compared to discover whether or not there is a link between two people, or a person and a scene.88 Fibres located on objects used in crime, such as vehicles and weapons can also be of significance.89 90 The persistence of fibres at crime scenes is easily recognised by the fact that they are ubiquitous in nature.9
25 A well cited and highly publicised case in forensic science involving fibre evidence is the “Wayne Williams and the Atlanta Child Murders” trial of December 1981-February 1982.13 15 This case was significant because before this trial, fibre evidence had not played such an important role in any case involving so large a number of murders.91 Associations were made between fibrous debris removed from the bodies of 12 murder victims and objects in the immediate, everyday surroundings of Wayne Bertram Williams. Peculiar and uncommon fibres consistent with these being used in carpets and rugs originating from his home and automobiles, animal hairs from his dog and African hair fibres originating from his scalp were recovered from the crime scenes.91 The amount of overwhelming and irrefutable fibre evidence was enough to convince the jury beyond reasonable doubt that Wayne Williams was guilty and was ultimately sentenced to serve two life sentences in prison.13
Human scalp hair is routinely collected from crime scenes as shown by their percentage frequency from a variety of crimes (Figure 1.9).92 For example, human hair is continually shed or deposited from the body through the normal hair-growth cycle (i.e. proliferation (anagen phase), involution (catagen phase), and resting (telogen phase)). It has been estimated that humans lose approximately 100 hair fibres per day;4 and therefore to forensic investigators, a large percentage of the physical trace evidence recovered from crime scenes is human hair. Natural fibres, such as cotton and wool from garments and carpets respectively, usually „donate‟ or „transfer‟ fibres more readily than synthetic or man-made fibres because they have a tendency to become loose and fray.3 87 93-96 Therefore, characterisation of fibres, both natural and synthetic, is a significant aspect of the forensic analysis of physical evidence.97 98
26
Murder and manslaughter 100 Violent
Rape
Assault without rape 50 Burglary
Other offences
Frequency in casework (per cent) (per casework in Frequency
0 human fibres small hair particles
Figure 1.9 - A histograph indicating the relationship between the frequency with which different types of trace evidence occurs in criminal cases. Adapted from Broad et al.92
1.4 Current Methods of Forensic Hair Analysis with the use of Microscopy and DNA Analysis
1.4.1 Macroscopic Analysis
In the forensic examination of hairs it is important to begin with visual examination followed by macroscopic examination of the morphology of individual hairs.83 Features such as hair length, shape or form, root appearance, tip appearance, colour, disease condition or abnormalities are all observed and measured.83
1.4.2 Microscopy
1.4.2.1 Optical Light Microscopy and Stereomicroscopy In optical microscopy, four types of microscope are used to examine and compare hair fibres from crime scenes. They include the stereomicroscope, the compound light/ polarising microscope and transmitted light comparison microscope and the scanning laser confocal microscope.99 100 The stereomicroscope and the light microscope are
27 used for rapid preliminary analysis to determine species, racial origin and the somatic (body location) origin.83 This is achieved through the analysis of hair characteristics such as the medulla (i.e. classification) and cuticle, colour, spatial configuration, diameter, cross-section, cortical cells, cortical fusi, birefringence and pigment features are analysed.18 83 99 The scanning laser confocal microscope is a fluorescence-based technique that allows the study of transverse cross-sections which is important in the examination of human hair.100 The transverse cross-sectional shape may be of assistance in determining the somatic origin or to assist in determining the ethnicity of the donor.100 However, there are certain features present in hair such as heavy pigmentation or the presence of an opaque medulla that can strongly interfere with the laser beam or collection of fluorescence and have an adverse affect upon cross-sectional image quality.100
The next phase of examination involves the direct comparison of questioned fibres from the crime scene and known fibres from the suspect, side-by-side, using a comparison microscope.99 101 When hair fibres are compared, it is difficult to associate questioned and known sources because the morphological features differ from fibre to fibre on an individual‟s scalp and from person to person. Morphological variation is an integral part of natural growth.99 Conclusions drawn from such comparisons are therefore subjective and rely upon the experience and skill of the examiner. Furthermore, the evidence has to be independently assessed by a second examiner to give weight to the primary assessment and reduce subjectivity of the conclusions.99
1.4.2.2 Scanning Electron Microscopy SEM is a powerful tool for the forensic analysis of trace physical evidence such as fibres, glass, paints and gunshot residues as it is a non-destructive means of examining morphological characteristics of a material.102 Sampling preparation is simple and the solid proteins of the hair fibre are relatively stable to the penetrating electron beam.103
In forensic hair fibre analysis by SEM, Taylor et al. state that “SEM highlights the surface topography of the external cuticle layer in great detail with greater depth of field than a stereomicroscope”.104 SEM is preferable to optical light microscopy as this also gives poor topographic resolution of hair features.105
28 SEM analysis is useful for identifying the species of an unknown hair fibre as the cell structure and thickness of the external cuticle layer is markedly different between humans and animals.106 107 For example, the cuticle layer in fine Merino wool fibres is normally one cell thick, whereas in human hair the cuticle is approximately 10 cell layers thick.108 Also, the surface topography of the fibres is different.
However, for matching and identification of fibres of the same species, i.e. human hair fibres, it was discovered that “SEM is difficult for comparison of human hairs because the variability in the surface architecture, distribution and appearance of the scales within one head are great, according to the natural and cosmetic history”.104 109 Additionally, there is considerable variability along the length of the fibre from root to tip as a result of natural weathering processes and even due to grooming such as brushing and combing.109 SEM is also limited by the fact that the morphological features used to compare evidentiary and exemplar hairs are within the hair fibre, not on the surface.20
Other studies involving SEM that have potential forensic applications concentrated on: understanding the morphological variations of hair from different parts of the body110, analysing the damage of the cuticle as a result of weathering (i.e. sun bleaching or photo-degradation65 111, combing and brushing65 63 112, shampooing65 113, mechanical stress)114, and cosmetic treatments (i.e. permanent waving, bleaching and dyeing65 63and lacquered hair).104
1.4.3 Fibre Evidence from Burial Scenes
1.4.3.1 Burial of Hair Fibres In homicide, murderers go to extreme lengths to avoid being apprehended and face the repercussions of their actions. For example, after having slain their victim, perpetrators will bury the body to disguise the human remains. Various locations and earth media are utilised, such as remote forest or bush land, beaches, mangroves, backyards, garages and cellars. The human body decomposes leaving behind the skeleton and hair fibres, which, therefore, become important for the forensic scientist to assist in the identification of the deceased. As the grave is being prepared, fibres from the
29 perpetrator can also be shed and remain buried until the victim‟s body is recovered. The forensic examiner is therefore faced with examining fibres that have been exposed to a variety of environmental conditions.
1.4.3.2 Environmental Weathering of Fibre Evidence With the burial of hair fibres, Rowe states “very little is known about how environmental conditions alter hair morphology”.20 However, several studies have considered the effect of microbial attack on the identification and comparison of hairs.23 115-119 Serowik et al. and Kundrat et al. performed investigations whereby human hair fibres were buried in garden soil for periods ranging from one to six months.116 119 The buried hairs were exhumed and compared microscopically with hairs from the same individual that had not been buried. Both those studies reported the tunnelling or boring of the hair shafts by keratinolytic micro-organisms such as fungal hyphae. Serowik et al.116 discovered up to four different types of fungal growths which were found to be associated with the buried hairs.
DeGaetano et al.120 have also reported fungal tunnels in the hair fibre from a buried body of a murder victim. SEM examinations revealed that the fungal hyphae had no preference in the site of penetration, entering under the free edge of the cuticular scales or directly through the scale surface. The damage caused by the fungal hyphae was therefore random. They also observed the development of small cavities or vesicles possibly caused by shrinkage in both the medulla and the cortex of the buried hairs. Some buried hairs showed total destruction of their shafts at random locations. Furthermore, the authors observed the appearance of darkened “necked” regions on the shafts of buried hairs. The darkening of the hairs in these areas are artefacts resulting from the etching of the shafts of the hairs as they are progressively destroyed by micro- organisms. The general conclusion was that the bio-deterioration of hair in a soil environment is likely to cause problems in forensic hair examinations.
30 1.4.4 DNA Analysis
Human DNA is the genetic “blueprint” material in the cell nucleus and in extra-nuclear organelles of the cell, known as mitochondria (singular: mitochondrion) that is responsible for determining our physical characteristics.121 Excluding identical twins, no two people have the same genetic code, and thus DNA is unique to the individual. From the forensic perspective this is most important as it provides a means for association.121
Common sources/origins of DNA containing material most frequently found at crime scenes are spattered blood, saliva, skin, seminal fluid and more importantly, hair fibres which are present as a result of crimes of a violent nature. As hair is the most common form of biological forensic evidence found at a crime scene, it is potentially a valuable source of DNA for forensic analysis.122
1.4.4.1 DNA Analysis of Human Hair Fibres As DNA is unique to the individual, DNA comparisons can form highly significant associations between known and unknown hair samples.17 However, hairs contain extremely small quantities of DNA.123 With hair fibre evidence, two sources of DNA are available for forensic analysis.17 Nuclear (nuc) DNA, i.e. the cells pertaining to the hair root and surrounding translucent follicular tissue, (root sheath cells), are the optimum source of DNA.17 A hair fibre with its root attached is evidence that the hair has been forcibly removed from the head. Unfortunately many, if not most human hairs recovered from crime scenes (ca. 90 %) are in the telogen phase (i.e. the resting phase of the normal hair growth cycle where the hair is naturally shed), and thus will not contain a growing root or adhering tissue.124 Telogen hair can be of three types: (1) club root without any soft tissue remnant (most common), (2) club root with a small amount of soft tissue attached, and (3) club root with a large amount of soft tissue attached.125 Hair roots with soft tissue remnants have been considered to contain some cells with nucDNA.125 Andreassson et al.126 performed a study to investigate the nucDNA content in anagen versus telogen hair fibres. The first centimetre of plucked hairs contained an average of 25, 800 nucDNA copies while no nucDNA copies were detected in the first centimetre of shed hairs.126
31 Even if sufficient amounts of DNA were extracted from hair, the DNA are not always successfully amplified by the polymerase chain reaction (PCR), suggesting the presence of PCR inhibitors (e.g. melanin, hair dyeing and sunlight oxidation) in the extracted samples.127 128 By typing DNA from telogen hairs a loss of signal is typically observed with larger STR (Short Tandem Repeats) fragment sizes due to the fact that the DNA has been fragmented into small pieces during hair development.129 Hence, in most cases, they are unsuitable for nucDNA analysis.7 123 130 131 Therefore, newly designed STR systems for shorted amplicons sizes needed to be used.124 Over the past decade, some laboratories have developed improved extraction methods and miniSTR kits (short-amplicon PCR) to increase the typing chance of highly degraded hair.128 In 2001, Hellmann et al.132 used a series of single STR typing steps while the extracted DNA from the hair was fixed onto a membrane during consecutive PCR reactions. In 2010, Bourguignon et al.125 proposed a new screening test to visualise DNA with 4-6- diamidino-2-phenylindole (DAPI) which is a fluorescent molecule that binds onto double-stranded DNA, between A and T base pairs.125 The use of a fluorescence microscope makes it possible to count the visible nuclear DNAs and quickly discard hairs less suitable for STR-typing, thereby focusing the attention towards hairs with the greatest potential for results.125
1.4.4.2 Mitochondrial DNA In those instances where telogen phase or naturally shed hairs are present, the analyst then becomes interested in isolating DNA from the mitochondrial cells in the hair shaft. For the analyst, this is a rich source of DNA because there are hundreds of mitochondria and thousands of copies of mtDNA in each cell. Human mtDNA is an extra- chromosomal, closed circular, organelle-specific genome consisting of approximately 16.5 kb (kilo-bases). The mtDNA genome consists of coding sequences for 2 ribosomal RNAs, 22 transfer RNAs, 13 proteins and a non-coding region (1,100 base pairs), called the displacement loop (D-loop). This non-coding region has the forensic potential as this is where most of the sequence variation between individuals is located.
MtDNA was first introduced as evidence in Tennessee v. Ware133 in 1996; it has now been applied in hundreds of cases.134 However, as with forensic microscopy examinations, DNA analyses also suffer from limitations in that (1) they are not as
32 informative about the characteristics regarding the species or race associated with the fibre, (2) mitochondrial (mt) DNA is inherited through the maternal lineage and (3) adverse environmental factors (e.g. burial and degradation, contamination from exogenous sources of DNA) affect the quality and quantity of DNA obtained from biological samples, such as hair, because it is not well protected. This is in contrast to DNA originating from forensic biological samples such as teeth.5 17 135 However, several studies have demonstrated that it is possible to successfully decontaminate modern hair shafts that have been contaminated with human saliva and blood.7 136 Gilbert et al.137 suggest that the survival of mtDNA in degraded hair samples and its protection from external sources of contaminant DNA derives from the unique manner in which hair grows during life. As the precortical cells keratinise to form the cortex, they undergo loss of cell cytoplasm, organelle destruction and dehydration.137 This apoptosis, associated with the programmed terminal differentiation of cortical keratinocytes is a characteristic which is the protracted retention of organelle integrity, most specifically mitochondrial integrity.138 The protracted maintenance of mitochondrial membrane integrity may be more likely to protect the mtDNA.137 Additionally, the hydrophobic nature of the proteins in the cuticle and the keratin packing of the cells helps provide a impermeable seal around the hair cortex and suggests a plausible explanation as to how samples resist the penetration of contaminant DNA.137
Despite some of the limitations, hair presents a useful source of mtDNA in forensic and ancient DNA analyses.135 It is believed that the majority of post-mortem DNA damage directly hinders PCR amplification, through events such as inter-strand cross-linking and fragmentation.139 However, a small proportion of the damage does not hinder amplification, but results in the generation of miscoding lesions. These miscoding lesions can potentially provide misleading results in genetic analyses that rely on directly amplified sequences from samples containing low levels of DNA.140 Histological screening of hair samples prior to mtDNA analysis has helped to alert researchers to the possibility of such errors.139
During the past 7 years, the forensic community has addressed the requirement to develop fast and reliable screening methods for mtDNA analysis.141 The DNA quantification methods used prior to the development of real-time quantification were
33 often not sensitive enough for the trace amounts of DNA present in the types forensic materials encountered today.126 In the past few years, the introduction of high- throughput sequencing techniques for mtDNA analysis are currently in use, including pyrosequencing , LINEAR ARRAYTM and TaqMan® analysis, and has vastly improved the yield from source materials as well as being more cost- and time-effecient .126 134 142 The analysis of SNPs (Single Nucleotide Polymorphisms) is characterised by primer design that results in the analysis of short DNA fragments that are more stable against degradation and therefore more successful when applied to even heavily damaged mtDNA.141
The analysis of hair is a challenge for both the forensic microscopists and biologists involved. Microscopy is subjective and provides only circumstantial evidence. In addition, optimal sources of DNA (nucDNA) are less common, forcing biologists to isolate mtDNA. However, in Queensland Australia (John Tonge Centre, Brisbane) mtDNA is not extracted from the hair and the analysis is expensive.143
Efforts have been made to discriminate hair through chemical analysis, which includes monitoring dye components, trace elements, proteins and the surface components (lipids) of human scalp hair.17 However, over a decade, the main focus or drive by a research group at Q.U.T. (Brisbane, Australia), has been towards utilisation of vibrational spectroscopy, namely IR22-24 26 and more recently NIR27 spectroscopy for structural elucidation. These techniques facilitate information on the molecular level about the nature of the hair fibre.
1.5 Vibrational Spectroscopy
Biological systems consist of interacting chemical compounds, and the most important structural and functional role is played by molecules. Molecules consist of atoms which have a certain mass and which are connected by elastic bonds. As a result, the bound atoms can perform periodic motions where the atoms alternately move towards and away from each other i.e. they vibrate.144
34 In spectroscopy, the electromagnetic radiation travels as an oscillating magnetic field perpendicular to an oscillating electric field with an energy and wavelength which is described by the following equations145: ΔE = hν Equation 1.1 where;
ΔE = Energy (kJ mol-1) h = Planck‟s constant 6.625 x 10-27 kJ sec υ = the frequency of light sec-1 Hertz (Hz) and; λ = c/ν Equation 1.2 where;
λ = the wavelength of the electromagnetic wave (cm) c = the velocity of light 3 x 1010 cm sec-1 υ = the frequency of light sec-1 Hertz (Hz)
A wavenumber is defined as;
= 1/λ (cm-1) Equation 1.3
Atoms of a molecule vibrate with a definite frequency that depends on the mass of the atoms, the force of their binding and the structure of the molecule. The molecule will absorb incident radiation at characteristic wavelengths corresponding to the energy of the molecular vibrations, providing that a change in dipole moment occurs with the vibration.146
Processes of change, including those of vibrations and rotations associated with infrared spectroscopy, can be represented in terms of quantised discrete energy levels E0, E1, E2, etc. Each atom or molecule in a system must exist in one of these levels. In a large assembly of molecules, there will be a distribution of all atoms or molecules among
35 these various energy levels. The latter are a function of an integer (i.e. the quantum number) and a parameter associated with the particular atomic or molecular process associated with that state.146 At a specified temperature, the molecules that make-up a system of oscillators is in the state of dynamic equilibrium determined by the Boltzmann energy distribution. Whenever a molecule interacts with radiation, a quantum of energy (i.e. a photon) is absorbed. The energy of the quantum of radiation must exactly fit the energy gap E1-E0 or E2-E1, etc. Hence, the selection rules must be obeyed. The requirement is that the transitions be quantised and the transitions between the respective levels are probable.146
If one oscillator passes to a lower state, another one will pass from a lower to a higher state to maintain the equilibrium. Thus the energy promotes the molecule from the ground state (E0) to the excited state (E1). Hence, the frequency of absorption of 146 radiation for a transition between the energy states E0 and E1 is given by :
υ = (E1 – E0) / h Equation 1.4
This can be represented on an energy diagram as a transition of the oscillator from the ground state to the excited state (absorption of energy) (Figure 1.10)
E1 Excited State
ΔE Ground State E0
Figure 1.10 – Absorption of energy for a vibration where the molecule is promoted from state E0 to state E1 and the molecule in the higher vibrational state (E1) dropping to the lower vibrational state (E0) emitting radiation of ΔE.
36 1.5.1 Infrared Spectroscopy
1.5.1.1 Infrared Absorptions Under normal conditions, the population ratio of a molecule is steady and increases with temperature. Incident radiation stimulates transitions between vibrational levels. The energy of most molecular vibrations corresponds to that of the mid-IR region of the electromagnetic spectrum. This includes radiation with wavelengths () between 2.5 m and 25 m, which correspond to a wavenumber range of 4000-400 cm-1.147 Reiterating, a transition can occur only if the dipole moment of a molecule is altered. This is the selection rule for infrared spectroscopy.146 As a consequence, during the vibration, the distribution of electric charge in the molecule must change. The negative charge deriving from the electron cloud around the positive charge of the nucleus frequently gives rise to a permanent dipole moment μ, (Equation 1.5):
μ = Q r Equation 1.5 where; μ = dipole moment, debye, D, (statcoulomb centimetre, statC cm 10-18) Q = charge (statC, 10-10) r = distance between the charges (angstrom, 10-8 cm)
Infrared absorptions are not infinitely narrow with several factor contributing to the broadening.146 The Doppler effect, in which radiation is shifted in frequency when the radiation source is moving towards or away from the observer. The collisions between molecules contribute to band or pressure broadening. Another source of band broadening refers to the lifetime of the states involved in the transition. The energy states of the system do not have precisely defined energies and this leads to lifetime broadening. The relationship between the lifetime of an excited state and the bandwidth of the absorption band associated with the transition to the excited state is a consequence of the Heisenberg Uncertainty Principle.146
37 1.5.1.2 Infrared Modes of Vibration A molecule can be looked upon as a system of masses joined by bonds with spring-like properties. Polyatomic molecules such as keratin containing many atoms (N) which have 3N degrees of freedom.146
In general, a molecule can only absorb radiation when the incoming infrared radiation is of the same frequency as one of the fundamental modes of vibration of the molecule. However, overtones and combination modes of vibration also occur.
Molecules have a number of vibrational modes that give rise to absorptions. These vibrations include the stretching and bending modes.148 The stretching vibration is associated with a motion of atoms causing elongation and shortening of the chemical bond. In Multi-atomic systems the motion can be classified as either symmetric or anti- symmetric in nature. Symmetric molecules will have fewer infrared-active vibrations than asymmetrical molecules. Symmetric vibrations are generally weaker than asymmetric vibrations since the former will not lead to a change in dipole moment.
A bending (scissoring) mode is an in-plane movement of atoms during which the angle between the bonds changes. The bending vibrations can be classified as: (1) rocking vibrations, which involves atoms swinging back and forth in phase in the symmetry plane of the molecule; (2) wagging vibrations, is an in-phase, out-of-plane movement of atoms, while other atoms of the molecule are in the plane and; (3) twisting vibrations, is the movement of the atoms where the plane is twisted. For example, the localised vibrations of the methylene group (Figure 1.11)147:
H H H H H H H H H H H H C C C C C C
Figure 1.11 – Localised vibrations of the methylene group highlighting the symmetric and anti-symmetric stretches, and the bending/scissoring, rocking, twisting and wagging vibrations respectively.
38 In human hair and wool keratin, the peptide bond is the most abundant.149 The atoms involved in this bond give rise to a number of vibrational bands that can be observed in the IR spectrum of -keratin (Figure 1.12). In the wavenumber region of interest for this investigation (1750-800 cm-1), the major characteristic absorptions of the peptide bond are the Amide I (1690-1600 cm-1), Amide II (1575-1480 cm-1), and Amide III bands (1320-1210 cm-1).
R H N R H R H O R N N O R O R
Figure 1.12 – Modes of Vibrations for the Amide I, Amide II and Amide III bands respectively for -keratin protein.
Other modes of vibration that are present in such a spectrum include the amino acid side -1 chains which have C-H deformations (1471-1460 cm ), CH2 and CH3 deformations (1453-1443 cm-1 and 1411-1399 cm-1), and the cystine oxide stretches which consists of the asymmetric and symmetric cysteic acid (1171 cm-1 and 1040 cm-1) symmetric cystine dioxide (1121 cm-1), and cystine monoxide (1071 cm-1) stretches.
39 1.5.2 The Fourier Transform Infrared Spectrometer
Fourier-transform infrared spectrometers are used and have improved the acquisition of infrared spectra. The schematic diagram, Figure 1.13, represents the Michelson Interferometer. Radiation from a broadband source (e.g. globar) strikes the beamsplitter. Some of the light is transmitted to a movable mirror and some of the light is reflected to a stationary mirror. The moving mirror modulates each frequency of light with a different modulation frequency. In general, the paths of the light returning from the stationary mirror and the moving mirror are not in phase. They interfere constructively and destructively to produce a pattern called an interferogram.148 150 The interferogram contains all the frequencies which make up the IR spectrum. The interferogram is a plot of intensity versus time (i.e. a time domain spectrum). By performing a mathematical operation known as a Fourier Transform, the interferogram can be decomposed into its component wavelengths to produce a plot of intensity versus frequency, i.e. an IR spectrum. 148 150
40
Fixed Mirror
Source
(Broadband Light)
Beamsplitter
Moving Mirror
Sample
Detector
Computer
IR Spectrum
Figure 1.13 - A schematic diagram of the Michelson Interferometer. Adapted from 146- 148
41 1.5.2.1 Fourier-Transformation The essential equations for a Fourier-transformation relating the intensity falling on the
detector, I(δ), to the spectral power density at a particular wavenumber, , given by B(
146 ), are as follows :
I(δ) = B( )cos(2 ) d Equation 1.6 0 which is one half of a cosine Fourier-transform pair, with the other being:
B( ) = I( ) cos(2 )d Equation 1.7
Equation 1.6 shows the variation in power density as a function of the difference in pathlength, which is an interference pattern. Equation 1.7 describes the variation in intensity as a function of wavenumber.
1.5.2.2 Advantages FT-IR instruments have several significant advantages over older dispersive instruments.146
1. Multiplex advantage (Felgett) – Improvement in the signal- to-noise ratio (SNR), proportional to the square root of the number of resolution elements. 2. Throughput advantage (Jacquinot) – The total source output can be passed through the sample continuously, resulting in a substantial gain in energy at the detector, translating to higher signals and improved SNRs. 3. Co-addition of scans – Increase SNR by signal-averaging, proportional to the square root of the time, as follows:
SNR α n1/2 Equation 1.8
42 4. High scan rate – The mirror has the ability to move short distances rapidly to acquire spectra on a millisecond timescale. 5. High resolution – By closing down the slits, a narrow band is achieved. 6. Laser Referencing (Connes Advantage) – By using a Helium-Neon laser as a reference, the mirror position is known with high precision. 7. Negligible stray light – The detector responds only to modulated light. 8. Powerful computers – Advances in computers and new algorithms have allowed for fast Fourier-transformation.
1.5.3 Forensic Investigations of Human Hair Fibres using FT-IR Spectroscopy
Across the major scientific fields, biological human hair fibres have been studied for a number of key purposes, i.e. for medical, environmental, cosmetic and more importantly, for forensic sciences. As indicated previously, hair fibres from questioned or unknown origins that are located on the victim and/or the immediate surroundings are taken as corroborating evidence to link a suspect to a crime.
In the mid 1970s, criminalists were aware that dyed and bleached hairs could be distinguished from untreated hairs by light microscopy.151 As mentioned earlier, this technique involves identifying and matching the morphological features of human hair fibres using known and unknown sources. However, the FT-IR spectroscopy facilitates matching the chemical structure of identified and questioned fibres utilising structural elucidation.
FT-IR Spectroscopy is a technique chosen for its sensitivity to the conformation and local molecular environment of molecules including that of the biopolymers. It has been suggested that “infrared spectroscopy is a powerful technique for the forensic examination of fibres”3, and that “FT-IR analysis can provide rapid and specific chemical information at the molecular level about the nature of the fibre and its
43 composition”.152 In early investigations in the late sixties, FT-IR spectroscopy had been utilised to study the effects of oxidative treatment on human hair fibres.153-156
Much later in 1985, in the first forensically directed applications, Brenner et al. performed an investigation on untreated and bleached hair fibres with the use of FT-IR spectroscopy that utilised a diamond anvil cell to obtain transmission spectra.47 For the bleached hair fibres, the authors discovered the presence of a peak at 1044 cm-1 which was attributed to the symmetric stretch of cysteic acid. As a result of this study it was suggested that “this peak may be used to differentiate treated and untreated hair samples”. Ohnishi et al. furthered this study by analysing permanently waved hair fibres.157 In this study, it was determined that the concentration of cysteic acid and random damage patterns increased from root to tip depending on the frequency of permanent waving.
In 1991, Hopkins et al. decided to investigate other IR absorptions of keratin by examining the ratio of the Amide I to Amide II bands to characterise human hair.158 However, the spectra did not appear to have sufficient discriminatory value for forensic use showing little or no difference in the Amide I/II ratio that could be correlated to gender, age, and hair colour. The final statements in this study were important - “If such differences do exist and can be detected by IR spectroscopy, they must be more subtle than the simplistic technique used in this study (ratio differences)”.158
Finally, in 1994, Bartick et al. used FTIR-ATR Spectroscopy to investigate the presence of hair spray on the hair fibre by subtracting the spectrum of an uncoated hair fibre from a coated one to reveal the characteristic absorptions of the hair spray.159 As a result, subtraction will be a tool used in this study.
Therefore, in summary, earlier FT-IR spectroscopic investigations showed some promise for the forensic analysis of human scalp hair fibres. It was possible to discriminate between untreated and cosmetically treated fibres through visual inspection - -1 of the spectra. The prominence and intensity of the SO3 vibrational band at 1040 cm was strong evidence indicating that the disulphide bond (S-S) had been cleaved and subsequently oxidised to cysteic acid residues by hydrogen peroxide during the
44 bleaching process. Unfortunately for the criminalists, no further discrimination was possible.
Several years later, Panayiotou22 endeavoured to apply FT-IR Micro-spectroscopy for structural elucidation. The spectra in this study were interpreted with the aid of Chemometrics. This approach had not been previously applied to the study of hair fibres. This amalgamation proved to be a very powerful one. As a result of this research, human scalp hair fibres could be discriminated on the basis of 152:
(a) the section of the fibre sampled, i.e. root, middle and tip, (b) section of the head where the fibre originated (e.g. left, right, top, middle and back), (c) gender, (d) untreated vs. cosmetically treated hair, (e) treatment vs. multiple treatment and (f) black Asian hair vs. black Caucasian hair.
Furthermore, unknown hair samples (i.e. blind samples with their history being withheld from the author) were submitted to a reference spectral database to assess the validity of the technique. It was discovered that this method predicted correctly approximately 83 % of samples with respect to the history of the unknown fibres.
1.5.3.1 Applications of Chemometrics to Forensic Science In forensic and criminalistic studies, PCA has been utilised to aid and solve numerous problems in different forensic science disciplines.160 161 The earliest applications in 1989162 and the mid-late 1990s163 164 concerned investigations in morphometry (i.e. skeletal gender determination of the skull and scapula), and in areas adjacent to forensic medicine (i.e. regional differences in alcohol and fatal injury165) differentiation between sharp force homicide and suicide.166
In Australia (with collaboration with the Royal Canadian Mounted Police), forensic arson studies using chemometrics involved the classification of unevaporated premium and regular gasoline167 and differentiation of polycyclic aromatic hydrocarbons on the basis of GC-MS data.168 169
45 Textile fibre studies performed by Kokot et al. have demonstrated that Diffuse Reflectance Infrared Fourier Transform spectroscopy (DRIFTS) taken from dye mixtures extracted from textile samples, cluster and match according to their sampling area on the test material.170 Gilbert et al. established that it was possible to differentiate between cotton-cellulose fabrics on the basis of the fabric dye, fabric type and level of textile processing.171 With continued study on cotton fabrics, Kokot et al. were able to show that fabric samples containing different states of a reactive dye and samples dyed with differently coloured unfixed reactive dyes could be discriminated on the basis of their DRIFTS spectra.172 Keen et al. reports that spectra from the same fibre type (polyester and polyamide) from different manufacturers have very similar spectra but can be separated using PCA.173
In two papers concerned with document examination, Thanasoulias et al. 160 and Kher et al. 161 were able to discriminate between different blue and black ball-point pen inks on the basis of their UV-Vis spectra and HPLC chromatograms respectively. Novel approaches in ballpoint ink analysis involved discrimination of ink-lines from 10 pens using non-destructive luminescence spectroscopy and PCA.174 Thanasoulias et al. were able to discriminate between 44 soil samples from five different areas, also on the basis of their UV-Vis spectra of the acid fraction of humus.175
Brody et al. have published results on the discrimination of dentine from six mammalian species and differentiated dentine from bone and cementum to counteract the illegal trade of African and Asian elephant ivory and identify legitimate and „fake‟ ivory respectively.176
Several investigations have been carried out by forensic laboratories concerned with linking seized illicit amphetamine and heroin samples to the source (common batch) of production177 178, classification on the basis of cocaine concentration179, and differentiation between illicit methaqualome containing tablet formulations.180
46 1.5.3.2 Previous Investigations using FT-IR Spectroscopy and Chemometrics
Panayiotou expanded her studies to include a wider range of -keratin fibres, namely those from animal fibres.24 In later work, Panayiotou developed a forensic protocol, which as defined by Barton is “a systematic approach for the analysis of unknown hair fibres from crime scenes with the use of FT-IR Spectroscopy”.23 The spectral evidence could then be used in conjunction with current methods of examination, such as microscopy and DNA analysis. It was proposed that the integration of these three techniques would improve identification of a hair „profile‟, giving information on the morphological, molecular and genetic levels.
The scope of this work was broadened by Paris25, adding yet another dimension to the ever growing area of forensic hair fibre analysis by FT-IR spectroscopy. Paris aimed to match and discriminate individuals after the hair fibres had been environmentally weathered. This is important to consider as hair fibres can be discovered in a wide variety of environmental conditions. The hair fibres of selected individuals were subjected to different surroundings (i.e. sand, soil and mud, which is assumed to range from moderate to harsh conditions respectively) for various time intervals. These media were chosen as they represent potential burial sites for the disguise of human remains in homicide cases.
From Paris‟s study, it was apparent that only approximate matching of individuals can be accomplished after the fibres have been both weathered and cleaned. From the forensic perspective this becomes a problem for positive identification of an individual.
1.5.3.3 Limitations to the Previous Investigations Through a critical examination, significant limitations could be attributed to the previous investigations carried out by Panayiotou and Paris.23 First and foremost, the authors did not have a large data set. Fibres were only sampled from two major races (i.e. Caucasian and Asian), whilst the third major race (i.e. African or African-type) was neglected. Although on the macroscopic level an African-type hair appears obvious, it cannot be so easily distinguished from pubic and beard hair which also has crimp. Therefore, the conclusions on the discrimination of individuals formulated by Panayiotou22 24 and Paris25 can only apply to Caucasian and Asian hair fibres. If
47 unknown African-type hair fibres were present at a crime scene, the forensic protocol would be rendered inadequate because the analyst would not be able to determine on what basis the questioned fibres are discriminated, therefore throwing the spectral analysis into jeopardy.
However, the most significant limitation concerned the sampling preparation of the hair fibre prior to spectral analysis. Spectroscopically, hair fibre investigation can involve the employment of a number of IR sampling techniques such as the traditional FT-IR Micro-spectroscopy (previous studies)22 24 25, FTIR-Photoacoustic spectroscopy (FTIR- PAS)181 182, Raman spectroscopy45 183-185, Near-Infrared spectroscopy (NIR)27 and the more novel (with respect to its involvement in this subject matter), FTIR-ATR spectroscopy.23 26
However, in general, these techniques have different spectral sampling methods as well as different spectral resolution and chemical information (IR vs. Raman) that can be extracted. This of course becomes an issue from the forensic perspective in that the investigator/s must draw as much information from the fibre that is physically possible, with acceptable precision and accuracy, to formulate conclusions that are beyond reasonable doubt for any later convictions and sentencing that may be made.
In the previous investigations the spectra were recorded in transmittance. As hair fibres absorb IR radiation strongly, they needed to be rolled and flattened to reduce lensing effects53, enhance the signal to noise ratio186, and decrease the path length of the IR radiation and subsequently the absorbance, as given by the Beer-Lambert law150 (Equation 1.9): A = bc Equation 1.9 where: A = Absorbance = molar absorptivity (M-1cm-1) b = pathlength (cm) c = concentration of the sample (M)
48 Panayiotou22 and Paris25 employed SEM to determine the approximate number of rolls required to flatten the fibre which left minimal physical damage, while still allowing sufficient transmission of the IR radiation through the fibre. Nevertheless, it was clear from the SEM images that the rolling technique was relatively destructive to the hair fibre. After four rolls of an untreated fibre, the hair began to stretch and produce splits and voids that ran along the length of the fibre. The damage was far greater with a bleached hair fibre after four rolls due to decreased structural stability. Robbins reported that when a fibre is stretched there is a transformation of the secondary structure of the protein from the -helix to the -pleated sheet arrangement, also known as -keratin.11 After 15 and 10 rolls of an untreated fibre and treated fibre respectively, the hair was virtually destroyed and useless for analysis. Although a satisfactory number of rolls were selected, in general the spectra recorded were of poor quality. The spectra suffered from what Kirkbride3 and Robertson187 describe as “peak saturation” or “band saturation”, where the Amide I and Amide II bands of each spectrum were apparently saturated, appearing as broad flattened peaks. However, it should be noted that application of chemometrics such as PCA reduced the influence of these broadening effects by appropriate pre-treatment and stepwise extraction of the PCs.
Nevertheless, to avoid “matrix” or saturation effects to obtain good quality spectra, and a better representation of the -keratin structure, Barton23 investigated the use of a different IR sampling technique. As opposed to sampling in transmittance, the information was collected from fibres using Attenuated Total Reflectance (ATR), which is a reflection method.
1.5.4 Fourier Transform Infrared Spectroscopy - Attenuated Total Reflectance
Fourier Transform Infrared - Attenuated Total Reflectance (FTIR-ATR) Spectroscopy otherwise known as Internal Reflection Spectroscopy (IRS), is just one of a wide range of IR sampling techniques available and is a well known method for measuring IR spectra.188-191 ATR was developed independently in the 1960‟s by Harrick and Fahrenfort.189 FTIR-ATR spectroscopy historically has been used for samples which are too thick for transmission measurements192-194, finding widespread use in studies which were concerned with the near-surface chemistry of forensic159, biological and
49 industrial materials which encompassed both natural and synthetic fibres 51 52 159 181 184 185 195-204, paints159 205, adhesive tapes206, coatings 207 208, human body specimens53 209 210, insect cuticular proteins and chitin211, polymers and rubbers191 212 213 and pharmaceuticals.214 215
FTIR-ATR spectroscopy is based on the phenomenon known as Total Internal Reflection (TIR) (Figure 1.14).188 216 In this sampling technique, infrared radiation is directed into an internal reflection element (IRE), which is a medium fabricated of a high refractive index crystalline material (eg. Diamond, ZnSe, ZnS, and KRS-5) and transmits radiation in the spectral region of interest.196 215 216 The angle of the incident
IR radiation, θi, exceeds the critical angle θc. When this radiation strikes the interface between the IRE and the sample composed of a lower refractive index, total internal reflection is achieved.215 216
A B C
IRE
Sample
Attenuated Total Reflection
Figure 1.14 – Total Internal Reflection in Attenuated Total Reflectance Spectroscopy. Adapted from188 196 215.
Evanescent Wave
Figure 1.15 – An evanescent wave that is produced upon Total Internal Reflection that eventually penetrates the sample. Adapted from159 215.
50 This internal reflectance creates an evanescent wave that extends beyond the surface of the crystal and penetrates only a short distance into the sample (Figure 1.15).159 215 216 As the sample absorbs IR radiation at certain frequencies, the resultant totally reflected radiation will be attenuated (altered) in regions of the infrared spectrum where the sample absorbs energy.215 216 The IR radiation exits the crystal and passes through the spectrometer to the detector where the spectrum is recorded.191
The intensity of the evanescent wave whose electric field amplitude decays exponentially with distance from the surface of the IRE crystal is given by188 215:
-z/dp E = Eoe Equation 1.10 where; E = electric field amplitude
Eo = external electric field -z = vector component of the evanescent wave
dp = depth of penetration
The depth of penetration (or sampling depth) for experiments involving ATR has been defined by Harrick 188 “as the distance required for the electric field amplitude to fall to e-1 of its value at the surface”, and is given by 188 217:
1 dp = 2 2 1/ 2 Equation 1.11 2n1 (sin 21 ) where:
dp = penetration depth
λ1 = λ/n1 is the wavelength in the IRE θ = is the angle of incidence with respect to the surface normal
η1 = refractive index of the IRE
η2 = refractive index of the sample
2 η21 = the ratio of the refractive indices of the sample and the IRE 1
51 An IR spectrum using an ATR accessory is not identical to the spectrum obtained using transmission.218 The ATR technique introduces relative changes in band intensity and absolute shifts in frequency. The relative intensity change is well-known and easily corrected using a simple algorithm in the (OMNIC) software (Equation 1.4)219:
Ycorr = Y / dp Equation 1.12 where;
Ycorr = Corrected intensity of a data point (a.u.) Y = Original intensity of a data point (a.u.)
dp = Depth of Penetration at wavelength λ
An advantage of ATR is that the penetration depth is dependent on these variables mentioned earlier; therefore depth profiling studies are possible.53 202 The depth of penetration remains relatively small, in the range of 0.05-0.12 (for most samples).220 -1 In this investigation, measuring keratin spectra between 1800-750 cm with η1 diamond -1 221 = 2.419 (at λ = 1000 cm ) and η2 human hair = 1.555 the penetration depth is approximately between 1.30 – 3.06 µm. It must also be taken into consideration that the pressure tower of the ATR accessory compresses the sample26, increasing the diameter of the fibre allowing the IR radiation to penetrate deeper into the fibre.
Hence, ATR is a powerful method as it is insensitive to sample thickness, permitting the surface or near-surface analysis of thick or highly absorbing materials, i.e. α-Keratin fibres51-53 159 195 196
52 1.5.4.1 Previous Investigations of Human Hair Fibres Utilising FTIR-ATR Spectroscopy with the aid of Chemometrics and SEM
The research conducted by Barton23 with the application of ATR spectroscopy proved to be successful, with reference to the proposed objectives. As a synopsis of a section of the results obtained from that study, it was concluded from the spectral evidence that FTIR-ATR Spectroscopy had a number of advantages over the earlier IR sampling method, these included:
(1) The spectra that were produced were of better quality. FTIR-ATR avoids excessive absorbance of IR radiation, which therefore also minimises the “peak saturation” or “band saturation” (i.e. avoids the saturation of the Amide I and Amide II bands).
A comparison of the -keratin spectra quality from the two techniques is shown in Figure 1.16. The saturation of the Amide I and Amide II bands at 1650 cm-1 and 1530 cm-1 respectively, in spectra sampled by Micro-spectroscopy are well illustrated. On the other hand, spectra sampled by ATR display Lorentzian/Gaussian line shape with relatively sharp peaks. However, it must be taken into consideration that the ATR technique samples only the cuticle and peripheral region of the cortex. More importantly, there is no loss of chemical structural information as generally the spectral profiles of the FT-IR Micro-spectroscopy and the FTIR-ATR methods are similar.
53 Transmittance
Amide I ATR Amide II
C-H Deformations Amide III
Cystine Dioxide
Cysteic Acid Absorbance(a.u.)
Cystine Monoxide
1600 1400 1200 1000 800 Wavenumber (cm-1)
Figure 1.16 – A spectral comparison of -keratin spectra using FTIR Micro- spectroscopy (blue line) and FTIR-ATR Spectroscopy (pink line).
54 (2) Essentially, the technique is economical on time. There is less instrumentation set-up and sampling preparation is simple as opposed to Micro-spectroscopy where the microscope has to be continually focused, and the fibre has to be rolled several times and positioned on the microscopic slide.
Thus, with FTIR-ATR, more spectra can be generated over a given time period which is important in forensic science as most government crime laboratories (e.g. Queensland Health Scientific Services) have an overwhelming back-log of criminal cases.222
(3) Sampling preparation is easy and considerably less destructive as opposed to the rolling technique utilised by the previous investigations.
The rolling technique required a couple of centimetres of the fibre to be rolled, which consequently stretched and split the fibre. The stretching of the fibre affects the secondary structure of the protein from the -helix to the -pleated sheet/random coil arrangement. With ATR, only a small point of the fibre is compressed by the pressure tower.
1.5.5 Alternative FT-IR Sampling Techniques for Analysing α-Keratin Fibres
1.5.5.1 FT-IR Photoacoustic Spectroscopy (PAS) of Human Hair Fibres Studies of keratin have involved FT-IR Photoacoustic Spectroscopy (PAS). This particular technique involves generating signals as a result of the absorption of radiation by the sample, producing a periodic temperature oscillation within the optical absorption depth.181
This technique allows scientists to discriminate between the surface and the underlying layers of solid materials, as only the photoacoustic signals generated within the thermal diffusion length are detected. The sampling depth or rather the thermal diffusion depth
(µs), is dependent upon both the optical velocity (ν) of the interferometer and the
55 wavenumber (cm-1) of the infrared radiation according to the Rosencwaig-Gtersho theory.223
In 1994, Jurdana et al. performed depth profiling studies to distinguish the between the cuticle and cortex layers of wool (Lincoln, Drysdale and Merino) and Caucasian hair fibres.181 FT-IR/PAS spectra were obtained at both low and high optical mirror velocities between 0.0256 to 2.56 cm s-1. These spectra exhibited significant differences in the fingerprint region (1000-2000 cm-1). At low optical velocities, all types of fibre displayed a greater degree of overlap of the Amide I and II bands as opposed to spectra obtained at high optical velocities. The authors suggested that the behaviour for these differences were due to signal saturation, peak broadening and the chemical composition between the cuticle and cortex.181
1.5.5.2 FT-Raman Spectroscopy of Human Hair Fibres FT-Raman Spectroscopy has been used to study the chemical structure of human hair.45 Raman is a complementary technique to infrared; they are not identical as they are governed by different selection rules. Whilst infrared relies upon a change in the dipole moment of the molecule during the vibration, Raman on the other hand is dependent upon a change in polarisability during the vibration which relates to the ease with which the electron cloud can be distorted by the electric field of light.224 Hence, the FT- Raman spectra for human hair exhibits some similar, however mainly different vibrational information. This includes the Amide I (1655 cm-1), υ(C=C) stretch (1585 -1 -1 -1 cm ), δ(CH2) deformations (1450 cm and 1315 cm ) and υ(C-C) skeletal stretches (1129 cm-1, 1084 cm-1, 1060 cm-1, 1041 cm-1 and 1003 cm-1) and υ(C-S) stretches (745- 700 cm-1 trans, 670-630 cm-1 gauche). Williams et al.45 performed an investigation concerning different human keratin biopolymers such as skin stratum corneum, callus, hair and nail. The results illustrated that the FT-Raman spectra from human hair was pigment dependent; blonde hair proving easier to analyse than dark hair due to fluorescence.45 Fluorescence can be avoided with 1064 or 780 nm lasers with the consequence of reduced sensitivity but can be improved using excitation wavelengths of 633 or 514 nm.24
56 Akhtar et al.183 carried out an investigation concerning the changes during bleaching which showed the decrease of the cystine (S-S) disulphide links at 540 cm-1, 525 cm-1 and 510 cm-1 which correspond to the trans-gauche conformation.
In summary, the alternative techniques suffer from a lack of important vibrational information. Therefore, in consideration of these limitations, the spectra derived from FTIR-ATR spectroscopy were sufficient to investigate single human hair fibres.
However, although the quality of the spectra has been appreciably improved through the utilisation of FTIR-ATR Spectroscopy, the vibrational spectrum of human hair keratin itself, particularly within the wavenumber range of 1750-800 cm-1 is extremely complex. The spectral complexity is governed by the fact that there are a number of vibrational bands, especially in the Amide I (1690-1600 cm-1), Amide II (1575-1480 cm-1) and cysteine oxidation (1200-1040 cm-1) region that are overlapped and provide no further qualitative information.
Thus, as a consequence of the intricacy within this spectral region, much structural information about the keratin protein remains hidden and non-participant in the IR spectrum. By delving more profoundly into the unprocessed spectrum allows one to justify their reasoning for identifying similarities or discrepancies between adjacent spectra.
This complication can be solved through the use of a mathematical manipulation method, by means of performing second derivative analysis on the IR spectra, which is a process that has not been used by previous investigations, this rendering it a novel approach.
1.5.6 Derivative Spectroscopy
The utilisation of differentiation to enhance the fine structure of empirical data was first proposed by Lord Rutherford in the early 1920s.225 A electromechanical technique was successful in obtaining the first derivative curve for the deduction of ionisation potentials in mass spectrometry. However, with the achievement of this early
57 inspiration, the employment of the derivative methodology in spectroscopy did not commence until the 1950s. Around that period, derivative spectroscopy had been utilised in the field of UV-Visible Spectroscopy for resolving overlapping peaks and was equally applicable to IR Spectroscopy.51
The application of derivative measurements has found practical use in many areas where the interpretation of the conventional spectra is complex, attributed to a high background signal or the superimposition of two spectral bands thus causing interference.226 The advantages that the derivative mode carries is that it facilitates the enhancement of the resolution between two overlapping bands; which assists quantitative assay of mixtures; the suppression background (matrix interference) effects to correct for systematic error; and improvement of fine spectral characteristics for qualitative analysis.226
The manner in which derivative spectroscopy operates is that the rate of change of a signal is recorded as a function of the wavelength or frequency.226 For a given absorbance curve, the first derivative (dA/dλ) is the gradient of the original spectrum at each wavelength. Further differentiation generates the second and higher derivatives:
d 2 A d n A . . . d2 dn
The general form of IR and Raman spectra have been shown to be characterised by the Lorentzian function as given by227:
1 Z 2 A = A 1 Equation 1.13 o 2 3 where: A = absorbance at wavelength λ
Ao = absorbance at λmax
Z = displacement (λ-λmax) σ = standard deviation
58 The derivative profiles of Lorentzian curves are sharper than those of Gaussian curves with the same amplitude and with the same full width at half maximum absorbance. By computing the differentials of simple Gaussian and Lorentzian peaks, it can be seen that the odd number derivatives exhibit a shift in the wavenumber of the peak whilst the even numbered derivatives display the main peak at the original wavelength of maximum absorbance.51
Successive differentiations of the signal obtained resolve any Gaussian or Lorentzian component peaks masked by overlapping. However, as the derivative order increases, the spectra become more complicated due to the presence of satellite peaks, thus second derivative spectra are the most optimum.
1.5.6.1 Properties of Derivative Profiles
1. Resolution Enhancement
Differentiation of even order derivatives of both Gaussian and Lorentzian functions results in a large reduction in bandwidth; the Lorentzian curves especially.226 In an investigation carried out by Fell228, it had been established that in regards to Lorentzian curves, the full width at half maximum absorbance (FWHM) falls to less than 1/3 of its zero-order value in second derivative mode.
2. Amplitude
With the utilisation of even-order derivatives, the amplitudes of the centroid peaks of Lorentzian and Gaussian curves differ with increases of the derivative order, n, with the Lorentzian curve being greater by an amount factorial n/2.226
3. Modes of Measurement
In derivative mode a number of methods of quantitative measurement exist where the suitability of the technique depends on the profile obtained. The preference of any particular measure of derivative amplitude for an analysis is governed by factors such as
59 the (a) presence and spectral characteristics of interference signals, (b) the useful linear range of the derivative signal, and (c) the relative amplitudes of the various derivative signals.226
The selection of an appropriate derivative order and measure is based upon deliberation of „interaction‟ graphs.226 Hypothetically, in the analysis of a bi-component system, derivative amplitudes are plotted against the concentration of the interfering component.226 The ideal derivative measure is the one that yields an amplitude which does not vary with the concentration of the interfering component.226
4. Satellite Interference
It has been established that as the derivative order increases, the number and amplitude of the associated satellite peaks increases.226 Another feature of the satellite pattern is that the displacement of the satellite peaks from the centroid peak is greater for Gaussian curves than for Lorentzian of equal derivative order. Outlying satellite peaks of Lorentzian bands are undetectable beyond approximately ±1.5σ (standard deviation) whilst those of Gaussian bands are still just discernible at ±3.5σ.228 Hence, it can be seen that in the higher order derivatives, peak resolution is enhanced, with the concurrent significant increase in satellite peak interference, especially with Gaussian curves.
5. Noise
It is apparent that the derivative modes provide a more characteristic profile of a substance than does the corresponding zero-order spectrum.226 However, the presence of noise reduces significantly the practical usefulness of the method. In electrical instruments such as FT-IR spectrometers, three common types of noise exist, random white noise; 1/frequency; and line noise.150 Random white noise, also known as Gaussian noise, arises from the random motion of electrons in a circuit. Drift noise or 1/f noise, is greatest at zero frequency and decreases in proportion to 1/frequency. Low-frequency noise, e.g. due to continuum background absorption or light scattering, is rejected in the higher order derivatives, whilst high-frequency random noise results in poor signal-to-noise ratios (SNR) compared to zero-order spectra.229 High-frequency
60 noise is a concern because even if it has a small amplitude compared to the true signal, it constitutes a sharp spectral feature.226 Drift arises from causes such as slow changes in instrument components with temperature and age and variation of power-line voltage to an instrument.150
Line noise, also characterised as interference or whistle noise occurs at discrete frequencies such as the 60 Hz transmission-line frequency or the 0.2 Hz vibrational frequency.
In zero order spectra, the presence of noise is not noticeable; however it grows to be more evident in the second order derivative profile. Proceeding then onto the fourth derivative, the signal arising from the noise is of such a magnitude that it inhibits any practical information to be interpreted from a spectrum.
A study carried out by O‟Haver et al.229 focused on the effects that random noise impacts on the derivatives of Gaussian bands where the authors discovered that on average the signal-to-noise decreases by a factor of approximately two with each successive differentiation. However as a consequence, a balance has to be established between the benefits of better resolution enhancement and reduction in systematic errors resulting from the higher derivative orders and the higher signal-to-noise ratio of the lower orders. Fortunately, Lorentzian peaks that are encountered in the infrared region provide greater derivative amplitude and bandwidth, therefore the signal-to-noise ratios are higher for the higher order derivatives.226
The effects of noise can be suppressed by the employment of various types of function for smoothing spectra in digitised form. However, one must take into consideration with smoothing functions that although the signal-to-noise ratio increases, there is a simultaneous reduction in resolution.
Several methods exist for smoothing and derivative calculation, the functions based mostly on the sliding average method.226 The Savitzky-Golay method is one of the most common techniques that has been utilised in this investigation for the analysis of second derivative spectra of α-Keratin proteins.
61 1.5.6.2 Generating Derivative Spectra: The Savitzky-Golay Method
The most common technique of calculating the second derivative is based on the Savitzky-Golay method.230 This method is based on a convolution function procedure, the nature of which is adjusted to yield the required degree of smoothing and order of differentiation. The process calculates the first nine derivatives, where the algorithm produces the least squares fit of the data to the selected polynomial.230
The simplest form of convolution to smooth fluctuating data is by using a sliding average.226 This process takes a fixed number of points, adds their ordinates together, and divides by the number of points to obtain the average ordinate at the centre abscissa of the group.230 Subsequently, the point at one end of the group is dropped, the next point at the opposite end added, and the process repeated.230
* Mathematically, the smoothed value of the central datum, Y i , is taken to be the simple average of a group 2n + 1 points distributed evenly around that central point given by226:
Y = (Yi-n + … + Yi-1 + Yi + Yi+1 + … + Yi+n) / (2n + 1) Equation 1.14
If a weighted average is substituted for the simple average, then each Yj (j = i-n to i+n) is multiplied by an analogous weighting factor Cj and the addition of CjYj is divided by a normalising N, given by:
n C jYi j Y = jn Equation 1.15 N
In the Savitzky-Golay algorithm, the weighting factors, Cj, are the integral coefficients of a polynomial (i.e. convolution function) of second to sixth order. The first and higher derivatives are produced by applying the coefficients of the differentiated polynomial. The number of convolution points can range from five to 10,000, although values greater than the number of points across a peak is not used. Only odd numbers
62 are used for the number of convolution points and even numbers are rounded up. The greater the number of convolution points results in greater smoothing of the peak line shape.
A complete set of tables for derivatives up to the fifth order for polynomials up to the fifth degree, using averages taken over five to 25 points are presented in the Appendices of the original paper by Savitzky-Golay (note: corrections to various arithmetic errors are presented by Steinier et al. 231) .
63 1.6 Aims and Objectives
Global Aim: To further the ongoing investigation concerning the identification and discrimination of single, naturally occurring fibres namely human scalp hair with the utilisation of FTIR-ATR Spectroscopy associated with Chemometrics and Multi- criteria Decision Making techniques for data interpretation.
1. To collect human scalp hair fibres from males and females of Caucasian, Asian and African-type backgrounds of a wide variety of ages. The collected hair fibre samples also varied between untreated and chemically treated hair fibres that have been subjected to different levels of cosmetic treatments (i.e. from mild to harsh).
2. To persevere in the investigation concerning the expansion and diversification of the provisional, unverified Forensic Protocol for analysing single human hair fibres using FTIR-ATR Spectroscopy and Chemometrics developed in previous studies. To achieve this a number of novel approaches were utilised:
a) Derivative spectroscopy i.e. second derivative spectra to unravel the complexity of the keratin spectra.
b) Spectral subtraction to determine the key spectral differences between various types of fibre i.e. gender, and illustrate the underlying principles for the separations and to assist the information gained from (a).
c) On the basis of (a) and (b) a novel investigation of potential classification of hair spectra with the aid of various chemometrics methods such as Fuzzy Clustering (FC), and PROMETHEE and GAIA over alternate wavenumber ranges selected on the basis of the detailed studies in (a) and (b).
64 3. To utilise the improved protocol to investigate a number of unremitting issues that warranted further investigation that had not been considered in previous studies:
a) To establish how African-type hair fibres fit the proposed method on the basis of chemical treatment, gender and race.
b) To study various chemically treated hair fibres from minimal or mild chemical treatment (i.e. cosmetic surface treatments such as gel and hairspray, straightening with an iron, etc.) to harsh oxidative chemical treatment (i.e. Bleaching and permanent dyeing).
c) To justify the basis of separation between male and female hair fibres with supporting evidence of difference and second derivative spectra.
d) To assimilate the major IR spectral differences between spectra of different racial origin, which are of the same hair treatment class/type and same gender.
65 2.0 EXPERIMENTAL: MATERIALS AND METHODS
2.1 Collection of Fibre Samples
Human scalp hair fibres were donated by 66 people. The hair fibres (i.e. a minimum of 10 hairs from each individual) were taken at random locations from the scalp in the telogen phase (i.e. as waste) and anagen/catagen phase (i.e. cut at the root) of the hair growth cycles. Forty-six were current residents of Brisbane, Queensland, Australia, and the remaining 20 were from Sugarland, Texas, United States of America. The fibres were placed in plastic sealable sample bags and permanently stored in an air-controlled environment (RH 65 % ± 2 %; 22oC ± 2oC %) to minimise water adsorption/absorption. Each person was requested to complete a survey form (Appendix I, p.290), giving general particulars and more importantly specific information about the nature of their hair (i.e. cosmetic treatments in the form of bleaching and dyeing, the use of hair products, level of sun exposure, whether or not they swam and how frequently, etc.) that would help aid the IR and Chemometric interpretation process. The samples were diverse, ranging from individuals of different (1) race (i.e. Caucasian, Asian and African-type), (2) gender (x Male and y Female), and (3) age (youngest 6 – 85 oldest) and (4) types of chemical treatment/s.
2.2 SEM Analysis
Randomly selected untreated hair fibres were cut into approximately 1 cm samples, positioned on carbon black sticky tape, then transferred to a metal grooved slug type SEM mount (ProSciTech). The stubs were then coated in an SC500 Gold Sputter Coater (BIO RAD Microscience Division) to prevent the sample from charging. SEM images were obtained using an FEI QUANTA 200 Scanning Electron Microscope (FEI Company, U.S.A.) at an accelerating electron voltage of 15.0 kV – 20.0 kV.
66 2.3 Cleaning Methodology
2.3.1 Revised IAEA Method for Cleaning Hair Fibres The procedure was originally used by Cargnello et al.232 for the cleaning of contemporary and well preserved historical hair samples in preparation for elemental analysis.
The revised method233 234 involves sonicating the hair fibres in each solution for shorter intervals to 10 minutes each to minimise the damage to the cuticle surface. Hair fibres are transferred to a small glass vial and filled with high purity acetone (AR grade, Assay 99.5 % (min), Banksia Scientific Co Pty Ltd). The vial was transferred to a MEGASON Ultrasonic Disintegrator (Figure 2.1) set to 20 kHz sonic intensity and the fibre was sonicated for 10 minutes. The acetone was decanted, and the fibre was rinsed with HPLC-grade water (18 M resistivity). This was subsequently decanted, filled again with HPLC-grade water and sonicated for 10 minutes. Finally, the fibre was rinsed and sonicated in de-ionised water for 10 minutes in a glass vial.
Sonicator
Sonic Intensity Control
Figure 2.1 - A photograph of the MEGANSON Ultrasonic Disintegrator that was used to sonicate the fibres for this study.
67 Once the fibres had been cleaned, they were transferred to an open petri-dish and then placed in a plastic desiccator (filled with silica desiccant), under vacuum and dried for two days. After this period, the fibres were transferred to small sample vials and capped. The fibres were analysed as soon as possible thereafter.
2.4 FTIR-ATR Spectroscopy
Hair fibre spectra were recorded on a NEXUS 870 FT-IR E.S.P Spectrometer fitted with a SMART ENDURANCETM Thermo Nicolet Diamond-ATR Smart Accessory (Figure 2.2).
Pressure Tower
Diamond Crystal
Figure 2.2 - A photograph of the NEXUS 870 FT-IR E.S.P Spectrometer fitted with a Diamond-ATR Smart Accessory. The arrows indicate the positions of the pressure tower and the diamond crystal.
68 The parameters of the FTIR-ATR analysis were as follows (Table 2.1):
Table 2.1 Specifications and Operating Parameters for the FTIR –ATR Analysis
Number of Co-added Scans 256 Scans
Resolution (cm-1) 8.0 cm-1
Detector DTGS
Aperture 100 m
Mirror Velocity (cm/s) 0.6329 cm/s
Gain 8.00
Beamsplitter KBr
Internal Reflection Element (IRE) Diamond
A background spectrum was recorded before collection of a spectrum from a fibre. For the spectral sampling process, the fibre was laid across the face of the diamond crystal and using the pressure tower, the fibre was compressed to ensure good contact between the fibre and the crystal. Once a spectrum had been recorded, it was collected and saved on the OMNIC E.S.P 5.2a Spectral Software Program (as .SPC files). Each spectrum was ATR corrected using the correction function which is built into the program to compensate for wavelength dependence (Section 1.5.4).
2.5 Spectral Processing
The OMNIC spectral (.SPC) files were imported into the spectral software package GRAMS/32AT (6.00, Galactic Industries Corporation, Salem, NH, U.S.A.) as GRAMS SPECTRAL (.SPA) files for spectral data processing. Firstly the spectra were baseline corrected and offset to zero. Secondly the spectra were truncated (cut or condensed) in the 1759-785 cm-1 range which contained the major characteristic -keratin absorption
69 bands. Using the Macro option in GRAMS, the spectral information was sampled and truncated to one data point every four wavenumbers (254 data points in total) and transferred to a Microsoft®Excel 2007 spreadsheet and saved (as an .XLS file).
In general, to facilitate spectral comparison, the spectra were normalised to the δ(CH2) deformation bend (ca. 1450 cm-1) as an internal standard. The justification behind this is that this particular molecular fragment is associated with the amino acid side chains and thus not affected by the peptide backbone conformation changes as a result of cosmetic chemical treatment from e.g. peroxides or thioglycolic acid or natural weathering processes.184 235 236
This raw data matrix was then pre-processed by the application of double mean centring and standardisation in preparation for Chemometrics and PCA.
2.5.1 Derivative Spectroscopy
For the derivative analysis FT-IR spectra, the raw spectra were imported into GRAMS/32AT (6.00, Galactic Industries Corporation, Salem, NH, U.S.A.) baseline corrected and truncated (Section 2.5). The final step involved converting the raw spectra into second derivative spectra using the Savitzky-Golay method. The second derivative was calculated using a 2o polynomial and a 5-point smoothing function. The spectra were then reduced to one data point in every four wavenumbers giving 254 data points in total. These spectra were transferred to a Microsoft®Excel 2007 spreadsheet and saved as an .XLS file.
2.6 Pre-processing of the Raw Data Matrix and Chemometric Analysis
Data pre-processing is defined as “the use of any mathematical manipulation of the data prior to the primary analysis”.237 It is utilised to eliminate or reduce irrelevant sources of variation (either random or systematic errors) for which the primary modelling tool may not account.
70 2.6.1 Variance Scaling
Scaling of data is used because the treatment concerns both the measurement unit of the values and the origin of the scale.238 In addition, scaling can be applied to variables or objects or both. Scaling has to be considered to include:
(1) Shift of the origin of the Cartesian system, (2) Expansion or contraction of the axes.
2.6.1.1 Double Centring
Double mean centring of a variable is accomplished by subtracting the mean of each row x, from each element in the row, this is known as x-mean centring. Also, the mean of each column, y, is subtracted from every element in the column; this is classified as y-mean centring. This procedure reduces the effect of the variance component reflected by PC1 of the un-pretreated data set and removes common spectral features.170 172 The process is described by Equation 2.1 and Equation 2.2238:
yim = xim – x.m Equation 2.1 followed by;
zim = yim - yi Equation 2.2 where;
yim = column centred datum
xim = datum in row I and column m before centring
x.m = mean of column m = xim / I i
zim = double centred datum
71 2.6.1.2 Standardisation
Weighting is performed on the variables to reduce or enhance the variables that influence the data analysis.237 When the variance of the variables used in the analysis, differs greatly in absolute size, systematic variation is often masked by the much larger absolute variance of the major variables. Several methods have been proposed for selecting the weight factors.237 Sirius includes six different options for weighting of the subset. One is to equalise the variance of each variable.237
Standardisation is achieved by dividing each element in a given column by the standard deviation of that particular column for that variable. Thus, every variable has variance equal to one after this weighting. The primary purpose of this method is to remove the weighting that is artificially imposed by the scale of the variables.237 This technique is useful because many data analysis tools place more influence on variables with larger ranges. The process is described by Equation 2.3 and Equation 2.4238:
yim = xim/sm Equation 2.3 where;
1/ 2 2 (xim x.m ) i sm = Equation 2.4 I 1
= the estimate of the standard deviation of the variable, xm, about its
mean.
Albano et al. and Derde et al. state that “standardisation of each subset separately gives a much better resolution in latent variable modelling of subsets”.239 240
2.6.1.3 Autoscaling
Autoscaling is the combination of column centring and standardisation i.e. the use of the t- transform (studentised variables). The process is described by Equation 2.5238:
zim = (yim - yi) / sm Equation 2.5
72 2.6.2 Chemometric Analysis
The double centred matrices were imported into the commercially available software package for multivariate analysis and experimental design, SIRIUS version 7.0 (© Copyright, Pattern Recognition Systems AS, Bergen, Norway, 1987-1998). These matrices were then processed to produce the resultant PCA scores-scores plots, loadings plots and fuzzy clustering tables.
2.6.3 Multi-criteria Decision Making (MCDM)
The multivariate ranking analysis methods, PROMETHEE and GAIA, rank order the objects according to the modelling of each variable of the matrix and explore the relationships between objects and variables respectively. The matrix data was imported into the commercially available Decision Lab software (Decision Lab 2000, Executive Edition, Visual Decision Inc. © 1999-2003) package for processing.
2.7 Chemometrics
In most fields of chemistry and biology in the 1950s, the processes requiring investigation had become increasingly complex because acquisition of data was a severely limiting step.241 242 What had resulted was an abundance of measured data that required reduction, display and extraction of the relevant information.243
In parallel, the development of computer science and technology allowed chemists to apply computers combined with advanced statistical and mathematical methods for data treatment and data interpretation. This eventually led to the formation of a new chemical discipline, called Chemometrics.243
The term „chemometrics‟ was first coined in 1972 by the Swedish physical organic chemist Svante Wold of the University of Umea in a grant proposal.244 Kowalski broadly defined chemometrics as “the application of mathematical and statistical methods to chemistry”.29 245 Frank et al. expanded on this definition to state
73 “chemometric tools are vehicles that aid chemists to move more efficiently on the path from measurements to information to knowledge”.246
The more recent definition28 243 describes chemometrics as “the chemical discipline that uses mathematical, statistical, and other methods employing formal logic;
(a) to design or select optimal measurement procedures and experiments (b) to provide maximum relevant chemical information by analysing chemical data and (c) to obtain knowledge about chemical systems”.
Chemometrics is utilised in numerous disciplines such as statistics, mathematics, computing, engineering, nutritional science, biology and particularly across all fields of chemistry.247 In chemistry, the major focus or drive of chemometrics has been towards solving numerous problems in analytical chemistry fields.241 247 This includes areas such as industrial chemistry and quality assurance, environmental science, and more importantly forensic science.247
2.7.1 Chemometrics and Forensic Science
Forensic science is a discipline that formulates conclusions on a purely objective basis. For example conclusions expressed or presented before a judge and jury pertaining to the analytical data/results should not show bias or favouritism to the parties involved in a criminal investigation. Thanasoulias et al. stressed that it is mandatory for forensic scientists to follow strict, rigid statistical protocols in reaching decisions regarding analytical data.160 The amalgamation of chemometrics with forensic science is therefore an important one, as it allows forensic chemists to access complex methods of analysis capable of generating multidimensional data.161 With chemometric tools available, efficient extraction of the information is possible, and this allows the forensic conclusions to be made on information, which is in agreement with forensic protocol.160 161 The advantage of coupling or uniting these disciplines lies in the fundamental objectives of forensic science (i.e. qualitative analysis such as identification,
74 matching/comparison (PCA and Loadings plots), discrimination and classification, SIMCA and FC)) being based on chemometric methods/techniques.
The comparison or association of crime scene evidence with known samples from the suspect can be achieved with pattern recognition methods such as PCA. Furthermore, once the groups have been identified, the evidence can be strengthened with classification methods such as SIMCA, and FC and then rank ordered using MCDM.
2.7.2 Principal Component Analysis (PCA)
The human eye is very good at perceiving similarities and differences between objects of different shapes.248 In chemometrics, the identification of the relationships among chemically characterised objects is important.242 Effective discrimination and identification of the objects can be achieved with the aid of exploratory PCA, which is a well-known pattern recognition method for multivariate data analysis problems.170 PCA is a data reduction technique whereby the information is arranged into a data matrix with the selected variables defining the columns and rows (i.e. objects) designating the sample measurements (e.g. spectra, chromatograms, voltammograms).244
The information is compressed by transforming the data into Principal Components (PCs), which are orthogonal to one another, with the use of linear combinations of the original variables (Equation 2.6).
PCjk = ajlxkl + aj2xk2 + …ajnxkn Equation 2.6 where;
PCjk = value for principal component j for object k (the score value for object j on component k)
aj1 = value of variable 1 on object k
xk1 = measurement for variable 1 on component j n = total number of the original variables
75 PCs are computed in such a way that PC1 accounts for the largest amount of data variance, PC2 describes the next largest amount, and the following factors explain less and less data variance which gradually fade into noise. Thus, much of the data is accounted for in the first few PCs. Information loss is virtually ruled out by this method of data reduction.180 249
As each object (sample) has a value (score) on each PC, PCA plots (or scores plots) provide a convenient means of displaying the data diagrammatically. This allows for subsequent investigations of relationships (clustering) and discrimination (separation) between the objects. Further information or evidence can be obtained from PCA plots by highlighting which variables have significant weighting on a PC (positive or negative), and also, indicating which objects are strongly related to those variables. This information is possible through the analysis of loadings (weights) plots for each
PC, where the values of the „ajn‟ coefficients in equation 2.6 are plotted against variables such as wavenumbers, time and voltage. High positive or negative values reflect the importance of those variables for that PC, whilst low loadings indicate that those variables are insignificant to that PC.
2.7.3 Classification
Classification of samples is one of the principal goals of pattern recognition.244 For the analyst, the objects to be classified can be samples for which chemical analysis of their constituents are obtained or the spectral data measured for a compound. Methods for classification can be divided into supervised (Soft Independent Modelling of Class Analogy SIMCA) approaches and unsupervised (Fuzzy Clustering FC).244
2.7.3.1 Soft Independent Modelling of Class Analogy (SIMCA) For the supervised method, a test (training) set of objects is required where the samples origins are known which quantitatively establish the basis on which those objects were classified, allowing objects of unknown class to be sorted.242 244 The most commonly used method of modelling is the SIMCA (soft independent modelling of class analogies) approach.245 250 In SIMCA, PCA is used to develop a model of each group or class within the training set. The members of such a set are selected by the user. The
76 number of statistically significant PCs that describe each class are determined by cross- validation.244 251 The data for each object in a class are partitioned into information that is explained by the class model and into residuals which describe the non-systematic variance.170
A model can be expressed by the following equation252 253:
p Xki = Xi + + ajiujk + eki Equation 2.7 ji where;
p = is the number of the principal components in the class model
eki = is the residual value of object k on variable i
Residual standard deviations (RSD) are computed for a class as a whole and for each object. The former measures the mean distance between the objects of a class and the class model; the latter measures the orthogonal distance between the object and the class model.170 This RSD indicates how well the object is explained by the class and is calculated using the following equation253:
1/ 2 ixc RSD[c] = 2 Equation 2.8 (ex[c]) / Nc P1) i1 where;
ex[c] = error of object x fitted to class model C
Nc = number of objects of class C P = number of principal components
RSD[c] = Residual Standard Deviation of class C
77 Assuming the residuals to be normally distributed, a critical F ratio for a selected level of significance can be computed which in turn will yield a critical distance (RSDcrit) that defines the class boundries.170 The distances between different sets of classes can also be established by selecting one class as the model set. The model set is chosen on the basis that the class contains a substantial number of objects.22 This is essential because SIMCA is a parametric method and is influenced by the number of samples in a class. Small sample sizes do not reflect the results of the true population253, and thus subsequently the significance of the results is questionable.
Once class models are established, further information can be obtained. The modelling power of each variable for each class gives the analyst an indication as to how significant the variable is for a given class model based on the distance values. Values of less than one indicate a very small degree of difference, while values greater than three signify that the two classes are quite different.253
Whereas PCA generally may display information in 2 or 3 dimensional space, SIMCA class models may include any number of statistically significant PCs. A completely different method to data classification is the unsupervised approach, an example of which is the Fuzzy Clustering method.254
2.7.3.2 Fuzzy Clustering (FC) Fuzzy clustering (FC) is a non-hierarchical cluster method; i.e. clusters are not formed either by merging small groupings into larger ones or, conversely, by subdividing large clusters.255 The FC method is a non-parametric method and is well described by Adams.256 The aim of FC is to highlight similar objects as well as to provide information regarding the relationship of each object to each cluster.256
In conventional classification a given object is considered to have unique membership of a class; i.e. its membership of any other class is zero. Alternatively, the FC approach attempts to assign a degree of class membership for a given object over a number of classes.241 256
78 Classification is performed with the aid of a membership function which may be specified, for example170:
m(x) = 1 – c|x – a|p Equation 2.9 where;
a = constant c = constant p = positive exponent
The classification could also be constructed on the basis of the data of interest. Thus, a membership value for each class is assigned for each object. In the SIRIUS software, the degree of fuzziness can be varied by a weighting exponent value between 1.0 to 3.0. The sum of the membership values for each object is between 0-1. The benefit of FC is that it facilitates the discrimination between objects that markedly belong to one cluster, i.e. values close to 1 yielding hard (unique) membership; and objects that are members of several clusters, i.e. a membership value of 1/No. of clusters (fuzzy membership).
As the Forensic scientist must be impartial to the analysis of any collected evidence (Section 2.7.1), this investigation has chosen FC so that the classifications of the spectra are un-biased.
2.7.4 Multi-criteria Decision Making Techniques (MCDM)
As human beings, we are faced with making decisions all the time. In 2002, Brans suggested that humans (in the context of the real world) naturally use a decision making approach, which is based on measurement, estimation and modelling. These models are usually approximations of reality.257 The decision making process is based on three elements: rationality, subjectivity and ethics.258 Out of this philosophy developed a non-parametric multi-criteria decision making method (MCDM) which is based on the ranking of objects.
79 MCDM is a multivariate data analysis technique that is principally concerned with the optimisation, selection and decision making of the response to a given procedure.258 The response is the criterion by which the procedure is evaluated, i.e. the optimisation criterion. Problems are solved by modelling the response as a function of the variables that influence that criterion after carrying out an experimental design.258 This technique permits large volumes of data to be processed, allowing the analyst to explore and understand the relationships between different parameters.259
For example, MCDM methods are broadly applied today to a multitude of problems, e.g., the comparison of baseball teams, development of negotiation support systems, selecting landmine detection strategies, etc.258 Also, many applications of MCDM can be found in scientific fields such as the environment, agriculture, civil engineering and medicine.
MCDM methods commonly offer partial pre-ordering as well as net full ordering or ranking of objects. In full ordering, the objects can be ordered either top-down or bottom-up depending on the index value (designated Φ+ or Φ-). Top-down or maximised ranking, the largest index value is preferred whereas bottom-up or minimised ranking the smallest index is preferred.258 Partial pre-ordering is concerned with the situation where objects may perform equally well but on different variables in that they cannot be compared and one object cannot be preferred to others.
Many MCDM methods exist for the handling of multi-variate situations. Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) is one of the better performing methods which is well established and is the technique that has been used in this work.258
2.7.4.1 PROMETHEE I and II Multivariate Techniques PROMETHEE is a non-parametric method applied in Euclidian space to rank objects.258 In PROMETHEE, each variable in the raw data matrix are set to maximise or minimise. It is then converted to a difference, d, matrix achieved by comparing all values pair wise by subtraction in all possible combinations.
80 The user then selects a so-called preference function for each criterion. A preference function P (a, b) defines how much outcome a has to be preferred to outcome b. If the values of the defined preference are between 0 and 1, then P = 0.1 is a weak preference whereas P = 0.9 is a strong preference. The degree of preference is expressed on a percentage scale. In practice, this preference function is a function of the difference, d, between the two evaluations260:
P(a, b) = P(f(a) – f(b)) Equation 2.10
A graph of the function is presented in Figure 2.3. It is a non-decreasing function, equal to zero for negative values of d = f(a) – f(b).
P(d) 1
0 d
Figure 2.3 – A preference function P(d).260
In general, one may consider a function H(d) which is directly related to the preference function, P260:
H(d) = {P (a, b), d ≥ 0 {P (a, b), d ≤ 0 Equation 2.11
81 This function is then represented in Figure 2.4.
H(d) 1
Preference of b over a Preference of a over b d 0
Figure 2.4 – Function H(d).260
The preference indices are then computed for each d value for each object with the use of one of six mathematical functions (Decision Lab 2000, Executive Ed., Visual Decision Inc., © 1999-2003), selected independently for each variable. The analyst can improve the quality and the reliability of the decision-making processes because of the structured procedure and the visual analytical aids. The information requested from the analyst is limited to a number of key parameters that can be precisely fixed, ensuring high quality results.261
Furthermore, the software allows the decision maker to directly use the data of the problem in a simple multi-criteria table.
82 The six types of preferences available are (1) Usual, (2) U-shape, (3) V-shape, (4) Level, (5) Linear and (6) Gaussian.260 The choice of the preference functions is crucial because they define how much one location has to be preferred to other locations.258
(1) Usual Criterion:
For this preference function, there is a difference between a and b if f(a) = f(b); as soon as the two evaluations are different, the decision maker has a strict preference for the action having the greatest evaluation. For this preference function, no parameter has to be defined.260
(2) U-Shape or Quasi-criterion
For this preference function, the two actions are indifferent to the decision maker as long as the difference between their evaluations, i.e. d, does not exceed the indifference q. For the U-shape preference to be utilised, the decision maker-must determine the value of q that is the greatest value of the difference between two evaluations that the decision maker considers indifferent.260
(3) V-Shape Criterion
For this preference function, if d is lower than p, the preference of the decision maker increases linearly with d.260 However, if d becomes greater than p, a strict preference situation is created known as the V-shape function. When the V-shape criterion is chosen, the decision maker has to determine the lowest value of d above which they consider there is strict preference of one of the corresponding actions.260
(4) Level Criterion
For this preference function, an indifference threshold q and a preference threshold p are simultaneously defined. If d lies between q and p, there is a weak preference situation (H(d) = ½). The decision maker has two thresholds to define.260
83 (5) Linear Criterion
In this scenario, the decision maker considers that the preference increases linearly from indifference to strict preference in the area between the two thresholds q and p. Two parameters are to be defined.260
(6) Gaussian Criterion
The Gaussian preference function requires the determination of the standard deviation, σ, which is made according to the experience obtained with the normal distribution in statistics. As this function has no discontinuity it provides stability to the results260 This refers to the influence of the thresholds on the rankings. Brans et al.260 state that “the results given by Gaussian criteria, with very „smooth‟ preference functions are still better”.
The six preference functions available in Decision Lab 2000, including the shape of the graphs and the mathematical justifications for each preference function are summarised in Table 2.2.
84 Table 2.2 List of Preference Functions Preference Function Shape262 Mathematical (Decision Lab 2000, Justification260 Executive Ed., Visual Decision Inc. 2003) Usual (no threshold) H(d) = 0 {d=0 H(d) = 1 {d≠0
U-shape (q threshold)* H(d) = 0 {-q ≤ d ≤ q H(d) = 1 {d < -q or d > q
V-shape (p threshold)† H(d) = d/p {-p ≤ d ≤ p H(d) = 1{d<-p or d > p
Level (q and p thresholds) H(d) = 0} [d] ≤ q H(d) =1/2} q<[d]≤p H(d) = 1} p < [d]
Linear (q and p thresholds) H(d) = 0} [d] ≤ q H(d) = ([d]–q)/(p-q)}q<[d]≤p H(d) = 1} p< [d]
Gaussian (σ threshold)‡ H(d) = 1-exp{-d2/2σ2}
NB: (*) = Indifference threshold, q, which represents the largest deviation that is considered negligible by the decision-maker. (†) = Preference threshold, p, represents the smallest deviation that is considered as decisive by the decision-maker. p cannot be smaller than q. (‡) = Gaussian threshold, σ, is the standard deviation
85 The next step involves calculating a preference index Π (a, b) of experiment (a) over experiment (b) for all criteria in the equation258:
k Π (a, b) = w j * Pj (a,b) Equation 2.12 j1 where;
k w j 1 Equation 2.13 j1 where;
k = is the number of criteria
wj = is the weight for each criterion
The values of Π (a, b) are between 0 and 1 and illustrate the global preference of (a) over (b).
From the individual preference indices the overall indices are computed for each object giving the positive Φ+ and negative Φ- flows. The positive flows are the best performing and expresses how each experiment outranks all the other experiments. The negative flows are the least performing objects and states how each experiment is outranked by all the other experiments. The higher Φ+ and the lower Φ- the better experiment.258
86 The Φ+ and the Φ- outranking flows are calculated as follows:
Ψ+ (a) = (a, x) Equation 2.14 xA and;
Ψ- (a) = (x,a) Equation 2.15 xA
PROMETHEE consists of pair wise comparisons of all the experimental results and leads to a partial ranking pre-order of the objects according to three rules258:
1. a outranks b if:
Ψ+ (a) > Ψ+ (b) and Ψ- (a) < Ψ- (b) Equation 2.16 or Ψ+ (a) > Ψ+ (b) and Ψ- (a) = Ψ- (b) Equation 2.17 or Ψ+ (a) = Ψ+ (b) and Ψ- (a) < Ψ- (b) Equation 2.18
2. a is indifferent to b if:
Ψ+ (a) = Ψ+ (b) and Ψ- (a) < Ψ- (b) Equation 2.19
3. a cannot be compared with b in all other cases where b does not outrank a (using a weighted sum of the two criteria)
If experiment a is every good on one criterion where experiment b is weak and, reciprocally, b is good on the other criterion where a is weak, then the two experiments cannot be compared because they are too different.
87 In PROMETHEE two types of ranking are possible:
1. PROMETHEE I - is partial ranking where objects a and b cannot be compared with one another (i.e. rule 3 included)
However, to establish a complete rank order, the user can calculate the PROMETHEE II, net outranking flow, Φ, by258:
Φ (a) = Φ+ (a) – Φ- (a) Equation 2.20
2. PROMETHEE II ranking eliminates the incomparability rule and therefore appears to be more efficient. However, it is less reliable than the results derived from PROMETHEE I
An outranking flow graph can be drawn for both the partial and complete pre-order to visualise the information, and to support the decision maker. However, when large matrices are used the PROMETHEE I diagrams become very complex and challenging to interpret, and PROMETHEE II net flows are preferred.
2.7.4.2 GAIA Since the assignment of weights to the different criteria is an important option for MCDM methods, a sensitivity analysis is a useful tool.258 The easiest way to achieve this for PROMETHEE is to apply GAIA (Geometrical Analysis for Interactive Aid). GAIA is a visualisation technique that complements the PROMETHEE ranking by providing guidance for the importance of the principal criteria.263 GAIA essentially provides a PC1 versus PC2 bi-plot, the matrix for which is generated by decomposing the net outranking flows Φ (a).258
The GAIA plane offers a visual representation of the data, with some clearly defined symbols.261 Criteria (or grouped categories of criteria) are represented by axes. On the GAIA plot, the longer a projected vector for a criterion, the more variance it explains. A criterion vector highlights the differences and similarities of the objects. If the
88 criteria vectors are oriented in the same direction, they are correlated; the preferences are similar. Independent criteria are characterised by almost orthogonal vectors and conflicting criteria have vectors in opposite directions.258 The objects or samples that are projected in the direction of a particular criterion vector are strongly related. Similar objects are therefore visualised as a cluster and dissimilar objects will be located in other directions.
The weight or decision vector, Π, is composed of the weights, normalised to one, of the different criteria. It is the weighted mean of the vectors of the different criteria.258 The projections on that vector follow the order of complete PROMETHEE net ranking. If the decision vector is short, the criteria are in conflict; where the decision vector is nearly orthogonal to the principal components plane and the decision power of the axis is therefore weak. However, if the vector is long, the most significant criteria are highlighted in that direction and as far from the origin as possible.258 Hence, the decision power of the axis is strong. A 3D-representation of the Π decision axis emphasises the position of the axis.
Although GAIA gives the best possible 2D-representation of the data, usually some information gets lost in the process. To control the quality of the GAIA plane, the Δ value is always displayed in the GAIA planes window, measuring the amount of information preserved in the GAIA plane.262 In practice, Δ values larger than 70 % correspond to reliable GAIA planes; Δ values lower than 60 % should be considered with care.262
This dissertation now advances towards the analysis of the morphological and structural properties of human hair keratin via SEM and FTIR-ATR Spectroscopy.
89 3.0 CUTICLE SURFACE TOPOGRAPHY AND FTIR-ATR SPECTRAL CHARACTERISTICS OF THE MORPHOLOGICAL- CHEMICAL STRUCTURE OF HUMAN HAIR FIBRES
Across the major scientific fields, biological human hair fibres have been studied for a number of key purposes, i.e. medical264, environmental264, cosmetic51 52 265 and more importantly, forensic science.24-27 47 158 266 267 In forensic science, fibre evidence is useful for matching fibres from a crime scene directly with known fibres from the alleged suspect or victim with the use of quality-assured comparative methods.
Structural elucidation techniques exist such as FT-IR spectroscopy, which facilitate the matching of the chemical structure of identified and questioned fibres. In general, FT- IR spectroscopy is a popular technique, chosen for its sensitivity to the conformation and local molecular environment of molecules in biopolymers.268 In the judicial field, it has been suggested by Robertson that “infrared spectroscopy is a powerful technique for the forensic examination of fibres”.3
Many FT-IR spectral sampling techniques are available for the study of structural chemistry of α-keratin hair fibres. However, some exhibit substantially better spectral resolution, and are able to yield more substantial chemical information. From the forensic perspective, the selection of an appropriate IR technique is critical.
The chosen technique should facilitate the extraction of reliable information from the fibre so as to obtain clear outcomes. Over the past few years there has been much debate and discussion as to what the optimum IR sampling technique for hair is. From the forensic perspective it is incumbent upon a forensic scientist to use tests that carry the highest discrimination power and be aware of (and express) the limitations in the technique.
Research at Q.U.T., Brisbane, Australia,22 23 25-27 over the past decade has endeavoured to improve the understanding of such complexities, and thus far the results have suggested a useful approach involves the utilisation of FTIR-ATR spectroscopy in
90 conjunction with chemometric methods for interpretation.23 FTIR-ATR spectroscopy produces spectra which are apparently clear of “peak saturation” or “band saturation” observed in the spectra of competing techniques.51 181 182 This observation has been well supported by Kirkbride, Robertson and Royds, from the Australian Federal Police force.3 187 269
Although the quality of the spectra has been appreciably improved with the amendment of the sampling technique, the vibrational spectrum of human hair keratin itself, particularly within the wavenumber range of 1750-800 cm-1, is very complex. This is a result of the chemistry of the protein-polypeptide structure of hair keratin. Three specific groups within the keratin protein give rise to different vibrational absorption bands that can be observed within this fingerprint section. They are:
(a) The peptide bond (primary protein structure). Formed by a condensation reaction between the carboxylic acid and amine group of adjacent amino acids. It is the most abundant within the keratin protein and yields the Amide I, II and III IR spectral bands.
(b) The polypeptide chain (secondary protein structure). Pertains to the C-C skeletal backbone of all keratin proteins and can exhibit one to three conformationally sensitive patterns, those being the α-helical, β-sheet and random coil or amorphous structures directly related to the Amide bands; and finally,
(c) The amino acid side chains (R groups). The C-H vibrations originating
from the -CH, -CH2 and -CH3 of the aliphatic and aromatic rings of phenylalanine, tyrosine, tryptophan and the significant vibrations of the - oxidative intermediates from the amino acid cystine (i.e. S=O, SO2, SO3 , and - - S-SO3 ).
Special mention should also be made of water, which is an integral part of the keratin supermolecular structure.270 Water affects both the amorphous and crystalline phases of keratin.
91 Keratin‟s high affinity for water is evident over the whole range of relative humidities, particularly within 65% RH to 95% RH. Under conditions of low temperature or short times for which no structural mobility can occur in an α-Keratin fibre, the mechanical properties of the fibre will depend primarily on the whole cohesive bond network.10 Although water vapour permeates the hair readily, there is some binding selectivity within the molecular structure and accessibility restraints in the filament and matrix texture.32
It has also been recognized that the nature of the structural chemistry can affect the % moisture content, which essentially refers to weathered and cosmetically treated hair fibres. Also, it has been well established that chemical disruption of the fibre contributes to increased swelling at moderate-to-high humidities.32
Water is a polar molecule and two types of water are associated with the α-Keratin protein, absorbed or „bound‟ water and adsorbed or „free‟ water. At low humidities, water molecules are principally bonded to hydrophilic side chains (guanidine, amino, carboxyl, phenolic, etc.) and peptide bonds through hydrogen bonds and Coulombic interactions. At higher humidities, water enters as „solution water‟ not attached to specific sites but with absorption resulting from the free energy difference arising from the entropy of mixing keratin with water.11 At very high % RHs (>80%), multi- molecular sorption (water-on-water) occurs, and this refers to the „free‟ water interacting and condensing onto the first „bound‟ layer.10 11
The thermal transitions of keratin have been discussed in many journals devoted to the properties of wool, horn or human hair fibres.270 In 1960, Schwenker et al. were one of the first groups to investigate the thermal properties of various keratin fibres by DTA under a nitrogen atmosphere. It can be concluded that as a hair fibre is heated, it goes through a number of changes/phases before its eventual degradation to charred residue. Between 80-140oC is the endothermic removal/evaporation of loosely and strongly bound water from the hair fibre. The main peak at approximately 110oC represents the loss of adsorbed water, whilst the shoulder peak at roughly 160oC refers to the endothermic loss of strongly bound water from the hydrophilic sites in the fibre.271
92 Therefore, as water plays a fundamental role in the overall mechanical strength of the hair fibre, it is reasonable to suggest that the –OH bands of absorbed and adsorbed water will be present in the IR spectrum of α-Keratin.
As a consequence of the dominance of the strong peptide bond vibrations, significant structural information (approximately 50% of the total absorptions) relating to the keratin protein remains concealed in the IR spectrum that has the potential to be utilised for identification and discrimination of human hair fibres, particularly for forensic purposes.
However, application of Derivative Spectroscopy can facilitate the unravelling and unveiling of the overlapped absorption bands. For this work, derivative analysis on raw spectra is a novel approach to extricating the convolution or complexity of the hair keratin spectrum.
This chapter critically examines and compares the complexity of various human hair FTIR-ATR spectra. The hair has been collected from many individuals of different genders and human races i.e. Caucasian, Asian, and African-type. To support the conclusions of the spectral examinations, a brief morphological analysis of the cuticle surface topography of typical hair fibre types was conducted with the use of SEM.
The proposed forensic protocol (Section 1.5.3.2) for analysing human hair evaluates the fibres in a systematic approach. The spectral comparisons and subsequent band assignments will involve a) contrasting the general raw or non- chemically treated fibres with cosmetically treated hair fibres which have been subject to differing levels of treatment, b) analysing mean difference spectra between gender and each race, and finally, c) an investigation of second derivative FTIR-ATR spectra of typical untreated and treated fibres.
93 3.1 Morphological Characteristics of the Cuticle Surface Topography of Human Hair Fibres Involving SEM
3.1.1 Comparison of Chemically Untreated and Cosmetically Treated Human Hair Fibres
In order to discern and identify the impact that various chemical treatments have on a human hair fibre, one must first understand the character of a fibre in its natural, untreated state.
Initially, in general, the term „non-treated‟ or „untreated‟ hair is strictly defined in this context as hair fibres that have not undergone any form of intentional cosmetic chemical treatment such as bleaching, permanent waving, straightening and permanent dyeing that results in causing oxidative damage to the fibre. The definition of cosmetic chemical treatment does not normally extend itself to the utilisation of shampoos and conditioners, because these products are essential daily requirements that assist in the hygienic maintenance of the hair and scalp, rendering it free of sebaceous oils, dirt and soils and dandruff.
However, past SEM studies have also indicated that the mechanical processes such as brushing, towel drying, weathering by exposure to rain, and dirt as well as the chemical damage from UV radiation all result in physical damage to the surface architecture of human hair fibres.48 65 67 72 272 The damage manifests itself as the jagged-like edges of the cuticle scales, sometimes causing them to lift and become completely removed from the surface, exposing the underlying cortical layers. With the protective external layer removed in some places, the damage renders the fibre more susceptible to further chemical degradation from natural chemical weathering, such as sunlight, salt and chlorinated water.
Hence, as these processes occur in normal every-day life for the majority of individuals in developed countries, the term „untreated hair fibre‟ is still adequate when used in this
94 context. However, the differences between untreated and physically treated fibres will be investigated further through the IR spectral and chemometric analyses.
3.1.1.1 SEM Analysis of Non-Treated Hair Fibres SEM micrographs were obtained from three typical untreated hair samples from both genders. An SEM micrograph of a 53 year old Asian female (Asian female No.17 in Appendix I) is displayed in Figure 3.1. The fibre is approximately 80 µm in diameter with the edges of each cuticle scale roughly 10 µm apart longitudinally.
Cuticle Scale
10 µm
Figure 3.1 – SEM image of an untreated Asian female hair fibre.
The external cuticle layer image shows each of the individual scales at high resolutions. Each cuticle scale is uniquely shaped - some have smooth rounded edges and others with jagged-like edges, overlapping each other as they ascend along the length of the fibre towards the tip. Overall, the fibre is structurally undamaged with very minimal cracking towards the centre of the image. Small (less than 1 m in size) pieces of debris or soil particles, represented by the white specs, adhere randomly to the fibre; nonetheless the fibre appears to be relatively clean. It is reasonable to suggest that some
95 dirt and debris or other foreign particles would be associated with the hair through normal everyday processes.
The SEM micrograph in Figure 3.2 is of a 23 year old Caucasian male (Caucasian male No. 4, Appendix I). The hair fibre is approximately 60 µm in diameter and the cuticle scales are spaced approximately 18 µm apart. There is no evidence of any damage or debris on the surface of the fibre as the cuticle scales are relatively smooth and spaced neatly apart.
Cuticle Scale
18 µm
Figure 3.2 – SEM image of an untreated Caucasian male hair fibre.
The final untreated fibre is of a 22 year old African male (African-type male No. 8, Appendix I; Figure 3.3). The fibre is approximately 70 µm in diameter and the cuticle scales are spaced approximately 8µm apart longitudinally. The surface appears to be covered by many cuticle scales compared to the fibres depicted in Figures 3.1 and 3.2. Some of the cuticle scales in-fact are jagged-like in appearance, however the fibre itself appeared relatively clean due to the lack of debris.
96 Cuticle Scale 8 µm
Figure 3.3 – SEM image of an untreated African hair fibre.
Hence, in summary, these three fibres illustrate typical untreated hair fibres sampled directly from the scalp at any given time.
3.1.1.2 SEM Analysis of Different Cosmetically Treated Hair Fibres SEM images were acquired from a number of hair fibres that had undergone different forms of cosmetic chemical treatment ranging from the gentle external cosmetics such as moisturisers and gels, to the harsh oxidative treatments such as permanent dyeing, bleaching and waving.
The majority of the African hair samples originated from the United States of America, Nigeria and Sudan. It was immediately apparent that a number of chemical treatments had been applied to the hair fibres such as perming, straightening and dyeing as well as the use of surface treatments such as moisturisers. Relatively few of the samples were completely free of chemical treatment according to analysis of the hair histories of these individuals. African-type hair fibres characteristically have more crimp, as compared to the other races. As a result, the hair has a greater tendency to knot (African-type male No. 6, Appendix I, Figure 3.4), making it often difficult to comb and style.
97 Knotted Fibre – African Male
Figure 3.4 – SEM image of the tip end of a treated African male hair fibre that has formed a knot possibly caused by the effects of grooming.
Compatibility tests have been conducted on African-type hair using a tress of hair attached to a strain gauge, which measures the force required to pull the comb through the tress. The results have illustrated that the engagement and motion of the comb lead to a displacement and intensification of individual curl entanglements, as reflected by the immediate and progressive rise in the combing force.32 However, in wet combing, the curly geometry of African hair resists fibre adhesion and clumping (as was also observed with Caucasian hair) with the curls slightly relaxing. This lessens the extent of individual entanglement. The torsion and bending moduli decrease, facilitating the unbending of curls and their twist passage between the teeth of the comb.32
Consequently, persons of African origin generally prefer, or are forced to have their hair straightened, relaxed or permed chemically and physically in order to render it more manageable, and also to maintain general hygiene of the hair as it is prone to the build- up of dirt and oils attributed by the geometry.
98 A cosmetically treated hair fibre SEM micrograph (Figure 3.5) is from an 18 year old African-American male (African-type male No. 6 in Appendix I; ca. 80 µm in diameter). The only form of cosmetic treatment claimed to have been used by this particular individual is the application of a moisturiser known as a “pink lotion”. This type of moisturiser is a popular product amongst African Americans, or persons of African origin because it protects the hair from dryness and brittleness as a result of blow drying, hot curling, or combing. The product is specially formulated to maintain the hairs natural moisture level.219
“Lifting” Cuticle Layers
“Chipped” Cuticle
Figure 3.5 – SEM image of the same treated African male hair fibre (Figure 3.4) which has been subject to a “pink” moisturising lotion. This image illustrates lifting and chipping of the cuticle scales.
In general, the cuticle surface of this fibre is inherently different to the surface topography of the untreated hair fibres. The edges of the cuticle scales are severely jagged in appearance with pieces of the cuticle seemingly “chipped away” in most places. At some locations of the cuticle scale edge, it is difficult to ascertain whether pieces have been torn off, or debris has adhered to the fibre. Furthermore, white areas of the cuticle layer, as indicated on the micrograph, are in fact regions where the cuticle cell has been up-lifted further from the surface, exposing the underlying layer.
99 As the fibre had not been subject to any form of chemical treatment, the micrograph suggests that the damage caused to the surface could be ascribed to physical or mechanical processes. This provides supporting evidence that combing or maintenance of African-type hair is difficult and abrasive.
Figure 3.6 is an SEM image of a hair fibre from a 23 year old Asian female with permanently dyed hair (ca. 77 µm in diameter). In direct contrast to the untreated female Asian hair fibre, the surface topography of the fibre appears to be markedly different. The majority of the cuticle scales of this fibre represent the trademark “jagged” or chipped” appearance, with the cuticle broken off in random locations along the length of the fibre. This is attributed to the affects of oxidative permanent dyeing. Hence, this observation suggests that chemical damage is not uniform along the surface of the fibre; the damage appears to be random.
“Jagged” Cuticle
“Breaking” Cuticle
Figure 3.6 – SEM image of a permanently dyed Asian female hair fibre.
100 Figure 3.7 shows the external cuticle layer from a randomly sampled fibre from a 53 year old Caucasian female with bleached hair which has been treated with a semi- permanent dye (ca. 60 µm in diameter). The fibre appears to be unaffected by the application of the semi-permanent dye. This is to be expected as semi-permanent dyeing involves no chemical reaction with the chemical structure of the fibre, only a diffusion of coloured molecules from solution into the hair cortex.31
“Smoothing”
“Lifting” Cuticle Layers
Figure 3.7 – SEM image of a bleached and semi-permanently dyed Caucasian female hair fibre that receives constant sun exposure.
The scales are characteristically jagged, yet not chipped, and the surface appears to be somewhat smoother in relation to permanent dyeing, suggesting that the cuticle has been removed in certain locations as indicated by the uplifting of the cuticle. The morphological analyses of each fibre provided information pertaining to the surface topography of different hair samples. These observations will be corroborated with the information drawn from the principle technique used in this study, FTIR-ATR spectroscopy.
101 3.2 Structural Elucidation of -Keratin Hair Fibres using FTIR-ATR Spectroscopy
3.2.1 Comparison of Chemically Untreated and Cosmetically Treated Fibres
3.2.1.1 Secondary Structure Conformations and Vibrational Modes of the Peptide Bond In keratin, the peptide linkage (i.e. primary protein structure) is quite rigid due to partial double bond character. This is caused by resonance of electrons between the oxygen and nitrogen atoms yielding a partial C=N bond.273 The modes of vibrations of the peptide bond give rise to the characteristic bands known as the Amide I, II and III bands. Their frequencies are sensitive to peptide conformation and the type of hydrogen bonding. This sensitivity of the peptide bond affects the secondary protein structure defined by the local conformation of its polypeptide backbone.274
These local conformations are specified in terms of regular folding patterns known as helices, pleated sheets or turns, which are established by their X-ray diffraction patterns. 274 275 These illustrate a regular repetition of particular structural units with certain repeat distances.274 Pauling and Corey demonstrated through X-ray analyses that the polypeptide chain can interact with itself in two major ways: through conformation of an α-helix and a β-pleated sheet.274
For the α-helical conformation, the right-handed helix (3.6 amino acid residues per turn and a repeat distance of 1.5 Å) is favoured. The structure is created through:
a) intra-molecular hydrogen bonding between the carbonyl oxygen of one peptide bond and the hydrogen atom of another as well as side chain amino and carboxyl groups
b) hydrogen bonding of water with amide, carboxyl and hydroxyl groups
c) coulombic interactions between the charged side chains of lysine, arginine, histidine and glutamic and aspartic acid, and
102 d) covalent, disulphide links between different chains or between different parts of the same chain.
It has also been suggested that if two or three strands of polypeptides are coiled or spiralled about each other analogous to a twisted rope, the structure is commonly referred to as the “coiled coil” model.
In contrast, the β-sheet pattern has a characteristic conformation pattern in an extended form arranged in sheets. This conformation is observed in feather keratin and stretched mammalian keratin. It relies on inter-chain hydrogen bonding between amide groups of adjacent chains.276 Small and medium sized R groups have enough room to avoid van der Waals repulsions. The structure has a longer repeat distance of 7.0 Å compared to that of the α-helix.274
The keratin peptide chain can also assume what is described as a random coil or amorphous arrangement. The structure is flexible, changing, and statistically random.274
Broad vibrational bands present in the spectra of hair fibres can be attributed to the presence of different types of secondary structure.149 Also, within one type of secondary structure the dihedral angles of the peptide backbone chain vary over a wide range.277
As a consequence of band broadening, the relative contributions of the different conformations are difficult to observe in the raw spectrum, but this will be more appropriately discussed and interpreted with the aid of derivative spectroscopy (Section 3.3.2).
3.2.1.2 FTIR-ATR Spectral Analysis of Untreated Hair Fibres A selection of 12 spectra of typical non-treated hair fibres originating from both male (M) and female (F) donors across the Caucasian (C), and Asian (A) and African-type (N (Negroid)) races are presented in Figure 3.8.
103 -1 1520 & 1511 cm -1 -1 1627 cm 1577 cm
- COO
-1 1445 cm -1 1392 cm -1 1234 cm
-1 1114 cm-1 1071 cm C=O -1 1037 cm NM2
NM1
NF21
NF20
AM6
Absorbance (a.u.) Absorbance AM20
AF18 AF17
CM8
CM3
CF2 CF1 1735 cm-1
1600 1400 1200 1000 800 Wavenumber (cm-1)
Figure 3.8 - A selection of 12 typical untreated FTIR-ATR spectra of human hair fibres from male (M) and female (F) donors of the major races: Caucasian (C), Asian (A) and African-type (N). (Note: The vertical lines designate the vibrational assignment and peak position of each functional group/molecular fragment. The arrows indicate the direction of the vibration).
104 The untreated hair fibre samples were received from individuals who had not performed any form of cosmetic treatment to their hair which also included the utilisation of surface applications such as hair gels, waxes, mousses, moisturisers, and did not spend exceedingly long periods in the sun. This strict sampling was purposefully carried out in order to ensure the integrity of the band assignments of typical untreated fibres.
Each spectrum has been normalised with the use of the δ(CH2) deformation bend (ca. 1450 cm-1) as an internal standard. The justification behind this is that this particular molecular fragment is associated with the amino acid side chains, and thus, not affected by the peptide backbone conformational changes as a result of cosmetic chemical treatment with e.g. peroxides or thioglycolic acid or natural weathering processes.184 235,236,278 It has been suggested that the intensity differences of this band from sample to sample are minimal.184
The untreated spectra will be discussed first, followed by the chemically treated ones, to illustrate the transformation of the structural chemistry within the keratinous fibre from the untreated state to the cosmetically treated one.
For the untreated fibre spectra, assigning from the higher wavenumber (cm-1) region, the vibrations of the three Amide bands from the peptide bond generally occur at 1700- 1590 cm-1, 1580-1500 cm-1, and 1320-1210 cm-1 respectively.24
The first absorption arises from the peptide linkage, and is the Amide I band, which involves about 80% C=O stretching coupled with an in-plane bending of the N-H and C-N stretching modes. The band is a broad and strong peak at approximately 1627 cm-1 and remains remarkably consistent between genders and race. This is illustrated by the lack of shift of each Amide I band across the vertical line. The complexity of the band is ascribed to either the coupling between two or more similar carbonyl stretching modes or the heterogeneity among the backbone carbonyl groups.273 Heterogeneity can occur from fundamental basic differences among carbonyls and/or from conformationally related differences in the strength of the hydrogen bonds associated with the carbonyls.273
105 An absorption from two of the amino acid side chains is masked by the strong intensity of the Amide I vibration. Hair keratin is made up of a composition of the 20 different amino acids; two of those are classified as carboxylic acid or acidic amino acids; aspartic and glutamic acid. These acidic side chain residues give rise to different IR absorptions dependent on the pH of their environment.184
At low pH values the carboxylic acid groups would be predominantly protonated. In the IR spectra, very weak evidence of the protonated carboxyl group (COOH) exists, as reflected by the small band of the carbonyl stretch (υC=O) at approximately 1735 cm-1.
The next band arising from the peptide bond is the Amide II band; it consists of a 60% C-N stretching mode coupled with N-H in-plane bending. However, in relation to the Amide I band, this absorption does not exhibit the same wavenumber position between the male and female fibres as highlighted by the two vertical lines. The Amide II absorption of spectra from male fibres appears as a sharp narrow band with a peak maximum at approximately 1511 cm-1, while the spectral line shape from the female fibres are somewhat broader (spectra CF1, AF17 and AF18) demonstrating an overall shift to a higher wavenumber with a peak maximum at approximately 1515-1520 cm-1.
The next series of absorptions in the keratin spectrum are attributed to the deformation and bending modes of the δ(C-H), (CH2) and (CH3) groups originating from the various amino acid (R) side chains.23 24 The bands are exemplified as medium, broad absorptions at approximately 1461 cm-1(shoulder peak), 1445 cm-1 and 1392 cm-1 respectively, and are quite similar in the spectra from fibres of both gender and race. This is attributed to the lack of chemical reactivity of these groups either during natural weathering or from cosmetic treatment.
The third commonly noted absorption arising from the peptide bond is the Amide III band, which involves 30% C-N stretching and 30% N-H bending modes of vibrations with additional contributions from the C-C stretch and CO in-plane bending. It exists as a very broad band of medium intensity at approximately 1234 cm-1.152 As per the Amide I band, this band shows no change in the wavenumber across both gender and race for the untreated fibres as delineated by the vertical line.
106 IR absorptions associated with the oxidation of the amino acid cystine, occur at approximately 1200-1000 cm-1. The bands in this region provide evidence of chemical changes arising from oxidative damage to the fibre as a consequence of bleaching, permanent dyeing and permanent waving. Under these conditions the cystine - disulphide cross-links are oxidised to cysteic acid (SO3 ) and the oxidative intermediates, cystine monoxide (S=O), cystine dioxide (SO2) and cysteine-S- thiosulphate.
However, in an untreated fibre, one also expects to observe some contribution from natural weathering. It would be virtually impossible to find a fibre that had not undergone some form of such exposure during its lifetime. Additionally, common physical processes such as combing and regular heating can also damage fibres as revealed by numerous SEM and AFM studies.48 65 67 72 272
For untreated fibres discussed here, each spectrum demonstrated a weak broad shoulder -1 between approximately 1130-1000 cm . This is attributed to the very weak S=O2 -1 -1 - (dioxide) band at 1114 cm , the S-S=O band (monoxide) at 1071 cm and the –SO3 (cysteic acid) band at 1040 cm-1. Not generally prominent in this region for an untreated fibre is the weak stretching band of the anti-symmetric cysteic acid at approximately 1171 cm-1 and, the stretching vibration band of cysteine-S-thiosulphate at approximately 1022 cm-1.
It is observed that the cysteic acid peak is quite distinct in the spectra of the Caucasian and Asian females, yet is rather weak and broad for the remaining spectra of the male and female samples. This observation can be explained by the overlap of cystine monoxide and cysteic acid bands because there is a higher concentration of cystine monoxide than cysteic acid.
Many FT-IR studies have sampled spectra from root to tip of naturally weathered, untreated hair fibres.52 279 They report that in the root end of the untreated fibre, the concentration of the cystine monoxide predominates over cysteic acid whereas in the tip the ratio is about one-to-one.279
107 Signori et al. sampled FT-IR spectra at five selected lengths from the tip of the hair fibre, and clearly illustrated that the intensity of absorption of cysteic acid from the middle to the tip significantly increases whereas the cysteine-S-thiosulphate band increases only slightly.52 This phenomenon of increased acid intensity from root to tip highlights further oxidation of cystine monoxide to cysteic acid. However, the concentration of the intermediate, cystine dioxide, remained constant throughout the length of the fibre.279 In fact, Hilterhaus et al. determined that the concentration of the oxidised groups (meq/kg) in the tip end (27.6 meq/kg) is approximately double of that in the root end (15.1 meq/kg) of the hair fibre.279
As the tip end is more exposed to the surrounding environment, it is thus more susceptible to degradation from UV radiation, moisture and mechanical processes, which fundamentally lead to the well-known-term as “split ends”. The same oxidative behaviour has been detected in a multitude of different wool fibres.184 280 281
In contrast to the tip end of the fibre, the root end near the follicle is more protected and is less subject to physical processes such as combing, towel drying, shampooing and conditioning.
The above discussion of IR band assignments of the untreated hair provides a general reference point with which spectra from chemically treated hairs may be compared.
3.2.1.3 Spectral Analysis of Cosmetically Treated Hair Fibres A selection of 12 spectra from 10 typical chemically treated hair fibres and 2 atypical chemically treated fibres presented in Figure 3.9 were obtained from both male (M) and female (F) donors across the Caucasian (C), and Asian (A) and African-type (N) races.
The treated hair samples were selected to illustrate the effects of different cosmetic methods that are likely to damage the structure of a keratin fibre, which could be reflected in the IR spectra.
108 -1 1631 cm
1531 & 1511cm -1
-1 1445 cm 1392 cm-1
C=O NM7
1234 cm-1 NF5 -1 1171 cm 1071 cm-1 1037 cm -1 NM6
NF4
Absorbance(a.u.) AM15
-1 S-SO 3 AM5 1022 cm -1 AF16
AF22
CM21
CM5
CF10
CF9 1735 cm-1
1580 1380 1180 980 780 Wavenumber (cm-1)
Figure 3.9 – A selection of 10 typical and 2 atypical chemically treated FTIR-ATR spectra of human hair fibres from male (M) and female (F) donors of the major races: Caucasian (C), Asian (A) and African-type (N).
109 The most striking difference is between both the African-type female (NF5) and male (NM7) spectra of treated hair fibres with the other samples in the group (Figure 3.9). These two particular sampled fibres are quite uncharacteristic of a normal α-keratin spectrum with reference to the typical untreated fibre assignments.
To the remaining chemically treated examples in Figure 3.9, in general were spectra that represent typical α-Keratin fibres. There appears to be no atypical bands present. These spectra will be discussed first to set a reference for comparison with the atypical ones.
Chemical cosmetic treatments with potential to cause structural damage to fibres include semi- and permanent dyes, bleaching and highlighting or a combination of several of these. With the exception of semi-permanent dyes, all other treatments involve oxidative chemical reactions to achieve the desired cosmetic outcome.
For these typical spectra (Figure 3.9), the Amide I band has a strong, broad maximum at approximately 1631 cm-1, which suggests an approximate shift of 4 cm-1 relative to the spectra of the untreated fibres (ca. 1627 cm-1). This peak shift for the Amide I band, whether great or small, typifies a change in the secondary structure of the keratin protein after the cosmetic process has taken place. This suggests an overall conformational change or modification in compositional balance of the two different forms in the fibre. The analysis of the conformation and structural modifications will be addressed with second derivative spectra (Sections 3.3.2).
Interestingly, the Amide II band in the spectra of both male and females display similar line shape and maxima, exhibiting a strong sharp absorption at approximately 1511 cm-1. This observation indicates a difference from the spectral comparison of the male and female untreated samples. Hence, the peak maximum position of the treated female spectra exhibits a shift to lower wavenumbers compared to the untreated female samples (i.e. from 1520-1515 cm-1), again suggesting a change in the protein conformation.
At pH values above 4.25, for fibres that have undergone cosmetic treatment with basic solutions, these carboxylic acid groups would be largely in their ionised forms, resulting - -1 in the anti-symmetric and symmetric –CO2 stretching modes at 1577 cm and
110 1400 cm-1 respectively.147 282 The spectrum of the sample, NF4, exhibits a large shift to the left to approximately 1531 cm-1 and the peak appears sharper in contrast to the other Amide II bands. Referring to the historical record for this sample (NF4, Appendix I), it was noted that the individual had straightened their hair. Mentioned in Section 1.2.6, hair straightening with NaOH can cause severe damage to the fibre by removing the cuticle cells, exposing the underlying cortex. Therefore, it is reasonable to suggest that the spectrum could be acquired from the cortical layer.
In the 1500-1200 cm-1 range of the treated α-Keratin spectrum, there is no significant evidence of any shift or changes in spectral line shape of the C-H deformation and bending modes, or Amide III band compared to the untreated spectra. This observation strongly suggests that these molecular groups of the keratin chain, i.e. the methyl, ethyl and conformation of the Amide III band (β sheet), are relatively stable and are suitable internal standards for comparison of FTIR-ATR spectra as supported by the literature.184 235-236
An important spectral region is the one that includes the cystine oxidation responses. This is a practical indicator of cosmetic treatment. Upon close examination, the underlying difference between the untreated and treated fibre spectra is the prominent increase in intensity of the symmetric cysteic acid band at 1037 cm-1. A similar effect is also observed with the weaker anti-symmetric cysteic acid band at 1172 cm-1, which often appears as a shoulder of the Amide III band. The intensity of both these absorptions are well illustrated in the spectrum, Caucasian female 9 (CF9, Appendix I), where the individual had bleached and semi-permanently dyed the hair. As the absorbance of the bands is quite strong, it is reasonable to suggest that the bleaching process had been extensive.
In addition to the formation of cysteic acid, there are the simultaneous responses of the oxidative intermediates that have not been converted in the reaction to such species as cystine dioxide (SO2) and cystine monoxide (S=O). The S=O band is clearly evident at -1 -1 1072 cm whereas the SO2 absorption is negligible with a slight shoulder at 1114 cm . There is no evidence for the presence of cysteine-S-thiosulphate or Bunte salt band at 1022 cm-1, which supports the findings by Signori et al. where it was established that the intensity of this band increases only slightly after cosmetic treatment.52 The above
111 discussion provides a basis for the analysis of the spectra with atypical behaviour and their comparison with the typical treated spectra. The African-type female (NF5) fibre (Figure 3.9) shows an intense, three-pronged set of absorption bands which is observed in the cystine oxidation region between approximately 1130-960 cm-1. Two weak absorptions at 922 cm-1 and 854 cm-1 are also atypical of the untreated and treated α- keratin spectrum.
In addition, the C-H deformation bands between 1460-1380 cm-1 are much more intense. All of these changes in the C-H and the cystine oxidation regions suggest that the relative concentrations of those molecular fragments have increased, perhaps due to some cosmetic surface treatment on the hair fibre.
To deduce the identity of this surface treatment, initially it was sufficient to evaluate the details that were given at the time of sampling of the individual‟s hair (Appendix I). In particular, the NF5 hair was permanently waved and an activator applied. This treatment involves a chemical treatment product, which is generally formulated to protect the hair from becoming too dry or brittle after the severe waving process. Permanent waving of hair is one of the most complex processes of all the cosmetic treatment methods (Section 1.2.5). It involves firstly the removal or lifting of the cuticle with NH3 solution followed by a reduction of the disulphide cross-links by thioglycolates or bisulphites to reduce the stability of the hair. This facilitates the hair to be manipulated into different shapes by hot curlers or curling irons, followed by subsequent re-oxidation of the S-S cross-links by peroxides to set the hair.58
However, permanent waving of African-type hair is rather different to Caucasoid hair in that the hair must be straightened prior to curling. Straightening is achieved with the use of ammonium thioglycolate and the rest of the treatment follows the normal procedure except that sodium bromate NaBrO3 is used as a neutraliser so as not to affect the natural colour of hair.58
As a consequence, the permanent waving process leaves the hair with decreased tensile strength, and more brittle as well as increased porosity. Hence, directly after completion, curl or wave activators are used, which are rich moisturising creams, to restore the manageability, glossiness and softness normally provided by the sebum.58
112 The moisturising creams consist of many chemicals such as deionised water, hydrocarbons, fatty acids, alcohols and esters, e.g. jojoba oil, propylene glycol, glycerine, cetearyl alcohol, panthenol and glycol stearate.219
Therefore, in the IR spectrum, one would expect to observe the stretches, and deformation/bending modes pertaining to the main functional groups of the constituents of the cream. These would include the carboxylic acid (COOH), alcohol (O-H) and ester (COOC) functional groups associated with the alkane and alkene (C-H) groups.
To investigate the hypothesis that the abnormal spectrum of the African-type female (NF5) was a result of the use of a surface treatment such as an activator, small samples of the fibres were cleaned according to a revised version of the IAEA method.233 234 The procedure was originally used by Cargnello et al.232 for the cleaning of contemporary and well preserved historical hair samples in preparation for elemental analysis. The revised procedure of the IAEA method is the same except that the sonication times at each wash (i.e. Acetone, HPLC-grade water and deionised water) were changed to shorter intervals of 10 minutes each. This was carried out in an attempt to remove this so called artificial layer, to leave the surface of the hair fibre clean. This approach also minimises any damage to the fibre.
After the fibres had been cleaned and appropriately dried for two days (Section 2.3.1.), they were analysed by FTIR-ATR spectroscopy. After an initial analysis of the spectra from each of the samples, it was apparent that the atypical bands were no longer present. The spectrum approximated that of a typical treated α-keratin spectrum. As per the typical chemically treated spectra, the Amide I and II bands have broad strong maxima at 1631 cm-1 and 1515 cm-1 respectively, which suggested evidence of transformation to the structural chemistry of the fibre.
Interestingly, the cysteic acid peak is apparently weak and is more or less masked by the intensity of the cystine monoxide absorption. The low intensity of the cysteic acid band is expected because the disulphide cross links are first reduced to thiol groups, and then re-oxidised to as far as the monoxide unit.
113 It can also be seen that a band which is normally dominated by cysteic acid, emerges as a weak shoulder the cysteine-S-thiosulphate as a weak shoulder at approximately 1025 cm-1.
Thus, given that the additional bands could be attributed to the presence of a cosmetic activator rather than the hair fibre itself, the spectrum of the cleaned fibre was subtracted from that of the contaminated fibre (Figure 3.10).
The difference spectrum shows a broad and medium intensity band between approximately 3430-3090 cm-1. Broad absorptions in this range are indicative of the stretches of the carboxylic acid (-COOH) group and the alcohol (-OH) group. These main functional groups are consistent with the active ingredients that are present in wave activator applications.219
Other observed absorptions are the aliphatic C-H stretches of the saturated and unsaturated long chain fatty acids, alcohols and esters. The absorption bands at -1 -1 -1 2944 cm , 2879 cm and 2829 cm are attributed to the υa (CH2), υs (CH3) and υs
(CH2) stretches respectively.
Between approximately 1120-820 cm-1, the fingerprint of molecular absorptions due to the activator occur. The two sharp bands of medium-to-strong intensity at approximately 1106 cm-1 and 991 cm-1 are characteristic of the O-C stretching frequency of the ester functional group.147 The strong and broad band at 1037 cm-1 corresponds to the C-O stretching vibration of the alcohol groups present in the chemical.147 The subsequent strong and medium absorbance bands at 993 cm-1 and 922 cm-1 can be associated with the C-H out-of-plane deformation of the alkene group -1 RCH=CH2 and the final band at 854 cm corresponds to the δ(C-H) (med.) deformation 147 of the R2C=CHR alkene group.
114 (a) Fibre + Activator
(b) Original -1 1037 cm C-O
COOH O-H Absorbance(a.u.) 3430-3090cm -1 CH (c) 3 -1 RCH=CH 2879 cm CH 2 2 -1 2829 cm -1 922 cm -1 Spectral Subtraction 854 cm
RCH=CR2
3550 3050 2550 2050 1550 1050 550
Wavenumber (cm-1)
Figure 3.10 – (a) FTIR-ATR spectrum of NF5 suspected to contain a hair activator on the surface, (b) FTIR-ATR spectrum of NF5 after cleaning of the surface and (c) the subtraction of (b) - (a) yielding the IR spectrum of the suspicious material.
115 The next assessment involves the investigation of the other atypical treated α-Keratin spectrum which is of the African-type male fibre (NM7, Appendix I). Following the same systematic approach as used in the previous example, analysing the individuals “hair history”, it was noted that this fibre (NM7) had been permanently dyed and the subject used a hair gel.
Morphologically, SEM analyses of the surface topography (section 3.1.1, Figure 3.5) showed that moderate to severe damage was caused to the cuticle layer as a result of the dyeing process, which utilises alkali solutions such as ammonia, to lift the cuticle and allow the dye to penetrate the cortex.
As the individual was of African descent, the hair fibres were naturally black, but they appeared to be dyed medium brown. With permanent dyeing, the dye remains until it is eventually washed out, which is a period of approximately 4-6 weeks and then colouring of proximal re-growth is required. However, in this case, there was no visible evidence of re-growth. Thus, the dye should still have been in the cortex, and the peripheral region of the cuticle.
For permanent dyeing to achieve brown hair from black hair, the oxidation reaction of primary intermediates such as para-aminophenols with hydrogen peroxide form benzoquinone monoamine. The monoimine product then reacts with couplers such as para-aminophenols to yield the brown tri-nuclear dye, commonly referred to as Bandrowski‟s base.283 Hence, as the dye pigment is associated with the cortex and perhaps the lower cuticle layer, it is reasonable to suggest that the IR radiation may not only be absorbed by the keratin protein, but also from the brown dye.
Considering the 1750-800 cm-1 region of the keratin spectrum only, the main functional groups of the dye are the stretches of the conjugated cyclic Imine R2C=N at 1660-1480 -1 -1 cm (very weak); the amine δ(NH2) bend at 1650-1560 cm (medium) and the hydroxyl δ(O-H) bending vibration at 1410-1260 cm-1 (medium).147
However, the Imine stretching vibrations are difficult to identify because the IR intensity is very weak and are close to the C=C stretching vibration.147 Therefore, probably this band will have very little impact on the spectrum especially as it is
116 situated between the strong Amide I and Amide II bands, and similarly the O-H bending vibration which is located near the Amide III band.
Furthermore, FTIR-ATR spectroscopy is a near-surface technique only, and thus the sampling penetration depth may not be sufficient to sample deep past the cuticle layer. Each cuticle cell is approximately 0.5 µm – 1.0 µm thick and the overall cuticle layer thickness varies between 5-10 layers.11 32 Hence, the average thickness of the cuticle layer for individuals can fluctuate between 2.5 µm – 10 µm with the median being approximately 6.25 µm or ~ 6.0 µm. The FTIR-ATR depth of penetration, dp, for a human hair fibre between 1700-1200 cm-1 (i.e. covering the Amide I, II and III bands) is approximately 1.24 µm – 1.75 µm (based on Equation 1.3, Section 1.6.4.).
It must also be taken into consideration that the ATR pressure tower compresses the fibre upon sampling to facilitate good contact between the sample and the diamond IRE. SEM studies22 23 25 26 have demonstrated that as a consequence of this sampling, the diameter of the fibres is approximately doubled, simultaneously reducing the overall thickness of the cuticle layer by approximately half.
Hypothetically, for a hair fibre with a cuticle thickness of 2.5 µm which is reduced to approximately 1.25 µm upon sampling, the penetration depth of the IR radiation would be sufficient to acquire structural information from the cortical layer. Conversely, for a hair fibre with an average cuticle thickness of 6.0 µm, the penetration depth is inadequate to sample structural information from the cortex. Corroborative evidence that FTIR-ATR spectroscopy samples from the cuticle layers only is discussed in Section 3.3, concerned with second derivative IR spectra.
In conclusion, it is reasonable to suggest that the dye pigment will have minimal impact on any FTIR-ATR hair fibre spectra acquired from this individual‟s hair samples.
In conjunction with the suspected IR absorption of the dye, is the hair gel. As hair gel is a cosmetic treatment that is applied externally to the hair, it is reasonable to suggest that the gel is responsible for the abnormal spectral bands, as seen in the previous example of the African female fibre (NM5) and the permanent wave activator.
117 An analysis of the NM7 spectrum suggests that inference appears to be valid. Considering the spectral line shape and intensity, especially in the cystine oxidation region between 1130-1000 cm-1; these bands are very obscure and markedly different from that of a typical treated hair fibre. In the previous assessment of the African-type female fibre (NF5), that particular region exhibited a fork-like appearance; in this example the equivalent region has a very broad band of medium intensity.
Additional irregularities or discrepancies from a typical treated fibre are further illustrated by the a) intensity of the Amide III band, exhibiting a sharp maximum at 1257 cm-1; b) a prominent, intense band at approximately 800 cm-1 and c) the uncharacteristic broadness and line shape of the Amide II band which exhibits a shift to higher frequency of approximately 20 cm-1 for a typical treated male fibre, associated with an irregular shoulder at 1573 cm-1.
Therefore, to test the hypothesis that the atypical spectrum is a consequence of the application of an external hair gel, the questioned fibre was cleaned via the revised IAEA method.232 This was carried out in order to remove the supposed artificial layer. The cleaned fibre was then subsequently analysed by FTIR-ATR spectroscopy. Immediately, it was apparent that the atypical bands had been removed from the spectrum by the cleaning procedure. Hence, the atypical fibre was investigated further by subtracting the cleaned fibre sample from the atypical fibre to reveal the characteristics of the external artefact.
The result of the spectral subtraction is presented in Figure 3.11. It can be seen that the additional vibrational bands in the atypical treated male African-type fibre are at 1260 cm-1, 1095 cm-1, 1020cm-1 and 800 cm-1. A search of these bands in the literature and by referencing to a spectral library using the OMNIC E.S.P 5.2a Spectral Software -1 Program, revealed that these absorptions can be attributed to the Si-CH3 (1260 cm and 800 cm-1) and Si-O (1095 cm-1 and 1020 cm-1) stretches.147 284 These bands are part of, and consistent with a long-chain siloxane resin, commonly seen in hair gel formulations and fixatives such as hair sprays, activators and mousses.11 285
118 Si-O Si-O
Si-CH 3 Si-CH 3 Absorbance(a.u.)
1350 1250 1150 1050 950 850 750 Wavenumber(cm-1)
Figure 3.11 – Resultant FTIR-ATR spectral subtraction of the chemically treated NM7 spectrum minus the cleaned version of the fibre revealing the characteristic bands of a long-chain silo-oxane resin used in hair gel and hairspray formulations.
119 The findings here are consistent with a previous study performed by Bartick et al.159 The authors employed the use of Micro-ATR Spectroscopy to enhance the surface contributions from a hair spray. By subtracting the spectrum of a clean fibre from the spectrum from a hair spray coated fibre, the identity of the hair spray was revealed.159
In summary, this section has discussed in detail the characteristics of treated hair as measured by FTIR-ATR spectroscopy. It was noted that in general, treated hair have consistent spectral profiles which may be seriously perturbed by application of specialised cosmetic surface treatments. These may be studied by spectral subtraction which at times allows specific identification of the treatment. This information could potentially be utilised forensically to link to a suspect‟s personal belongings/surroundings. Therefore, the following section focuses on the application of subtracted spectra, to discern the differences between genders for each race.
3.2.2 Analysis of Difference FTIR-ATR Spectra of Human Hair Fibres between Gender
3.2.2.1 Spectral Differences between Genders of each Race A number of difference spectra were obtained by subtracting typical untreated male spectra from typical untreated female spectra for each of the three races. To minimise the error due to differences in intensity, each spectrum had been baseline corrected and -1 normalised to the δ(CH2) bend at approximately 1452 cm . Typical untreated fibres were selected to understand the raw structural differences between male and female human hair fibres. Representations of the gender differences between Caucasian, Asian and African-type races are presented in Figures 3.12, 3.13 and 3.14 respectively. The individual spectrum of each person was summed and averaged using the software to obtain an average spectral profile or representation.
Beginning with the typical gender differences between Caucasian fibres (Figure 3.12), the subtraction is of the average spectral profile of Caucasian male No. 3 from the average spectral profile of Caucasian female No. 1 (Appendix I). The peak maxima pertain to absorbance bands of the female fibres whereas the peak minima correspond to absorbance bands of the male fibres.
120 CFUN1CFUN1-CMUN3-CMUN3
1538cm-1
1573cm-1 1635cm-1
1469cm-1 1396cm-1
-1
1330cm-1 1141cm Absorbance(a.u.) 1488cm-1 1222cm-1 1056cm-1 1022cm-1
1716cm-1
1600 1400 1200 1000 800 Wavenumber (cm-1) Figure 3.12 – A subtraction FTIR-ATR spectrum of the average of untreated Caucasian female No. 1 (peak maxima) minus the average of untreated Caucasian male No. 3(peak minima).
121 Strong intensities for Caucasian female fibres were generally observed for the Amide I and Amide II bands at approximately 1635 cm-1 and 1538 cm-1 respectively, which also - -1 included the anti-symmetric –CO2 stretch at approximately 1573 cm . The Caucasian female fibres also showed medium intensities at 1469 cm-1 and 1396 cm-1 which are attributed to the bending modes of the δ(C-H) and (CH3) groups respectively.
In contrast, the Caucasian male fibres generally exhibited a strong intensity of the carbonyl, υ(C=O) stretch, of the carboxyl group (aspartic and glutamic acid) at 1716 -1 -1 -1 cm and medium intensities at 1488 cm (δ(C-H)), 1330 cm (δ(CH2) tryptophan), 1222 cm-1 (Amide III band, β-sheet) and the cystine oxidation region between approximately 1150-1000 cm-1.
For the gender differences between typical untreated Asian hair fibres, the average spectral profile of Asian male 19 (AM19) was subtracted from Asian female 17 (AF17) (Appendix I) and is presented in Figure 3.13. The peak maxima for the females include - -1 -1 the anti-symmetric –CO2 stretch at approximately 1577 cm , δ(C-H) 1481 cm , δ(CH3) -1 -1 - -1 1396 cm , SO2 1133 cm and SO3 1040 cm .
The final spectrum (Figure 3.14) involved the subtraction of the average spectra of African-type male No.1 from the average spectra of African-type female No.21 as listed in Appendix I. In this scenario, the African-type female is characterised by the random coil and the β-pleated sheet of the Amide I band at approximately 1670 cm-1, the anti- - -1 symmetric –CO2 stretch at approximately 1577 cm , the deformations of the C-H bands between 1465-1376 cm-1 and the cystine oxidation region between 1122-1040 cm-1. The vibrational bands related to the African male include the carbonyl, υ(C=O) stretch, of the carboxyl group at 1774 cm-1, β-pleated sheet of the Amide I band at 1616 cm-1, tryptophan at 1550 cm-1, the Amide II, III and IV at 1519 cm-1, 1241 cm-1 and 979 cm-1 respectively.
122 AFUN17-AMUN20-AMUN20
1481 cm-1
1419 cm-1 -1 1396 cm-1 1040 cm 1133 cm-1
1577 cm-1 1712 cm-1
1234 cm-1
Absorbance(a.u.) 1546 cm-1
1627 cm-1
1600 1400 1200 1000 800 Wavenumber (cm-1) Figure 3.13 - A subtraction FTIR-ATR spectrum of the average of untreated Asian female No. 17 (peak maxima) minus the average of untreated Asian male No. 20 (peak minima).
123
Figure 3.14 - A subtraction FTIR-ATR spectrum of the average of untreated African- type female No. 21 (peak maxima) minus the average of untreated African-type male No. 1 (peak minima).
124 In summary, of the spectral evidence of cosmetically treated fibres (Figure 3.9), structural changes to the hair protein are not only specific to the disulphide linkages (as highlighted by the increase in concentration of the cysteic acid), but are also found with the stable peptide bonds, which are the backbone of each protein fibre. Spectral shifts of approximately 5-10 cm-1 were observed for both the Amide I and Amide II bands after some form of oxidative chemical treatment.
It was established that these two vibrational bands have unequal contributions of the different conformational forms, i.e. random coil, α-helix and β-pleated sheets. The observations suggest that the secondary structure of the fibre is transformed, such that as one conformational form decreases another increases as a result of the chemical treatment.
However, experimentally one is only able to illustrate these conformational changes resulting from chemical treatment through the unravelling of the overlapped bands, permitting those absorptions to be examined prior-to and subsequent-to the treatment process. This work with the difference spectra leads onto the next topic concerned with the use of second derivative spectra and its underlying importance towards its potential as a forensic procedure for hair fibre analysis.
3.3 The Application of Derivative Spectroscopy for Interpretation of FTIR-ATR Spectra of Single Hair Fibres
3.3.1. Optimisation of the Savitzky-Golay Method for Second Derivative Analysis
In the previous section, the main focus had been concerned with the discussion of α- Keratin spectra in its raw form (including the use of the difference spectra). As mentioned previously, the spectrum of α-Keratin between 1750-800 cm-1 is exceptionally intricate, because there are many overlapping bands. This section describes the application of second derivative spectra for the interpretation of keratin spectra.
125 Derivative spectroscopy, particularly, where the second derivative is involved, facilitates the unravelling of the complex overlapping bands.226 This method has to be optimised in order to acquire the maximum information from each spectrum at high resolution while simultaneously minimising the inherent background noise. Thus, it is common to apply the Savitzky-Golay method (GRAMS/32AT, 6.00, Galactic Industries Corporation, Salem, NH, U.S.A.). This approach is based on the application of an n- degree polynomial (n = 1, 2 …) with a peak smoothing function i.e. the description of the spectrum by a polynomial is arranged to give a compromise between smoothness of the resulting curve and the accuracy of the fit.226
The spectral profile is approximated by a polynomial of degree, n:
2 n y = k1 + k2x + k3x + … + kn+1x Equation 3.1 where: x = wavelength y = signal amplitude (e.g. absorbance)
For smoothing of spectral derivatives, the order of the derivative is limited by the degree of the polynomial used to describe the spectrum.226 Hence, for a second derivative spectrum, a second degree polynomial was selected.
For spectral smoothing, the number of points which may be used for the smoothing operation is a function of the experimental curve under examination. Minimum profile distortion will occur when the polynomial accurately describes the spectrum, and will increase as the polynomial departs from the true curve.230 The underlying rules for selecting smoothing points are:286 (i) the number of convolution points must be an odd number, and even points are rounded up, (ii) this number must be at least five or one more than the degree of the polynomial (whichever is greater) and (iii) the number must be no more than three less than the number of points in the trace. Thus, a large number of convolution points will ultimately provide more smoothing in the result and reduce noise.
126 Second derivative spectra of a second degree polynomial using 5, 7, 9, and 11 point smoothing are presented in Figure 3.15. The significant absorptions in the spectra are now delineated as minima. It is well illustrated here that as the number of smoothing points increases, the resolution between component peaks of some of the individual absorption bands decreases, (particularly between the Amide I and Amide II bands) with concurrent reduction in intensity of the bands. The signal given by the 5-point smoothing function is more intense than the signal recorded by the 11-point smoothing function.
127
5 Point Smooth 7 Point Smooth 9 Point Smooth 11 Point Smooth Absorbance(a.u.)
11 Point Smooth
5 Point Smooth
1600 1400 1200 1000 800 Wavenumber (cm-1)
Figure 3.15 – Second derivative FTIR-ATR spectra of an untreated Caucasian female fibre using a two degree polynomial and comparing different number of smoothing points (5, 7, 9 and 11). Increase in smoothing points shows that resolution between the bands decreases. Thus a 2o polynomial with 5-points was selected.
128 The intention of the second derivative analysis in this work is to study more deeply the underlying differences in the α-Keratin spectrum between gender, race and the changes that occur through the use of chemical treatment. Hence, the five points smoothing model provides good resolution between component peaks and was selected as optimum condition for the analysis of hair fibre spectra.
3.3.2. Assessment of Typical Second Derivative FTIR-ATR Spectra of Untreated α- Keratin Fibres
The same untreated female and male samples that were used for the raw spectral analysis (Figure 3.8) were selected for the untreated second derivative spectral analysis.
A typical example of an untreated second derivative spectrum (CF1, Appendix I) is presented in Figure 3.16, and from now forth will be a reference (CFUN1) throughout the remainder of the dissertation. The spectral differences between untreated female and male second derivative spectra are illustrated in Figure 3.17. In general, it became apparent that the broad peaks that were present in the raw α-Keratin spectrum were resolved into a number of intense but sharp absorptions. In the raw spectrum approximately 10 bands can be clearly distinguished whilst in the second derivative spectrum approximately 20 bands can be identified.
129
Figure 3.16 – Typical untreated second derivative FTIR-ATR spectrum of hair from a Caucasian female untreated No. 1(CFUN1).
130 NM1
NF20
AM19
AF17 Absorbance(a.u.)
CM3
C=O - α α β/r COO - SO O=C- SO3 2 S=O CF1 CH N 3 β - C-H CH2 SO α 3 β α β
1600 1400 1200 1000 800 Wavenumber (cm-1)
Figure 3.17 – A comparison of six typical (alleged according to hair history) untreated second, derivative FTIR-ATR spectra of hair from both male (M) and female (F) of the Caucasian (C), Asian (A) and African-type (N) races.
131 The Amide I band originally at 1627 cm-1 in the raw spectrum of Keratin, is separated into three more distinguishable bands of unequal intensity. Thus, what appeared to be a single absorption band is actually a number of bands of different secondary structural forms of the protein. With reference to the spectral literature, the strong, broad absorption at 1627 cm-1 is attributed to the carbonyl stretch, υ (C=O), of the β-pleated sheet conformation in both the male and female spectra.
As the FTIR-ATR technique facilitates sampling of the near-surface chemistry only, the dominance of the β-pleated sheet suggests the cuticle is comprised of rather an amorphous matrix as opposed to a fibrous α-helical matrix that makes up the cortical cells. This inference is supported by Church et al.184 where it has been reported that the cuticle layer is rich in β-sheet and/or random coil forms, having a higher proportion of cystine, proline, serine, and valine residues that have generally been considered by Bradbury et al.287 288 as non-helical forming amino acid residues.
The second Amide I absorption, which emerges as a shoulder to the left of the β-pleated sheet, is correlated to the υ(C=O) stretch of the α-helix confirmation at approximately 1650 cm-1 and 1647 cm-1 for the female and male spectra respectively. Interestingly, the α-helical band for the AF17 spectrum exhibits much stronger intensity than the β- pleated sheet, which suggests that the spectrum has been sampled from the underlying cortex layer i.e. it has been sampled from that area. Although the historical record for AF17 suggests that the fibre had not undergone any chemical treatment, the age of the individual (53 years) must also be taken into account. This inference is supported by the strong intensity of the cysteic acid band at 1041 cm-1 for this sample, which suggests that age leads to deterioration of cystine to cysteic acid. The cuticle is removed, exposing the cortical layer, which is ultimately reflected in the IR spectra given the strong contributions of the α-helical Amide I and Amide II bands and carboxylic acid, υ(C=O) stretch.
However, for most of the second derivative spectra between both gender and race, the α-helix emerges as a shoulder or is completely absent. Explanations for the absence of the α-helix absorption in the Amide I band exist, and are based on two separate phenomena or a combination of these. Firstly, Kuzuhara et al.235 performed a Raman
132 spectroscopic investigation on human hair fibres and established that at a depth of about 1 µm from the fibre surface, the skeletal C-C stretch of the α-helix (normally at ca. 932 cm-1) did not appear, which led to the suggestion that the α-helix form did not exist in the hair cuticle.
Another plausible rationalisation for the lack of α-helical evidence is attributed to the
H-O-H bend of OHwater …..OHwater Hydrogen bond interactions, relating to adsorbed water at approximately 1633 cm-1. This absorption band is situated directly between the bands attributed to the α-helix and β-pleated sheet and evidence of this band can be observed in the AF17 spectrum. The presence of water depends on the relative humidity (% RH) or level of cosmetic chemical treatment. As the AF17 spectrum shows a high intensity of cysteic acid, it is reasonable to suggest that the surface is hydrophilic, thus increasing the hydrogen bonding interaction with water molecules.
The final section of the Amide I absorption between 1750-1660 cm-1 is complex, as it is made up of a composite of different conformational forms and amino acid contributions, varying significantly across the gender and race related spectra.
Hair keratin is made up of a composition of the 20 different amino acids; two of those are classified as carboxylic acid or acidic amino acids; aspartic and glutamic acid. These acidic side chain residues thus give rise to different IR absorptions dependent upon the pH of their environment.184 At low pH values the carboxylic acid groups would be predominantly protonated. In the IR spectra, evidence of the protonated carboxyl group (COOH) exists, demonstrating a sharp, yet very weak (with the exception of AF17) band of the carbonyl stretch υ(C=O) at approximately 1736 cm-1.184
The anti-symmetric stretch at 1577 cm-1 is scarcely below the baseline, associated with the symmetric stretch at 1400 cm-1 which is negligible, present as a shoulder only. These observations further strengthen the argument that the carboxyl groups are protonated in an untreated fibre.
The penultimate absorption within this particular region of the Amide I band is assigned to the amide (CONH2) stretch (sharp-weak) of the asparagine and glutamine side chains at approximately 1685 cm-1. The final of absorption of the Amide I absorption is
133 correlated with a combination of the υ(C=O) stretch (sharp, weak) of the β-pleated sheet (1670 cm-1) and random coil (1665 cm-1) conformation, yielding a overall maximum at approximately 1669 cm-1. This is observed for both female and male spectra. However, in some of the second derivative spectra of both male and female fibres, the two peaks are not well resolved, and a broad, weak to medium absorption is observed at 1685 cm-1. Apart from the presence of “free” or mobile water associated with the surface of the fibre, absorbed or „bound‟ water is principally bonded to the hydrophilic side chains and peptide groups and aids structural stability.
Evidence of these strong OHwater…OHwater interactions are also reflected in the IR spectra, resulting in an O-H bending absorption band at 1693 cm-1, justifying the 268 broadening and intensity in the CONH2 and β/r stretching region. The absorbed water band is prominent in the AF17 sample. The presence of “bound/free” water and relative humidity effects concerning hair keratin will be considered in the following section.
The Amide II band has two strong, sharp peaks of different intensities at an average of 1543 cm-1 and 1511 cm-1 for the females; and 1540 cm-1 and 1511 cm-1 for the male sources. However, with reference to the spectral literature289, the band at 1543 cm-1 essentially consists of two bands which are ascribed to the υ(C-N) stretch (60%) and δ(N-H) (40%) in-plane-bending of the α-helical conformation at 1545 cm-1 and the random coil/amorphous form at 1536 cm-1, both of medium intensity. However, with the exception of the AM19 spectrum, the spectral evidence illustrates that there is generally no spectral resolution between the two bands.
The strong, sharp absorption at 1511 cm-1 is directly correlated to the υ(C-N) stretch (60%) and δ(N-H) (40%) in-plane-bending of the β-pleated sheet conformation. Once more, the assignments suggest that the β-pleated sheet dominates the structural conformation of the cuticle layer.184 Therefore in general, the layer is less ordered i.e. amorphous, as opposed to the underlying cortex. However, in the CF1 and AF17 spectra, the intensity of the α-helical band is very strong. This suggests two possible scenarios, some woman tend to have more of the α-helix in the cuticle, or the IR spectra was sampled from the cortical layer.
134 Hence, it can be seen that the second derivative spectra of the untreated fibres has substantiated the differences in wavelength of the Amide II band between genders (Section 3.2.1.2), and explains the overall shift from 1511 cm-1 to 1515-1520 cm-1 for some of the untreated female raw spectra. This occurs because the strong intensity of the α-helical band shifts the overall peak position of the Amide II band.
The next set of absorptions between approximately 1470-1310 cm-1 is attributed to the different deformation modes of the of the aliphatic and aromatic C-H groups which are present in the protein structure. The sharp, weak peak at 1470 cm-1 corresponds to the δ(C-H) deformation stretch. The subsequent peak of medium intensity at 1454 cm-1 also contains a slight shoulder at somewhat higher frequency; this is because it consists of both bending modes of the δ(CH2) and δ(CH3) groups respectively. The bands at 1389 cm-1 and 1369 cm-1 (shoulder) are furthermore attributed to the symmetric deformations of the δ(CH3) group.
The final two stretches within this region at 1342 cm-1 and 1315 cm-1 are most interesting because in the normal raw spectrum they are no more than two extremely weak peaks between the δ(C-H) deformations and the Amide III band. The band at -1 1342 cm can be assigned to the δ(CH2) deformation bend from the amino acid -1 tryptophan and the band at 1315 cm is the υs symmetric cystine dioxide (SO2) stretch.
The next group of spectral bands between 1300-1200 cm-1 is exclusively associated with the Amide III mode of vibration. Weak shoulders are observed at 1284 cm-1 and 1257 cm-1 which are related to the υ (C-N) stretch (30%) and δ (N-H) (30%) in-plane- bend of the α-helical form, respectively. However, the main band at 1235 cm-1 is associated with the υ(C-N) stretch (30%) and δ (N-H) (30%) in-plane-bend of the β- pleated sheet conformation, with a small contribution from the deformation of the O=C-N bending mode.
The final part of the spectrum between 1200-1000 cm-1 contains the absorptions arising from the oxidation of cystine with peaks at 1195 cm-1 and 1015 cm-1 corresponding to the anti-symmetric and symmetric absorptions of cysteine-S-sulphonate; the anti- symmetric and symmetric vibrations of cysteic acid at 1172 cm-1 and 1040 cm-1; the symmetric stretch of cystine dioxide at 1115 cm-1 and the symmetric stretch of cystine
135 monoxide at 1074 cm-1. Amongst that group of absorption bands, there are a number of very weak shoulder peaks at approximately 1151 cm-1, 1129 cm-1 and 1084 cm-1 all of which correspond to the stretching mode of the C-N bond. These are more active or discernible in the Raman spectrum of α-Keratin.149
There is a lone band at approximately 933 cm-1 and it is attributed to Amide IV modes of vibration, which primarily consists of O=C-N bending.152
In conclusion thus far, it can be seen that the chemical make-up of the α-Keratin protein is complex, and not as simple as it appears in the raw untreated spectrum. Second derivative spectroscopy revealed over 20 bands providing more discriminatory power to identify the differences and similarities between single hair fibres between gender and race.
3.2.3. Assessment of Typical Second Derivative FTIR-ATR Chemically Treated α- Keratin Spectra
Unfortunately, as a side-effect to chemical treatment, strongly oxidising alkaline solutions not only act on the melanin itself, but also attack the accessible reaction sites of the protein. These include the peptide bond, hydrogen bonds, side chain amino groups the cystine disulphide bridges. In Section 3.2.1.3, spectral evidence illustrated that the Amide I and II bands of chemically treated fibres had exhibited shifts of approximately 5-10 cm-1 when directly compared to the spectral assignments of untreated hairs. Thus, the transformations of the protein conformation are reflected by the shifts observed in the FTIR-ATR spectra.
Many previous FT-IR, Raman and X-ray spectroscopy investigations have explored the structural change in the conformation of hard keratin fibres resulting from physical modifications (i.e. stretching and %RH) and chemical treatments. Each of the studies utilised different conventional quantitative-qualitative approaches to deduce or illustrate the structural modifications to the protein such as peak or curve-fitting analysis (relative peak area intensities), Wide-angle X-ray diffraction (WAXD) and 13C and 15N NMR as the chemical shifts are conformation dependent.24 235 278 289 290
136 The phenomenon of the α-β transition in hard keratin fibres was first discovered and observed in the early 1930s and 1960s by X-ray spectroscopy.291-293 Preliminary IR and Raman investigations were concerned with the analysis of stretched keratins such as horsehair and wool fibres. Bendit294 and Frushour and Koenig295 respectively, demonstrated the α-helix to β-sheet conformational transition upon stretching, illustrating the dramatic increase in intensity particularly for the Amide I band.
Church et al.184 performed Raman and FTIR-ATR spectroscopic analyses with the aid of mathematical software on both cortical and cuticle cells isolated from fine Merino wool fibres. Curve fitting analysis of the deconvolution of Raman spectra of the two layers illustrated the significant difference in relative intensities whereby the cortical cells exhibited much higher α-helical content, while the cuticle cells were richer in the β-sheet and/or random coil conformations.
The results further illustrated that the increases in both relative intensity and width of the Amide I component of the cuticle cell compared to the cortical cell is a result of an increase in disordered content at the expense of the α-helical content.184
FTIR micro-spectroscopic analyses demonstrated that the Amide I band exhibited a significant shift of 14 cm-1 to higher wavenumber after flattening and was very similar to IR spectra of cuticle cell fragments.184
Further, curve fitting analyses of the Amide I band performed by Lyman et al.195 and Kreplak et al.296 on stretched horsehair suggested that physical extension gives rise to anti-parallel β-sheet structures and is also affected by relative humidity and temperature.
With hair fibres from aged individuals, Kuzuhara et al. reported that the disulphide (-S- S-) content of virgin black hair from Japanese females in their fifties decreased compared with Japanese females in their twenties.297 They were able to manifest from the curve-fitting analyses that the β/r and the α-helical contents remained constant.
Apart from the study of physical modification to the keratin fibre, a number FTIR and Raman investigations have been carried out for the cosmetic treatment of hair and wool fibres respectively.
137 With hair bleaching, Panayiotou24 used curve-fit analysis to examine the changes in the keratin fibre as a result of chemical oxidation and compared those to peak areas of untreated hair fibres. The results showed a 27 % decrease in the Amide I α-helix (from untreated to 5 hour chemical treatment) with a simultaneous increase in the Amide I random-coil of almost 15 % over the same time period. The Amide I β-sheet remained relatively stable. The α-helix of the Amide II band demonstrated a 56 % decrease in peak area after 1 hour of bleaching, but returned to 100 % after 5 hours due to the simultaneous increase in the random coil structure. The Amide III (β-sheet) remained relatively stable during chemical treatment.
With permanent waving, Kuzuhara et al.235 278, Nishikawa et al.289 and Ogawa et al.290 investigated the mechanism leading to the reduction in tensile strength. Curve fitting analyses illustrated that the β-sheet and/or random coil content (β/R) (Amide I band) and the Amide III (β-sheet) band intensity existing throughout the cortex region remarkably increased, while the α-helix content slightly decreased. For the Amide II band, a slight increase at 1537 cm-1 attributed to the random coil is observed after 1 hour, in contrast with the slight decrease of the shoulder at 1545 cm-1 owing to the α- helix structure. The absorption region of the Amide II β-sheet was scarcely changed by the treatment.
In this investigation, to explore and highlight the conversion of the protein conformation, a broad number of different cosmetically treated fibres were selected from both male and female donors across the three races. The second derivative spectra of the treated fibres were separated into mild chemical treatment and oxidative chemically treated fibres presented in Figure 3.18 and Figure 3.19 respectively.
Figure 3.18 is a selection of four spectra from males CM6, NM6, AM5 and AM14, which have been chosen to illustrate the general effects of mild treatment to the hair fibre pertaining to age (CM6), physical damage (NM6) and use of surface treatments such as hair wax and gel (AM5 and AM14).
138 AM14
AM5 Absorbance(a.u.) COO NM6 OH
OH
CM6 - C=O r TRP SO- SO S-SO O=C-N β CH2 3 2 S=O 3 - CH3 β SO CH 3 α 2 α β β
1600 1400 1200 1000 800 Wavenumber (cm-1)
Figure 3.18 - A comparison of four typical mildly treated, second derivative FTIR-ATR spectra of hair from both male (M) and female (F) of the Caucasian (C), Asian (A) and African-type (N) races.
139 The spectrum CM6 is from a 51 year old Caucasian male. The historical record indicates that the hair has started to grey. Kuzuhara et al. studied the internal structure changes in virgin black hair fibres due to aging using Raman spectroscopy.297 Spectra were acquired from eight females in their mid-twenties and compared to eight females in their mid-fifties. The spectral evidence demonstrated that the cystine content decreased with the increase in age as illustrated by the reduction in intensity of the disulphide (-S-S-) stretch.297
Hordern298 and Panayiotou24 focused on the FTIR spectroscopic analysis of black and melanin poor-to-white hair fibres from the scalps of the same individuals. The melanin poor fibres demonstrated higher levels of Cysteic acid and black hair fibres showed stronger Amide I and Amide II bands, thus supporting the findings by Kuzuhara et al.297
It was suggested that because melanin‟s principal role is to protect the hair fibre proteins from ionising radiation of the sun's ultraviolet rays via preferential oxidation (due to a high electron density); grey-to-white fibres which are melanin deficient are therefore more susceptible to cystine oxidation, resulting in the production of increased levels of cysteic acid.298
Evidence of aging in this fibre can be seen based on the strong intensity of the cysteic band at 1041 cm-1 relative to the samples.
The spectrum NM6 is from an 18 year old African American male who uses a hair moisturiser. In Section 3.1.1.2, an SEM image of this fibre showed severely jagged and chipped cuticle edges as well as up-lifting of the cuticle cells, exposing the underlying cortical layer. The damage was ascribed to physical processes such as grooming.
The spectrum displays little evidence of oxidative treatment based on the weak intensity of the cysteic band at 1041 cm-1. The intensities of the α-helix for the Amide I and Amide II band are stronger than the β-sheet, which suggests that the spectrum has been acquired from the cortex layer which has been exposed due to the physical damage. This is supported by the strong intensity of the υ(C=O) stretch from the acidic amino acids, where the concentrations are higher in the cortex than the cuticle. The spectrum
140 also indicates a strong presence of water based on the intensity of the absorbed H2O bend at 1693 cm-1.
The final two spectra in Figure 3.18 are from Asian males AM5 and AM14 who use wax and hair gel respectively. In Section 3.2.1.4, the analysis of the atypical treated fibres illustrated that the use of surface treatments could affect the absorption spectrum of keratin. However, in these two examples there are no irregular bands present other than those pertaining to the keratin spectrum. The presence of external treatments depends upon the time the product was last applied, when the hair was last cleaned and the amount that is applied to the fibre. Both spectra exhibit the presence of water based on the broad intensity of the Amide I, β-sheet conformation at 1633 cm-1.
Figure 3.19 is a selection of 7 spectra from individuals AF3, AF16, CF9, CF10, CM21, CF20 and NF41 which have been chosen to highlight the effects of cosmetic chemical treatment. The spectra are in order (bottom to top) from weak to strong oxidative chemical treatment. The spectrum of Asian female No. 3 is of a fibre that has been treated with a semi-permanent dye. As mentioned in Section 3.2.1.3, the semi- permanent dye is unlikely to be observed in FTIR-ATR spectroscopy as the dye- pigment penetrates deep into the cortex layer. The strong intensity of the cysteic acid band and the age of the Asian woman (40 years of age) suggest that the melanin pigment is starting to be chemically reduced thus increasing its susceptibility to UVA and UVB radiation to impair and oxidise the –S-S- bond. The fibre samples received from the Asian female No. 16 have been permanently dyed in conjunction with “frosting” or bleaching of the tip towards the shaft which are both oxidative procedures. This is noticeably discernible by the strong intensity of the cysteic acid band and the weak intensity of free carboxylic acid (COO-) group. As the cuticle scale has been lifted or perchance removed to allow the dye pigment to enter the cortex, the intensity of the α-helix has increased as the cortex of this conformational form.
141 NF41
CF20
CM21
Absorbance(a.u.) CF10
CF9 OH
OH AF16
COO -
AF3 SO - r C-H CH - 2 S-SO O=C-N C=O β TRP CH SO S=O 3 CH 3 2 β 3 α 2 - α SO β β 3
1600 1400 1200 1000 800 Wavenumber (cm-1)
Figure 3.19 - A comparison of seven typical chemically treated, second derivative FTIR-ATR spectra of hair from both male (M) and female (F) of the Caucasian (C), Asian (A) and African-type (N) races.
142 The FTIR-ATR spectrum of Caucasian female No. 9 exhibits severe damage which illustrates the most intense level of cysteic acid of the 66 persons which spectra were acquired from. The intensity of this band can be attributed to a number of factors. The hair fibre is white blond which has been bleached with concomitant photo-oxidative bleaching from periodic tanning in the sun because of a decrease in melanin pigment as the subject is 53 years of age. Thus, the hair fibre is highly hydrophilic as denoted by the lack of resolution of the Amide I band. This evidence can be correlated to the microscopic evidence, Figure 3.7; Section 3.1.1.2, which highlights severe lifting of the cuticle scales associated with smoothing of the cuticle layer for the cuticle scales on the uppermost layer should be slightly tilted.
According to the “hair histories” of CF9 and CF10 (Appendix I), their particulars are almost identical except that the individual, CF10, spends minimal time outdoors which illustrates the strength of photo-oxidative bleaching from harmful UVA and UVB rays in Brisbane, Australia. Therefore, as the CF10 spectrum represents a typical example of chemical treatment it will now forth be referred to as CFTR10 (TR = treated) for reference purposes.
The spectrum of CM21 is very similar to that of CF10 with a slight increase in cysteic acid and the amino acid tryptophan emerges as a shoulder peak to the left of the Amide II α-helical band. The final two spectra, CF20 and NF4 have been purposefully chosen to demonstrate the “top-end” and most damaging of the chemical cosmetic treatment scale. The spectrum of CF20 is of an 18 year Hispanic woman who has had their hair permanently-waved. The chemical process as outlined in Section 1.2.5.1, explains that the cuticle scales are lifted to allow the reductive solution to reach the cortex with concomitant cleavage of the disulphide linkages. Oxidising agents are then used to reform the links and the cuticle scales return to their original position. However, the second derivative FTIR-ATR spectrum contradicts this supposition. The intensity of α- helix, of both the Amide I and Amide II bands, have increased dramatically more so in the Amide II bands which suggests that layers of the cuticle have been peeled free of the fibre allowing the evanescent wave of the IR radiation to penetrate the cortical layer, which is rich in the α-helix conformation.
143 It is apparent that ammonium hydroxide has reacted with the acidic aspartic and glutamic amino acid side chains, which is noticeably discernible by the strong intensity of the free carboxylic acid (COO-) group at approximately 1573 cm-1. The strong intensity of the cysteic acid band reveals that the oxidising agent used has not fully reformed the disulphide cross-links.
Finally, NF4 is a spectrum of a 24 year woman from Ghana who had her hair straightened/relaxed and used hair spray to hold it in place. Straightening African-type hair is different to Asian and Caucasian hair because the hair is chemically treated with sodium hydroxide to cleave the disulphide bonds whereas Asian and Caucasian hair can be straightened with a straightening iron. Again, there is strong evidence to suggest that the treatment has been severe by stripping off layers of the cuticle as exemplified by the strong intensities of the α-helix and free carboxylate group of the acidic amino acids.
The comparison of the FTIR-ATR vibrational bands assignments for this investigation is summarised in Table 3.1. These results are compared against literature values and the IR vibrational band assignments using FTIR Micro-spectroscopy from previous investigations.
144 Table 3.1 – Major Vibrational Band Assignments of Human Hair Keratin
Assignments Literature Previous Current Values Investigation Investigation (cm-1)152 (cm-1)24 (cm-1) (ATR)
Amide I 1690-1600 1670 1669 80 % C=O stretch 1650 1631-1627 C-N stretch C-CN
Amide II 1575-1480 1548 1580-1481 60 % C-N stretch 1545 1534 40 % N-H in plane bend 1532 1517 1520-1511 Minor contributions C-C, N-C stretch, C=O in plane bend
(C-H) deformation bend 1471-1460 1470 1461
δ(CH2) deformation bend 1453-1443 1453 1445
δ(CH3) deformation bend 1411-1399 1397 1392 Amide III 1320-1210 1311 1322-1211 30 % N-H in plane bend 1260-155 30 % C-N stretch 1241-1231 1239 1234 Contributions from C-C stretch, C=O in plane bend 1225 O 1121 1121 1114 S S
O Cystine Dioxide stretch S S 1071 1072 1071
O Cystine Monoxide stretch
- -SO3 Cysteic Acid stretch 1040 1041 1037
145 3.3 Chapter Conclusions
Investigating the surface topography of both untreated and chemically treated human hairs at a microscopic level, has provided a basis for the understanding of the chemistry of keratin fibre on a structural level. In general, the SEM analyses of the surface topography suggest that a hair fibre can be of three types:
(1) Untreated fibres with relatively negligible damage to the cuticle, (2) Mildly Treated fibres which are a result of physical/chemical treatment and show moderate chipping and jaggedness of the cuticle edges and, (3) Chemically Treated fibres as a consequence of oxidative chemical reactions and display the highest amount of damage to the cuticle, and also; (4) Combing or maintenance of African-type hair is difficult and abrasive, (5) Chemical damage along the fibre appears to be random from root to tip.
The comparison of male-female untreated and treated hair using raw and second derivative FTIR-ATR spectra highlighted the conformational transformation of the α- helical protein to the β-pleated sheet and random coil conformation as a consequence of cosmetic chemical treatment/s. Untreated male spectra exhibit greater intensity of the β-sheet with a maximum at 1511 cm-1 (Amide II) whilst females exhibit more of the α-helical conformation with a maximum between 1520-1515 cm-1. However, through chemical treatment, the α-helix is untwisted to the β-sheet formation which results in a peak shift to 1511 cm-1. Difference spectra between male and female fibres within each race suggest that female spectra exhibit greater intensity of the amino acids tryphtophan (1554 cm-1) and aspartic and glutamic acid (1577 cm-1).
In general FTIR-ATR spectra showed the dominance of the β-pleated sheet, which suggests the cuticle is comprised of an amorphous matrix as opposed to a fibrous α- helical matrix that makes up the cortical cells. The morphological and structural similarities and differences of untreated, mildly treated and treated fibres have provided a foundation on which the statistical data can be corroborated within the subsequent chapters.
146 4.0 FORENSIC PROTOCOL FOR ANALYSING HUMAN HAIR FIBRES USING FTIR-ATR SPECTROSCOPY WITH THE AID OF CHEMOMETRICS AND MCDM
Panayiotou24 endeavoured to expand her preliminary findings vis-à-vis the discrimination of single hair fibres, which were concerned with object discrimination on the basis of chemical treatment, gender and major race. The intention was to develop a forensic protocol, which is a formal procedure, intended to be followed by forensic scientists when analysing single human hair fibres. The protocol design involved a systematic approach to analysing recovered unknown single hair fibres from crime scenes with the use of FT-IR micro-spectroscopy and interpreting the spectral data with the use of chemometrics. It was envisaged that in the future, the protocol would be used in conjunction with current and legally accepted techniques such as microscopy and DNA analysis. More importantly, it was proposed that the combination of these three techniques would enable improved identification of a hair profile, as there would be information on the morphological, molecular and genetic properties.
In general, when taking an unknown fibre from a crime scene, it is first necessary to compare it microscopically to control fibres from the victim or the alleged suspect (if available) for association purposes. If the questioned fibres are believed to be different based on their morphological features, then, on that basis those accused person/s are excluded from further examination and scrutiny from investigators. If however, the hair fibres are found to be similar in morphological appearance, the fibres are then examined further to remove the subjective nature of the microscopic analysis, for which the conclusions provide circumstantial evidence only. DNA may be present around the follicular sheath which is generally present where the hair fibre has been forcibly removed. However, the majority of fibres found at crimes scenes are naturally shed (i.e. in the telogen phase) and contain no root. Therefore minimal nuclear DNA is present, only mitochondrial DNA (in the hair shaft) which is inherited through the maternal lineage.
147 In conjunction with morphological and DNA analyses, it is also feasible to execute FT- IR spectroscopic measurements on the questioned fibres to gather structural molecular information. Such spectra are examined in conjunction with control fibres.
In the instances where no control fibres are available, or where the questioned fibres cannot be matched when compared to control fibres, then the forensic scientist can still employ FT-IR spectroscopy with the aid of chemometrics whilst following a strict forensic protocol. The results of this analysis provide the investigating police officers with a hair profile identification, supplying them with information on the race, gender and cosmetic treatment (if any) of the alleged suspect‟s hair.
4.1 The Protocol – A Systematic Approach to Hair Fibre Analysis
In the ideal case, the forensic crime scene officer collects unknown hair fibres from the victim and the immediate surroundings of the body.
Before any spectral analysis is carried out on the questioned hair fibre, it is imperative that a suitable spectral database or reference set is assembled, which covers a wide range of individuals of known background/history. Variables such as race and ethnic background; age; cosmetic chemical treatments; including level of sun exposure, medication and even social activities such as swimming in chlorinated or saltwater must all be considered. This information is necessary because it builds up an individual‟s “hair history” that provides evidence, which may aid in the identification process.
With the reference set in place, spectra can be sampled from the unknown fibre and together with the reference spectra, can subsequently be processed by chemometrics for comparison. The flow diagram (Figure 4.1) outlines the methodology for the investigating forensic scientist to follow so as to determine the origin of the unknown hair fibre. In the first instance the spectra are processed using chemometrics and submitted to PCA for pattern recognition (i.e. comparison/discrimination), loadings analysis (i.e. variable separation/s) to justify the basis of the separation, and Fuzzy Clustering for spectral classification.
148
Unknown Hair Fibre
Chemical Treatment
Yes No
Gender Gender
Female Male Female Male
Race Race Race Race
Caucasian Asian Caucasian Asian Caucasian Asian Caucasian Asian
African-type African-type African-type African- type
Figure 4.1 – The proposed forensic protocol24, for the analysis of unknown hair fibres using FTIR spectroscopy and Chemometrics with the inclusion of the novel African-type group (green).
149 PCA facilitates the user to observe clustering between certain scores (objects i.e. spectra) and simultaneously highlights the discrimination or discrepancies between individual groups, allowing inferences and conclusions about the relationships and associations to be established on this basis. Further information/evidence can be obtained from loadings (weightings) plots where the values of the scores are plotted against variables (i.e. wavenumbers cm-1), highlighting which variables have significant weighting on a PC (positive or negative) and also indicates which objects are strongly related to those variables.
The first separation of the scores is on the basis of chemical treatment. For example, if it had been established that the unknown hair fibre had not been chemically treated, all the untreated reference spectra including the unknown fibres are taken from the data matrix and subsequently processed again, whilst the chemically treated spectra are excluded from further analysis.
The computation of the new data subset then separates the spectra on the basis of gender, and for arguments sake it has been recognised that the unknown fibres have originated from a male individual. Thus, taking all the reference untreated male and unknown/suspect male spectra and compiling another novel subset, subsequent processing illustrates the final separation is on the basis of major race (i.e. Asian, Caucasian or African-type).
Unfortunately, the original protocol design suffered from some significant limitations as were outlined in section 1.5.3.3. However, the major deficiency present that affected the potential of the protocol and which required major consideration was the fact that Panayiotou24 did not incorporate African-type hair fibres into the methodology, nor had such hairs been studied spectroscopically in previous studies. Hence, this protocol excluded a significant portion of the population globally and is therefore restrictive.
Barton23 studied African-type hair fibres and attempted to re-construct the proposed protocol to include this class. Thus, it appeared that human hair spectra could be separated on the basis of race, gender and chemical treatment, which validated the prospective protocol methodology with but one unusual exception (section 1.5.4.1). When the African-type hair fibres were partitioned on the basis of chemical treatment,
150 the spectra of untreated African-type hair fibres behaved similarly to Asian and Caucasian chemically treated spectra, while some chemically treated African-type hair fibres displayed similar properties to Asian and Caucasian untreated hair fibres. This is an obvious contradiction to the separations observed for the Caucasian and Asian fibres.152
However, Barton‟s23 African-type fibre sample set contained hair from only eight individuals, and could only be considered as a guideline.
Hence, it is reasonable to suggest that the current protocol methodology has only a basic skeletal framework, which requires improvement to become a comprehensive identification procedure. At this stage, certainty has only been given to FTIR-ATR spectroscopy over Micro-spectroscopy as an acceptable technique for acquiring spectral data based on improved spectral quality. Warranting further investigation however, is a meticulous analysis of the protocol design itself, where ambiguity remains between the separation of spectra of male and female fibres and between each of the races. This specifically refers to the principal differences in the conformational structural chemistry.
Hence, this chapter deals with a detailed investigation of analysing human hair fibres by FTIR-ATR spectroscopy aided by Chemometrics and MCDM. The aim ultimately is to design a forensic protocol which would cover the hair characteristics from the three races – Asian, Caucasian and African-type. The following issues from previous and present investigations were addressed, specifically:
a) To explore the potential spectral regions that could provide optimum discrimination of FTIR-ATR keratin spectra (i.e. the entire fingerprint region between 1750-800 cm-1 and/or different combinations of the Amide I, II and III bands only).
b) To incorporate other chemometric techniques for classification, namely Fuzzy Clustering, for the identification of specific classes of spectra i.e. untreated and chemically treated hair fibres.
151 c) To apply multi-criteria decision making (MCDM) techniques to rank- order the spectra (PROMETHEE) and examine the relationship within and between the classes (GAIA).
d) To compare the second derivative FTIR-ATR keratin spectra and with zero order raw spectra for the proposed protocol.
On completion of the above aims, the objective is to then use the optimum chemometric conditions to investigate the potential of the protocol as a viable hair fibre identification procedure.
4.2 Optimisation of the Proposed Forensic Protocol for Spectroscopic Analysis of Human Hair Fibres with the aid of Chemometrics
A spectral database was obtained from 66 individuals of known hair history (Appendix I). In total, the database contained 550 spectra acquired from 2-3 randomly selected fibres (depending on the length) from each individual, where 3-5 spectra (again, depending on the length) were recorded, in close proximity, along the shaft (i.e. root to middle) of the fibre. The number of fibres examined is less than what would be selected by a forensic examiner, but it must also be taken into consideration that the aim of the investigation was to initially build a database on single or minimal hair fibres and then expand and diversify the protocol appropriately, based on the conclusions.
These 550 spectra were further classified into untreated and chemically treated groups according to the hair history survey. Spectra (350, 170 African-type, 90 Caucasian and 90 Asian spectra) were acquired from individuals with untreated hair (i.e. no chemical treatments or use of external products such as gels, waxes or moisturisers). Conversely, the chemically treated database is based on 200 spectra originating from 90 African- type, 40 Caucasian and 70 Asian spectra, again with an approximate balance between genders within each race.
152 The raw data matrix was double-centred and the resultant matrix was submitted to PCA (Section 2.6).
4.2.1 Spectral Regions and Fibre Discrimination
4.2.1.1 Spectral Range 1750-800 cm-1 The analysis of FTIR-ATR spectra by chemometrics focused on the wavenumber region within 1750-800 cm-1.22-24 26 This included the Amide bands (I, II, and III), δ(C-H) deformations and the cystine oxidation region. In earlier investigations 22 24 152, this spectral region has proven to be successful for the separation of individuals on the basis of chemical treatment, gender and race (Caucasian and Asian hair fibres only). However, more recent studies23 have suggested that there may be some ambiguity between spectra from untreated and chemically treated fibres, especially those from African-type hair.
The uncertainty arises from the fact that although individuals claim in their hair fibre histories that their hair has not been subject to any form of cosmetic chemical treatment, their hair may in-fact have undergone some form of physical/mechanical stress or photo-chemical oxidation. These processes include moderate to severe bleaching by
UVA and UVB radiation from sunlight (and excessive tanning) resulting in fission of the C-S bond; damage to the cuticle surface from rigorous combing, shampooing and towel drying; and the excessive use of hot curling and straightening irons which contributes to the breakage of the disulphide (S-S) linkages. These phenomena and inferences have been observed and well supported in the literature by a number of SEM examinations pertaining to those specific effects.6 11 48 52 60 72
Ultimately, these unmanageable occurrences result in increasing the concentration of cysteic acid and reactive intermediates within the cuticle and cortical layers as damage to the protective surface layers, exposing underlying layers, rendering the fibre susceptible to chemical structure modifications.
This raises the unremitting issue of the discrimination of untreated and chemically treated hair fibres. Hence, it is important to investigate the spectral region between
153 1200-800 cm-1 (i.e. cystine oxidation region) and its importance or otherwise for the discrimination between human hair fibres (with the inclusion of African-type hair fibres) for the forensic protocol.
Initially, when the entire spectral database was processed, the PCA PC1 vs. PC2 scores- scores plot for the 1750-800 cm-1 wavenumber region appeared complex. This plot showed that there were significant atypical spectra present from specific individuals. These objects influenced the core group to cluster heavily around the origin. The atypical spectra originated from the hair fibres of the African-type female number (NF5) and African-type male number (NM7) (Appendix I). They were analysed in the previous chapter (Section 3.2.1.3), and it was established that those individuals had utilised external surface treatments such as hair gels, hairspray and moisturisers. Clearly, the constituents of these treatments would contribute to their IR spectra and hence, distinguish them from the typical untreated hair spectra. It would be noted that the amounts of such treatment need not be large so as to be easily detected in the spectra.
As a result, for the purposes of the protocol concerning questioned fibres, the hair fibres must not be enclosed by an external layer of a cosmetic hair product or any debris that may have adhered to the fibre (e.g. through burial). For example, if fibres are located at grave/burial sites, depending on the environmental surroundings they will contain numerous aggregates of soil particles, micro-organisms, fungal hyphae and debris.23
Fortunately, cleaning/washing methods of hair fibres have been trialled in a number of past investigations23 25 233 234 where it has been established that the revised acetone-water method recommended by the IAEA (Section 2.3.1) is the most efficient. These studies have also suggested that time, intensity and type of sonication are very significant for the cleaning methodology of human hair fibres.233 234 These investigations have illustrated that short time periods at low intensity in a sonication bath are vital in maintaining the integrity of the cuticle layer morphology. Hence, if a hair fibre displays atypical structural behaviour from the keratin protein, it firstly must be cleaned before being processed and compared to a spectral database.
154 These atypical samples were removed and the database was processed again yielding another PC1 vs. PC2 scores-scores plot (Figure 4.2). In total, 86.5 % spectral data variance is explained by the first two PCs with 75.7 % variance on PC1 and 10.8 % variance on PC2.
Untreated Treated African-type
15
10
5
0
-5
-10
PC2(10.8%) -15
-20
-25
-30
-35 -50 -40 -30 -20 -10 0 10 20 30 40 PC1 (75.7%)
Figure 4.2 - PCA scores plot of PC1 (75.7 %) vs. PC2 (10.8 %) of the untreated fibres (blue), the chemically treated fibres (pink) and the entire African-type fibre database (green) using the traditional spectral region between 1750-800 cm-1.
This new plot showed an intense cluster of spectra (denoted by the arbitrary elliptical circle) with low-to-moderate scores on both the positive PC1 and PC2 axes. This group contained the majority of the African-type fibre (green scores) spectral objects of both untreated and cosmetically treated spectra. Hence, although an original African-type spectral subset has been added to the entire database, no distinct separation of untreated and chemically treated African-type spectra was evident because of the intense clustering. This trend of the African-type fibre spectra is consistent with the previous investigation.23 Furthermore, the plot shows little evidence for the discrimination between untreated (denoted in blue) and chemically treated (denoted in pink) fibres when the African-type fibres were included.
155 This phenomenon is inconsistent with the protocol, and thus, the African-type spectra will be addressed independently in the subsequent chapter in order to avoid any further confusion regarding the separations/associations between untreated and chemically treated Caucasian and Asian spectral objects. Hence, the work described in the rest of this chapter will be to assemble an “ideal” spectral data matrix based on typical samples from the Caucasian and Asian subjects. Such data could be used as a reference set for comparison with untreated-treated African-type fibres or those of unknown origin.
Thus, the remaining Asian and Caucasian spectra (292 spectra) were processed to produce a PCA scores-scores plot (Figure 4.3). Overall, 89.2 % spectral data variance was explained by the first two PCs with 74.8 % variance on PC1 and 14.4 % on PC2. It appears that on the PC1 axis, there is a slight trend for the separation of untreated hair fibre spectra (blue) with negative scores on PC1 from chemically treated hair fibre spectra (pink) with positive scores. This is broadly consistent with previous investigations.22-24
Untreated Chemically Treated 15
10
5
0
-5 Caucasian Female Untreated Caucasian Female Treated CFUN 1 CFTR 10
-10 PC2 PC2 (14.4%) -15
-20
-25
-30 -50 -40 -30 -20 -10 0 10 20 30 40 PC1 (74.8%)
Figure 4.3 - PCA scores plot of PC1 (74.8 %) vs. PC2 (14.4 %) of the untreated fibres (blue) and the chemically treated fibres (pink) of Caucasian and Asian fibres between 1750-800 cm-1.
156 This inference is supported by the locality of the typical untreated and treated female spectra that were designated as the reference spectra for each group in the previous chapter. The untreated Caucasian female (CFUN 1) spectral objects have moderate to high scores on negative PC1 whilst the treated Caucasian female (CFTR 10) objects have moderate to high scores on positive PC1. Hence, the large variance between the two groups reflects the difference of the structural chemistry between them. However, it is clear that there are scores from both spectral object sets which overlap each other. Thus, 34 chemically treated spectral objects were associated with the untreated ones, whilst 29 untreated spectral objects were overlapping the chemically treated spectral group. It is unlikely that over sixty fibres were wrongly sampled and measured i.e. they are unlikely to be outliers. Rather, it is more likely that they are atypical objects, which brings into question the reliability of the collected „hair histories‟ collected from the donors, and consequently, their use for the classification of the fibres.
Nevertheless, the explanation for the above misclassification of the fibre spectra is twofold. To begin with, for untreated fibres to demonstrate similar characteristics to those of chemically treated fibres, where the individual claims to not have used cosmetic enhancement, the justification may possibly be a combination of physical/mechanical processes and area of sampling of the fibre.
Reiterating, many FT-IR spectroscopic studies have shown that the levels of cysteic acid and cystine monoxide increase along the length of the fibre from the root to tip. This is a result of weathering processes such as sun bleaching or photo-oxidative attack5 51 52 279, or physical processes such as brushing, combing, styling with hot straightening and curling irons, shampooing and towel drying.48 65 272
Using chemometrics, Panayiotou 22 was able to discriminate between spectra collected at the root, middle and tip off a fibre; illustrating that spectra pertaining to those sampling areas were chemically different, based principally on the amount of cysteic acid in the fibre. Spectra sampled from the root and the middle (shaft) of the fibre was separated along the PC1 axis from spectra sampled at the tip. Furthermore, spectra from the root were separated from the shaft spectra along the PC2 axis. Hence some of the untreated fibres behaved as outliers because the spectra have been sampled from a region between the shaft and tip of the fibre, where cysteic acid concentration is higher.
157 Of particular interest is the majority of the untreated Caucasian male fibre spectra which displayed scores in this dense cluster located on positive PC1 and PC2. The spectra originated from 5 individuals (Caucasian males 4-8 in Appendix I) of European origin, with ages varying from 23 to 54. The commonality between the samples donated by the last three mature male subjects was that they were between 50 and 55 years old and that their hair has proceeded to grey and/or whiten, suggesting that age, weathering and deterioration/absence of melanin pigment are responsible for the association of the spectral objects with chemically treated spectral objects on positive PC1.
Reiterating from the previous chapter (Section 3.3.2), Kuzuhara et al. studied the internal structure changes in virgin black hair fibres as a function of age with the use of Raman spectroscopy.297 FTIR studies with the aid of Chemometrics24 298 focused on the analysis of black and melanin poor-to-white hair fibres from the scalps of the same individuals. Hordern298 established from PCA that Caucasian male, black or melanin rich fibres could be discriminated from those of the Caucasian male white hair fibres from the same individuals along the PC2 axis. Corroborative evidence from the PC2 Loadings plot298 described that the separation was on the basis of white fibres demonstrating higher levels of Cysteic acid and black hair fibres exemplifying stronger Amide I and Amide II bands, thus supporting the findings by Kuzuhara et al.
Hence, grey-to-white fibres which are melanin deficient appeared to be more susceptible to cystine oxidation, resulting in the production of increased levels of cysteic acid.
The hair samples donated by the relatively younger Caucasian male donors are short in length which means that the boundaries (i.e. root, shaft and tip) along the length of the fibre are also shorter. Spectra were likely to have been sampled towards the tip end of the fibre. This suggests that the area of sampling along the fibre is a contributing factor in the discrimination of untreated hair fibres and also a logical explanation for their presence with chemically treated fibres in Figure 4.3.
Alternatively, the grouping of cosmetically treated spectral objects with untreated spectral objects can also be rationalised. Although an individual may claim to have
158 performed a variety of cosmetic enhancements to their hair, the separation is primarily dependent on the time since the chemical treatment had been carried out.
In the scalp, each hair grows progressively at rate of approximately 1 cm per month.32 Hence as the hair fibre grows, the natural melanin pigment is gradually restored from the root to the tip (i.e. regrowth), coupled with the reformation of the stable cystine disulphide links that return mechanical stability to the fibre.5 Simultaneously, for hair dyeing, permanent and semi-permanent dyes are slowly washed out of the hair fibre which is a process that may take up to six weeks.54 Therefore, at the time of sampling if the individual states that the cosmetic process had taken place at least 6-8 weeks prior to sampling, then chemically the fibre would have lower concentrations of cysteic acid, cystine monoxide and cystine dioxide, thus the spectra would display characteristics similar to that of an untreated fibre.
To resolve the difficulty of identifying the spectral objects, other methods of classification were applied to investigate the possibility of the presence of other classes of hair fibre. Fuzzy Clustering (FC, Section 2.7.3.1) method was applied initially to explore how many classes may be present in the data matrix. SIMCA was not as practical because the user has to nominate members of the classes.244
Hence, the Caucasian and Asian spectral database was submitted to FC for modelling. A three-cluster model was calculated with a hard weighting (p = 1.2) based on 4 PCs (96 % data variance). A simple three cluster model was selected allowing, at this stage, for just one other class apart from the untreated and treated fibre. SEM images (Section 3.1) and second derivative spectra (Section 3.3) suggested that a third (intermediate) type of hair fibre existed in nature. The p exponents were chosen so that the results were comparable and consistent with FC results of previous investigations.22 24 152
The FC membership values for Classes 1, 2 and 3 for hard clustering (p=1.2) are presented in Appendix II. With reference to the CFUN1 samples (typical untreated fibres), the table illustrates that these spectral objects (blue) display membership values of 1 or close to one with a hard exponent in Class 3.
159 Alternatively, with reference to the class membership values of CFTR10 (typical chemically treated fibres; pink) exhibit values of 1 or close to one with a hard exponent in Class 2.
The third cluster (Class 1, green), can be attributed to hair samples that have not been subjected to oxidative cosmetic treatment but have been subjected to either mild chemical treatment having moderate levels of cysteic acid due to age, section of the fibre sampled or intense photo-chemical oxidation, surface treatment from gels and waxes, or experienced physical treatment from rigorous grooming. Therefore, the third cluster has provided strong evidence that a third class of hair fibre exists, with the possibility of sub-classes (i.e. mild physical or mild chemical), and has not yet been thoroughly investigated.
In addition to the three classes, as the FC modelling suggests some fibres demonstrated „fuzzy‟ membership with values that vary between 0-1 across the extremes for the three clusters (white). Two types of „fuzzy‟ membership exist in Appendix II. The first type of „fuzzy‟ membership can be observed with fibres that pertain to Asian male, AM19 (i.e. AM191 – AM199 = three fibres with three spectra from each), which claimed to have had no prior chemical treatment. The FC membership values suggest that one fibre that was sampled is treated and the other fibres sampled is untreated.
The second type, which makes up the majority of the „fuzzy‟ class, illustrates that spectra acquired from the same fibre demonstrate membership of all three classes. This type of „fuzziness‟ most likely occurred because spectra were sampled at different locations along the length of the fibre. In total, 116 of the 292 spectra displayed fuzzy membership which is approximately 40 % of the Asian and Caucasian spectral database.
The fuzzy membership of some individual fibres illustrated that each hair sampled randomly from the scalp of an individual may be different chemically, due to moderate- to-harsh weathering from chemical or physical processes. Fibre position and time since cosmetic treatment are contributing factors to the misclassification of the overall chemical state (untreated vs. treated) of each hair fibre on the basis of „hair treatment history‟ as supported by the hair donor.
160 Therefore, it is suggested that a larger number of samples should be randomly selected from an individual‟s scalp to compensate for the variance and reduce the amount of “fuzziness”. However, it was not within the scope and timeframe of the project to analyse many fibres from a particular individual which would only yield data based on fewer individuals. Furthermore, from the forensic perspective, one must take into account that crime scenes are not ideal, and the analyst may only be working with single hair fibres or fragments of fibre. The research, however, does encompass the analysis of single fibres from a vast number of individuals from many ethnic backgrounds to construct a much broader database.
The objects that were classified in the PCA of the original spectral database (Figure 4.3) were reassessed and labelled according to their reclassified chemical state based on the FC results. Each individual contributed approximately 10-15 spectra from two to three fibres. The fibres were labelled as untreated (blue), treated (pink), mildly treated (green) or fuzzy objects (black).
The reclassified PCA scores plot is presented in Figure 4.4. It can be seen that with the inclusion of the 116 „fuzzy‟ samples to the database, the objects are widely spread along the PC1 axis and no discernible trends were found. Therefore, to simplify the scenario, fuzzy objects bearing only those clear cut memberships in the three classes were omitted from the database.
161 Untreated Treated Mild Treatment Fuzzy Samples
15
10
5
0
-5
-10 PC2 PC2 (14.4%) -15
-20
-25
-30 -50 -40 -30 -20 -10 0 10 20 30 40 PC1 (74.8%)
Figure 4.4 – Re-classified PCA scores plot of PC1 (74.8 %) vs. PC2 (14.4 %) of the untreated fibres (blue), the chemically treated fibres (pink), the mild treated fibres (green) and the „fuzzy‟ samples (black) of the Caucasian and Asian fibres.
The PCA scores of the spectral data matrix without the „fuzzy‟ samples are presented in Figure 4.5. It is immediately apparent that untreated fibres (blue objects) with negative scores are discriminated on PC1 from the chemically treated fibres (pink objects) with positive scores on the same PC. Further separation of the spectral database can be delineated along the PC2 axis which explains the next highest amount of spectral data variance (14.4 %). The spectral objects from mildly treated hair fibres (green), cluster tightly positive on PC2, and are separated from untreated and chemically objects, which negative scores on PC2.
162 Untreated Treated Mild Treatment
15 Mildly Treated 10
5
0
-5 CFUN 1 CFTR 10
-10 Untreated PC2 PC2 (14.4%) -15
-20
-25 Chemically Treated -30 -50 -40 -30 -20 -10 0 10 20 30 40 PC1 (74.8%)
Figure 4.5 – Re-classified PCA scores plot of PC1 (74.8 %) vs. PC2 (14.4 %) of the untreated fibres (blue), the chemically treated fibres (pink) and the mildly treated fibres (green) of the Caucasian and Asian hair fibres between 1750-800 cm-1.
Also, the mildly treated group of objects is separated on PC1 into two groups, one with spectra from fibres that have mild physical treatment (negative on PC1) and those that have been exposed to mild chemical oxidation (positive on PC1) according to the “hair history” records.
To explore the possible separation and sub-division of the above mildly treated group, fuzzy clustering was repeated on the database using a four class model based on 4 PCs (96 % total data variance). The FC membership values of the 4 clusters are presented in Appendix III. This table supports the presence of a fourth group. With reference to the typical untreated (blue) and chemically treated (pink) fibres (CFUN1 and CFTR10), they display membership in Clusters 3 and 4, respectively. The mildly treated fibres are segregated into classes‟ noted above „mild physical treatment‟ (turquoise) and „mild chemical treatment‟ (green) which consist of spectral objects with membership in Clusters 1 and 2 respectively. However, the calculation of a fourth cluster increased the
163 number of „fuzzy‟ samples (white) from 116 to 132 or about 45 % of the Caucasian and Asian database.
The PC1 versus PC2 scores plot based on the four class FC model is presented in Figure 4.6. It shows that the mildly treated group has been divided along the PC1 axis. The spectral objects from fibres subjected to mild physical treatment (turquoise), adjacent to the untreated group, have negative scores on PC1 and positive ones on PC2, and are separated from the objects from fibres subjected to mild chemical treatment (light green) (positive scores on PC1 and PC2). However, the main difference to the PCA scores plot of the 3 class model is that the number of chemically treated fibres has increased. This refers to the cluster of spectral objects on positive PC2, which appear to segregate the physical and mild treated groups. The boundaries between the three groups are indistinct, which increases the likelihood of misclassification. But importantly it should be noted that this comparison between the FC modelling and the 2-dimensional representation should only be regarded as an approximation because the FC modelling was carried out with information from 3 and 4 dimensional spaces i.e. 3 or 4PCs, rather than just 2.
Untreated Chemically Treated Mild Chemical Treatment Mild Physical Treatment 15
10 Mild Chemical Treatment Mild Physical Treatment 5
0
-5 CFTR 10 CFUN 1 -10
Untreated PC2 PC2 (14.4%) -15
-20
-25 Chemically Treated
-30 -50 -40 -30 -20 -10 0 10 20 30 40 PC1 (74.8%)
Figure 4.6 – PCA scores plot of PC1 (74.8 %) vs. PC2 (14.4 %) of the untreated fibres (blue), the chemically treated fibres (pink), the mildly physically treated fibres (turquoise), and the mild chemically treated fibres (light green) of the Caucasian and Asian hair fibres between 1750-800 cm-1based on a four class FC model.
164 Nevertheless, the PCA plot (Figure 4.5) still reflects the observations seen in the FC results which illustrate that a third class of hair fibre is apparently present. This observation indicates that the original protocol design is inadequate (Figure 4.1)24 as it considers only two classes: untreated or chemically treated. This suggests that on initial inspection of the unknown or questioned fibre, spectral objects potentially could belong to one of three classes (or possibly four) which relate to the chemical state of the fibre. Hence, with this evidence, a third branch should be added to the tree diagram (Figure 4.1) which stems away from the unknown fibre to the third fibre type coined as mildly treated.
Supporting the evidence for separation of the untreated from chemically treated spectral objects is available in the PC1 loadings plot presented in Figure 4.7. Analysing the positive loadings, which correlate to the scores of the chemically treated and approximately half of the mildly treated fibres positive on PC1, it can be seen that these spectral objects are most heavily influenced by the frequencies between 1200-1000 cm-1 (denoted in purple). Thus, the loadings plot supports the spectral evidence which indicates that when a hair fibre is chemically treated, the products of the oxidation of cystine are cysteic acid (1172 cm-1, anti-symmetric stretch; and 1040 cm-1, sym str.; cystine dioxide (1121 cm-1 sym str.); and cystine monoxide (1071 cm-1; sym str.).
165
Figure 4.7 – PC1 Loadings plot of the chemically treated and mildly treated fibres (positive loadings), and the untreated and mildly treated fibres (negative loadings) between 1750-800 cm-1 region.
Chemically treated fibres have spectra which are also consistently biased towards the frequencies between 1750-1700 cm-1(dark blue). This is attributed to the υ (C=O) stretch of the COOH group. Previous IR and Raman spectroscopic investigations have focused on the variations in amino acid composition in wool and hair as a consequence of chemical treatments such as bleaching and permanent waving.184,236,290,299 Those studies found that the aspartic and glutamic amino acids increased slightly (within a magnitude of µmoles/gram) as a result of cosmetic treatment.
To a lesser extent, weak positive loadings indicate that treated hair fibres are also influenced by frequencies between 1350-1265 cm-1 (dark green) which can be assigned to the δ(CH2) deformation bending mode from the amino acid tryptophan at 1342 cm-1.184 This bond has also been observed to increase slightly as a consequence of 269 -1 treatment. The υs symmetric stretch of cystine dioxide (SO2) stretch at 1315 cm , and finally the vibrational stretches at 1284 cm-1 and 1257 cm-1, which pertain to υ (C-
166 N) stretch and δ (N-H) in-plane-bend of the α-helix and random coil of the Amide III band are also involved.
Conversely, the negative PC1 loadings which refer to the untreated fibres and approximately half of the mildly treated fibres, are related to the frequencies between 1700-1350 cm-1 and 1260-1220 cm-1 which are attributed to the Amide I and Amide II bands (black) at approximately 1627 cm-1 and 1515 cm-1 respectively. The deformation and bending modes of the δ(C-H), (CH2) and (CH3) groups (blue) at approximately 1461 cm-1, 1445 cm-1 and 1392 cm-1 respectively, and lastly, the Amide III band (black) of the β-sheet at approximately 1238 cm-1 are also involved. The results of the negative PC1 loadings suggest that the stable peptide linkage of the polypeptide backbone remains relatively undamaged. The hairs from this group have not been subject to any form of chemical treatment. However, with reference to approximately half of the mildly treated group, these may have undergone some weak form of mechanical/physical stress according to the „hair history‟.
Supporting evidence of the discrimination between mildly treated and untreated- chemically treated hair fibre spectra is presented on the PC2 loadings plot (Figure 4.8). The positive PC2 loadings, which are attributed to the scores of the mildly treated hair fibre spectra, are heavily influenced by variables within the wavenumber region of -1 1500-1241 cm . It includes the deformation and bending modes of the δ(C-H), (CH2) and (CH3) groups (dark blue), υs symmetric cystine dioxide stretch (dark blue); and the stretching frequencies which pertain to the Amide III υ (C-N) stretch and δ (N-H) in- plane-bend of the α-helix and random coil(dark green). The separation is also partially influenced by the cystine dioxide and cystine monoxide stretches within 1115-1050 cm-1 (light blue). The positive loadings suggest that cystine monoxide and cystine dioxides are products of mild oxidation of the cystine bond as a result of weak physical/chemical processes. These processes therefore attribute to the formation of mildly treated or intermediate hair fibres.
167
Figure 4.8 – PC2 Loadings plot of the mildly treated hair fibres (positive loadings), and the untreated and chemically treated fibres (negative loadings) between 1750-800 cm-1.
Alternatively, the negative PC2 loadings are heavily correlated to the scores of the chemically treated hair fibre spectra and are influenced strongly by the variables within the 1240-1120 cm-1 and 1115-1050 cm-1 range (purple). These sections refer to the Amide III of the β-pleated sheet and the asymmetric and symmetric cysteic acid stretches respectively. The negative loadings highlight that these fibres have undergone strong oxidation of the cystine bond, producing the final product cysteic acid.
Hence, for the protocol using the current spectral region between 1750-800 cm-1, exploratory PCA with the aid of FC highlighted the separation of untreated and chemically treated FT-IR spectra along the PC1 axis. The separation is predominantly based of the formation of cysteic acid and intermediates from the oxidation of the amino cystine. However, it has been illustrated that there is some ambiguity between the two groups based on the cystine oxidation region between 1200-1000 cm-1 which suggested that a third spectral group exists. Mildly treated fibres are separated from untreated and chemically treated fibres along the PC2 axis.
168 However, exploratory PCA and FC alone are not suitable indicators to identify the relationships between the three groups as the SIRIUS software does not accommodate performance ranking. PROMETHEE and GAIA (Section 2.7.4.1 and Section 2.7.4.2) however are designed specifically for ranking and investigating scenarios concerned with decision making.300
4.2.1.2 PROMETHEE and GAIA Analysis: 1750-800 cm-1 Spectral Range Previous studies concerning the forensic analysis of hair fibres have utilised MCDM methods to investigate the relationship and differences between human and various animal keratin fibres based on their differences in molecular structure.24 However, with reference to the proposed forensic protocol, no investigations have been carried out making it a novel approach.
Hence, this chemometrics technique was applied to the proposed protocol. The spectral objects for ranking were selected from:
i. untreated fibres which were minimally oxidised and formed a relatively tight PCA cluster (Figure 4.5). ii. mildly treated fibres. iii. chemically treated fibres which showed high levels of cysteic acid and formed a loose cluster (Figure 4.5).
GAIA analysis was performed to investigate the relationships between PC1 and PC2 from the previously evaluated analysis (Section 4.2.1.1) used as criteria.
The 176 x 2 matrix of the PC1 and PC2 scores from the hair fibre spectra were imported into the commercially available Decision Lab 2000 Software301 for MCDM analysis. Table 4.1 outlines the MCDM scenario which shows the assignment of the ranking sense (maximise/minimise), choice of the preference functions, P (a, b), and the associated threshold value, σ, for the two criteria.
169 Table 4.1 Data matrix for ranking of Untreated, Mildly Treated and Chemically Treated Hair Fibre Spectra by PROMETHEE (3-Class Model) Criterion PC1 PC2 Function Type Gaussian Gaussian Minimised True True p - - q - - σ 14.4263 6.8363 Unit (a.u.) (a.u.) Weight 1.00 1.00
The rationale for the selection of various parameters is discussed below. As a necessity of the PROMETHEE model, each criterion must be maximised or minimised. If a criterion is maximised, this implies that the objects with high values are best performing or conversely, if a criterion is minimised the best performing samples have low values. For this scenario, the PC1 and PC2 criteria were minimised. This implies that the PROMETHEE net ranking flow should be dominated by the untreated fibre spectral objects which have negative scores on PC1 and low scores on PC2. This was followed by the mildly treated and chemically treated spectral objects which have positive scores on PC1. From the six preference functions available in Sirius, the Gaussian preference function was selected for the PC1 and PC2 criteria. It was chosen because the PC1 and PC2 scores are derived from the decomposition of the spectra and measurements at any spectral point are normally distributed.302 The weighting for each criterion was set to 1.
The PROMETHEE II net ranking flow chart derived from the above model is illustrated in Table 4.2. The φ values range was +0.813<φ<-0.932, and the groupings showed that the untreated samples (Un) (blue), are the most preferred samples occupying approximately the first 33 ranks ranging from φ = +0.81 - (+0.45). Within these ranks are the 10 spectra from the typical untreated reference sample, CFUN1, i.e. CFUN1 - CFUN110 which verify that the other objects around them are of similar type.
170 Table 4.2 – PROMETHEE II Net Flows of the 1750 – 800 cm-1 Database Net φ Net φ Net φ Rank Object Index Rank Object Index Rank Object Index 1 CF18 0.813 56 MT 0.209 111 MT -0.162 2 Un 0.81 57 Tr 0.177 112 Tr -0.162 3 Un 0.797 58 Tr 0.16 113 MT -0.163 4 CF19 0.795 59 MT 0.16 114 MT -0.174 5 Un 0.764 60 CF106 0.154 115 MT -0.177 6 CF10 0.749 61 MT 0.154 116 Tr -0.177 7 Un 0.747 62 MT 0.143 117 Tr -0.187 8 Un 0.704 63 CF105 0.143 118 MT -0.198 9 Un 0.677 64 MT 0.1395 119 Tr -0.205 10 CF17 0.674 65 MT 0.131 120 MT -0.211 11 Un 0.671 66 MT 0.131 121 MT -0.211 12 Un 0.67 67 MT 0.127 122 MT -0.215 13 Un 0.659 68 MT 0.115 123 MT -0.216 14 Un 0.647 69 MT 0.098 124 MT -0.217 15 Un 0.639 70 MT 0.076 125 MT -0.249 16 Un 0.631 71 MT 0.076 126 CF108 -0.256 17 Un 0.625 72 MT 0.069 127 Un -0.265 18 Un 0.606 73 Tr 0.064 128 MT -0.274 19 Un 0.606 74 MT 0.059 129 MT -0.281 20 Un 0.593 75 MT 0.038 130 MT -0.294 21 Un 0.588 76 MT 0.038 131 MT -0.294 22 Tr 0.535 77 MT 0.035 132 MT -0.297 23 Un 0.534 78 MT 0.029 133 Tr -0.305 24 CF1 0.528 79 Tr 0.029 134 MT -0.314 25 Tr 0.525 80 MT 0.027 135 Tr -0.323 26 Tr 0.518 81 Tr 0.023 136 Tr -0.327 27 CF16 0.516 82 MT 0.019 137 MT -0.334 28 CF14 0.504 83 MT 0.019 138 MT -0.334 29 Un 0.498 84 Tr 0.014 139 MT -0.342 30 Tr 0.477 85 MT 0.005 140 MT -0.362 31 CF13 0.473 86 Tr 0 141 MT -0.376 32 CF12 0.46 87 MT -0.003 142 MT -0.387 33 CF15 0.447 88 MT -0.021 143 MT -0.402 34 MT 0.44 89 MT -0.021 144 MT -0.405 35 MT 0.421 90 MT -0.027 145 MT -0.427 36 MT 0.381 91 MT -0.031 146 MT -0.431 37 MT 0.381 92 MT -0.035 147 MT -0.471 38 MT 0.371 93 MT -0.035 148 MT -0.471 39 Tr 0.365 94 Tr -0.052 149 MT -0.477 40 MT 0.364 95 MT -0.052 150 MT -0.48 41 Un 0.364 96 MT -0.056 151 MT -0.486 42 MT 0.358 97 MT -0.058 152 MT -0.499 43 MT 0.323 98 MT -0.058 153 MT -0.505 44 CF102 0.306 99 CF109 -0.061 154 MT -0.507 45 MT 0.306 100 MT -0.075 155 MT -0.507 46 CF103 0.306 101 CF101 -0.09 156 MT -0.519 47 CF1011 0.299 102 MT -0.092 157 MT -0.525 48 CF1010 0.295 103 MT -0.115 158 MT -0.555 49 MT 0.295 104 CF107 -0.117 159 MT -0.557 50 Tr 0.294 105 CF104 -0.135 160 MT -0.575 51 Un 0.288 106 MT -0.141 161 MT -0.577 52 MT 0.243 107 MT -0.146 162 Tr -0.595 53 MT 0.243 108 MT -0.148 163 MT -0.615 54 Un 0.241 109 MT -0.152 164 MT -0.615 55 MT 0.211 110 MT -0.152 165 MT -0.637 171
Table4.2 - Contined
Net φ Legend Rank Object Index 166 MT -0.637 Untreated (Un) = Blue 167 MT -0.697 168 MT -0.71 169 MT -0.72 Mildly Treated (MT) = Green 170 MT -0.72 171 MT -0.723 Treated (Tr) = Pink 172 MT -0.723 173 Tr -0.773 174 Tr -0.789 175 MT -0.811 176 Tr -0.932
The mildly treated (MT) objects (green) dominate the middle and lower ranks from φ = 0.44-(-0.059) and φ = -0.14-(-0.72). Inter-dispersed within the mildly treated objects are the chemically treated (TR) ones (pink) in the φ ranges of +0.31 – (+0.29) and φ = - 0.06-(-0.13) which are the typical treated reference spectra, CFTR10, i.e. CFTR101 – CFTR1011. The high scattering amongst the mildly treated and treated objects is attributed to the relatively high and non-uniform band intensity of the cysteic acid in those fibre samples as compared to that present in the untreated fibres.
The GAIA bi-plot (Figure 4.9) for this matrix provides a display of the PC1 and PC2 criteria and the 176 spectral objects, decomposing the net outranking flows, providing additional information to PROMETHEE II. In total, 100 % of the data variance is accounted for by the first two PCs, hence all the information has been retained on the GAIA plane. This bi-plot shows that the spectral objects can be separated into three groups, with the untreated fibres forming a tight cluster with high scores on positive PC1; the mildly treated fibres forming a moderate cluster on mainly negative PC1 and positive PC2; and the chemically treated fibres spread across the PC1 axis and on negative PC2. Hence, the plot demonstrates a similar distribution of the 176 spectra in the PCA scores-scores plot of the three classes providing supporting evidence that three classes of fibre exist.
172
PC2
Mild Treatment
PC1
Untreated
Chemically Treated
Δ 100 %
Figure 4.9 – GAIA analysis of the 176 spectra for the Caucasian and Asian hair fibre database between 1750-800 cm-1; ■ untreated fibres, ■ chemically treated fibres, ■ mildly treated hair fibres, ● pi (Π) decision-making axis, and ■ Original PC1 and PC2 criteria using a Gaussian preference function.
The two criteria vectors, PC1 and PC2 (dark green), are orthogonal to each other where PC1 favours the better performing untreated hair spectra, and are separate from the chemically treated objects which are favoured by the PC2 criterion. The Π decision axis (red line) is very strong, indicating a robust decision, pointing towards the untreated fibre spectral group.
173 To explore the possible sub-division of the mildly treated objects into mild physical and mild chemical classes, PROMETHEE II and GAIA was performed on the PCA scores data (PC1 through to PC3, 96 % data variance) from Figure 4.6 (Table 4.3).
Table 4.3 Data matrix for ranking of Untreated, Mildly Treated and Chemically Treated Hair Fibre Spectra (4-Class Model) Criterion PC1 PC2 PC3 Function Type Gaussian Gaussian Gaussian Minimised/Maximised Minimised Minimised Minimised p - - - q - - - σ 14.6998 7.0119 3.7362 Unit (a.u.) (a.u.) (a.u.) Weight 1.00 1.00 1.00
Table 4.4 represents the net flow PROMETHEE ranking chart of the 1750-800 cm-1 database based on a 4-class model. The φ values range was +0.831<φ<-0.64, illustrating that the untreated (Un) samples (blue) are the most preferred samples occupying approximately the first 27 ranks ranging from φ = +0.831 – (+0.393). The treated (TR) objects are the next preferred samples, occupying ranks between φ = +0.366-(-0.006) followed by the mild physical treated (MPT) objects between φ = +0.006 - (-0.042) and φ = -0.206 – (-0.236). The mild chemical treated (MCT) samples are the least preferred objects dominating the lower ranks from approximately φ = -0.33-(-0.42). The ranking of the objects using a 4-class model suggests a trend for the relationship between four hair classes, however the boundaries between each class are indefinite. The model does favour the 3-class model as delineated by PCA (Figure 4.5) and GAIA (Figure 4.10), showing three distinct groups. Hence, the evidence suggests that the 3-class model is sufficient for discrimination and classification of hair fibre spectra.
174 Table 4.4 – PROMETHEE II Net Flows of the 1750 – 800 cm-1 Database (4 Class Model)
Net φ Net φ Rank Object Index Rank Object Index 1 Un 0.831 56 CFTR1011 0.125 2 CFUN17 0.814 57 MPT 0.121 3 Un 0.767 58 MPT 0.097 4 Un 0.747 59 Tr 0.086 5 CFUN18 0.743 60 CFTR107 0.076 6 Un 0.733 61 MPT 0.069
7 CFUN19 0.705 62 Tr 0.062 8 Un 0.698 63 Tr 0.058 9 Un 0.697 64 Tr 0.058 10 CFUN16 0.673 65 Tr 0.054 11 Un 0.634 66 Tr 0.050 12 Un 0.627 67 Un 0.050 13 CFUN15 0.579 68 Tr 0.044 14 Un 0.548 69 Tr 0.039 15 Un 0.539 70 Tr 0.039 16 Un 0.528 71 CFTR109 0.027 17 CFUN12 0.518 72 Tr 0.022 18 Un 0.502 73 Tr 0.022 19 CFUN13 0.494 74 Tr 0.014 20 Tr 0.491 75 Tr 0.014 21 CFUN110 0.474 76 Tr 0.006 22 Un 0.464 77 Tr 0.005 23 Tr 0.437 78 Tr 0.005 24 CFTR102 0.41 79 MPT 0.005 25 Un 0.41 80 CFTR108 -0.000 26 CFUN14 0.395 81 MPT -0.003 27 CFUN11 0.393 82 Tr -0.004 28 Tr 0.366 83 CFTR104 -0.012 29 Un 0.361 84 CFTR106 -0.012 30 Tr 0.335 85 MPT -0.022 31 Un 0.332 86 MPT -0.032 32 Tr 0.327 87 MPT -0.035 33 CFTR103 0.317 88 MPT -0.041 34 Un 0.317 89 Tr -0.042 35 Tr 0.295 90 Tr -0.046 36 Tr 0.287 91 MCT -0.058 37 Un 0.284 92 Un -0.059 38 Tr 0.284 93 Tr -0.065 39 Un 0.282 94 MPT -0.070 40 Tr 0.267 95 Tr -0.089 41 CFTR1010 0.248 96 MPT -0.093 42 Tr 0.224 97 MPT -0.099 43 Tr 0.206 98 Tr -0.101 44 Tr 0.206 99 MCT -0.107 45 MCT 0.195 100 MPT -0.109 46 Tr 0.194 101 MPT -0.121 47 Tr 0.183 102 MCT -0.128 48 Tr 0.182 103 MCT -0.131 49 CFTR105 0.166 104 MCT -0.151 50 Tr 0.166 105 Tr -0.167 51 Tr 0.147 106 MPT -0.169 52 MPT 0.137 107 MCT -0.184 53 Tr 0.135 108 MPT -0.206 54 Tr 0.131 109 MPT -0.209 55 Tr 0.129 110 MPT -0.210 175
Table 4.4 - Continued
Net φ
Rank Object Index Legend
111 MPT -0.213
112 MPT -0.228 Untreated (Un) = Blue
113 Tr -0.229 114 Tr -0.232 Mild Physical Treatment 115 MPT -0.235 (MPT) = Turquoise 116 Tr -0.249
117 Un -0.253 Mild Chemical Treatment 118 MPT -0.263 (MCT) = Green 119 MCT -0.273 120 Tr -0.276
121 Tr -0.281 Treated (Tr) = Pink 122 MPT -0.289 123 MPT -0.306 124 Tr -0.317 125 Tr -0.320 126 Tr -0.329 127 MCT -0.330 128 MCT -0.331 129 MPT -0.335 130 MPT -0.341 131 MCT -0.355 132 MPT -0.356 133 MCT -0.357 134 MPT -0.361 135 MPT -0.363 136 MCT -0.365 137 MCT -0.372 138 Tr -0.374 139 MPT -0.374 140 MCT -0.382 141 Tr -0.394 142 Tr -0.400 143 MCT -0.401 144 MCT -0.409 145 MCT -0.418 146 MCT -0.418 147 Tr -0.425 148 MPT -0.427 149 Tr -0.443 150 MCT -0.446 151 MCT -0.45 152 MPT -0.459 153 Tr -0.467 154 Tr -0.491 155 MPT -0.491 156 MCT -0.510 157 MPT -0.517 158 MPT -0.547 159 Tr -0.564 160 Tr -0.567 161 Tr -0.581 162 Un -0.588 163 MCT -0.588 164 MPT -0.640 176
PC2
Mild Chemical Treatment Chemically Treated
PC1
Untreated
Mild Physical Treatment
Δ 73.1 %
Figure 4.10 - GAIA analysis of the 164 spectra for the Caucasian and Asian hair fibre database between 1750-800 cm-1using a 4-cluster model; ▲untreated fibres, ■ chemically treated fibres, ■ mild chemical treatment hair fibres, ■ mild physical treatment hair fibres, ● pi (Π) decision-making axis, and ■ PC, PC2 and PC3 criteria using a Gaussian preference function.
177 4.2.1.3 Conclusions: 1750-800 cm-1 Database The PC1 versus PC2 scores plot (Figure 4.3) showed a complicated scenario in which many spectral objects were classified according to the historical record of the hair fibres provided by the donors. These objects did not fall into the expected treated-untreated classes.
Fuzzy clustering analysis using a three class model indicated the presence of a third group and also some fuzzy objects. In total, 116 spectra (40 %) of 292 spectra displayed fuzzy membership and could not be used for the spectral database. By discarding those samples the robustness of the database is reduced, hence, the separations of the three fibre classes are based on fewer samples.
When this fuzzy group was removed, the PCA plot also indicated a possible third group. The PC2 loadings suggested that the group belonged to a mildly treated class, which was generally characterised by much lower intensity cysteic acid bands. Furthermore, the PC1 versus PC2 scores plot showed that the mildly treated group could be further separated based on the historical record into the mild physical and mild chemical treated groups. This conclusion is in reasonable agreement with the SEM observations, which generally showed that hair fibres can be classified on a morphological basis, into three groups, which reflected the level of fibre oxidation.
Fuzzy clustering using a four class model separated the mildly treated group into mild physical treatment (e.g. from a combination of rigorous shampooing, towel drying, combing, styling and surface treatments such as gel, wax, mousse etc.) and mild chemical treatment (due to aging and photo-chemical oxidation). However, using PCA and PROMETHEE, the boundaries between the four fibre classes were indefinite, due to the non-uniform intensity of the cysteic acid vibrational band.
The PC1 loadings plot (Figure 4.7) also demonstrated that chemically and mildly treated spectra are strongly influenced by the υa(C=O) stretch of the carboxylic acid group and -1 δ(O-H) bending vibration of H2O between 1750-1690 cm because the of the increase in intensity of the aspartic and glutamic acid vibrational bands, and the hydrophilic nature of the fibre
178 Hence, alternative spectral ranges were investigated within the 1700-1200 cm-1 region. Thus, excluded were the cystine oxidation region between 1200-800 cm-1, the acidic side chain residues and the carboxylic acid and water region between 1750-1690 cm-1. With the removal of these specific regions from the spectra, the major differences were now attributed to the contributions of the different conformational forms - α-helix, β- sheet and random coil.
Hence, the main data matrix of the keratin FTIR-ATR spectral database were pre- processed into a number of sub-set data matrices, 1690-1200 cm-1(Amide I, II and III), and 1690-1500 cm-1(Amide I and II). The 1690-1360 cm-1 (Amide I, II and δ(C-H) deformation and bending) and second derivative spectral objects was also investigated but it gave poor results. All the regions investigated are summarised in Table 4.9 and in Appendices II, III, IV and X.
4.2.2 Investigation of the Alternative Spectral Regions
4.2.2.1 Spectral Range - 1690-1200 cm-1 The 1690-1200 cm-1 spectral region is exclusive to the vibrations of the Amide I – III bands, and the δ(C-H), (CH2), (CH3) deformation and bending absorptions. The previous 1750-800 cm-1 example illustrated that the PC scores for the spectral database could not be designated according to the hair history because some fibres displayed „fuzzy‟ membership between three classes of fibre. Hence, the 1690-1200 cm-1 spectral database was submitted to FC for classification.
To segregate the spectra into the untreated, mildly treated and chemically treated groups, a three-cluster model was calculated with a hard (p = 1.2) weighting exponent based on 4 PCs which explained 98.76 % data variance. The FC membership value for classes 1, 2 and 3 is presented in Appendix IV.
With reference to the typical untreated CFUN 1 spectral objects, the table illustrates that untreated fibres (blue) display memberships values of 1 or close to 1 with a hard exponent in column/class 2. The reference chemically treated fibre objects, CFTR 10,
179 (pink) are found in class 1 and this supports the view that other objects in that class are either chemically or otherwise treated. This is confirmed by the initial classification of the fibres. The third cluster, the mildly treated fibres (green), belongs to class 3. The spectra in the table highlighted in red represent the „fuzzy‟ samples which have membership in multiple classes.
In total, there were 77 spectral objects out of 212 which were fuzzy. This is approximately 26 % of the total Asian and Caucasian spectral database. Hence, by excluding the cystine oxidation spectral region, 39 less spectral objects exhibited fuzzy membership as opposed to the 116 (fuzzy) spectra using the traditional 1750-800 cm-1 region.
The PCA scores-scores plot of the 1690-1200 cm-1 wavenumber region minus the „fuzzy‟ samples is presented in Figure 4.11. In total, 87.8 % of the total spectral data variance is explained by the first two PCs with 79.5 % variance on PC1 and 8.3 % variance on PC2.
Untreated Chemically Treated Mildly Treated
25
Untreated 20
15
10 Chemically Treated CFUN 1
5
PC2 PC2 (8.3%) CFTR 10
0
-5 Mildly Treated Increase in Physical/Chemical Treatment -10 -40 -30 -20 -10 0 10 20 30 PC1 (79.5%)
Figure 4.11 - PCA scores plot of PC1 (79.5 %) vs. PC2 (8.3 %) of the untreated fibres (blue), chemically treated fibres (pink), mildly treated fibres (green) using the alternate spectral region between 1690-1200 cm-1.
180 With the aid of the typical spectral references CFUN 1 and CFTR 10, it can be seen that PC1 favours the separation of untreated (blue) and chemically treated (pink) hair fibres. The mildly treated group forms a tight cluster with negative scores on PC2 and is more or less separated along the same axis from the other two classes, demonstrating some overlap with the chemically treated group. The mildly treated spectra that overlap with the chemically treated spectra pertain to samples that have been subject to mild chemical oxidation (i.e. photo-chemical oxidation) as opposed to damage by physical processes which contribute to the majority of the mildly treated group.
The main difference between the PCA plots of the 1750-800 cm-1 and the 1690-1200 cm-1 spectral regions relates to the variance within untreated and treated spectral groups. In the 1750-800 cm-1 plot, the untreated spectral group forms a very tight cluster suggesting little variance between such samples, whereas in the 1690-1200 cm-1 region the untreated samples form a very loose cluster which illustrates samples within the group are different.
The spectra of untreated spectral objects in the former region displayed little presence of cysteic acid and the spectra were similar in contrast to the treated, however when the cystine oxidation region was removed the main differences within the group are based on the proteins conformation which appears to vary.
The opposite effect is seen with the chemically treated spectral objects which form a very loose cluster in the 1750-800 cm-1 plot and a very tight cluster in the 1690-1200 cm-1 plot. The intensity of the cysteic acid and the associated intermediates peaks varied for the chemically treated samples. This was dependent on the level of chemical treatment, and hence there were significant spectral differences and more spectral objects spread in the 1750-800 cm-1 plot. When the cystine oxidation region was removed, the objects appear to have similar spectral band structure and hence form tight clusters.
The spectral regions that separate the three classes of hair fibre within the 1690-1200 cm-1 are shown in the PC1 and PC2 loadings plots (Figure 4.12 and Figure 4.13). For the PC1 loadings, chemically treated fibres (positive loadings) are heavily influenced by the vibrations of δ(C-H), (CH2), (CH3), (CH2)TRP, and the υs(C=O) stretch
181 of the carboxyl anion between approximately 1490-1310 cm-1 (green) and the Amide III band between approximately 1310-1200 cm-1 (purple).
Untreated fibres are strongly influenced by the absorptions of the Amide I and Amide II vibrational bands between approximately 1681-1490 cm-1(black), again indicating that untreated fibres represent stable peptide linkages.
Figure 4.12 - PC1 Loadings plot of the chemically treated fibres (positive loadings) and the untreated and mildly treated fibres (negative loadings) between 1690-1200 cm-1.
The PC2 loadings analysis demonstrates that the untreated and chemically treated
(positive loadings) samples are heavily influenced by the anti-symmetric υa(C=O) carbonyl of the carboxyl anion and Tryptophan stretches between 1580-1500 cm-1 as well as the deformation band of the δ(CH2) and (CH3) groups between approximately 1480-1440 cm-1. To a lesser extent, such fibres are also influenced by the Amide II -1 band between 1550-1515 cm and the deformation of δ(CH2)TRP of the tryptophan residue and the symmetric υs(C=O) stretch of the carboxyl anion.
182 The negative loadings, which are attributed to the mildly treated fibres, are influenced by the stretches of the β-sheet, random coil and α-helix modes of vibration of the Amide I band between approximately 1690-1590 cm-1 and the Amide III band between 1315-1200 cm-1.
Figure 4.13 – PC2 Loadings of the untreated and chemically treated fibres (positive loadings) and mildly treated fibres (negative loadings) between 1690 -1200 cm-1.
183 To investigate the relationship between the three groups, the 212 x 2 matrix of the PC1 and PC2 scores from the hair fibre spectra were submitted to an MCDM analysis. Table 4.5 outlines the MCDM modelling showing the assignment of the ranking sense, preference function, P (a, b), and the associated threshold values for the two criteria.
Table 4.5 1690-1200 cm-1 Data matrix for ranking of Untreated, Mildly Treated and Chemically Treated Hair Fibre Spectra by PROMETHEE II Criterion PC1 PC2 Function Type Gaussian Gaussian Minimised / Minimised Minimised Maximised p - - q - - σ 9.6782 3.6142 Unit (a.u.) (a.u.) Weight 1.00 1.00
As per the previous model, the data required for the PROMETHEE model is the same. The PROMETHEE II net ranking flow φ indices are given in Table 4.6. The outflow order, φ, was +0.93<φ<-0.62 which highlights that the untreated hair fibres are the most preferred samples occupying the first 28 ranks ranging from φ = +0.93 – (+0.51).
The mildly treated samples (green) are the second most preferred samples which occupy rankings between φ = +0.46 - (-0.27) inter-dispersed amongst approximately 1/3 of the treated objects. The treated objects are the least preferred objects dominating the lower ranks from φ = -0.33 – (-0.57). The main difference between the 1750-800 cm-1 and 1690-1200 cm-1 PROMETHEE II flow charts is that by excluding the cysteic acid region, the treated group became more defined than scattered (as with the 1750-800 cm-1 region).
184 Table 4.6 - PROMETHEE II Net Flows of the 1690 – 1200 cm-1 Database
Net φ Net φ Rank Object Index Rank Object Index 55 MT 0.181 Legend 1 CF18 0.928 56 Un 0.176 2 Un 0.928 57 Un 0.167 3 CF19 0.904 Untreated (Un) = Blue 58 MT 0.163 4 Un 0.902 59 Un 0.159 5 Un 0.889 Mildly Treated (MT) = Green 60 MT 0.157 6 CF110 0.883 61 MT 0.150 7 CF17 0.879 Treated (Tr) = Pink 62 MT 0.117 8 Un 0.863 63 Tr 0.110 9 Un 0.844 64 MT 0.108 10 Un 0.835 65 Tr 0.106 11 Un 0.817 66 MT 0.104 12 CF13 0.816 67 MT 0.097 13 CF16 0.801 68 MT 0.097 14 Un 0.793 69 Tr 0.089 15 Un 0.787 70 MT 0.088 16 CF11 0.770 71 MT 0.083 17 CF14 0.759 72 CF105 0.065 18 CF15 0.753 73 MT 0.064 19 Un 0.745 74 MT 0.054 20 Un 0.713 75 Tr 0.041 21 Un 0.692 76 MT 0.039 22 Un 0.687 77 MT 0.033 23 Un 0.686 78 MT 0.027 24 Un 0.679 79 MT 0.026 25 Un 0.646 80 Un 0.025 26 CF12 0.639 81 MT 0.024 27 Tr 0.604 82 MT 0.023 28 Un 0.512 83 MT 0.014 29 MT 0.460 84 MT 0.01 30 MT 0.457 85 MT 0.006 31 MT 0.450 86 Tr -0.009 32 Tr 0.45 87 MT -0.015 33 MT 0.372 88 MT -0.017 34 Un 0.363 89 MT -0.017 35 MT 0.354 90 MT -0.020 36 Un 0.330 91 MT -0.025 37 MT 0.320 92 CF101 -0.026 38 MT 0.317 93 MT -0.036 39 MT 0.310 94 CF106 -0.036 40 Tr 0.284 95 Un -0.054 41 Tr 0.274 96 Tr -0.056 42 MT 0.265 97 MT -0.059 43 MT 0.264 98 MT -0.061 44 MT 0.259 99 MT -0.061 45 Tr 0.250 100 MT -0.063 46 Tr 0.249 101 CF104 -0.064 47 CF103 0.236 102 Tr -0.100 48 MT 0.234 103 MT -0.103 49 Tr 0.231 104 Un -0.106 50 CF102 0.225 105 MT -0.106 51 Un 0.223 106 MT -0.119 52 MT 0.216 107 MT -0.123 53 Tr 0.195 108 MT -0.123 54 Un 0.191 109 MT -0.125 185
Table 4.6 - Continued Net φ Net φ Rank Object Index Rank Object Index 110 MT -0.134 165 Tr -0.31 111 CF1011 -0.134 166 Tr -0.310 112 CF1010 -0.137 167 Tr -0.311 113 MT -0.140 168 MT -0.311 114 MT -0.146 169 Tr -0.312 115 MT -0.149 170 MT -0.314 116 MT -0.153 171 Tr -0.315 117 Un -0.154 172 MT -0.316 118 Tr -0.163 173 MT -0.316 119 MT -0.163 174 Tr -0.318 120 Tr -0.164 175 MT -0.323
121 Tr -0.167 176 MT -0.325 122 MT -0.177 177 MT -0.325 123 MT -0.180 178 MT -0.326 124 MT -0.182 179 Tr -0.327 125 Tr -0.183 180 MT -0.330 126 MT -0.186 181 CF108 -0.335 127 MT -0.194 182 MT -0.336 183 Tr -0.344 128 Tr -0.195 184 MT -0.346 129 Tr -0.201 185 Tr -0.351 130 MT -0.203 186 MT -0.359 131 Tr -0.204 187 MT -0.361 132 MT -0.204 188 MT -0.364
133 Tr -0.204 189 Tr -0.364 134 Tr -0.207 190 MT -0.366 135 MT -0.208 191 MT -0.367 136 MT -0.210 192 Tr -0.368 137 MT -0.215 193 Tr -0.369 138 MT -0.216 194 MT -0.377 139 MT -0.224 195 CF107 -0.396 140 Tr -0.225 196 Tr -0.402 141 Tr -0.22 197 Tr -0.405 142 MT -0.237 198 Tr -0.418 143 Tr -0.239 199 Tr -0.420 144 MT -0.242 200 Tr -0.433 145 MT -0.243 201 Tr -0.437
146 Tr -0.246 202 Tr -0.458 147 MT -0.248 203 MT -0.459 148 Un -0.249 204 Tr -0.465 149 Tr -0.250 205 MT -0.467 150 MT -0.252 206 Tr -0.472 151 MT -0.255 207 Tr -0.478 152 MT -0.269 208 MT -0.495 153 MT -0.269 209 Tr -0.505 154 MT -0.272 210 MT -0.546 155 CF108 -0.282 211 Tr -0.572 156 MT -0.283 212 MT -0.62 157 Tr -0.286 158 Tr -0.287 159 Tr -0.288 160 Tr -0.292 161 MT -0.299 162 MT -0.301 163 MT -0.304 164 MT -0.304 186
The GAIA bi-plot of the criteria and the 212 spectra for the 1690-1200 cm-1 matrix is presented in Figure 4.14. In total, 100 % of the data variance is accounted for by the first two PCs, hence all the information is retained. From the plot, one is able to conclude that spectral objects are roughly separated into three groups. However, the majority of the mildly treated spectra form a tight cluster with scores on positive PC1 but clearly disperse across the PC1 axis and integrate with the chemically treated spectra which form a tight cluster on negative PC1. The untreated fibres form a tight cluster on both positive PC1 and PC2. This group is relatively separate from the other two groups which further illustrated their difference in chemical structure.
The two criteria vectors, PC1 and PC2 (dark green), are orthogonal and moderately surround the majority of the mildly treated hair spectra. The Π decision axis (red line) is very strong, indicating a robust decision, pointing towards the mildly treated fibre spectral group with minor influence from some untreated and chemically treated spectra. Hence, for this matrix, the mildly treated fibres are the better performing samples.
In comparison to the GAIA plot the 1750-800 cm-1 matrix (Figure 4.10), there is more overlap between the mildly and chemically treated groups which creates a grey area for the discrimination between those fibre types. However, the main difference between the two GAIA plots concerns the decision axis vector which favours the mildly treated fibres over the untreated fibres.
187
PC2
Mild Treatment Chemically Treated
PC1
Untreated
Δ 100 %
Figure 4.14 - GAIA analysis of the 212 spectra for the 1690-1200 cm-1 hair fibre database; ▲ untreated fibres, ■ chemically treated fibres, ■ mildly treated hair fibres, ● pi (Π) decision-making axis, and ■ PC1 and PC2 criterion variables using a Gaussian preference function.
For the protocol, using the 1690-1200 cm-1 spectral region, having the mildly treated fibres as the stronger performing samples is not feasible. The PCA, PROMETHEE and GAIA evidence demonstrate that the group is not isolated because the spectra share similar characteristics to chemically treated fibres. However, the evidence illustrates that the untreated fibres are an isolated group which represent spectra in the “raw” chemical state and thus should be used as the reference set.
188 The loadings analysis for the 1690-1200 cm-1 region revealed that the Amide III band (β-pleated sheet), affects the separation between treated and untreated fibres. The IR evidence in the previous chapter (Section 3.2.3) illustrated that the band slightly increases with chemical treatment due to an increase in the random coil conformation. Therefore, the assessment of the next alternative spectral region excluded the Amide III band from the spectrum.
4.2.2.2 Chemometric Analysis of Single Human Hair Fibres using Alternative Spectral Regions - 1690-1500 cm-1 The 1690-1500 cm-1 IR region for hair keratin is restricted only to the vibrations of the Amide I and Amide II bands. FC analysis of a 3-cluster model with hard weighting (p = 1.2) was performed on the 292 spectra. The FC results for the 1690-1500 cm-1 database are presented in Appendix V. The untreated (CFUN1) and chemically treated (CFTR10) reference spectra illustrate that those classes show membership to clusters two and one respectively. The mildly treated class shows membership to class three. The samples highlighted in red have fuzzy membership. In total, 83 spectra had fuzzy membership; a loss only 28 % of the total database which is an improvement in comparison to the current 1750-800 cm-1 analysis region.
The PCA scores-scores plot of the 1690-1500 cm-1 wavenumber region is presented in Figure 4.15. In total, 88.9 % of the total spectral data variance is explained by the first two PCs with 72.3 % variance on PC1 and 16.6 % variance on PC2 (4 PCs 97 % data variance). It can be seen that with the exclusion of the amino acid side chain contribution from the spectrum, the total % data variance is similar to the data variance explained by the 1700-850 cm-1 PCA plot (89.2 %).
189 Untreated Treated Mildly Treated
20
Untreated 15
10 Chemically Treated CFUN 1
5
CFTR 10 PC2 PC2 (16.6%) 0
-5 Increase in Mildly Treated Physical/Chemical Treatment -10 -20 -15 -10 -5 0 5 10 15 20 PC1 (72.3 %)
Figure 4.15 - PCA scores plot of PC1 (72.3 %) vs. PC2 (16.6 %) of the untreated fibres (blue), mildly treated fibres (green) and the chemically treated fibres (pink) using the alternate spectral region between 1690-1500 cm-1.
With respect to the reference samples (CFUN 1 and CFTR 10), the untreated spectra (blue) form a loose cluster with positive scores on PC1 and PC2 and are separated across the PC1 axis from the chemically treated spectra which form a tight cluster with negative scores on PC1. The mildly treated fibres form a tight cluster with negative scores on PC2, adjacent to the chemically treated and untreated group. The overlap of scores between the mildly treated and chemically treated groups is low, compared to the 1690-1200 cm-1 and 1690-1360 cm-1 PCA scores plots. The lack or reduction in overlap is important because it decreases the likelihood of object misclassification. This is especially important for classifying fibres of unknown origin.
The keratin spectra had been truncated to about 200 cm-1, and the bands responsible for the discrimination of untreated and treated fibres within 1690-1500 cm-1 are reflected in the loadings plots (Figures 4.16 and 4.17).
190
Figure 4.16 - PC1 Loadings plot of the untreated and mildly treated fibres (positive loadings) and the chemically treated fibres (negative loadings) between 1690-1500 cm-1.
Figure 4.17 - PC2 Loadings plot of the untreated and chemically treated fibres (positive loadings) and the mildly treated fibres (negative loadings) between 1690-1500 cm-1.
191 For the PC1 loadings (Figure 4.16), it can be seen that the untreated and mildly treated fibres (positive loadings) are influenced by the α-helical and β-pleated sheet of the Amide I and Amide II bands (black) between 1660-1600 cm-1 and 1550-1500 cm-1 respectively. Conversely, the treated fibres are influenced by the changes occurring to the Amide I υ(CONH2) stretch of the asparagine and glutamine side chains and υ(C=O) stretch of the β-pleated sheet and random coil conformation between approximately -1 1690-1670 cm (dark blue); the anti-symmetric υa(C=O) carbonyl stretch of the aspartic and glutamic acid between 1590-1570 cm-1 (green); and the vibration of the 3- substituted indole ring of tryptophan between 1570-1550 cm-1 (blue).
The PC2 loadings (Figure 4.17) are complex because the positive loadings represent the untreated fibres and approximately half of the chemically treated spectral group whereas the negative loadings represent the mildly treated fibres and the other half of the chemically treated fibres.
To investigate the relationship and ranking between the three groups, the 209 x 2 matrix of the PC1 and PC2 scores from the hair fibre spectra was submitted to an MCDM analysis. Table 4.7 outlines the MCDM scenario showing the assignment of the ranking sense, preference function, P (a, b), and associated threshold values for the two criteria.
Table 4.7 1690-1500 cm-1 Data matrix required for ranking of Untreated, Mildly Treated and Chemically Treated Hair Fibre Spectra by PROMETHEE (3-Class) Criterion PC1 PC2 Function Type Gaussian Gaussian Maximised True True p - - q - - σ 6.1065 3.0013 Unit (a.u.) (a.u.) Weight 1.00 1.00
192 Maximisation of the PC1 and PC2 criteria for this scenario suggests that the PROMETHEE net ranking flow should be dominated by the untreated fibres group which have positive scores on PC1 and PC2 (best-performing samples) followed by the mildly treated and chemically treated fibres (worst-performing samples).
The PROMETHEE II net ranking chart for the 1690-1500 cm-1 region is presented in Table 4.8. The Φ values range was +0.95<φ<-0.57, which demonstrated that the untreated samples are the most preferred samples occupying the first 27 ranks from φ = 0.95 – 0.47. The mildly treated and chemically treated fibres are the next preferred samples which are well dispersed across the remaining 170 ranks between φ = 0.37 – (- 0.57).
The GAIA bi-plot of the 209 spectra for the 1690-1500 cm-1 database is presented in Figure 4.18. In total, 100 % of the data variance is accounted for by the first two PCs. The untreated fibres form a very tight cluster on +PC1 and –PC2, which is well separated from the mildly treated and chemically treated fibres. The mildly treated fibres form a dense cluster on +PC1 separated across the PC1 axis from the chemically treated fibres forming cluster on –PC1. However, some overlap exists between the mildly treated and chemically treated groups because of the close relationship in conformation.
193 Table 4.8 - PROMETHEE II Net Flows of the 1690 – 1500 cm-1 Database
Net φ Net φ Rank Object Index Rank Object Index Legend 1 Un 0.949 56 CF105 0.123 2 Un 0.931 57 MT 0.121 Untreated (Un) = Blue 3 CF18 0.914 58 Un 0.118 4 Un 0.905 59 MT 0.115 Mildly Treated (MT) = Green 5 Un 0.893 60 MT 0.115
6 Un 0.887 61 MT 0.114
7 CF19 0.884 62 MT 0.103 Treated (Tr) = Pink 8 Un 0.875 63 Un 0.099 9 Un 0.872 64 MT 0.097 10 Un 0.859 65 MT 0.096 11 CF110 0.857 66 MT 0.094 12 CF17 0.832 67 Tr 0.084 13 Un 0.811 68 MT 0.063 14 Un 0.805 69 MT 0.061 15 Un 0.781 70 MT 0.046 16 Un 0.781 71 MT 0.045 17 CF16 0.773 72 MT 0.037 18 CF13 0.743 73 Tr 0.037 19 Un 0.698 74 MT 0.032 20 CF14 0.688 75 MT 0.019 21 CF11 0.681 76 MT 0.015 22 CF15 0.649 77 Tr 0.013 23 CF12 0.6318 78 MT 0.008 24 Tr 0.546 79 MT 0.003 25 Un 0.501 80 Tr 0.001 26 MT 0.475 81 Tr 0 27 Un 0.47 82 MT -0.007 28 MT 0.377 83 CF1010 -0.024 29 MT 0.376 84 MT -0.027 30 MT 0.357 85 MT -0.03 31 MT 0.305 86 MT -0.033 32 Tr 0.304 87 CF106 -0.034 33 MT 0.301 88 MT -0.036 34 Tr 0.285 89 MT -0.04 35 CF102 0.262 90 MT -0.054 36 Tr 0.258 91 CF104 -0.056 37 MT 0.247 92 CF106 -0.056 38 Tr 0.241 93 MT -0.06 39 Tr 0.239 94 Tr -0.063 40 MT 0.23 95 Tr -0.064 41 MT 0.223 96 MT -0.065 42 MT 0.217 97 MT -0.065 43 MT 0.215 98 MT -0.066 44 Tr 0.206 99 MT -0.066 45 MT 0.202 100 MT -0.068 46 MT 0.17 101 MT -0.068 47 Tr 0.164 102 MT -0.071 48 MT 0.163 103 Tr -0.071 49 Tr 0.141 104 MT -0.076 50 MT 0.135 105 MT -0.076 51 CF103 0.132 106 MT -0.079 52 MT 0.132 107 MT -0.09 53 Tr 0.131 108 MT -0.093 54 MT 0.129 109 MT -0.096 55 Tr 0.123 110 MT -0.097 194
Table 4.8 - Continued Net φ Net φ Rank Object Index Rank Object Index 111 MT -0.107 166 Tr -0.277 112 Tr -0.107 167 MT -0.279 113 Tr -0.11 168 MT -0.28 114 MT -0.111 169 MT -0.285 115 MT -0.111 170 MT -0.289 116 MT -0.119 171 Tr -0.296 117 MT -0.123 172 MT -0.297 118 MT -0.134 173 Tr -0.302 119 CF109 -0.137 174 MT -0.315 120 MT -0.139 175 Tr -0.318 121 MT -0.144 176 Tr -0.319 122 MT -0.147 177 Tr -0.323 123 MT -0.148 178 MT -0.323 124 Tr -0.152 179 Tr -0.324 125 MT -0.155 180 Tr -0.324 126 MT -0.159 181 Tr -0.324 127 MT -0.163 182 Tr -0.332 128 MT -0.165 183 Tr -0.338 129 Tr -0.172 184 Tr -0.342 130 Tr -0.175 185 MT -0.345 131 Tr -0.176 186 MT -0.362 132 CF1011 -0.179 187 MT -0.381 133 CF102 -0.18 188 Tr -0.384 134 Tr -0.182 189 Tr -0.389 135 Tr -0.182 190 MT -0.391 136 Tr -0.183 191 Tr -0.393 137 MT -0.185 192 Tr -0.394
138 MT -0.186 193 MT -0.397 139 MT -0.187 194 MT -0.405 140 MT -0.19 195 Tr -0.42 141 MT -0.191 196 MT -0.425 142 MT -0.191 197 MT -0.425 143 Tr -0.196 198 Tr -0.431 144 CF107 -0.198 199 Tr -0.435 145 Tr -0.2 200 Tr -0.436 146 MT -0.2 201 Tr -0.439 147 Tr -0.201 202 MT -0.445 148 MT -0.205 203 MT -0.458 149 Tr -0.207 204 Tr -0.461 150 Tr -0.212 205 MT -0.512 151 Tr -0.221 206 Tr -0.523 152 MT -0.222 207 Tr -0.547 153 Tr -0.229 208 MT -0.567 154 Tr -0.231 155 MT -0.237 156 Tr -0.247 157 MT -0.251 158 Tr -0.256 159 Tr -0.258 160 Tr -0.26 161 MT -0.261 162 CF108 -0.261 163 MT -0.263 164 Tr -0.268 165 MT -0.272 195
PC2
Mild Treatment Chemically Treated
PC1
Untreated
Δ 100 %
Figure 4.18 - GAIA analysis of the 208 spectra for the 1690-1500 cm-1 hair fibre database; ▲ untreated fibres, ■ chemically treated fibres, ■ mildly treated hair fibres, ● pi (Π) decision-making axis, and ■ PC1 and PC2 criterion variables using a Gaussian preference function.
196 4.2.3 Chemometric Analysis of Further Alternative Spectral Regions of Keratin FTIR-ATR and Second Derivative Spectra
The 292 spectra of the Caucasian and Asian fibres were converted into second derivate spectra as outlined in Section 2.5.1. The double-centred second derivative matrix was then submitted to Sirius for chemometric analysis of the current and alternative spectral regions. However, it must be taken into account that by taking the second derivative of the spectra, the downwards peaks or troughs are related to the keratin spectrum and the upwards peaks do not apply to the separation. Nevertheless, it was important to consider whether second derivate spectra enhanced the separation of the three classes of hair fibre. The results using second derivative did not enhance/improve the discrimination of the spectral objects. The chemometric analyses of the alternative regions are presented in Appendix X.
4.3 Chapter Conclusions
In summary, this chapter has dealt with a detailed study to determine the optimum spectral conditions in which to investigate single human hair fibres as part of a forensic protocol. The analysis used raw spectra, and for the first time second derivative spectra were trialled. In preparation of the optimised protocol, some information came to light that had not been discovered by previous investigations and is summarised below:
The historical record cannot be used for classification because of the vague discrimination of an untreated and chemically treated fibre. FC for unsupervised, non-biased classification was applied to the database to determine how many classes of fibre were present. 3 types of hair fibre exist – untreated, mildly treated and chemically treated. The mildly treated fibres exhibit an intermediate level of cystine oxidation. The mildly treated group can be sub-divided into the mild physical and mild chemical treated groups. PROMETHEE II rank orders the objects from untreated, moderate to harsh oxidation.
197 The GAIA bi-plot illustrates the clustering of the groups and indicates the most preferred samples in the database. Second derivative spectra are useful for qualitative analysis; however, Chemometric analysis does not provide evidence for the basis of the separations as loadings (variables) plot are complex.
After exploration of the traditional and several alternate spectral regions of the keratin spectrum, the 1690-1500 cm-1 region (raw spectra) provided satisfactory results for discrimination based on the robustness (No. of objects used), and the PCA and GAIA separations. The results for the investigation of the protocol are summarised in Table 4.9.
198 Table 4.9 Summary of Chemometric Results for Current and Alternative Spectral Regions of Raw and Second Derivative Spectra
Spectral Region PCA PCA PROMETHEE GAIA (cm-1) (No. of non- Separation* (Best Performing Separation* fuzzy (No overlap Samples) (No Overlap Objects) to Heavily to Heavily Overlapped) Overlapped) 1750-800 cm-1 176/292 Good Untreated Average- (3-Class Model) 60 % Good 1750-800 cm-1 164/292 Good Untreated Average- (4-Class Model) 56.2 % Good 1690-1200 cm-1 212/292 Average Mildly Treated Poor-Average (3-Class Model) 72.6 % 1690-1360 cm-1 202/292 Average Untreated Average (3-Class Model) 69 % (Appendix X) 1690-1500 cm-1 209/292 Good Untreated Average- (3-Class Model) 72.0 % Good 1750-800 cm-1 176/292 Good Mildly Treated Average- Second Good Derivative 60 % (3-Class Model) (Appendix X) 1690-1500cm-1 200/292 Average Untreated/Mildly Poor-Average Second Treated Derivative 68.5 % (3-Class Model) (Appendix X)
* PCA and GAIA Separation = The evaluation in the table above is only a subjective visual analysis method based on the effectiveness of the separation of the three fibre groups untreated, mildly treated and chemically treated. For example, in the first
199 scenario, the PCA plot of 1750-800 cm-1 revealed that each group was separated by the PC1 (mildly treated from untreated and chemically treated) and PC2 axis (untreated from chemically treated) with very little overlap. However, the PCA plot of 1690-1200 cm-1 it can be seen that there is a lot of overlap between the mildly treated and chemically treated groups which increases the risk of misclassification for unknown spectra in that particular area. In the new, alternate region, 1690-1500 cm-1, it provides good separation of the three hair classes and less fuzzy objects are encountered.
200 5.0 APPLICATIONS OF THE FORENSIC PROTOCOL AS AN IDENTIFICATION PROCEDURE FOR SINGLE HUMAN HAIR FIBRES
5.1 Principles of the Forensic Protocol
Panayiotou24 envisaged that the protocol would be utilised by forensic authorities as a procedure for the identification of questioned hair fibres that would corroborate the information obtained from microscopic and genetic examinations. The „Blue Sky Vision‟ of the ongoing research and development in this field is to create a comprehensive database of hair fibre spectra to be utilised for comparison of hair fibres of unknown origin. The database should encompass IR spectra from many different types of hair sample to compensate for age; race/mixed race; grooming habits; cosmetic desires; and personal lifestyle (i.e. swimming and tanning). Additionally, the database should incorporate information regarding the whole hair fibre, which has been shown to be different from root to tip.22 26 The information that would be extracted with aid of this protocol should be employed for initial screening to narrow the scope and direction of the forensic investigation.
However, the main disadvantage of Panyiotou‟s protocol was that it was limited to Caucasian and Asian hair only and did not include the third important African-type group. Also, it did not consider the possibility of sub-classes other than untreated and treated hair e.g. light or heavily treated hair.
In the light of the above two disadvantages, Barton‟s work (2004) is significant. 23 He collected an FTIR-ATR spectral database from a wide array of individuals and included for the first time African-type hair fibres. The spectra were processed by PCA to establish if the separations that were observed with the Asian and Caucasian fibres with the use of FTIR-Micro-spectroscopy in the earlier Panayiotou study, were valid.24 As a result, it was determined that with the introduction of African-type hair fibres, the separation of those hairs on the basis of chemical treatment (i.e. the first separation of
201 the spectra as proposed by the protocol) appeared to contradict the initial protocol model (Figure 4.2, Chapter 4, Section 4.2.1.1).
Interestingly, the PCA scores plot illustrated that some untreated African-type spectra clustered with the chemically treated spectra with positive PC1 scores. Chemically treated African-type hair spectra were observed to be associated with the untreated fibre spectra with negative PC1 scores. No plausible results or evidence existed at the time to explain these observations. However, at that stage, it was suggested that the phenomenon could be explained through an understanding of the morphology and chemical composition of the African-type hair fibre e.g. African hair fibres characteristically have more crimp compared to the other races.303
With the PCA of the untreated African-type fibres and with reference to the “hair history”, there was no evidence to suggest that these fibres could be considered outliers or rather chemically treated fibres. The fibres had not been subjected to any hair product/s, received minimal sun exposure and the individuals swam only rarely. Thus, it was hypothesised that African-type hair fibres have elevated levels of cystine and moderate to high levels of cysteic acid in comparison to the Caucasian and Asian races. Consequently, any form of light to moderate natural weathering (i.e. photo-oxidative bleaching) increases the concentration levels of cysteic acid in the hair fibre and when processed by chemometrics, a spectrum from such a fibre would be recognised as that of a treated fibre.
The treated African-type hair fibres also displayed atypical results at the time. The main reason suggested for this behaviour was the use of surface treatments such as gel and hairspray. It was also reasonable to suggest that there was further discrimination of the treated African-type spectral objects from the other treated spectral objects (i.e. Asian and Caucasian) on the basis of multiple treatments versus single treatment, which had been indicated by Panayiotou in previous investigations.22
Thus, this complex issue was explored further in this work so as to define analytical methodology, which would resolve the problem observed. Therefore, this final chapter explores the strength and potential of the optimised forensic protocol (Chapter 4) as a technique to differentiate between the structural
202 characteristics of single human hair fibres, which relate to chemical treatment, gender and race.
The aims were:
In general, to analyse thoroughly the similarities and differences between FTIR- ATR spectra of Asian, Caucasian and African-type human hair with the ultimate aim of proposing a protocol, which could be applied in forensic investigations.
Specifically to: 1. Analyse Chemically Treated Hair Fibres To study various chemically treated hair fibres from mild chemical treatment (i.e. cosmetic surface treatments such as gel and hairspray, straightening with an iron, etc.) to harsh oxidative chemical treatments (i.e. bleaching and permanent dyeing).
2. Understand the Structural Differences between Hairs of Different Gender Sources To investigate the basis of separation between male and female hair fibre spectra with supporting evidence from second derivative and IR difference spectra (Section 3.2.2.1).
3. Investigate the Structural Differences of Hair from Subjects of Different Race To investigate the IR spectral variables that are significant in the discrimination of hairs of each of the three major races, Asian, Caucasian and African-type.
203 5.2 African-type Hair Fibres
Racial differences in scalp hair have been the subject of much interest.112 The term African-type, refers to a major human racial classification traditionally distinguished by physical characteristics. Black African-type hair, from the indigenous people of mainly southern African (sub-Saharan Africa), Melanesia and Papua New Guinea (PNG) region, is characterised by the tight spring-like coiling of the hair shaft.112 Additionally, there are varying degrees of curl, and it has been hypothesised that these geometric differences can influence the mechanical properties of hair.304 There are six main physical and chemical attributes of African-type hair that separate them from Asian and Caucasian hair fibres.
5.2.1 Physical and Chemical characteristics of African-type hair fibres: 1. Diameter and Cross-section: African-type hairs demonstrate a high degree of irregularity in diameter and have an elliptical cross-section.114 The diameter is smaller than that of the other two races.91 2. Shape: The shape of a hair fibre resembles a twisted oval rod.114 3. Mechanical Properties: The hair has low tensile strength and breaks more easily than Caucasian hair.114 Porter et al.304 suggest that as the hair becomes more curly, it has a smaller curve diameter, extends less when strained and is more susceptible to breakage. It also has a tendency to form longitudinal fissures and splits along the hair shaft.112 SEM studies have highlighted that the majority of the tips had more fractured ends compared with Asian and Caucasian hairs.112 Similarly, the basal end often exhibited evidence of breakage in contrast to the Asian and Caucasian samples in which the majority of hair had attached roots. 4. Combing Ability: The hair is difficult to comb because of its very curly configuration.114 The physical effect of washing, drying and combing may increase knotting (Figure 3.4) and
204 intertwining by stretching out the coils, which then interlock when they spring back.112 5. Chemical Composition: There are no significant differences in the amino acid composition of hair of different ethnicity.114 6. Hair Moisture: African-type hair has less moisture content than Caucasian and Asian hair, and thus, has a tendency to become dry and brittle.114
The cause of the geometry of African-type hair is unknown.32 However, the results of examination of scalp biopsies taken from African Americans indicate that highly curled hair follicles may be a strong contributing factor. In summary, Khumalo et.al claim that African-type hair is less fragile compared to that from the other races.32 In their study, TEM micrographs of hair from African, Caucasian, Asian origins and persons with that suffered from trichorrhexis nodosa (weathering due to physical damage) exhibited similar observations. This demonstrated that there is no abnormality in the cystine-rich proteins compared to other groups.
Therefore, the excessive structural damage observed in African-type hair is consistent with physical trauma (e.g. grooming) rather than an inherent weakening due to any structural abnormality.32 Thus, given the evidence and research on African hair fibres, it is important to note that it is very unusual to find/collect a African fibre in an untreated or virgin state.
5.2.2 FTIR-ATR Spectroscopic-Chemometric Analysis of African-type Hair Fibres
For the analysis of African-type hair fibres, 215 spectra (2-3 fibres per person and 3-5 spectra from each (dependent upon length of the fibre)) were acquired from 23 individuals of African and PNG origin. The historical record pertaining to these individuals is presented in Appendix I. The spectra were pre-processed (Section 2.6) and submitted to Sirius and Decision Lab for chemometric analysis.
205 5.2.2.1 Comparison of the 1750-800 cm-1 and 1690-1500 cm-1 regions In the previous chapter (Section 4.2.1.1), African-type hair fibres were processed along with Asian and Caucasian hair fibres. The PCA scores plot (Figure 4.2) appeared complex due to the intense clustering on the PC2 axis and the high number of „fuzzy‟ samples present. These African-type spectra were not processed further (as per Section 4.2.1.1) at that stage; the optimisation of the protocol was based on the separations of Caucasian and Asian spectra only. Although it was determined that the 1690-1500 cm-1 was the most suitable range (Chapter 4, Section 4.3) to discriminate single hair fibres, it was imperative to demonstrate that the results were similar for the African-type hair. Therefore, the currently accepted spectral region (1750-800 cm-1) was compared to the proposed alternative region (1690-1500 cm-1). The 215 available spectra were processed by FC using a hard weighting exponent (p=1.2) and based on a 3-cluster model (i.e. untreated, mildly treated and chemically treated, 4PCs 98 % variance) for the 1750-800 cm-1 and 1690-1500 cm-1 spectral regions. The FC membership values for both spectral regions are presented in Appendices VI and VII respectively.
It can be established from both the FC tables that untreated fibres (blue values), demonstrate strong membership to column or cluster 2, chemically treated fibres (pink) display membership of cluster 1, and mildly treated fibres (green) belong to cluster 3. In relation to „fuzzy‟ samples (white), 104 (48 % of the total) spectra had fuzzy membership in the 1750-800 cm-1 database and 91 spectra in the 1690-1500 cm-1 (42 %) one. Hence, the FC analysis of the African-type hair fibres is similar to the FC analysis of the Caucasian and Asian fibres (Section 4.2.1.4 and Table 4.13 (Section 4.3)), which demonstrated that more non-fuzzy samples are available with the use of the 1690-1500 cm-1 region. The fuzzy samples were removed from the data matrix (215 spectra).
This matrix, free of fuzzy objects, was submitted to PCA and the scores-scores plots of the African-type fibre database using the 1750-800 cm-1 and 1690-1500 cm-1 spectral regions are presented in Figures 5.1 and 5.2 respectively.
206
Figure 5.1 – PC1 vs. PC2 scores plot of untreated♦, mildly treated▲ and chemically treated fibres■ for the African-type hair fibres between 1750 - 800 cm-1.
Figure 5.2 – PC1 vs. PC2 scores plot of untreated♦, mildly treated▲ and chemically treated fibres■ for the African-type hair fibres between 1690-1500 cm-1.
207 In Figure 5.1 (1750-800 cm-1), 87.5 % of the total spectral data variance is explained by the first two PCs. Untreated fibres (blue) have negative scores on PC1 and are separated along the PC1 axis from chemically treated fibres (pink) which have positive scores on PC1. However, the mildly treated fibres (green) have scores that are centred about the origin of the PC1 and PC2 axis, in between the untreated and chemically treated groups. As the scores of the mildly treated group are not separated by a PC, this makes it difficult to decipher the boundaries between the three classes of fibre. In Figure 4.5 of the Caucasian-Asian 1750-500 cm-1 database, the mildly treated group was separated along the PC2 axis from the untreated and chemically treated fibres. This result demonstrates that African-type hair fibres fit the proposed protocol on the basis of untreated or treated object separation (Figure 4.1), and also illustrates that the results of the previous study23 (Section 1.6.4.1) were inconclusive because the total number of samples was too small.
In Figure 5.2, 85.9 % of the total spectral data variance is explained by the first two PCs, comparable to the total data variance explained in Figure 5.1. However, the discrimination of the three classes is different. Untreated fibres (blue) have moderate to high positive scores on PC1 adjacent to the mildly treated fibres which have low to moderate positive scores on PC1. These two groups are separated along the PC1 axis from the chemically treated fibres (pink) which have scores on negative PC1.
In Figure 5.1 the untreated scores are spread across PC2 from +10 to -10, whereas in Figure 5.2 they are spread from +1 to -2. It is also the case that due to the FC analysis, more fibres are classified as untreated in the 1750-800 cm-1 (33 spectra) spectral region compared to 10 spectra using the 1690-1500 cm-1 region. Therefore, when the spectral analysis region is shortened, i.e. the cystine oxidation region is removed, approximately 2/3 of the untreated fibres in the 1750-800 cm-1 region are then classified as mildly treated in the 1690-1500 cm-1 region. It would appear that the bands related to the above oxidation region are so strong that the relationship between an untreated fibre and a chemically treated fibre is exaggerated.
The apparent classification of more untreated hair objects as mildly treated, when -1 studied within 1690-1500 cm , suggests that the presence of different [SOn] modes of vibration included in the 1750-1500 cm-1 region, are so varied from sample to sample
208 that they override the spectral effects of the smaller of the conformational changes of the α-helix, random coil and β-sheet vibrations detected in the former region.
In the 1750-800 cm-1 region, the untreated and chemically treated fibres form a loose cluster whereas in the 1690-1500 cm-1 the scores form tight clusters. For PCA models, tight clustering of similar scores is important because this reduces the boundary of the group on the plot, and in turn, reduces the overlap of the scores with other groups, which can ultimately reduce the risk of misclassification of an unknown object. The more scattered cluster noted above also suggests that the variability in the nature and composition of the cystine oxidation products is quite significant and reduces the possibility of spectral discrimination.
To test the hypothesis that the African-type mildly treated fibre class can be subdivided into mild physical and mild chemical treatments (PCA Figure 4.6), an FC, 4-cluster model of the 1750-800 cm-1 and 1690-1500 cm-1 African-type databases were calculated (p=1.2 weight exponent). The resultant PCA scores plots (Figure 5.3 and Figure 5.4), demonstrate that four clusters spread across the PC1 axis.
Untreated Treated
20 Mild Physical Treatment Mild Chemical Treatment
15 Mild Physical Treatment Increase in Chemical Treatment
10
5
0
-5 PC2 (9.1 %) (9.1 PC2 Mild Chemical -10 Untreated Treatment Chemically Treated -15 -40 -30 -20 -10 0 10 20 30 40 PC1 (78.4 %) Figure 5.3 - PC1 vs. PC2 scores plot of the African-type 1750-800 cm-1 spectral database based on a 4-cluster FC model illustrating the untreated♦, mild physical treatment▲, mild chemical treatment■ and chemically treated■ spectral objects.
209 Treated Untreated Mild Physical Treatment Mild Chemical Treatment 10
8 Mild 6 Physical Untreated 4 Treated
2
0 .
-2 PC2 (9.1 %) (9.1 PC2
-4 Mild Chemical -6 Increase in Chemical Treatment -8 -15 -10 -5 0 5 10 15 20 PC1 (78.4 %) Figure 5.4 - PC1 vs. PC2 scores plot of the African-type 1690-1500 cm-1 spectral database based on a 4-cluster FC model illustrating the untreated■, mild physical treatment▲, mild chemical treatment and chemically treated♦ spectral objects.
The first cluster of spectral objects (blue) is untreated hair from the African-type male No. 1 (Appendix I), with low negative scores on PC1 and PC2 (Figure 5.3) and positive scores on PC1 and PC2 (Figure 5.4). No fibres from the other 22 African and PNG donors were classified as untreated according to this model. The next cluster with moderate scores on negative PC1 and positive PC2 (Figure 5.3) and positive PC1 and PC2 (Figure 5.4) is attributed to fibres that have experienced mild physical treatment (turquoise). The cluster situated at the centre of the PC1 and PC2 axis (green) relates to fibres that have been mildly treated (chemically). Finally, the cluster with high scores on positive PC1 (Figure 5.3) and low scores on negative PC1 (Figure 5.4) is of the African-type fibres that have been chemically treated (pink). Hence, in comparison to Figure 4.6, a hair fibre of any race, at any given time, could be potentially in four different chemical states as it progresses from the untreated to mildly physical, mildly chemical and chemically treated states.
210 The PC1 loading variables discriminate the untreated and mildly treated fibres from the chemically treated fibres, Figure 5.5 (1750-800 cm-1) and Figure 5.6 (1690-1500 cm-1). The PC1 loadings plot for the 1750-800 cm-1 region is analogous to the plot observed for the Caucasian and Asian hair fibres (Figure 4.7). The plot illustrates that the chemically treated and approximately half of the mildly treated spectral group (positive loadings) are influenced by the frequencies between 1200-1000 cm-1 (purple) relating to the products of the oxidation of cystine (cysteic acid at 1172 cm-1 (anti-symmetric stretch) and 1040 cm-1 (symmetric stretch), cystine dioxide 1121 cm-1 and cystine monoxide at 1071 cm-1 (symmetric stretch)).
Figure 5.5 - PC1 Loadings plot of the chemically treated and mildly treated African- type spectral objects (positive loadings), and the untreated and mildly treated African- type spectral objects (negative loadings) between 1750-800 cm-1 IR region.
211
Figure 5.6 - PC1 Loadings plot of the untreated and mildly treated African-type spectral objects(positive loadings) and the chemically treated African-type spectral objects (negative loadings) between 1690-1500 cm-1 IR region.
Chemically treated fibres also show higher loadings between 1750-1700 cm-1(dark blue), attributed to the υ (C=O) stretch of the COOH group, and to a lesser extent, weak loadings between 1350-1265 cm-1 (dark green) assigned to the overlap of bands from -1 δ(CH2) deformation bending mode from the amino acid, tryptophan, at 1342 cm , the -1 -1 υs(SO2) stretch at 1315 cm , and finally the vibrational stretches at 1284 cm and 1257 cm-1 which pertain to the υ (C-N) stretch and δ (N-H) of the α-helix and random coil (Amide III).
Conversely, the untreated fibres and the other half of the mildly treated spectral group are related to the frequencies between 1700-1350 cm-1 and 1260-1220 cm-1 which are attributed to the Amide I, Amide II and Amide III bands (black) at approximately 1627 -1 -1 cm and 1515 cm respectively, deformation and bending modes of the δ(C-H), (CH2) -1 -1 -1 and (CH3) groups (blue) at approximately 1461 cm , 1445 cm and 1392 cm respectively; and lastly the Amide III band (black) of the β-sheet at approximately 1238 cm-1.
212 The PC1 loadings plot for the 1690-1500 cm-1 region (Figure 5.6) is also similar to the loadings analysis of the Caucasian and Asian hair fibres (Figure 4.16). The untreated and mildly treated fibres (positive loadings) are influenced by the α-helical and β- pleated sheet of the Amide I and Amide II bands (black) between 1660-1600 cm-1 and 1550-1500 cm-1 respectively. Conversely, the treated fibres are influenced by the changes occurring to the Amide I υ(CONH2) stretch of the asparagine and glutamine side chains and υ(C=O) stretch of the β-pleated sheet and random coil conformation -1 between approximately 1690-1670 cm (dark blue); the anti-symmetric υa(C=O) carbonyl stretch of aspartic and glutamic acid between 1590-1570 cm-1 (green); and the vibration of the tri-substituted indole ring of tryptophan between 1570-1550 cm-1 (blue). Hence, the pattern of loadings bands of African-type untreated, mildly treated and chemically treated hair spectral objects are similar to those from the Caucasian and Asian hair as per the proposed forensic protocol (1750-800 cm-1) and the alternate region (1690-1500 cm-1). The current and prospective regions were further compared using MCDM analysis.
5.2.2.2 MCDM Analysis of African-type Hair Fibres The 111 x 2 (1750-800 cm-1) and 124 x 2 (1690-1500 cm-1) matrices i.e. both without the fuzzy samples, were submitted for PROMETHEE ranking and GAIA analyses. Tables 5.1 and 5.2 show the modelling involved for analyses of the matrices.
Table 5.1 PROMETHEE Model for African-type Untreated, Mildly Treated and Chemically Treated Hair Spectra (1750-800 cm-1) Criterion PC1 PC2 Function Type Gaussian Gaussian Minimised/Maximised Minimised Maximised p - - q - - σ 14.8184 4.7933 Unit (a.u.) (a.u.) Weight 1.00 1.00
213 Table 5.2 PROMETHEE Model for ranking of African-type Untreated, Mildly Treated and Chemically Treated Hair Spectra (1690-1500 cm-1) Criterion PC1 PC2 Function Type Gaussian Gaussian Minimised/Maximised Maximised Minimised p - - q - - σ 6.0607 2.1530 Unit (a.u.) (a.u.) Weight 1.00 1.00
As per Sections 4.2.1.2, the African-type spectra were analysed using a Gaussian preference function and Minimised/Maximised settings were selected such that spectral objects from untreated samples were preferred on each PC criterion.
Tables 5.3 and 5.4 illustrate the PROMETHEE II net ranking charts for the African-type „fuzzy‟ free objects of the 1750-800 cm-1 and 1690-1500 cm-1 database.
For the 1750-800 cm-1 database (Table 5.3), the ranking showed that the untreated samples (blue) are the most preferred objects in the first 28 ranks (φ = +0.981 to +0.199). The chemically treated fibres (pink) clearly dominate the lower ranks between φ = -0.513 to -0.825. It seems that a few treated objects mix in with the untreated ones and vice versa, and the mildly treated objects (green) ranks 25 to 101 (φ = +0.242 to (- 0.509)) mix into the two groups with some tending to favour the treated end.
214 Table 5.3 – PROMETHEE II Net φ Ranking of the African-type 1750-800 cm-1 Spectral Database Net φ Net φ Rank Object Index Rank Object Index Legend 1 Un 0.981 56 MT -0.045 2 Un 0.85 57 MT -0.047 Untreated (Un) = Blue 3 Un 0.842 58 MT -0.049 4 Un 0.827 59 Tr -0.052 Mildly Treated (MT) = Green
5 Un 0.804 60 MT -0.07 Treated (Tr) = Pink 6 Un 0.739 61 Tr -0.071 7 Un 0.708 62 Tr -0.071 8 Un 0.684 63 Tr -0.073 9 Tr 0.683 64 Un -0.073 10 Un 0.645 65 MT -0.081 11 Un 0.562 66 Tr -0.092 12 Un 0.476 67 Tr -0.094 13 Un 0.45 68 MT -0.101 14 Un 0.447 69 Tr -0.106 15 Un 0.409 70 Tr -0.106 16 Tr 0.38 71 Un -0.11 17 Un 0.373 72 Un -0.115 18 Tr 0.335 73 Tr -0.128 19 Un 0.296 74 MT -0.14 20 Un 0.278 75 MT -0.152 21 Un 0.277 76 Tr -0.154 22 Un 0.269 77 MT -0.154 23 Tr 0.268 78 Tr -0.157 24 Un 0.254 79 MT -0.168 25 Tr 0.242 80 MT -0.173 26 Tr 0.225 81 MT -0.181 27 Un 0.217 82 Tr -0.19 28 Un 0.199 83 MT -0.19 29 Tr 0.176 84 Tr -0.196 30 Tr 0.166 85 Tr -0.204 31 MT 0.164 86 Tr -0.206 32 Un 0.16 87 MT -0.213 33 Tr 0.159 88 Tr -0.24 34 Tr 0.155 89 Tr -0.269 35 Tr 0.137 90 MT -0.276 36 MT 0.131 91 MT -0.287 37 Tr 0.126 92 Un -0.293 38 Tr 0.122 93 MT -0.331 39 MT 0.095 94 MT -0.403 40 Un 0.077 95 Tr -0.407 41 Tr 0.067 96 MT -0.408 42 Un 0.066 97 Tr -0.463 43 MT 0.058 98 MT -0.476 44 MT 0.044 99 Tr -0.477 45 MT 0.043 100 MT -0.493
46 Tr 0.022 101 MT -0.509 47 Un 0.02 102 Tr -0.513 48 MT 0.015 103 Tr -0.546 49 MT 0.014 104 Tr -0.554 50 Un 0.01 105 Tr -0.582 51 Un 0.002 106 Tr -0.6 52 Un -0.003 107 Tr -0.649 53 Tr -0.007 108 Tr -0.656 54 MT -0.007 109 Tr -0.67 55 Tr -0.031 110 Tr -0.814 111 Tr -0.825 215
Table 5.4 – PROMETHEE II Net φ Ranking of the African-type 1690-1500 cm-1 Spectral Database Net φ Net φ Net φ Rank Object Index Rank Object Index Rank Object Index 1 Tr 0.951 56 Tr 0.008 111 MT -0.414 2 MT 0.804 57 MT -0.008 112 Tr -0.430 3 MT 0.748 58 Tr -0.008 113 Tr -0.463 4 Un 0.674 59 MT -0.016 114 MT -0.495 5 Un 0.666 60 Tr -0.016 115 Tr -0.536 6 MT 0.658 61 MT -0.024 116 MT -0.544 7 MT 0.650 62 MT -0.024 117 Tr -0.552 8 Un 0.650 63 MT -0.048 118 Tr -0.577 9 MT 0.626 64 MT -0.065 119 Tr -0.577 10 Un 0.561 65 MT -0.073 120 Tr -0.593 11 MT 0.548 66 Tr -0.097 121 Tr -0.601 12 MT 0.516 67 MT -0.105 122 Tr -0.626 13 Tr 0.504 68 Tr -0.105 123 Tr -0.666 14 MT 0.504 69 MT -0.130 124 Tr -0.869 15 Un 0.479 70 MT -0.134 16 Un 0.475 71 Tr -0.138 17 MT 0.463 72 MT -0.138 Legend 18 Un 0.430 73 MT -0.138 19 MT 0.430 74 MT -0.138 Untreated (Un) = Blue 20 MT 0.422 75 Tr -0.146 Mildly Treated (MT) = Green 21 MT 0.422 76 MT -0.154 Treated (Tr) = Pink 22 MT 0.422 77 Tr -0.154 23 Un 0.398 78 MT -0.170 24 MT 0.365 79 Tr -0.170
25 MT 0.357 80 MT -0.178 26 MT 0.341 81 Tr -0.187 27 MT 0.317 82 Tr -0.187 28 MT 0.317 83 Tr -0.187 29 MT 0.300 84 MT -0.195 30 MT 0.300 85 MT -0.211 31 MT 0.300 86 MT -0.219 32 MT 0.284 87 Tr -0.243 33 Un 0.268 88 MT -0.243 34 Tr 0.260 89 Tr -0.252 35 Tr 0.260 90 Tr -0.260 36 Un 0.252 91 MT -0.268 37 Tr 0.243 92 MT -0.268 38 MT 0.211 93 Tr -0.272 39 MT 0.187 94 MT -0.284 40 MT 0.178 95 Tr -0.292 41 MT 0.178 96 MT -0.325 42 MT 0.170 97 Tr -0.325 43 Tr 0.162 98 MT -0.325 44 MT 0.154 99 MT -0.341 45 Tr 0.146 100 Tr -0.345 46 MT 0.122 101 MT -0.349 47 Tr 0.122 102 Tr -0.357 48 MT 0.089 103 Tr -0.357 49 MT 0.081 104 MT -0.365 50 Tr 0.081 105 Tr -0.374 51 MT 0.065 106 MT -0.382 52 Tr 0.065 107 Tr -0.382 53 Tr 0.048 108 MT -0.382 54 Tr 0.048 109 Tr -0.398 55 MT 0.024 110 Tr -0.406 216
In Table 5.4 (1690-1500 cm-1), the mildly treated fibres dominate approximately the first 42 ranks between φ = +0.804 – (+0.170) in which the small number of the untreated samples is scattered. The chemically treated fibres dominate the middle to lower ranks (objects 87 to 124) between φ = -0.243 to (-0.869). Again, the objects in the middle (φ = 0.163 to (-0.22)) scatter indicating the similarity of the hair classes.
Tables 5.3 and 5.4 emphasise that the majority of African-type fibres are likely to be physically and/or chemically treated, partly because of the shape and curvature of the hair. Normal grooming habits with African-type hair place extra stress on the fibres as compared to the grooming of Asian and Caucasian hair which is less curly.281
The GAIA biplots for the African-type 1750-800 cm-1 and 1690-1500 cm-1 database are presented in Figures 5.7 and 5.8 respectively. In total, 100 % of the data variance is accounted for by the two GAIA PCs, hence, all the information has been retained on the GAIA planes. These biplots show that the spectral objects are separated into three somewhat overlapping groups analogous to Figure 5.1 and 5.2 - the untreated (blue) mildly treated (green) and treated (pink) fibres.
In the 1750-800 cm-1 region (Figure 5.7), the two criteria vectors, PC1 and PC2 (black), are orthogonal to each other where PC1 favours the untreated objects with positive scores while the chemically treated objects mostly have negative scores. Similarly PC2 separates mostly chemically treated objects (positive scores) from untreated ones (negative scores). Thus, the two groups are separated on that basis. The Π decision axis (red vector) is very strong, indicating a robust decision, pointing towards the untreated fibre class. In Figure 5.8 the PC1 criterion vector is related to the untreated spectral objects whilst the PC2 criterion vector favours the mildly treated objects. The decision axis points along the PC2 axis. Hence, the plot demonstrates a similar distribution of the 176 spectra as in the PCA scores-scores plot (Figure 4.5, Section 4.2.1.1) of the three classes providing supporting evidence that three classes of fibre exist.
217
PC2
Chemically Treated Mild Treatment
PC1
Untreated
Δ 100 %
Figure 5.7 - GAIA analysis of the 111 spectra for the African-type hair fibre database between 1750-800 cm-1; ▲untreated fibres, ■ chemically treated fibres, ■ mildly treated hair fibres, ● pi (Π) decision-making axis, and ■ PC1 and PC2 criteria using a Gaussian preference function.
218
PC2 Chemically Treated
PC1
Mild Treatment Untreated
Δ 100 %
Figure 5.8 - GAIA analysis of the 124 spectra for the African-type hair fibre database between 1690-1500 cm-1; ■untreated fibres, ■ chemically treated fibres, ■ mildly treated hair fibres, ● pi (Π) decision-making axis, and ■ PC1 and PC2 criteria using a Gaussian preference function.
219 Thus, the similar discrimination between the untreated, mildly treated and chemically treated spectral objects from the African-type hairs compared well with Asian and Caucasian objects, and now suggests that the spectral objects from the three races may be compared.
5.3.1 Incorporation of the African-type Hair IR Spectra to the Protocol According to Panayiotou‟s forensic protocol (Figure 4.1, Section 4.1), the systematic order of separation of the spectral objects is based sequentially on treatment, gender and race. Consequently, in this section this protocol was applied for the first time to a matrix which included African and PNG IR spectral objects.
5.3.1.1 Chemometric Analysis of the Entire (3 Races) Database The inclusion of the African-type spectral database with the Asian and Caucasian spectral database was approached with caution to avoid any misrepresentations of the data. As observed in Figure 4.2, the inclusion of the African-type spectra produced severe overlapping of the objects precluding any useful analysis. Therefore, to reduce the complexity of the analysis, only the untreated and treated Asian and Caucasian spectral objects as well as with the untreated and treated African-type spectra were first processed by PCA using the alternative region, 1690-1500 cm-1 (Figure 5.9). In this matrix, in addition to the samples of the three races, the two typical reference groups, CFTR10 and CFUN1 (Chapters 3.0 and 4.0), were included for comparison. With respect to these spectral objects, CFUN1 and CFTR10, it can be seen that the untreated spectral objects (blue) have positive scores on PC1 and PC2, and are separated by the PC1 axis from the treated spectral objects (pink) with negative scores on PC1. Situated amongst the loose cluster of untreated objects are the African-type untreated spectral objects (black), and the treated African-type objects (purple) form a tight cluster with the treated objects. At the cross-section of the PC1 and PC2 axes, there is a clear separation of the untreated and treated spectral objects.
220
Figure 5.9 – PCA scores plot of the 1690 -1500 cm-1IR Database; Caucasian and Asian untreated fibres●, chemically treated fibres■, with the inclusion of the untreated African-type untreated♦ and chemically treated■ African-type spectral objects.
When the mildly treated Asian and Caucasian (green) and the mildly treated African- type objects (brown) were added to the data matrix, the resulting PCA plot is shown in Figure 5.10. In total 88.9 % of the total data variance is explained by the first two PCs with 72.3 % on PC1 and 16.6 % on PC2. The mildly treated Caucasian, Asian and African-type spectral objects form a fairly tight cluster mostly with negative scores on PC2 and between the chemically treated and untreated clusters. Thus, the African-type spectral objects in the 1690-1500 cm-1 region, are found together with the respective spectral objects of the Caucasian and Asian objects, i.e. the African-type hairs behave similarly to the Asian and Caucasian ones.
221 UntreatedUntreated TreatedTreated MildlyMildly Treated Treated UntreatedUntreated Negroid African-type ChemicallyTreated African Treated-type Negroid MildlyMildly Treated Treated AfricanNegroid-type 20
Untreated 15
10 Chemically Treated CFUN 1
5
CFTR 10 PC2 PC2 (16.6%) 0
-5 Increase in Mildly Treated Physical/Chemical Treatment -10 -20 -15 -10 -5 0 5 10 15 20 PC1 (72.3 %)
Figure 5.10 - PCA scores plot of PC1 vs. PC2 of the Entire 1IR Database between 1690 -1500 cm-1. Caucasian and Asian untreated fibres●, chemically treated fibres■, mildly treated fibres▲ and African-type untreated♦, mildly treated▲ and chemically treated■ hair fibres.
A PROMETHEE II model (Table 5.5) was created to rank order the spectral objects of the entire (3 race) database for the 1690-1500 cm-1 spectral analysis region.
Table 5.5 PROMETHEE II Model of the Entire Spectral Database (257 spectra x 3PC Criteria) within the 1690-1500 cm-1 Spectral Region Criterion PC1 PC2 PC3 Function Type Gaussian Gaussian Gaussian Minimised/Maximised Maximised Maximised Maximised p - - q - - σ 6.19 2.76 1.82 Unit (a.u.) (a.u.) (a.u.) Weight 1.00 1.00 1.00
222 The Net φ ranking (Table 5.6) for the entire spectral database was between +0.833>φ>0-0.153 with the untreated spectral reference, CFUN 1 – CFUN 110 samples (blue) as the most preferred objects between +0.833 to (+0.544). This was followed by the African-type untreated (NUN) spectral objects (grey) between approximately +0.484 to (+0.395).
The remainder of the spectral objects between ranks 43 and 194 is very scattered. The middle ranking from 80 to 166 is dominated by the Asian and Caucasian chemically treated samples (TR, pink) and the African-type treated objects (NTR, purple) between φ = +0.162 to (-0.168). The mildly treated objects (MTR, green) dominate the lower rankings (195-257) from φ = -0.267 to (-0.557). Therefore, the ordering starts from the untreated spectra to the chemically treated spectra and finishes with the mildly treated spectra.
The GAIA bi-plot for the 1690-1500 cm-1 spectral analysis region is presented in Figure 5.11. The untreated Asian-Caucasian (blue ■) and untreated African-type (black ■) objects have positive scores on PC1 and negative scores on PC2, and are separated along the PC1 axis from the treated Asian-Caucasian (pink ■) and African-type (purple ■) which have negative scores on PC1 and PC2. The mildly treated Asian-Caucasian (light green ■) and African-type (brown ■) have positive scores on PC2 and spread across the PC1 axis. The PC3 (turquoise ■) vector favours the untreated samples whilst the PC1 and PC2 vectors point towards the periphery of the mildly treated and treated samples respectively. The decision axis (red ●) favours the untreated spectral objects which contain the reference CFUN1-CFUN110 samples.
223 Table 5.6 - PROMETHEE II Net φ Ranking of the 3 Race IR Spectral Database 1690-1500 cm-1 Net φ Net φ Net φ Rank Object Index Rank Object Index Rank Object Index 1 CFUN18 0.833 56 NUN 0.288 110 NTR 0.0352 2 CFUN17 0.816 57 TR 0.271 111 MTR 0.0217 3 CFUN13 0.767 58 NUN 0.269 112 TR 0.0153 4 CFUN16 0.705 59 NMT 0.267 113 MTR 0.0134 5 CFUN14 0.703 60 NMT 0.26 114 NMT 0.0107 6 CFUN110 0.674 61 NTR 0.257 115 MTR 0 7 NUN 0.665 62 MTR 0.257 116 NMT -0.017 8 NUN 0.654 63 NUN 0.256 117 MTR -0.021 9 CFUN11 0.648 64 UN 0.248 118 TR -0.033 10 NUN 0.647 65 MTR 0.246 119 NMT -0.035 11 CFUN15 0.644 66 NMT 0.243 120 CFTR106 -0.037 12 CFUN19 0.628 67 MTR 0.231 121 MTR -0.038 13 UN 0.586 68 NUN 0.218 122 CFTR104 -0.039 14 UN 0.581 69 TR 0.212 123 NTR -0.043 15 UN 0.547 70 MTR 0.204 124 MTR -0.043 16 CFUN12 0.544 71 MTR 0.192 125 MTR -0.043 17 NUN 0.538 72 NTR 0.189 126 TR -0.044 18 UN 0.523 73 NMT 0.185 127 MTR -0.052 19 NUN 0.508 74 NMT 0.184 128 TR -0.054 20 UN 0.501 75 MTR 0.175 129 MTR -0.057 21 NMT 0.499 76 MTR 0.172 130 TR -0.064 22 NMT 0.485 77 MTR 0.17 131 NTR -0.069
23 NUN 0.484 78 MTR 0.169 132 TR -0.069 24 NUN 0.474 79 NUN 0.164 133 NTR -0.07 25 NUN 0.474 80 TR 0.162 134 NMT -0.077 26 UN 0.453 81 NMT 0.159 135 TR -0.082 27 NUN 0.451 82 NUN 0.136 136 NMT -0.089 28 UN 0.444 83 TR 0.132 137 TR -0.091 29 NUN 0.442 84 NMT 0.126 138 MTR -0.104 30 NUN 0.438 85 TR 0.111 139 TR -0.106 31 NUN 0.436 86 TR 0.105 140 NMT -0.106 32 NMT 0.434 87 NMT 0.102 141 MTR -0.106 33 NMT 0.416 88 MTR 0.099 142 NTR -0.108 34 NUN 0.405 89 MTR 0.095 143 MTR -0.114 35 NUN 0.395 90 TR 0.089 144 NTR -0.122 36 UN 0.39 91 TR 0.08 145 NTR -0.122 37 UN 0.382 92 NTR 0.086 146 MTR -0.125 38 UN 0.377 93 MTR 0.081 147 NTR -0.128 39 TR 0.374 94 TR 0.08 148 MTR -0.1299 40 UN 0.367 95 MTR 0.08 149 NTR -0.131
41 UN 0.366 96 NTR 0.078 150 TR -0.131 42 UN 0.36 97 MTR 0.076 151 NTR -0.131 43 MTR 0.359 98 MTR 0.07 152 TR -0.132 44 MTR 0.359 99 CFTR105 0.07 153 MTR -0.132 45 MTR 0.354 100 NTR 0.066 154 MTR -0.134 46 NUN 0.346 101 TR 0.066 155 TR -0.1358 47 NTR 0.344 102 CFTR103 0.065 156 MTR -0.1359 48 TR 0.334 103 NTR 0.062 157 MTR -0.1391 49 NUN 0.33 104 MTR 0.05 158 TR -0.141 50 MTR 0.324 105 MTR 0.047 159 MTR -0.143 51 NUN 0.322 106 CFTR101 0.0447 160 TR -0.145 52 TR 0.314 107 CFTR102 0.0436 161 TR -0.147 53 TR 0.305 108 MTR 0.0415 162 CFTR1011 -0.153 54 NMT 0.303 109 TR 0.0414 163 NMT -0.153
224 Table 5.6 - Continued
Net φ Net φ Rank Object Index Rank Object Index 164 NTR -0.162 218 TR -0.329 165 TR -0.164 219 MTR -0.335 166 TR -0.168 220 MTR -0.342 167 MTR -0.172 221 MTR -0.342 168 MTR -0.173 222 MTR -0.343 169 MTR -0.176 223 MTR -0.343 170 MTR -0.183 224 MTR -0.344 171 TR -0.187 225 MTR -0.345 172 MTR -0.19 226 NTR -0.351 173 TR -0.192 227 TR -0.353 174 NTR -0.196 228 MTR -0.364 175 TR -0.197 229 MTR -0.364 176 MTR -0.198 230 CFTR109 -0.37 177 MTR -0.2 231 TR -0.37 178 NMT -0.208 232 MTR -0.371 179 MTR -0.214 233 MTR -0.374 180 NTR -0.216 234 MTR -0.383 181 NTR -0.217 235 NTR -0.387 182 MTR -0.219 236 CFTR1010 -0.398 183 TR -0.225 237 MTR -0.399 184 NTR -0.226 238 MTR -0.409 185 TR -0.231 239 NTR -0.411 186 MTR -0.237 240 MTR -0.415 187 TR -0.238 241 MTR -0.417 188 NTR -0.241 242 MTR -0.419 189 TR -0.251 243 MTR -0.43 190 MTR -0.254 244 MTR -0.434 191 CFTR108 -0.256 245 MTR -0.434 192 NTR -0.257 246 MTR -0.436 193 NTR -0.257 247 TR -0.44 194 TR -0.263 248 MTR -0.444 195 MTR -0.267 249 MTR -0.46 196 MTR -0.27 250 MTR -0.46 197 TR -0.271 251 MTR -0.463 198 MTR -0.273 252 MTR -0.475 199 MTR -0.273 253 MTR -0.477 200 MTR -0.273 254 MTR -0.493 201 TR -0.275 255 MTR -0.513 202 MTR -0.278 256 MTR -0.514 203 MTR -0.279 257 MTR -0.557 204 MTR -0.279 205 MTR -0.28 Legend 206 MTR -0.28 207 TR -0.283 208 TR -0.288 Untreated (UN) = Blue 209 MTR -0.29 African-type Untreated (NUN) = Black 210 NTR -0.292 Mildly Treated (MTR) = Green
211 TR -0.299 African-type Mildly Treated = Brown 212 TR -0.301 Treated (TR) = Pink 213 MTR -0.303 African-type Treated (NTR) = Purple 214 TR -0.303 215 MTR -0.314 216 MTR -0.317 217 MTR -0.326 225
PC2 MILDLY TREATED
PC1
TREATED
UNTREATED
Δ 74.5 %
Figure 5.11 – GAIA analysis of the 257 spectra for the Entire (3 Race) IR database between 1690-1500 cm-1; ■untreated fibres, ■ untreated African-type fibres, ■ chemically treated fibres, ■ chemically treated African-type fibres, ■ mildly treated hair fibres, ■mildly treated African-type fibres, ● pi (Π) decision-making axis, and ■ Original PC1, PC2 and PC3 criteria using a Gaussian preference function.
226 The results are different when the spectral objects of the entire database were examined over the 1750-800 cm-1 region. For the current 1750-800 cm-1 region (Figure 5.12), 92.8 % of the total data variance is explained by the first two PCs with 78.4 % on PC1 and 14.4 % on PC2. The PC plot is complex and does not offer a clear separation of the fibre classes. However, the plot provides a trend rather than groupings, and is most useful as a 2-D pattern. If all the objects are projected onto PC2, then there is very little definitive separation observed. However, if the distribution of objects is viewed in the two dimensional PC space, a trend pattern emerges which suggests that all treated samples are grouped together with positive scores on PC1, while the untreated samples, group on PC1 with negative scores. The mildly treated groups are evident between the previous two, and arguably, most mildly treated African-type samples (▲) are separated on PC2 with positive scores from most of the mildly treated objects (▲) with negative scores. Thus, the overall pattern of objects suggests a trend which indicates grouping according to treated, mildly treated and untreated classes on PC1. In addition, while the treated groups remain unseparated, the mildly treated ones indicate some separation and the untreated ones form loose unique groups.
Untreated Treated Mild Treatment Untreated African-type Treated African-type Mild Treatment African-type 20
15 Mildly Treated 10
5
0
-5 CFUN1 CFTR10 -10
-15 Untreated PC2 (14.4%) PC2 -20
-25 Chemically Treated
-30 -50 -40 -30 -20 -10 0 10 20 30 40 PC1 (74.8%) Figure 5.12 - PCA scores plot of PC1 vs. PC2 of the 1750-800 cm-1IR Database. Caucasian and Asian untreated fibres●, chemically treated fibre■, mildly treated fibres▲, and African-type untreated♦, mildly treated▲ and chemically treated■ spectral objects.
227 It is reasonable to suggest that each of the African-type hair classes (i.e. untreated, mildly treated and chemically treated) is not associated with their respective Caucasian and Asian hair classes using the 1750-800 cm-1 spectral region. The structural chemistry at the molecular level of mildly treated and chemically treated African-type fibres is different from similarly treated Caucasian and Asian fibres for one main reason. A goal of cosmetic treatments for African men and women is to have straightened/permed and coloured hair. This requires that the hair is subjected to a number of multiple treatments to achieve the desired outcome. Hence, this would increase the moderate levels of cysteic acid in the chemically untreated hair to quite high levels, which as PCA in this study indicated, differentiates the treated African-type fibres from treated Caucasian and Asian hair. The latter types of hair usually will have had only one treatment. This supports the finding from Panayiotou22 who was able to demonstrate the discrimination of chemically treated hair on the basis of single versus multiple cosmetic treatments.
These results support the conclusions from the previous chapter, which suggested that the optimum region for analysing hair keratin IR spectra was between 1690-1500 cm-1. Furthermore, the results also provided an explanation for why African-type spectral objects did not fit into the protocol design from the previous investigation (Section 1.6.4.1) where it was established that the separation of the African-type spectra on the basis of chemical treatment appeared to contradict the model. In that case, the studied region was between 1750-800 cm-1, which contained spectral elements i.e. products of cystine oxidation, as described above, that precluded the separation of the various classes. When using the 1690-1500 cm-1 region to analyse keratin FTIR-ATR spectra, the principal differences between the spectra are fundamentally based on α-helical, β- sheet and random coil conformations. This region of the spectrum is more suitable for the matching and discrimination of the spectra from different fibres than the 1750-800 cm-1 range. In this region, FC and PCA misclassify an untreated African-type fibre for a mildly or chemically treated fibre due to inconsistent amounts of cysteic acid in the cuticle. Hence, subsequent sections focus on the analysis of keratin spectra between 1690-1500 cm-1.
228 5.3 Gender: Male vs. Female Hair Fibres
In criminal cases, it is relevant to forensically identify the gender of the hair sample. In one of the earliest studies, Hopkins et al.158 using peak ratio differences concluded that no differences could be discerned between the Amide I and II bands. However, in more recent studies, Panayiotou24 and Barton23 had proposed that the Amide I and II vibrational bands were responsible for the discrimination of male and female, untreated and chemically treated hair fibres. This result was demonstrated with the use of Chemometrics, which was a more sophisticated approach. Hence, the rationale of this section is to investigate the protocol for matching and discriminating spectral objects by comparing untreated, mildly treated and chemically treated hair fibres from subjects of different genders
5.3.1 Gender Differences between Untreated, Mildly Treated and Chemically Treated Fibres
5.3.1.1 Untreated Hair Fibres Thirty nine male and female spectra (29 female (20 Caucasian, 9 Asian) and 10 male (African-type)) from untreated fibres (excluding any fuzzy objects) were selected from the entire database (Section 5.2.2.1), and processed separately by FC and PCA. This data subset included the spectral reference sets; Caucasian female No. 1(Appendix I), which is a collection of spectra from untreated hair fibres. A 2-cluster FC analysis (male and female groups) was performed to exclude misclassified objects. Of the entire database of male hair fibre spectra, and 10 spectra pertaining to African-type male No. 1 (NMUN 1) were deemed as untreated by this classification method. The resultant PCA scores plot is presented in Figure 5.13. In total, 89.9 % of the total data variance is retained by the first two PCs with 60.3 % on PC1 and 29.6 % on PC2. The spectra of NMUN 1 (blue) form a cluster on PC2 (positive scores), and are separated along the PC2 axis from untreated female fibres (pink), which exhibit negative scores on PC2. The separation of spectral objects from hairs of different gender is confirmed by the position of the CFUN1 reference objects which have negative scores on PC2 and consist of female untreated spectra. The separation of untreated hair fibres by gender is consistent with previous investigations.22 23
229
Figure 5.13 - PCA scores plot of PC1 vs. PC2 of the Untreated Hair Fibre Spectral Database illustrating the separation of untreated African-type Male No.1♦ from untreated Female■ spectral objects along the PC2 axis.
With reference to the PC2 loadings plot (Figure 5.14), the vibrational bands significant to each gender can be discerned. The positive loadings (black), attributed to the male spectra are influenced by the β-sheet conformation of the Amide I and Amide II bands between 1690-1600 cm-1 and 1520-1500 cm-1 respectively. The negative PC2 loadings correspond to the female untreated spectral objects on PC2 (negative scores). The IR -1 - spectral region between 1590-1520 cm include the υa(CO2 ) (green), tryptophan (blue) and α-helix (purple) of the Amide II band.
Comparing these results with the raw and second derivative spectra (Figure 3.8, Section 3.2.1.2 and Figure 3.17, Section 3.3.2), it appears that the untreated male hair fibres are discriminated by the β-pleated sheet conformation (Amide II band) in the protein of the cuticle in the fibre. Alternatively, the untreated female fibres are described by the α-helical conformation of the Amide II in the hair cuticle. With correlation to the chemical composition of male and female spectra, the PC2 loadings plot provided corroborative evidence for the difference spectra between genders within each race (i.e. of untreated spectra (Section 3.3.2)). From that evidence, it is suggested that female hair IR spectra exhibit more intense absorption of the amino acids tryptophan, aspartic and glutamic acid.
230
Figure 5.14 – PC2 Loadings plot of the untreated African-type Male No. 1 spectral objects (positive loadings) and the untreated Female spectral objects (negative loadings).
MCDM analysis was utilised to provide further verification of the separation (i.e. quantitatively) between NMUN 1 (10 spectra) and the untreated female spectra (29 spectra CFUN1 inclusive). The 39 spectra x 2 (PC Criteria) matrix was submitted to PROMETHEE ranking and GAIA analysis (Model - Table 5.7).
Table 5.7 PROMETHEE II Model of Untreated African-type Male (NMUN 1) and Untreated Female Hair Spectra Criterion PC1 PC2 Function Type Gaussian Gaussian Minimised/Maximised Maximised Minimised p - - q - - σ 5.65 3.95 Unit (a.u.) (a.u.) Weight 1.00 1.00
231 Table 5.8 illustrates the PROMETHEE II ranking for the two selected individuals from the untreated database. The φ values ranged from 0.731<φ<-0.770. The ranking showed that the untreated female objects (pink) are the most preferred samples between φ = +0.731 – (-0.023) and φ = -0.107 – (-0.242), which contain the reference untreated CFUN 1 samples. The untreated African-type male spectral objects (NMUN 1) dominate the lower ranks between φ = -0.30 - (-0.77). The separation of gender is indicated by the large change in φ indices between ranks 30 and 31. The GAIA bi-plot (Figure 5.15) shows that PC1 and PC2 criteria favour the female untreated spectral objects (pink) as indicated by the decision axis (PC; red line). The untreated female spectral objects are separated on PC2 from the untreated African-type male objects (blue) which have positive scores on this PC. As with PROMETHEE ranking, there are a few overlapping spectral objects.
232 Table 5.8 - PROMETHEE II Net φ Ranking of the Untreated Spectral Database
Net φ Rank Object Index 1 FUN1 0.731 2 FUN2 0.653 3 CFUN18 0.592 4 FUN4 0.518 5 FUN5 0.507 6 FUN6 0.498 7 FUN7 0.481 8 CFUN19 0.421 9 FUN9 0.385 10 FUN10 0.360 11 CFUN110 0.334 12 FUN12 0.32 13 CFUN17 0.281 14 FUN14 0.137 15 FUN15 0.120 16 FUN16 0.112 17 FUN17 0.046 18 FUN18 0.020 19 CFUN16 0.016 20 CFUN13 -0.003 21 FUN21 -0.023 22 NMUN17 -0.060 23 CFUN11 -0.090 24 NMUN15 -0.104 25 FUN25 -0.107 26 CFUN15 -0.170 27 CFUN14 -0.183 28 FUN28 -0.222 29 CFUN12 -0.223 30 FUN30 -0.242 31 NMUN12 -0.299 32 NMUN16 -0.354 33 NMUN11 -0.500 34 NMUN13 -0.572 35 FUN35 -0.597 36 NMUN14 -0.609 37 NMUN18 -0.649 38 NMUN110 -0.756 39 NMUN19 -0.770
Legend
Female Untreated (FUN) = Pink African-type Male Untreated (NMUN) = Blue
233
PC2
African-type Male Untreated No. 1
PC1
Female Untreated
Δ 100 %
Figure 5.15 - GAIA analysis of the 39 spectra for the Untreated hair fibre database; ■ Male untreated fibres, ■ Female untreated fibres, ● pi (Π) decision-making axis, and ■ PC1 and PC2 criteria using a Gaussian preference function.
234 5.3.1.2 Mildly Treated Hair Fibres In total, 161 spectra (Sections 4.2.2.2 and 5.2.2.2) were classified as Mildly Treated by FC (Appendix III) within the 1690-1500 cm-1 spectral range. The data matrix consisted of 50 female spectra (15 Asian, 20 Caucasian and 15 African-type) and 111 male spectra (41 Asian, 18 Caucasian, and 52 African-type spectra). As African-type spectral data were the novel subset with relation to the protocol, the mildly treated Asian and Caucasian spectral subset were analysed by PCA initially.
The PCA scores plot of the Asian and Caucasian mildly treated database is presented in Figure 5.16. In total, 81.0 % of the total data variance is retained by the first two PCs with 67.0 % on PC1 and 14.0 % on PC2. No separation could be discerned along the PC1 axis, however, the objects were discriminated along the PC2 axis, where the male mildly treated objects (blue) formed a cluster with negative scores on PC2 and the mildly treated female objects (pink) have positive PC2 scores. Subsequently, the male and female African-type mildly treated spectral objects were added and calculated by PCA (Figure 5.17). The majority of the 67 male-female African-type spectral objects ((green) with the exception of approximately 7 objects) were scattered along the PC1 axis and inter-dispersed with the mildly treated female objects with positive scores on PC2. As the majority of the mildly treated African-type database consisted of male spectra (78 %), the separation across the PC2 axis demonstrates that male mildly treated African-type spectra have minute structural similarities with male mildly treated Asian and Caucasian spectra. Hence, in terms of the outline of the protocol methodology, the African-type female-male mildly treated objects should be processed by PCA separately from the mildly treated Asian and Caucasian spectral objects.
235
Figure 5.16 - PCA scores plot of PC1 vs. PC2 of the Mildly Treated Hair Fibre Spectral Database illustrating the separation of mildly treated male♦ from mildly treated female♦ spectral objects.
Figure 5.17 - PCA scores plot of PC1 vs. PC2 of the Mildly Treated Hair Fibre Spectral Database illustrating the separation of mildly treated male♦ from mildly treated female♦ and mildly treated African-type▲ spectral objects.
236 The structural differences between the mildly treated male and female spectral objects (Asian and Caucasian) are described by the PC2 loadings diagram (Figure 5.18). The female mildly treated objects (positive loadings) are ascribed to the intensity increase of the β-pleated sheet and concomitant shift of the Amide I and Amide II band, tryptophan, - and asymmetric carboxylate νa(CO2 ) vibrational band as a result of treatment. The negative loadings, which describe the male mildly treated spectral objects are assigned to the β-sheet, random coil and α-helix of the Amide I vibration.
Figure 5.18 – PC2 Loadings plot of the Mildly Treated spectral database showing the separation of mildly treated female spectral objects from mildly treated male spectral objects on the PC2 axis illustrated in Figure 5.16.
Again, as per the untreated hair spectra scenario (Section 5.3.1.1.), the correlation between the second derivative spectra (Figure 3.18) and PC2 loadings suggest that mild chemical treatment has a greater effect on females than males due to the increase in intensity of the β-sheet and random coil (Amide I and II band) protein conformations and de-protonation of aspartic and glutamic acid in females fibres. The loadings also support the hypothesis that female spectra exhibit strong intensity of the tryptophan vibration at 1554 cm-1.
237 A PROMETHEE model was constructed (Table 5.9) using 2PC criteria (81 % data variance) to provide a quantitative analysis of the separation between Asian and Caucasian, male-female, mildly treated objects. As the mildly treated male spectra made up the majority of the database the PC1 and PC2 criteria were maximised and minimised respectively so they would be the preferred objects.
Table 5.9 PROMETHEE II Model of Male and Female Mildly Treated Hair Spectra Criterion PC1 PC2 Function Type Gaussian Gaussian Minimised/Maximised Maximised Minimised p - - q - - σ 6.25 2.65 Unit (a.u.) (a.u.) Weight 1.00 1.00
Table 5.10 demonstrates the complete ranking of the spectra of the 94 male and female mildly treated spectral objects. The net φ values ranged from 0.911>φ>-0.747. The male mildly treated (MMTR) spectral objects dominate approximately the first 48 ranks from φ = +0.911 to (-0.012) followed by the female mildly treated (FMTR) which approximately dominate the last 44 ranks between φ = -0.027 – (-0.747). It can be seen that there is some scatter between the male and female spectral objects. Nevertheless, it is suggested that the genders are well separated on the extremities of the ranking.
238 Table 5.10 - PROMETHEE II Net φ Ranking of the Mildly Treated Spectral Database
Net φ Net φ Legend Rank Object Index Rank Object Index 1 MMTR 0.911 49 MMTR -0.015 Male Mildly Treated 2 MMTR 0.872 50 FMTR -0.027 (MMTR) = Blue 3 MMTR 0.794 51 FMTR -0.031
4 MMTR 0.743 52 FMTR -0.034 5 MMTR 0.557 53 FMTR -0.04 Female Mildly Treated 6 MMTR 0.517 54 MMTR -0.052 (FMTR) = Pink 7 MMTR 0.509 55 FMTR -0.061 8 MMTR 0.427 56 MMTR -0.063 9 MMTR 0.400 57 MMTR -0.068 10 MMTR 0.394 58 MMTR -0.070 11 MMTR 0.391 59 FMTR -0.070 12 MMTR 0.386 60 MMTR -0.075 13 MMTR 0.385 61 FMTR -0.081 14 MMTR 0.368 62 MMTR -0.099 15 MMTR 0.364 63 MMTR -0.111 16 MMTR 0.358 64 FMTR -0.126 17 MMTR 0.344 65 MMTR -0.128 18 FMTR 0.316 66 FMTR -0.134 19 MMTR 0.286 67 FMTR -0.157 20 MMTR 0.270 68 MMTR -0.177 21 MMTR 0.259 69 MMTR -0.186 22 MMTR 0.241 70 MMTR -0.193 23 MMTR 0.229 71 FMTR -0.195 24 FMTR 0.204 72 MMTR -0.204 25 MMTR 0.203 73 FMTR -0.215 26 MMTR 0.197 74 FMTR -0.226 27 FMTR 0.1877 75 MMTR -0.248 28 MMTR 0.175 76 MMTR -0.253 29 MMTR 0.170 77 FMTR -0.261 30 MMTR 0.161 78 FMTR -0.276 31 MMTR 0.136 79 FMTR -0.294 32 MMTR 0.121 80 MMTR -0.337 33 FMTR 0.109 81 MMTR -0.371 34 MMTR 0.108 82 MMTR -0.395 35 MMTR 0.094 83 FMTR -0.408 36 MMTR 0.092 84 FMTR -0.466 37 FMTR 0.088 85 MMTR -0.494 38 MMTR 0.076 86 FMTR -0.564 39 MMTR 0.061 87 FMTR -0.602 40 MMTR 0.046 88 MMTR -0.628 41 FMTR 0.039 89 FMTR -0.648 42 MMTR 0.023 90 FMTR -0.656 43 FMTR 0.020 91 FMTR -0.703 44 FMTR 0.017 92 FMTR -0.713 45 MMTR 0.011 93 FMTR -0.725 46 FMTR -0.009 94 FMTR -0.747 47 MMTR -0.010 48 MMTR -0.012
239
A GAIA bi-plot for the Asian and Caucasian mildly treated spectra would have been superfluous as it is very similar to Figure 5.16. In its place, a GAIA bi-plot (Δ 70.86 %) was processed which included the African-type male and female mildly treated spectra (Figure 5.19) which included PC3 as a third criterion. The mildly treated male spectral objects (blue) have negative scores on PC2 separated from the female (pink) and African-type mildly treated (green) objects which have positive scores on PC2. The criteria vectors for GAIA can be useful as they illustrate what samples are associated with which variables, so when unknown samples are added the analyst has an approximate estimation of what type of samples they are. In this scenario, the PC scores from PCA are the criteria. The PC1 criterion is approximately associated with the female mildly treated samples; the PC2 criterion allied with the male mildly treated objects and the PC3 criterion correlated with the African-type male and female mildly treated spectral objects.
240
Female and African-type (Female PC2 and Male) Mildly Treated
PC1
Male Mildly Treated
Δ 70.86 %
Figure 5.19 - GAIA analysis of the spectra for the Mildly Treated hair fibre database; ■ Male mildly treated fibres, ■ Female mildly treated fibres, ■ African-type male- female mildly treated fibres, ● pi (Π) decision-making axis, and ■ PC1, PC2 and PC3 criteria using a Gaussian preference function.
241 5.3.1.3 Chemically Treated Hair Fibres The 123 male and female chemically treated spectra were separated from the main database (Section 5.2.2.1) and initially processed by FC (2-cluster model to allow for male and female classes, p = 1.2 (hard exponent), n = 0.5). With the two cluster model, a total of 38 spectra were misclassified, where 25 spectra pertained to the African-type female fibres. They potentially belong to a group referred as “multiple-treated” fibres, which had been proposed by Panayiotou.22 Hence, a FC 4-cluster model (Appendix IX, p=1.2, 4PCs 96.7 %) was applied in an attempt to include the African-type male and female “multiple treated” spectral objects. The 4-cluster model indicated only 14 misclassified spectra.
The PCA plot of the remaining 109 chemically treated spectral objects is presented in Figure 5.20. This data subset included the spectral references, treated Caucasian female No. 10 (CFTR10, Appendix I), which is a collection of spectra from chemically treated hair fibres. In total, 82.8 % of the total data variance is retained by the first two PCs with 65.2 % on PC1 and 17.6 % on PC2. Most chemically treated male spectral objects (blue) have a range of positive scores on PC1 and mostly negative scores on PC2 whereas the chemically treated female spectral objects (pink) have high scores on positive PC1 and PC2. These objects have positive PC2 scores and compare well with the typically treated CFTR10 spectral objects. In somewhat similar circumstances to the previous scenario (Section 5.3.1.2.), the chemically treated male and female African-type spectral objects cluster with the treated male objects which have moderate positive scores on PC1 and negative ones on PC2.
242
Figure 5.20 - PCA scores plot of PC1 vs. PC2 of the Chemically Treated Hair Fibre Spectral Database illustrating the separation of treated male■, African-type male treated■ African-type female treated▲ from treated female♦ on the PC2 axis.
The loadings plot variables that approximately separate the genders described in Figure 5.18 are the same for the chemically treated fibres. This outcome further reinforces the hypothesis that female spectra are characterised by the α-helix of the Amide II band and male spectra are described by the concomitant increase in intensity of the β-pleated sheet in both the Amide I and II bands as a consequence of treatment.
A PROMETHEE II model using the PC1, PC2 and PC3 scores (c.a. 93 % data variance) as criteria was constructed (Table 5.11) to provide a quantitative analysis of the separation between male and female chemically treated spectral objects. To set a reference point, the PPROMETHEE model was setup in order for the typically treated Caucasian female No. 10 (CFTR10) samples to be the preferred objects.
243 Table 5.11 PROMETHEE II Model of Male and Female Chemically Treated Hair Spectra Criterion PC1 PC2 PC3 Function Type Gaussian Gaussian Gaussian Minimised/Maximised Minimised Minimised Minimised p - - q - - σ 5.82 2.89 2.14 Unit (a.u.) (a.u.) (a.u.) Weight 1.00 1.00 1.00
The PROMETHEE II ranking output (Table 5.12) for the chemically treated database was in the φ range of +0.725>φ>-0.552, where female treated objects (FTR, pink) were the most preferred objects (φ: +0.725 to (-0.007)), CFTR10 treated reference samples inclusive. Scattered amongst the ranking of FTR and male treated (MTR) spectral objects were the African-type female treated objects (NFTR, green) and φ -0.01 to (- 0.137) and φ -0.245 to (-0.322). The treated male spectral (MTR, blue) objects dominate the lower ranks from φ -0.141 to (-0.391). The treated African-type male spectral (NMTR, turquoise) objects provide no practical information as they are scattered across the 109 ranks.
244 Table 5.12 - PROMETHEE II Net φ Ranking of the Chemically Treated Spectral Database
Net φ Net φ Legend Rank Object Index Rank Object Index 1 FTR 0.725 56 MTR -0.007 Female Treated (FTR) = Pink 2 FTR 0.685 57 FTR -0.008 3 NMTR 0.6 58 NFTR -0.01 4 CFTR1010 0.567 59 NFTR -0.016 Male Treated (MTR) = Blue 5 FTR 0.509 60 NFTR -0.024 6 FTR 0.471 61 MTR -0.049 African-type Male Treated 7 NFTR 0.461 62 NMTR -0.065 (NMTR) = Light Blue 8 FTR 0.459 63 NFTR -0.065 9 FTR 0.456 64 CFTR101 -0.066 African-type Female Treated 10 FTR 0.438 65 NFTR -0.118 (NFTR) = Green 11 FTR 0.421 66 NFTR -0.131 12 CFTR1011 0.41 67 FTR -0.135 13 FTR 0.396 68 NFTR -0.137 14 FTR 0.389 69 MTR -0.141 15 FTR 0.348 70 MTR -0.15 16 NMTR 0.343 71 MTR -0.15 17 CFTR102 0.342 72 MTR -0.166 18 FTR 0.336 73 NMTR -0.169 19 NFTR 0.325 74 MTR -0.179 20 FTR 0.281 75 MTR -0.192 21 MTR 0.265 76 MTR -0.193 22 CFTR109 0.261 77 NFTR -0.195 23 CFTR105 0.249 78 MTR -0.195 24 NFTR 0.247 79 MTR -0.204 25 NMTR 0.236 80 MTR -0.223 26 FTR 0.214 81 MTR -0.24 27 FTR 0.2 82 MTR -0.24 28 MTR 0.199 83 NFTR -0.245 29 NFTR 0.198 84 MTR -0.249 30 NMTR 0.191 85 MTR -0.258 31 MTR 0.164 86 MTR -0.267 32 FTR 0.158 87 NFTR -0.283 33 NFTR 0.147 88 NFTR -0.287 34 NFTR 0.144 89 NFTR -0.29 35 CFTR106 0.143 90 MTR -0.308 36 MTR 0.132 91 NFTR -0.312 37 MTR 0.103 92 MTR -0.322 38 FTR 0.1 93 NFTR -0.322 39 FTR 0.09 94 MTR -0.327 40 CFTR104 0.081 95 MTR -0.328 41 NFTR 0.073 96 MTR -0.339 42 FTR 0.071 97 MTR -0.351 43 NFTR 0.071 98 FTR -0.354 44 FTR 0.063 99 MTR -0.363 45 NFTR 0.059 100 MTR -0.368 46 FTR 0.053 101 MTR -0.371 47 NFTR 0.052 102 NFTR -0.373 48 CFTR107 0.041 103 MTR -0.38 49 NMTR 0.037 104 MTR -0.391 50 NFTR 0.033 105 NFTR -0.416 51 CFTR108 0.023 106 NFTR -0.475 52 FTR 0.01 107 MTR -0.516 53 NFTR 0.002 108 NMTR -0.543 54 MTR -0.006 109 NFTR -0.552 55 FTR -0.007 245
The GAIA bi-plot (Figure 5.21, Δ 73.7 %) shows that the male spectral objects (blue) have negative scores on PC1 and mostly positive scores on PC2, and are favoured by the original PC2 criterion. These spectral objects are approximately separated along the PC2 axis from the female spectral objects (pink, CFTR10 inclusive) which have mostly negative scores on PC2 and are favoured by the PC1 criterion. These two clusters mentioned above, are approximately separated from the African-type female and male (green and turquoise respectively) spectral objects which have mostly positive scores on PC1 and PC2 and are favoured by the PC3 criterion.
PC2 African-type Female and Male Treated
Male Treated
PC1
Female Treated
Δ 73.7 %
Figure 5.21 - GAIA analysis of the 109 spectra for the Chemically Treated hair fibre database; ■ Male mildly treated fibres, ■ Female mildly treated fibres,■ African-type male, ■ African-type female,, ● pi (Π) decision-making axis, and ■ PC1, PC2 and PC3 criteria.
246 The PCA and Loadings plots (PC2 Loadings) analyses, in association with the second derivative spectra suggest that the separation of gender – sourced spectra, that male hair fibres (intensity-wise) prefer, the β-sheet conformation; however, the female hair fibres displayed more of the α-helical conformation (i.e. Amide II band) in the cuticle layers. The loadings also illustrate that as a consequence of chemical treatment, there is a related increase in intensity of aspartic and glutamic acid as shown by the - -1 carboxylate, νa(CO2 ), at 1577 cm
5.4 Race: Asian, Caucasian and African-type Hair Fibres
The variability of the morphological, physical and chemical properties of human hair in each race is greater than the variability of hairs on a single individual‟s head.105 Human hair can be characterised into three major racial groups (or major population groups) that include: Caucasoid (principally of European ancestry), African-type (races of Africa, Melanesia and Papua) and Asian (i.e. Sinetics, Mongols, American Indians and Eskimos).11 19 The populations of the Indian subcontinent are allied with the European populations in terms of anthropological kinship and closely allied with the hair type of the East Asian populations.18
Numerous studies have described the physical differences in hair from people of different ethnicities.10 11 38 62 64 305-307 Fibre curvature and cross-sectional shape vary between the three major races, and human scalp hair varies from 40-120 m in diameter.
Asian hairs have a greater diameter (c.a. 69 – 86 µm; mean 77 µm) with circular cross- section, are usually straight to wavy in curvature, round to slightly oval, and dark-brown to black.11 32 66 114
Caucasian hairs have an intermediate diameter (c.a. 67-78 µm; mean 72 µm), are generally straight to curly in curvature, round to slightly oval in cross-sectional shape and blonde to dark brown in colour. 32 66 114
247 African-type hair fibres have a high degree of irregularity in diameter (54-85 µm; mean 66 µm); are wavy to woolly, are the most elliptical in cross-sectional shape and brown- black in colour.11 32 66 114
In terms of chemical composition, the proteins and amino acids of keratin are similar in African-type, Asian and Caucasian hair.32
Finally, in terms of cuticle thickness, African-type hair is thin whilst Asian hair is thick and Caucasian hair varies widely. It must be taken into account that FTIR-ATR is a sample depth dependent technique that monitors the near surface chemistry of samples only. As African-type hair has the thinnest cuticle of the three races, it is suggested that the IR evanescent wave may be able to penetrate past the cuticle layer and sample information from the peripheral area of the cortex which is comprised of α-helical proteins.18
According to the proposed protocol for analysing single human hair fibres (Figure 4.1, Section 4.1), the last separation of the spectral objects is on the basis of the major races mentioned above. In total, there are six scenarios for the three hair classes/types i.e. male-female untreated, male-female mildly treated and male-female chemically treated. There is also the possibility of more scenarios if the mildly treated group is sub-divided into mild physical and mild chemical, and the chemically treated group is sub-divided into single vs. multiple treatments which in total equals 10 possible scenarios. However, for this investigation it is not feasible to explore all 10 scenarios because a) more evidence of the existence of sub-groups must be obtained, and b) some scenarios (including the theorised new scenarios) did not have enough spectral objects to make any valid conclusions or deductions. Hence, only two scenarios per gender of the possible 10 will be analysed.
In previous investigations, Panayiotou22 24, through the use of PC loadings plots was able to determine the underlying spectral differences for the discrimination of untreated Caucasian and Asian FTIR spectra. Asian hair fibres were characterised by the vibrational bands at 1690 cm-1 (random coil /β-pleated sheet of the Amide I band), with minor contributions from 1614-1550 cm-1 (β-sheet Amide I band, Tryptophan and Phenylalanine), 1500 cm-1 (β-pleated sheet Amide II band), 1470-1390 cm-1 and 1470-
248 1390 cm-1 δ(C-H) deformations, and 1310-1225 cm-1 (Amide III band). Caucasian hair fibres were characterised by the carbonyl stretch ν(C=O) at 1710-1742 cm-1 of the acidic amino acids and the cystine oxidation spectral region between 1121-1040 cm-1. According to Table 1.1, Section 1.2.2.1, (that contrasts the amino acid composition in human hair fibres), the only significant difference between the major races is that Caucasian hair has a higher concentration (µmole/gram) of cystine and cysteic acid than Asian hair.
5.4.1 Racial Spectral differences between Female Hair Fibres
5.4.1.1 Untreated Female Hair Fibres The 29 untreated female spectra were chosen from the untreated spectral database (Section 5.3.1.1.), which included the 10 typical untreated reference CFUN No.1 spectra. In total, 89.6 % of the total data variance is retained by the first two PCs with 62.5 % on PC1 and 27.1 % on PC2. This dataset did not include any untreated female African-type hair spectra, because it is difficult to find such genuinely untreated hair given the damage caused to the hair by common grooming practices. The PCA scores plot of the female untreated database is presented in Figure 5.22. With reference to the CFUN1 samples, Caucasian female spectral objects (blue) have mostly positive scores on PC1 and are approximately separated along the PC1 axis from the Asian female spectral objects (pink) which have negative scores on PC1.
249 Caucasian Female Untreated Asian Female Untreated
12
10 Caucasian Female Untreated 8
6
4 Asian Female Untreated 2
0 PC2 PC2 (27.1%) CFUN 1 -2
-4
-6
-8 -20 -15 -10 -5 0 5 10 15 PC1 (62.5%)
Figure 5.22 – PCA scores plot of PC1 vs. PC2 of the Untreated Female spectral database which illustrates the separation of untreated Caucasian female♦ spectra from untreated Asian female■ spectra on the PC1 axis.
The PC1 loadings plot (Figure 5.23) illustrates that the female Asian spectra (positive loadings) are characterised by the Amide I and Amide II bands (black) whilst the Caucasian female bands (negative loadings, including the reference CFUN1 spectra) are - related to the β-sheet of the Amide I (dark blue), νa(CO2 ) (green) of aspartic and glutamic acid and tryptophan (light blue) vibrational bands. This result supports the suggestion that untreated Caucasian hair is characterised by its higher levels of cystine, cysteic acid and possibly the amino acid tryptophan (Table 1.1).
250
Figure 5.23 – PC1 Loadings plot of the Untreated Female spectral database. The Amide I and II vibrational bands (positive loadings) correlate to the untreated Asian female spectral objects whilst the β-sheet, νa(CO2) and Tryptophan bands (negative loadings) are associated with the untreated Caucasian female spectral objects.
A PROMETHEE II model (Table 5.13) using PC1-PC3 (c.a. 97 % data variance) as criteria was constructed to provide a quantitative description of the separation of the female, untreated Asian and Caucasian hair spectra. The PC criteria were minimised, maximised and minimised respectively in order for the CFUN1 typical untreated samples to be the reference objects.
Table 5.13 PROMETHEE II Model of the Untreated Female Spectral Database
Criterion PC1 PC2 PC3 Function Type Gaussian Gaussian Gaussian Minimised/Maximised Minimised Maximised Minimised p - - q - - σ 5.76 3.80 1.90 Unit (a.u.) (a.u.) (a.u.) Weight 1.00 1.00 1.00
251 The PROMETHEE φ ranking (Table 5.14) of the 29 spectra was between φ: +0.546 to (-0.883) where Caucasian female spectral objects (blue, CFUN1-CFUN110 inclusive) occupy ranks between φ: +0.546 to (+0.027) and Asian female objects (pink) are the weaker performing samples between φ = -0.038 to (-0.883). The GAIA bi-plot (Figure 5.24, Δ 73.0 %) shows the approximate PC1 separation of Caucasian and Asian spectral objects where untreated Caucasian female objects (blue ■) have mostly positive scores on PC1 and untreated Asian female (pink ■) have negative scores on PC1 and positive PC2. The original PC1, PC2 and PC3 criteria strongly favour the untreated Caucasian female samples, CFUN1 reference samples inclusive.
Hence, the loadings analysis of the untreated female IR spectral subset has demonstrated that Caucasian females have higher levels of the amino acid cystine, aspartic and glutamic acid. However, it would be essential to compare untreated African-type female spectra to establish how they are different from Asian and Caucasian ones.
252 Table 5.14 – PROMETHEE II Net φ Ranking of the Female Untreated Hair Database
Rank Object φ 1 CFUN13 0.546 2 CFUN15 0.506 3 CF3 0.494 4 CFUN14 0.364 5 CF2 0.36 6 CFUN16 0.309 7 CFUN19 0.304 8 CF1 0.302
9 CFUN18 0.245 10 CFUN17 0.135 11 CFUN12 0.113 12 CF5 0.078 13 CF9 0.071 14 AUN5 0.067 15 CFUN110 0.064 16 CFUN11 0.037 17 CF4 0.027 18 AUN8 -0.038 19 AUN4 -0.099 20 CF18 -0.117 21 AUN3 -0.204 22 CF20 -0.206 23 AUN2 -0.219 24 AUN1 -0.296 25 CF16 -0.345 26 CF17 -0.461 27 AUN7 -0.473 28 AUN9 -0.683 29 AUN6 -0.883
Legend
Caucasian Female Untreated (CFUN) = Blue
Asian Female Untreated (AUN) = Pink
253
PC2
Asian Female Untreated
Caucasian Female Untreated
PC1
Δ 73.0 %
Figure 5.24 - GAIA analysis of the 29 spectra for the Untreated Female hair fibre database; ■ Caucasian Female untreated spectral objects, ■ Asian Female untreated spectral objects, ● pi (Π) decision-making axis, and ■ Original PC1, PC2 and PC3 criteria using a Gaussian preference function.
254 5.4.1.2 Chemically Treated Female Hair Fibres The 35 female treated spectra (5 Asian, 25 Caucasian; CFTR10 samples included, and 5 African-type) were removed from the treated dataset (Section 5.3.1.3) and processed by PCA (Figure 5.25). Unlike the untreated female spectra, three distinct clusters can be seen along the PC2 axis which relate to the three races. Asian spectral objects (pink) have positive scores on PC1 and high positive scores on PC2, the treated Caucasian spectral objects (blue), which contain the typical reference CFTR No. 101-1011 samples form a cluster that spreads along the centre of the PC1 and PC2 axis with mostly negative scores on PC2; the treated African-type (green) objects have negative scores on PC1 and low negative scores on PC2.
Caucasian Female Treated Asian Female Treated African-type Female Treated 10 8 6 Asian Female Treated 4 Caucasian Female 2 Treated 0 -2
PC2 (24.4%) PC2 -4 CFTR10 -6 African-type Female Treated -8 -20 -15 -10 -5 0 5 10 15 PC1 (58.9%)
Figure 5.25 - PCA scores plot of PC1 vs. PC2 of the Female Treated spectral database illustrating the segregation of Asian■, Caucasian♦ and African-type▲ spectral objects.
The PC2 loadings plot (Figure 5.26) demonstrates the spectral loadings that approximately separate treated female Asian spectral objects from treated Caucasian and African-type ones. The Caucasian and African-type spectral objects (negative loadings) are described by the β-pleated sheet of the Amide I and Amide II (black) vibrational bands whilst the Asian spectral objects are related to the anti-symmetric - carboxylate stretch νa(CO2 ) of aspartic and glutamic acid (green), tryptophan (blue) with small loadings from the α-helix (purple) of the Amide II band.
255
Figure 5.26 – PC2 Loadings plot of the Female Treated database where the treated Asian spectral objects (positive loadings) are separated from the treated Caucasian and African-type spectral objects (negative loadings).
To rank order the 35 spectral objects of the female chemically treated database a PROMETHEE II model was constructed using PC1, PC2 and PC3 criteria (Table 5.15).
Table 5.15 PROMETHEE II Model of the Chemically Treated Female Spectral Database
Criterion PC1 PC2 PC3 Function Type Gaussian Gaussian Gaussian Minimised/Maximised Maximised Minimised Maximised p - - q - - σ 5.89 3.48 2.17 Unit (a.u.) (a.u.) (a.u.) Weight 1.00 1.00 1.00
256 The PROMETHEE II net φ ranking (Table 5.16) was φ +0.467>φ -0.54 where the African-type female spectral objects were the most preferred samples φ = +0.373 to (+0.312), followed by the typically treated (CFTR101 – CFTR1011 inclusive) Caucasian samples φ = +0.287 to (-0.286) and the treated Asian samples dominate the lower ranks from φ = -0.296 to (-0.50). The GAIA bi-plot (Figure 5.27) depicts the PROMETHEE II ranking of the spectral objects which illustrates the 2-D separation of the three races along the PC2 axis, analogous to Figure 5.25. The PC1 and PC3 criteria favour the Caucasian (blue) spectral objects whilst the PC2 criterion favours the African-type (green) objects.
The results indicate that female Asian hair fibres are separated from female Caucasian and African-type hair fibres on the basis of the amino acids tryptophan, aspartic and glutamic acid.
257 Table 5.16 - PROMETHEE II Net φ Ranking of the Female Chemically Treated Hair
Net φ Rank Object Index 1 CFTR9 0.467 2 CFTR7 0.435 3 CFTR106 0.374 4 NFTR184 0.373 5 CFTR105 0.342 6 NFTR185 0.321 7 NFTR183 0.313 8 NFTR181 0.312 9 CFTR103 0.287 10 CFTR102 0.279 11 CFTR101 0.257 12 CFTR4 0.191 13 CFTR5 0.189
14 CFTR107 0.182 15 NFTR182 0.169 16 CFTR33 0.153 17 CFTR104 0.141 18 CFTR8 0.125 19 CFTR1010 0.104 20 CFTR1011 -0.02 21 CFTR31 -0.101 22 CFTR23 -0.214 23 CFTR108 -0.222 24 CFTR25 -0.246 25 CFTR28 -0.286 26 AFTR221 -0.296 27 CFTR109 -0.308 28 CFTR22 -0.311 29 AFTR222 -0.332 30 CFTR24 -0.359 31 AFTR224 -0.372 32 CFTR26 -0.422 33 AFTR223 -0.486 34 AFTR225 -0.5 35 CFTR29 -0.54 Legend
Caucasian Female Treated (CFTR) = Blue
Asian Female Treated (AFTR) = Pink
African-type Female Treated (NFTR) = Green
258
PC2 Asian Female Treated
Caucasian Female Treated
PC1
African-type Female Treated
Δ 80.22 %
Figure 5.27 - GAIA analysis of the 35 spectra for the Chemically Treated Female hair fibre database; ▲ Caucasian female treated objects, ■ Asian female treated objects, African-type female objects■, ● pi (Π) decision-making axis, and ■ Original PC1, PC2 and PC3 criteria using a Gaussian preference function.
259 5.4.2 Racial spectral differences between Male Hair Fibre Spectra
5.4.2.1. Mildly Treated Male Hair Fibres In total, the 92 male mildly treated spectra (41 Asian, 10 Caucasian and 41 African- type) were removed from the male-female mildly treated spectral database (Section 5.3.1.2) and processed by PCA (Figure 5.28). The African-type spectral objects (green) form a large cluster with positive scores on PC2 and spread across the PC1 axis. They are separated on the PC2 axis from the mildly treated Asian (pink) and Caucasian (blue) spectral objects which have negative scores on PC2. It is difficult to discern a separation of the Asian and Caucasian spectral objects as the Asian objects form a cluster which has large variance across the PC1 axis.
Caucasian Male Mildly Treated Asian Male Mildly Treated African-type Male Mildly Treated 8 African-type Male Mildly 6 Treated
4
2
0
-2 PC2 (13.5%) PC2 -4
-6 Asian + Caucasian Male Mildly Treated -8 -25 -20 -15 -10 -5 0 5 10 15 20 PC1 (67.4%)
Figure 5.28 – PCA scores plot of PC1 vs. PC2 of the Male Mildly Treated spectral database illustrating the separation of African-type male objects▲ from Asian■ and Caucasian♦ objects on the PC2 axis.
260 The PC2 loadings plot (Figure 5.29) demonstrates that mildly treated African-type objects (positive loadings) are influenced by the β-pleated sheet (green) of the Amide I band, tryptophan (turquoise) and to a lesser degree the α-helix of the Amide II band (red), whilst the Asian and Caucasian spectral objects are associated with the β-sheet and random coil of Amide I (black) and to a minor degree the α-helix Amide I (blue). The spectral objects of the male mildly treated database were rank ordered using a PROMETHEE II model using PC1-PC3 scores as criteria (Table 5.17).
Figure 5.29 – PC2 Loadings plot of the Male Mildly treated database which illustrates spectral variables that separate African-type male mildly treated (positive loadings) from Asian and Caucasian (negative loadings) mildly treated fibres.
261 Table 5.17 PROMETHEE II Model of the Mildly Treated Male Spectral Database Criterion PC1 PC2 PC3 Function Type Gaussian Gaussian Gaussian Minimised/Maximised Maximised Maximised Minimised p - - q - - σ 5.98 2.67 2.17 Unit (a.u.) (a.u.) (a.u.) Weight 1.00 1.00 1.00
The PROMETHEE II net φ ranking (Table 5.18) order for the male mildly treated objects was φ +0.677>φ>-0.917. The African-type spectral objects (NMTR, green) were the most preferred samples φ = +0.677 – (+0.129) whilst the Asian spectral objects (AMTR, pink) dominated the middle to lower ranking φ = +0.090 – (-0.917). Interspersed between the African-type and Asian spectral objects are the mildly treated Caucasian objects (CMTR, blue) that provide little information as to its actual rank order of the races. However, it must also be taken into consideration that there are only 10 Caucasian spectral objects.
The GAIA bi-plot (Figure 5.30) indicates the approximate separation of Asian (pink) and Caucasian (blue) spectral objects from African-type (green) ones on the PC1 axis. The original PC1 criterion favours the Asian and Caucasian spectral objects, whilst the original PC2 and PC3 criteria favour the African-type spectral objects.
262 Table 5.18 PROMETHEE II Net φ Ranking of the Male Mildly Treated Hair Database
Net φ Net φ Rank Object Index Legend Rank Object Index 1 NMTR 0.677 48 AMTR -0.005 49 AMTR -0.007 African-type Male Treated 2 NMTR 0.603 (NMTR) = Green 3 NMTR 0.597 50 NMTR -0.027 4 AMTR 0.593 51 AMTR -0.029
5 AMTR 0.590 52 AMTR -0.062 Asian Male Treated (AMTR) 6 NMTR 0.588 53 NMTR -0.073 = Pink 7 NMTR 0.576 54 AMTR -0.093 8 NMTR 0.490 55 NMTR -0.102 Caucasian Male Treated 56 AMTR -0.109 9 NMTR 0.471 (CMTR) = Blue 10 NMTR 0.449 57 AMTR -0.110 11 NMTR 0.438 58 AMTR -0.118 12 NMTR 0.424 59 NMTR -0.122 13 NMTR 0.409 60 NMTR -0.130 61 AMTR -0.141 14 NMTR 0.377 15 NMTR 0.37 62 NMTR -0.150 16 NMTR 0.368 63 CMTR -0.150 64 CMTR -0.165 17 NMTR 0.339 18 NMTR 0.328 65 NMTR -0.203 19 NMTR 0.302 66 AMTR -0.208 20 CMTR 0.290 67 AMTR -0.241 21 NMTR 0.281 68 NMTR -0.251 69 CMTR -0.258 22 AMTR 0.280 23 CMTR 0.280 70 AMTR -0.265 24 NMTR 0.262 71 NMTR -0.268 25 CMTR 0.252 72 AMTR -0.269 26 AMTR 0.249 73 AMTR -0.272 74 AMTR -0.274 27 AMTR 0.238 28 NMTR 0.226 75 AMTR -0.284 29 NMTR 0.222 76 CMTR -0.304 77 AMTR -0.352 30 NMTR 0.191 31 NMTR 0.179 78 NMTR -0.390 32 NMTR 0.169 79 AMTR -0.400 33 AMTR 0.163 80 AMTR -0.410 34 NMTR 0.158 81 AMTR -0.412 82 NMTR -0.420 35 NMTR 0.152 36 NMTR 0.136 83 AMTR -0.448 37 NMTR 0.129 84 AMTR -0.453 38 AMTR 0.105 85 AMTR -0.489 39 NMTR 0.104 86 CMTR -0.528 87 AMTR -0.559 40 AMTR 0.090 41 AMTR 0.086 88 AMTR -0.576 42 CMTR 0.023 89 AMTR -0.589 43 NMTR 0.015 90 AMTR -0.756 91 AMTR -0.895 44 CMTR 0.008 45 AMTR 0.008 92 AMTR -0.917
46 AMTR 0.003 47 AMTR -0.003
263
PC2
African-type Male Mildly Treated
PC1
Asian and Caucasian Male Mildly Treated
Δ 78.0 % Figure 5.30 - GAIA analysis of the 92 spectra for the Male Mildly Treated hair fibre database; ■ Caucasian male mildly treated objects, ■ Asian male mildly treated objects, African-type male mildly treated objects■, ● pi (Π) decision-making axis, and ■ Original PC1, PC2 and PC3 criteria using a Gaussian preference function.
264 5.4.2.2. Chemically Treated Male Hair Fibres The final scenario involves the analysis of the male chemically treated database which contains 41 spectra (14 Asian, 9 Caucasian, and 18 African-type) of the total chemically treated spectral database (Section 5.3.1.3). The PCA scores plot of the male treated spectral database is presented in Figure 5.31. The scenario is similar to Figure 5.28 of the male mildly treated database except that Asian (pink) and Caucasian (blue) spectral objects have positive scores on PC2 whilst African-type (green) spectral objects have negative scores on PC2. However, the PC2 spectral variables (Figure 5.32) that separate the hair races are not similar to the Figure 5.29 PC2 loadings plot. The treated African-type spectral objects (negative loadings) are described by the β-sheet of the Amide I and Amide II bands (black). The Asian and Caucasian spectral objects (positive loadings) are mainly associated with the tryptophan (light green) with minor contributions from the β-sheet and random coil Amide I (dark blue), α-helix Amide I - (light blue), anti-symmetric carboxylate stretch νa(CO2 ) of aspartic and glutamic acid (dark green), and the α-helix of the Amide II band (turquoise).
Caucasian Male Treated Asian Male Treated 12 African-type Male Treated 10
8
6 Asian + Caucasian Male 4 Treated
2
0 PC2 (13.4%) PC2 -2
-4 African-type Male Treated -6 -15 -10 -5 0 5 10 15 20 25 PC1 (74.3%) Figure 5.31 – PCA scores plot of PC1 vs. PC2 of the Male Chemically Treated Database which illustrates the separation of Asian■ and Caucasian♦ from African- type▲ spectral objects on the PC2 axis.
265
Figure 5.32 – PC2 Loadings plot of the male treated spectral database illustrating the variables which separate the Asian and Caucasian (positive loadings) from the African- type (negative loadings) spectral objects.
Table 5.19 explains the PROMETHEE II model used to rank order the male chemically treated spectral objects.
Table 5.19 PROMETHEE II Model of the Chemically Treated Male Spectral Database
Criterion PC1 PC2 PC3 Function Type Gaussian Gaussian Gaussian Minimised/Maximised Minimised Minimised Maximised p - - q - - σ 6.17 2.68 1.91 Unit (a.u.) (a.u.) (a.u.) Weight 1.00 1.00 1.00
266 Table 5.20 PROMETHEE II Net φ Ranking of the Male Chemically Treated Hair Database
Net φ Rank Object Index 1 NMTR 0.802 2 NMTR 0.643 3 NMTR 0.550 4 NMTR 0.541 5 NMTR 0.4 6 NMTR 0.398 7 AMTR 0.320 8 NMTR 0.245 9 NMTR 0.213 10 AMTR 0.183 11 NMTR 0.168 12 NMTR 0.077 13 NMTR 0.074 14 CMTR 0.067 15 AMTR 0.049 16 CMTR 0.027 17 AMTR 0.016 18 CMTR -0.012 PC1 19 NMTR -0.023 20 AMTR -0.037 21 AMTR -0.065 22 AMTR -0.080 23 AMTR -0.091 24 AMTR -0.097 25 CMTR -0.098
26 CMTR -0.099 27 AMTR -0.107 28 AMTR -0.115 29 CMTR -0.135 30 CMTR -0.142 31 AMTR -0.149 32 AMTR -0.196 33 NMTR -0.290 34 NMTR -0.304 35 CMTR -0.322 36 NMTR -0.350 37 CMTR -0.387 38 AMTR -0.387
39 NMTR -0.409 40 NMTR -0.424 41 NMTR -0.449
Legend
African-type Male Treated (NMTR) = Green
Asian Male Treated (AMTR) = Pink
Caucasian Male Treated (CMTR) = Blue
267
PC1
Caucasian Male Treated
PC2
Asian Male Treated
African-type Male Treated
Δ 76 %
Figure 5.33 - GAIA analysis of the 41 spectra for the Male Chemically Treated hair fibre database; ■ Caucasian male treated objects, ■ Asian male treated objects, African-type male treated objects■, ● pi (Π) decision making axis, and ■ Original PC1, PC2 and PC3 criteria using a Gaussian preference function.
268 The PROMETHEE II net φ ranking (Table 5.20) of the chemically treated male spectral database was φ = 0.802>φ>-0.449. The African-type spectral objects dominated the upper and lower ranks φ = +0.802 to (+0.074) and φ = -0.290 to (-0.449). The Asian spectral objects occupied the middle ranking φ = +0.037 to (-0.196) and as observed in the previous scenario the Caucasian spectral objects were scattered amongst the Asian spectral objects due to a small population size.
The GAIA bi-plot (Figure 5.33, Δ 76 %) illustrates the approximate separation of the African-type spectral objects (green) with negative scores on PC1 from the Asian and Caucasian spectral objects with positive scores on PC1 and PC2. As per the GAIA bi- plot of the male mildly treated database (Figure 5.30), the PC1 criterion somewhat favours the Asian and Caucasian spectral objects whilst the PC2 and PC3 criteria favour the African-type spectral objects. The overall decision axis is in preference of the African-type spectral objects as according to the setup of the model (Table 5.17).
With male hair fibres, Asian and Caucasian spectra are similar, and are separated along the PC2 axis from male African-type hair fibres. African-type spectra are described by the β-pleated sheet of the Amide I band.
269 5.5 Potential Extension of the Forensic Protocol
These studies have demonstrated the necessity for further investigation and extension of the forensic protocol for the analysis of single human hair fibres, predominately with the aid of a wider variety of samples. The variety of the samples used in the investigation did not permit all possible scenarios of the protocol to be analysed. Further sampling is therefore needed to compensate for the variation of human hair in our society. This will hopefully allow analysis of FTIR-ATR spectra in each category of the protocol. Of the male subset of IR spectra, only one sample was classified as untreated by FC analysis (African-type Male No. 1, NUN1). Of the untreated variety there were also no African-type female hair fibres available as described by FC and PCA. In the mildly treated hair class, the female hair subset lacked spectra for distinct discrimination of the objects. As a result, the protocol remains as a preliminary, yet developed methodology (Figure 5.34) in comparison to previous investigations.22-24 Furthermore, the results from Section 4.2.1.1 and Section 5.2.2.1 provided adequate evidence to warrant the sub-division of the mildly treated database into mild physical treatment (e.g. from grooming, combing, towel drying shampoo and conditioning) and mild chemical treatment (e.g. photo-oxidative bleaching, swimming in chlorinated water, use of hair styling products and hair straightening) hair classes. This was achieved with the utilisation of a 4-cluster FC model. Hence, treatment specific sampling would be required to analyse and verify that hypothesis. There is also reasonable data to suggest that the chemically treated spectral objects (Section 5.3.1.3) can be sub-divided into single versus multiple treatments, especially observed with African-type female hair, as hair of that type requires a number of cosmetic processes to achieve the desired straight or permed hair geometry. At the racial level, it may be possible to further discriminate hair fibres from each race into their respective ethnic/national groups (i.e. African-type hair spectra – African, Papa New Guinea, Torres Strait Islands, Samoan, Tongan, etc.).
Therefore, the more that the analysis methodology can be segregated at each interval or tier in the protocol (i.e. treatment and race), the more accurate and informative the spectral identifications of unknown hair fibres can become.
270 Unknown Fibre
* PCA (+FC ) PCA (+FC*) PCA (+FC*)
Untreated Mildly Treated Treated
Male Female Male Female Male Female
Caucasian Asian African African Caucasian Asian African
Caucasian Asian Caucasian Asian
Figure 5.34 – Preliminary Forensic Protocol for Analysis of Single Human Hair Fibres by FTIR-ATR Spectroscopy with the aid of Chemometrics. *FC Classification- Preferred Classification Method (if available) 271 5.6 Chapter Conclusions
Firstly, before the protocol could be modelled, it was imperative to examine if African- type hair IR spectra would fit the proposed forensic protocol in both the 1750-800 cm-1 and alternative 1690-1500 cm-1 IR vibrational regions. This had only been attempted in the previous investigation23 where the results indicated a contradiction to the protocol. In the current study, when compared with Asian and Caucasian spectra, PCA illustrated that African-type fibre IR spectra from each hair class (i.e. untreated, mildly treated or treated) clustered strongly with the respective classes of the Asian and Caucasian in the 1690-1500 cm-1 region, and not in the 1750-800 cm-1 spectral region. This suggested that when the cystine oxidation region is used for comparison, the levels of cysteic acid and oxidative intermediates of cystine is much higher in African-type hair than Asian and Caucasian hair. It was therefore proposed that a low percentage of African-type hair fibres would be collected in an untreated or virgin state from scenes of crime etc. It was suggested that because of the crimp of African-type hair fibres, normal grooming habits tend to be more destructive than on straight to oval shaped hair. This fact is supported by the literature studies using SEM. Hence, the observation explained the contradiction that was suggested in the previous investigation23 which showed untreated African-type spectral objects clustering with treated spectra and vice versa.
The spectra from the three hair classes were then separated into three data sub-sets. The next separation of the IR spectra for the methodology was on the basis of gender. Spectral objects of male and female spectra are separated along the PC2 axis. The PC2 loadings plots for each class indicated that the separation of gender is on the basis of the β-pleated sheet Amide I for male spectra and the α-helix Amide II vibrational band for female spectra. This supported the observations of the raw and second derivative IR spectra. In relation to the differences in chemical composition between genders for untreated, mildly treated and treated fibres, it is hypothesised that female IR spectra demonstrated strong intensity of the amino acid tryptophan (1554 cm-1). As a consequence of treatment of female fibres, there is a concomitant increase in intensity - -1 of the carboxylate, (νa(CO2 ) 1577 cm ), of the acidic amino acids aspartic and glutamic acids.
272 The spectra for each hair type of each gender were furthered divided into four smaller sub-sets. The final separation of the spectra was on the basis of racial origin. Not all scenarios of the protocol for race (6 scenarios) could be analysed because those subs- sets had little to no spectra available. With female spectra, Caucasian and African-type spectra are separated from Asian spectra on the basis of the amino acids tryptophan and aspartic and glutamic acid. With male hair spectra, Asian and Caucasian spectra are separated from African-type spectra on the basis of the β-pleated sheet and random coil of the Amide I vibrational band.
273 6.0 Conclusions and Future Investigations
6.1 Concluding Remarks
This dissertation is arguably the first comprehensive investigation of human scalp hair fibres by FTIR-ATR spectroscopy supported by chemometrics. The IR spectroscopic measurements made on a single hair fibre were sampled approximately in the middle of the hair shaft region. The measurements refer to spectral information collected at a beam penetration depth of 1.30 – 3.06 µm in the IR range of 1750 - 800 cm-1 in a hair compressed by the ATR tower. This essentially corresponds to spectral information being collected from the cuticle or near-cuticle and minimal cortex regions. Human scalp waste hair fibres were collected from 66 individuals, male and female, of Asian, Caucasian, African-type; varying in age (6-74); un-weathered or variously treated or coloured. From these hair fibres, 550 spectra were recorded to build a relatively large database that covered typical hair samples that could be recovered from scenes of crime. FTIR-ATR spectroscopy carries a number of advantages over FTIR micro-spectroscopy used in previous investigations: (i) the technique required less sample preparation offering greater throughput advantage and is relatively less destructive, (ii) greater spectral resolution between the vibrational bands and do not suffer from “peak saturation” or “band saturation”, and (iii) the advance in technology of FTIR-ATR spectrometers has allowed portable use which permits real-time analysis at crime scenes. In relation to the study‟s contribution to the field of forensic science, it has provided a novel methodology to systematically identify and discriminate single unknown human hair fibres. This proposed protocol can be used as a complementary technique to the current forensic methods of hair analysis. Before this no protocol existed. The methodology yields information pertaining to the chemical structure of the fibre including the presence of cosmetic treatment, its gender, and major racial origin of the subject.
6.1.1 Conclusions of Chapter 3 The “raw” spectra, spectral subtractions and second derivative spectra were compared to demonstrate the subtle differences in FTIR-ATR spectra between untreated and
274 cosmetically treated hair, its gender and race origins. SEM images were used as corroborative evidence to demonstrate the surface topography of untreated and treated hair. SEM images indicated that the condition of cuticle surface could be of three types: relatively “untreated” with minor damage as seen with hair having no physical or chemical treatment, “mildly treated” hair consistent with physical-mechanical damage, and “treated” hair from the use of cosmetic treatments.
Chemical changes in the form of oxidative damage to the fibre are a consequence of bleaching, permanent dyeing and permanent waving. Additionally, common physical processes (such as combing, and straightening) also damage fibres as revealed by SEM micrographs (Section 3.1.1.2.). For the comparison of untreated and treated hair fibres, the important IR spectral region consisted of the cystine oxidation bands. The cystine - disulphide cross-links (S=S) are oxidised to cysteic acid (-SO3 ) as shown by the prominent increase in intensity at 1037 cm-1 concurrent with the weaker anti-symmetric cysteic acid band at 1172 cm-1, which is actually a shoulder of the Amide III band. The oxidation bands appear together with the responses from the oxidative intermediate -1 species such as cystine monoxide (S-S=O) at 1071 cm , cystine dioxide (S=O2) at 1114 cm-1and cysteine-S-thiosulphate (Bunte Salt) at 1022 cm-1. At higher wavenumbers, there is a peak shift of the Amide I band from approximately 1627 cm-1 to a strong, broad maximum at approximately 1631 cm-1and a shift of the Amide II band from 1520-1515 cm-1 to 1511 cm-1 This suggested a conformational modification of the secondary structure of the keratin protein, i.e. α-helix to β-pleated sheet transition.
For gender comparison, the Amide II band is significant for differentiation. In general, for untreated male fibre spectra, there is a sharp narrow band at approximately 1511 cm-1, whilst untreated female spectra demonstrated a peak maximum at approximately 1515-1520 cm-1. Interestingly, for chemically treated female hair spectra, the Amide II band becomes narrow and sharp at 1511 cm-1 as per the untreated male fibre spectra. This observation again indicates a conformational change of the protein as a consequence of the treatment. Apart from the evidence given by the difference in conformational structural chemistry, IR difference spectra between genders within each race were practical to identify the main spectral variables that are consistent for each gender. Female spectra exhibited greater intensity of the amino acid -1 - -1 tryptophan at 1554 cm and aspartic and glutamic acid, ν(CO2 ) at 1577 cm .
275 Therefore, to support the hypothesis of a conformational change in the protein structure (α-helix to β-sheet) which apparently occurs as a result of chemical treatment, second derivative (derivative spectroscopy) analyses were performed. The results illustrated that male hair exhibit a strong intensity of the β-sheet conformation (1511 cm-1) in the Amide II band whereas female hair spectra exhibited more intense α-helical conformation spectral pattern (1515-1520 cm-1).
The more intense β-pleated sheet bands in the male hair spectra suggested that the cuticle is comprised of a rather amorphous matrix as opposed to a fibrous α-helical matrix that makes up the cortical cells. This inference is supported by the literature163 where it has been reported that the cuticle has a higher proportion of cystine, proline, serine, and valine residues that have generally been considered as non-helical forming amino acid residues.
6.1.2 Conclusions of Chapter 4 The main objective of the research was concerned with the expansion and diversification of the provisional, unverified Forensic Protocol for hair fibre analyses over the 1700-850 cm-1 region. To achieve this, a relatively large database of spectra was required that covered hair samples of different racial backgrounds and treatment types. Previous investigations only used methodologies based on Asian and Caucasian hair spectra. In the penultimate study to this one23, African-type hair spectra highlighted a contradiction of the protocol concerning the separation of spectra on the basis of treatment. In the present work, African-type hair spectra were also initially removed from the preliminary model because of classification or borderline ambiguity between untreated and treated fibres. To eliminate any uncertainty of multiple class membership, Fuzzy Clustering (a non-parametric classification method) was employed as an unbiased test for other class membership and multiple class belongings of objects. A 3-cluster model was calculated to allow for another hair fibre class. There was immediate evidence that a third class of fibre existed. This hair fibre class was categorised as the Mildly Treated fibre group. The remaining „fuzzy‟ or misclassified objects were removed from the database. Pattern recognition (PCA) illustrated that a third fibre group existed. This group consisted of spectra from hairs that had been subjected to mild forms of physical and/or chemical treatment. MCDM (quantitative
276 object ranking order) showed that the groups would be quantitatively separated. Upon further examination using a 4-cluster FC model there was some evidence that the mildly treated group could potentially be separated into mild physical and mild chemical treatments.
Based on the above reduction of the data matrix after exclusion of the „fuzzy objects‟, a new aim was proposed to analyse the spectra in the 1690-1200 cm-1 region. FC 4- cluster modelling showed that the mildly treated group could be further sub-divided into mild physical treatment and mild chemical treatment. Ultimately, the most appropriate region for analysing the FTIR-ATR hair keratin spectra, which gave the least number of “fuzzy samples” was found to be the 1690-1500 cm-1 IR wavenumber region which contained principally the Amide I and II absorption bands.
6.1.3 Conclusions to Chapter 5 The global perspective and rationale of this investigation endeavoured to provide analysts with a rapid methodology (i.e. Forensic Protocol) for analysing single unknown human hair fibres via FTIR-ATR Spectroscopy coupled with Chemometrics as a complementary technique to the current methods. Initially, African-type hair fibre spectra were processed using the proposed 1690 -1500 cm-1 spectral region which is novel for the development of the Forensic Protocol. It now appears that in the previous work23 where Forensic Protocol ambiguities were apparent, the inclusion of the cystine oxidation region in the spectral range (1750 – 800 cm-1) confused the spectral classification because of the chemical inconsistency. It appears that this chemical inconsistency arises from the composition of the oxidised products from „cystine‟ and is reflected between 1200-1000 cm-1. This is particularly so with the African-type hair samples which are robust in the 1690-1500 cm-1 range because the discrimination of the spectra is reliant on the change in conformation (α-helical to β-pleated sheet and/or random coil) of the fibre.
On the basis of the separation of gender – sourced spectra, the PC2 loadings plots for the untreated, mildly treated and chemically treated hair fibres suggest that male hair fibres exhibit more (intensity-wise) of, or prefer, the β-sheet conformation; however, the female hair fibres displayed more of the α-helical conformation (i.e. Amide II band) in
277 the cuticle layers. In terms of amino acids, it is suggested that female spectra are defined by greater intensity of the amino acids tryptophan (1554 cm-1), aspartic and glutamic - -1 acid (νa(CO2 ) 1577 cm ). These inferences are both supported by the IR spectral evidence (derivative and difference spectra) from chapter 3.
For the separation of samples based on racial differences, untreated Caucasian hair is discriminated from Asian hair as a result of having higher levels (µmole/gram) of the amino acid cystine and cysteic acid. Due to the common grooming habits of African- type hair, no untreated fibres were available for comparison, as demonstrated by FC modelling. However, in mildly or chemically treated hair fibres, Asian and Caucasian hair fibres are similar, whereas African-type fibres are relatively different as illustrated by the separation on the PCA scores plot (Figure 5.28 and Figure 5.31). It is suggested that the difference is based on the geometry of the hair. Caucasian and Asian have straight to elliptical shaped hair, whilst African-type hair have a highly curled geometry.
Of the mildly treated and chemically treated databases especially, 34 % and 66 % respectively of the African-type hair IR spectra were misclassified by the 2 class FC model. These spectra cannot be outliers in a 2-class model. From previous investigations, it is suggested that the spectra belong to another class of fibre known as multiply treated hair, mainly seen in some African women. This is a result of a multitude of treatments to acquire straight or permed hair geometry. Furthermore, if permanent colouring is also involved then the amount of cysteic acid is further increased.
The conclusions described in this investigation have furthered the scientific understanding pertaining to the structural chemistry of human hair fibres. Structural elucidation FTIR-ATR spectroscopy and Chemometric analysis has facilitated the development of a novel protocol to analyse unknown single human hair fibres proposed for viable forensic purpose. The protocol has been modelled in such a way so that the hair fibre is analysed in three logical, systematic steps i.e. treatment, gender and racial origin. Advances in FTIR-ATR technology has made it possible for on-site, real-time analyses.
278 6.2 Future Investigations
In general, the main outcome of this investigation has allowed for the modelling of a proposed protocol, with the purpose of identifying and gaining information about the origins of unknown or suspect human hair fibres which can complement the current forensic methods of hair analysis. Human hair fibres are commonly found at crime scenes or associated suspect/s. The problem is that crime scenes are not ideal and that fibres are found in a wide variety of circumstances from effects from types of chemical treatment or environmental weathering, racial origin and mixed origins. The database of IR spectra used to build the forensic protocol in this investigation did not allow analysis of all scenarios. That is why crime authorities e.g. Federal Bureau of Investigation (FBI) constantly update their databases of DNA and fingerprints of criminals/suspects.308
Therefore warranting future studies within this topic:
(a) A wider variety of hair fibre sampling is needed to compensate for the variation of human hair in our society. In addition, a larger number of hairs per donor are needed to give a better understanding of inter and intra individual variation. Also, to conduct trials where individuals hair has been subjected to specific cosmetic treatment regimes. This will hopefully allow analysis of FTIR-ATR spectra in each category of the protocol.
(b) With respect to the donated African-type hair samples from 23 persons in this investigation, the protocol indicated (with the exception of the samples from African-type male No. 1, NUN1), that there were no male or female untreated African-type hair fibres, only those of the Mildly Treated and Chemically Treated classes. Therefore, this suggests that upon hair sampling, the possibility of classifying an African-type hair fibre in the “untreated” state would be low. Furthermore, with the Asian and Caucasian male hair samples, the FC modelling highlighted there were no untreated hair fibres. Hence, to reinforce the inference that male hair fibres are seldom
279 in an untreated state, another randomly sampled set of alleged untreated male hairs would be required for FTIR-ATR analysis.
(c) The preliminary results in Chapter 4 indicated that the Mildly Treated group has the potential to be sub-divided into mild physical and mild chemical treatments using a 4-cluster FC model. To validate that hypothesis, treatment specific sampling in those classes would be imperative. In terms of analysis, it would be necessary to explore the use of more PC‟s rather than the first two PC1 and PC2, which only provided a 2-dimensional trend across the PC1 axis. Conceivably, the use of the third, fourth, PC etc., may draw out more data variance and with the aid of PROMETHEE II net φ flows, the objects can be ordered into distinct classes. The sub-division of the mildly treated group can allow the identification process to be more accurate, rather than creating an inaccurate hypothesis of the chemical state of the fibre. At the racial level of the protocol, it could be favourable to explore the separation of spectra of the same treatment and gender into spectra of the same ethnicity (i.e. Indian-Pakistani-Bangladesh etc. vs. Chinese-Japanese-Korean etc.).
(d) To explore the hypothesis that female hair fibres have a greater concentration of the amino acid tryptophan over male fibres, it would be necessary to perform a hydrolysis of the fibres in a strong acid to free the amino acids and subsequently separate and analyse using HPLC.309
(e) The database for this project was concerned with hair keratin spectra sampled at the shaft (middle) of the fibre only. Previous work has suggested that a hair fibre can be classified according to section i.e., root, middle and tip. Therefore it would be essential to build a comprehensive database which covers all three sections of the fibre because fibres collected at crime scenes could be in fragment form. This could be achieved by a specific comparison of the surface vs. internal chemistry using cross sections of hairs.
(f) Additionally in reference to the spectral database of keratin IR spectra, it may be necessary to sample and acquire spectra from many fibres from one
280 individual as each hair on a person‟s scalp is not uniformly treated or weathered by the environment or through grooming.
(g) To experiment with blind trials to test how accurate FTIR-ATR chemometrics is in determining treatment history, gender and race.
281 7.0 References (1) Kokot S., Encyclopaedia of Materials: Science and Technology 2001, pp. 4363- 4368.
(2) Erinclioglu Z., The Illustrated Guide to Forensics: True Crime Scene Investigations; Carlton Books Ltd: 20 Mortimer Street, London W1T 3JW, England, 2004.
(3) J. Robertson; Grieve, M. Forensic Examination of Fibres; Second Edition ed.; Taylor and Francis, 1999.
(4) Choudry M.Y., Kingston C.R., Kobilinsky L., Forest, P.R.D., Journal of Forensic Sciences. JFSCA 1983, 28, 293-306.
(5) Benner B.A., Goodpaster J.V., DeGrasse J.A., Tully L.A. Levin B.C., Journal of Forensic Sciences. JFSCA 2003, 48, 554-563.
(6) Tungol M.W., Bartick E.G., Montaser, A., Applied Spectroscopy 1993, 47, 1655-1658.
(7) Wilson M.R., Polanskey D., Butler J., DiZinno J.A., Replogle J., Budowle, B., BioTechniques 1995, 18, 662-669.
(8) Kirk P.L., Journal of Criminal Law Criminology Police Science 1941, 31, 486- 496.
(9) Grieve M.C., Wiggins K.G., Journal of Forensic Sciences. 2001, 46, 835-843.
(10) Feughelman M., Mechanical Properties and Structures of Alpha-Keratin Fibres. Wool, Human and Related Fibres, University of New South Wales Press: Sydney, 1997.
(11) Robbins C.R., Chemical and Physical Behaviour of Human Hair; Third Edition ed., Springer-Verlag: New York, 1994.
(12) Jolles P., Zahn H., Hocker H., Formation and Structure of Human Hair, Birkhauser Verlag, Basel, 1997.
(13) Owen D., Hidden Evidence: Forty true crimes and how forensic science helped solve them; Firefly Books Ltd: 3689 Victoria Park Avenue, Willowdale, Ontario M2H 3K1, Canada, 2000.
(14) Masters B., Kennedy J., Real-Life Crimes, and how they were solved 2003, 4, 1165-1168.
(15) Evans C., The casebook of forensic detection: how science solved 100 of the world's most baffling crimes; Wiley: New York, 1996.
(16) Fido M., Masters B., Kennedy J., Every Contact Leaves a Trace, 2003; Vol. 1.
282 (17) Goodpaster J.V., Drumheller B.C., and Benner B.A., Journal of Forensic Sciences. 2003, 48, 299-306.
(18) Ogle Jr. R.R., Fox M.J., Atlas of Human Hair - Microscopic Characteristics; CRC Press: Boca Raton, Florida, 1999.
(19) Houck M.M., Budowle B., Journal of Forensic Sciences. 2002, 47, 964-967.
(20) Rowe W.F., Forensic Taphonomy, The Post-mortem Fate of Human and Remains, CRC Press: Boca Raton, FL, 1997.
(21) Rendle D. F., Chemical Social Review 2005, 34, 1021-1030.
(22) Panayiotou H., Vibrational Spectroscopy of Human Hair Keratin Fibres, Master‟s Thesis, Queensland University of Technology, 1998.
(23) Barton P., A Forensic Taphonomy Investigation of Single α-Keratin Fibres under Environmental Stress Using a Novel Application of FTIR-ATR Spectroscopy and Chemometrics, Honours Thesis, Queensland University of Technology, 2004.
(24) Panayiotou H., Vibrational Spectroscopy of Keratin Fibres a Forensic Approach, PhD Thesis, Queensland University of Technology, 2004.
(25) Paris B., Vibrational Spectroscopy of Human Hair with Forensic Application, Honours Thesis, Queensland University of Technology, 1998.
(26) McCarthy A.M.J., Aspects of Matching and Discrimination of Human Head and Body Hair, Honours Thesis, Queensland University of Technology, 2006.
(27) Brandes S., FTIR-NIR Spectroscopy of Human Hair Fibres, Master‟s Thesis, Queensland University of Technology, 2009.
(28) Massart D.L., De Jong S., Lewi P.J., and Smeyers-Verbeke J., Handbook of Chemometrics and Qualtimetrics Part A, Elsevier: Amsterdam, 1997.
(29) Deming S.N., Clinical Chemistry 1986, 32, 1702-1706.
(30) Zviak C., The Science of Hair Care, Marcel Dekker, Inc., 270 Madison Avenue, New York, 1986.
(31) Johnson D. H., Hair and Hair Care; Marcel Dekker, Inc, New York, 1997.
(32) Wolfram L.J., Journal American Academy of Dermatology 2003, 48, S106- S114.
(33) Jones L.N., Swift J.A., Structure of the fully formed hair shaft; Micelle Press: Weymouth, Dorset, England, 1998.
(34) Phan K.H., Thomas E., Heine H., In Proceedings of the 9th International Wool Textile Research Conference Biella, Italy, 1995.
283 (35) Swift J.A., Smith J.R., Wool Science Review 2001, 204, 203.
(36) Bradbury J.H., In Advances in Protein Chemistry, Anfinsen C.B., Richards F.M., Academic Press, U.S.A., 1986, Vol. 28, p 111-211.
(37) Formanek F., De Wilde Y., Luengo G.S., Querleux B., Journal of Microscopy 2006, 224, 197-202.
(38) Kreplak L., Briki F., Duvault Y., Doucet J., Merigoux C., Leroy F., Leveque L., Miller L., International Journal of Cosmetic Science 2001, 23, 369-374.
(39) Clement J.L., Pareux P.F., Le Pareux A., Ceccaldi P.F., Annal. Dermatol. Venereol. 1981, 108, 849-857.
(40) Mahrle G., Orfanos C.E., Arch. Derm. Res. 1971, 241, 305-316.
(41) Nagase S., Shibuichi S., Ando K., Kariya E., Satoh N., Journal Cosmetic Science 2002, 53, 89-100.
(42) Swift J.A. Fundamentals of Human Hair Science, Micelle Press: Weymouth, Dorset, 1997, Vol. 8.
(43) Commo S., Gaillard O., Bernard B.A., British Journal of Dermatology 2004, 150, 435-443.
(44) Creighton T.E., Proteins: Structures and Molecular Properties; W.H. Freeman and Company, New York, 1993.
(45) Williams A.C., Edwards H.G.M., Barry B.W., Journal of Raman Spectroscopy 1994, 25, 95-98.
(46) Pescatore E.L., Wu M.-S. Journal Society Cosmetic Chemistry 1985, 36, 363- 371.
(47) Brenner L., Squires P.L., Garry M., Tumosa C.S., Journal of Forensic Sciences. 1985, 13, 420-426.
(48) Jachowicz J., Journal Society Cosmetic Chemistry 1987, 38, 263-286.
(49) Robbins C., Kelly, J. C., Journal Society Cosmetic Chemistry 1969, 20, 555.
(50) Robbins C.R. Journal Society Cosmetic Chemistry 1971, 22, 339.
(51) Joy M., Lewis, D.M., International Journal of Cosmetic Science 1991, 13, 249- 261.
(52) Signori V., Lewis, D.M., International Journal of Cosmetic Science 1997, 19, 1- 13.
(53) Rintoul L., Panayiotou H., Kokot S., George G., Cash G. Frost R., Bui T., Fredericks, P.M., Analyst 1998, 123, 571-577.
284 (54) Motz-Schalck L., Lemaire J., Journal of Photochemistry and Photobiology A: Chemistry 2002, 147, 225-231.
(55) Motz-Schalck L., Lemaire J. Journal of Photochemistry and Photobiology A: Chemistry 2002, 147, 233-239.
(56) Corbett J.F., Journal of the Society of Cosmetic Chemistry 1984, 35, 297-310.
(57) Tsujino Y., Yokoo Y., Sakato K., Journal of the Society Cosmetic of Chemistry 1991, 42, 273-282.
(58) Bolduc C., Shapiro J. Clinics in Dermatology 2001, 19, 431-436.
(59) Chao J., Newsom A.E., Wainwright I.M., Mathews R.A., Journal Society Cosmetic Chemistry, 1979, 30, 401.
(60) Pande C.M., Journal of the Society of Cosmetic Chemistry, 1993, 44, 109-122.
(61) Fair N., Gupta B.S., Journal of the Society of Cosmetic Chemistry, 1982, 33, 229.
(62) Speakman J.B., Journal Textile Institution, 1927, 18, 431.
(63) Swift J.A., Brown A.C., In Cosmetics, Toiletries and Health Care Products; Owens, G.W., Noyes Data Corporation: New Jersey, 1978.
(64) Garcia M.L., Epps J.A., Yare, R.S., J. Am. Acad. Dermatol. 1977, 29, 155-175.
(65) Robinson V.N.E., Journal of the Society of Cosmetic Chemistry, 1976, 27, 155- 161.
(66) LaTorre C, Bhushan B. Ultramicroscopy, 2005, 105, 155-175.
(67) Beard B.C., Johnson A., Cambria F.M., Trinh P.N., Journal of Cosmetic Science, 2005, 56, 65-77.
(68) Bhushan B. Principles and Applications of Tribology, Wiley, New York, 1999.
(69) Bhushan B. Handbook of Micro/Nanotribology, Second Ed, CRC Press, Boca Raton, FL, 1999.
(70) Bhushan B. Introduction to Tribology, Wiley, New York, 2002.
(71) Bhushan B. Springer Handbook of Nanotechnology, Springer, Heidelberg, Germany, 2004.
(72) Ruetsch S.B., Kamath Y., Weigmann H.D. Journal of Cosmetic Science, 2000, 51, 103-125.
(73) Smyth F. Cause of Death: The Story of Forensic Science, Orbis Publishing, London, 1980.
285 (74) Martin E.A., 4th edition, Oxford University Press, New York, 1997.
(75) Eckert W.G., Introduction to forensic sciences; Second Edition, Elsevier Science Publishing Co. Inc. New York, 1992.
(76) Fox R.H., Cunningham C.L. Crime Scene Search and Physical Evidence Handbook, Paladin Press, Boulder, Colorado 80306, U.S.A., 1992.
(77) DeForest P.R., Proceeding of the International Workshop on the Forensic Examination of Trace Evidence 1998, 7-8.
(78) DeForest P.R., Gaensslen R.E., Lee H.C., Forensic Science: An Introduction to Criminalistics, McGraw-Hill, New York, 1983.
(79) Sudo T., Seta, S. Proceedings of the First International Symposium on Biology and Diseases of Hair 1975, 1, 543-553.
(80) White P. Crime Scene to Court. The Essentials of Forensic Science, The Royal Society of Chemistry: Thomas Graham House, Science Park, Milton Road, Cambridge CB4 4WF, UK, 1998.
(81) Fong W., Journal of Forensic Sciences, 1989, 34, 295.
(82) Phillips J.H., Forensic Science and the Expert Witness, The Law Book Company Limited, Sydney, N.S.W., 1989.
(83) Robertson, J. Forensic Examination of Human Hair; Taylor and Francis: London, 1999.
(84) Aitken C.G.G, Stoney, D.A., The use of Statistics in Forensic Science; Ellis Horwood Limited: Market Cross House, Cooper Street, Chichester, West Sussex, PO19 1EB, England, 1991.
(85) Reik T. The Unknown Murderer, Prentice-Hall, New York, 1945.
(86) Roux C., Langdon S., Waight D., Robertson, Journal of Science and Justice 1999, 39, 239-251.
(87) Cantrell S., Roux C., Maynard P., Robertson J., Forensic Science International 2001, 123, 48-53.
(88) Wiggins K.G., Crabtree S.R., March B.M. Journal of Forensic Sciences, 1996, 41, 1042-1045.
(89) Cook R., Wilson C., Forensic Science International 1986, 32, 267-273.
(90) Jackson G., Cook R., Forensic Science International 1986, 32, 275-281.
(91) Saferstein R., Criminalistics: An Introduction to Forensic Science; Pearson Education, Inc., Upper Saddle River, New Jersey 07458, 2004, Vol. 8th Edition.
286 (92) Broad J. Science and Criminal Detection, MacMillan Education Ltd, London, 1988.
(93) Trueb R.M., Experimental Gerontology 2002, 37, 981-990.
(94) Ellis J.A., Expert Reviews in Molecular Medicine 2002.
(95) Sinclair R. D., Cutis. Chatham 2001, 68, 35-40.
(96) Morell V., Science 1994, 265, 741.
(97) Gaudette B.D., The forensic aspects of textile fibre examination; Prentice Hall, New Jersey, 1988.
(98) Grieve M. C. Journal of Forensic Sciences, 1983, 28, 877-887.
(99) Saferstein R., Forensic Science Handbook; Prentice Hall Regents: Englewood Cliffs, New Jersey 07632, 1982.
(100) Kirkbride K.P., Tridico S.R., Forensic Science International 2010, 195, 28-35.
(101) Bisbing, R.E., In Forensic Science Handbook; 2nd Ed., Saferstein, R., CRC Press: Englewood Cliffs, NJ, 2002. Vol. I.
(102) Zadora G., Brozek-Mucha, Z., Materials Chemistry and Physics 2003, 81, 345- 348.
(103) Forslind B., Scanning Microscopy 1989, 3, 1177-1182.
(104) Taylor M.E., Scanning Electron Microscopy 1971, 545-553.
(105) Verhoeven L.E., The Journal of Criminal Law, Criminology and Police Science 1972, 63, 125-128.
(106) Schneoder V., The Role of Hair in Forensic Investigations; Springer-Verlag, Berlin, 1981.
(107) Chernova O.F. Biology Bulletin 2002, 29, 238-247.
(108) Jones L.N., Clinics in Dermatology 2001, 19, 95-103.
(109) Brown A.C., Swift J.A., Scanning Electron Microscopy 1974, 67-74.
(110) Hess W.M., Seegmiller R.E., Gardner J.S., Allen J.V., Barendregt S. Scanning Microscopy 1990, 4, 375-386.
(111) Ruetsch S.B., Kamath Y., Weigmann H.D., Journal of Cosmetic Science 2000, 51, 103-125.
(112) Khumalo N.P., Doe P.T., Dawber R.P.R., Ferguson, D.J.P. J. Am. Acad. Dermatol. 2000, 43, 814-820.
287 (113) Breuer M.M., J. Soc. Cosmet. Chem. 1981, 32, 437-458.
(114) Franbourg A., Hallegot P., Baltenneck F., Toutain C., Leroy, F., J. Am. Acad. Dermatol. 2003, 48, S115-119.
(115) Hawks C.A., Rowe W.F., Deterioration of Hair by Airborne Microorganisms: Implications for Museum Biological Collections; Elsevier Science, New York, 1988.
(116) Kundrat J.A., Rowe W.F., A Study of Hair Degradation in Agricultural Soil, Plenum Press, New York, 1989.
(117) Kupferschmid T.D., Van Dyke R., Rowe W.F., Scanning Electron Microscope Studies of the Bio-deterioration of Human Hair Buried in Soil and Immersed in Water, Plenum Press, New York, 1994.
(118) Serowik J.M., Biodeterioration of Hair in a Soil Environment, Master‟s Thesis, Department of Forensic Sciences, George Washington University, D.C., 1987.
(119) Serowik J.M., Rowe W.F., Bio-deterioration of Hair in a Soil Environment, Plenum Press, New York, 1987.
(120) DeGaetano D.H., Kempton J.B., Rowe W.F. Journal of Forensic Sciences, 1992, 37, 1048-1054.
(121) Campbell N.A., Reece, J.B., Biology; 8th Edition, Pearson Education Australia: Frenchs Forest, N.S.W., 2008.
(122) Baker L.E., McCormick W.F., Matteson K.J. Journal of Forensic Sciences, 2001, 46, 126-130.
(123) Higuchi R.G., vonBeroldigen C.H., Sensabaugh G.F., Ehrlich H.A., Nature 1988, 332, 543-546.
(124) Bender, K., Schneider, P.M., Forensic Science International 2006, 161, 52-59.
(125) Bourguignon, L., Hoste B., Boonen T., Vits K., Hubrecht F., Forensic Science International-Genetics 2008, 3, 27-31.
(126) Andreasson, H., Nilsson, M., Budowle B., Lundberg H., Allen M., Forensic Science International 2006, 164, 56-64.
(127) Suenaga, E., Nakamura H., Journal of Chromatography B-Analytical Technologies in the Biomedical and Life Sciences 2005, 820, 137-141.
(128) Muller K., Klein R., Miltner E., Wiegand P., Electrophoresis 2007, 28, 2835- 2842.
(129) Matsuda H., Imaizumi K., Kubota S., Miyasaka S., Yoshino M., Seta S., Rep. Nat. Inst. Police Sci. 1997, 50, 23-28.
288 (130) Linch C.A., Whitting D.A., Holland M.M. Journal of Forensic Sciences, 2001, 46, 844-853.
(121) Linch C.A., Smith S.L., Prahlow J.A. Journal of Forensic Sciences, 1998, 43, 305-314.
(132) Hellmann A., Rohleder U., Schmitter H., Wittig M., International Journal of Legal Medicine 2001, 114, 285-287.
(133) Davis C.L., Profiles in DNA 1998, 1, 6-7.
(134) Roberts K. A., Calloway C., Journal of Forensic Sciences 2007, 52, 40-47.
(135) Gilbert M.T.P., Janaway R.C., Tobin D.J., Cooper A., Wilson A.S., Forensic Science International 2006, 156, 201-207.
(136) Jehaes E., Gilissen A., Cassiman J.J., Decorte R., Forensic Science International 1998, 94, 65-71.
(137) Gilbert M.T.P., Menez L., Janaway R.C., Tobin D.J., Cooper A., Wilson A.S., Forensic Science International 2006, 156, 208-212.
(138) Hacker G., Cell. Tissue Res 2000, 301, 5-17.
(139) Lindahl T., Nature 1993, 362, 709-715.
(140) Gilbert M.T.P., Willerslev E., Hansen A.J., Rudbeck L., Barnes I., Lynnerup N., Am. J. Hum. Genet. 2003, 72, 32-47.
(141) Parson W., Fendt L., Ballard D., Borsting C., Brinkmann B., Carracedo A., Carvalho M., Coble M.D., Real F.C., Desmyter S., Dupuy B.M., Harrison C., Hohoff C., Just R., Kramer T., Morling N., Salas A., Schmitter H., Schneider P.M., Sonntag M.L., Vallone P.M., Brandstatter A., Forensic Science International-Genetics 2008, 2, 61-68.
(142) Ho S.Y.W., Gilbert M.T.P., Mitochondrion 2010, 10, 1-11.
(143) Miller B, Bright G. Queensland Health Scientific Services, Brisbane, Queensland, Australia, Personal Communication, 2009.
(144) Schrader B. Infrared and Raman Spectroscopy. Methods and Applications, VCH Verlagsgesellschaft, D-69451 Weinheim (Federal Republic of Germany), 1995.
(145) Nakamoto K. Infrared and Raman Spectra of Inorganic and Coordination Compounds. Part A: Theory and Applications in Inorganic Chemistry, 5th Edition, John Wiley & Sons, Inc, New York, 1997.
(146) Stuart B. Infrared spectroscopy: fundamentals and applications; John Wiley & Sons, West Sussex, England, 2004.
289 (147) Williams D.H., Fleming I. Spectroscopic Methods in Organic Chemistry; McGraw-Hill: Glasgow, Great Britain, 1995.
(148) Pavia D.L., Lampman G.M., Kriz G.S. Introduction to Spectroscopy; Second Edition, Saunders College Publishing: U.S.A., 1996.
(149) Carter E.A, Vibrational Spectroscopic Studies of Wool, PhD Thesis, Queensland University of Technology, 1997.
(150) Harris D.C. Quantitative Chemical Analysis, 5th Ed, W.H. Freeman and Company, U.S.A, 1999.
(151) FBI Law Enforcement Bulletin 1976, 45, 1-71.
(152) Panayiotou H., Kokot S. Analytica Chimica Acta 1999, 392, 223-235.
(153) Strasheim A., Buijs K., Biochimica Biophysical Acta, 1961, 47, 538-541.
(154) Robbins C. Textile Research Journal 1967, 37, 811-813.
(155) Shah R.C., Gandhi R.S., Textile Research Journal 1968, 38, 87.
(156) Alter H., Bit-Alkhas M. Textile Research Journal 1969, 39, 479.
(157) Ohnishi K., Sakano T., Fujiwara N. Journal of Toxicology and Environmental Health, 1992, 39, 247-250.
(158) Hopkins J., Brenner L., Tumosa C.S., Forensic Science International 1991, 50, 61-65.
(159) Bartick E.G., Reffner J.A., Analytical Chimica Acta, 1994, 288, 35-42.
(160) Thanasoulias N.C., Parisis N.A., Evmiridis N.P., Forensic Science International 2003, 138, 75-84.
(161) Kher A.A., Green E.V., Mulholland M.I., Journal of Forensic Sciences, 2001, 46, 878-883.
(162) Johnson D.R., O‟Higgins P., Moore W.J. Forensic Science International 1989, 41, 41-53.
(163) Vella G.D., Campobasso C.P., Dragone M., Boll. Soc. Ital. Biol. Sper. 1994, 70, 299-305
(164) Hsiao T-H., Chang H-P., Liu K-M., Journal Forensic Science, 1996, 41, 792- 795.
(165) Cherpitel C.J., Journal Studies Alcoholism 1996, 57, 244-248.
(166) Karlssonl T., Forensic Science International, 94, 183-200.
290 (167) Doble P., Sandercock M., Pasquier E.D., Petocz P., Roux C., Dawson M., Forensic Science International 2003, 132, 26-39.
(168) Sandercock P.M.L., Pasquier E.D., Forensic Science International 2003, 134, 1- 10.
(169) Sandercock P.M.L., Pasqui E.D., Forensic Science International 2004, 140, 71- 77.
(170) Kokot S., Carswell S., Massart D.L., Applied Spectroscopy 1992, 46, 1393- 1399.
(171) Gilbert C., Kokot S. Vibrational Spectroscopy 1995, 9, 161-167.
(172) Kokot S., Crawford K., Rintoul L., Meyer U., Vibrational Spectroscopy 1997, 15, 103-111.
(173) Keen I.P., White G.W., Fredericks P.M., Journal of Forensic Sciences, 1998, 43, 82-89.
(174) Adam C.D., Forensic Science International 2008, 182, 27-34.
(175) Thanasoulias N.C., Piliouris E.T., Kotti M-S.E., Evmiridis N. P. Forensic Science International 2002, 130, 73-82.
(176) Brody R.H., Edwards H.G.M., Pollard A.M., Analytica Chimica Acta 2001, 47, 223-232.
(177) Krawczyk W., Parczewski A. Analytica Chimica Acta 2001, 446, 107-114.
(178) Klemenc S. Forensic Science International 2001, 115, 43-52.
(179) Ryder A.G., O'Connor G.M., Glynn T.J. Journal of Raman Spectroscopy 2000, 31, 221-227.
(180) Francios Van Zyl E., Louw M., Journal of Forensic Sciences, 1995, 40, 1072- 1076.
(181) Jurdana L.E., Ghiggino K.P., Leaver I.H., Barraclough C.G., Cole-Clarke P., Applied Spectroscopy, 1994, 48, 44-49.
(182) Jurdana L.E., Ghiggino K.P., Leaver I.H., Cole-Clarke P. Applied Spectroscopy, 1995, 49, 361-366.
(183) Akhtar W., Edwards H.G.M., Farwell D.W., Nutbrown M. Spectrochimica Acta Part A 1997, 53, 1021-1031.
(184) Church J.S, Woodhead A.L., Biopolymers, 1997, 42, 7-17.
(185) Church J.S., Woodhead A.L., Mikrochimica Acta 1997, 14, 179-180 Suppl.
(186) Humecki H.J.. Practical Guide to Infrared Spectroscopy, Maracel Dekker, New York, 1995.
291 (187) Robertson J, Australian Federal Police Force, Canberra, ACT, Australia, Personal Communication, 2004.
(188) Harrick N.J., Internal Reflection Spectroscopy, Wiley-Interscience, New York, 1967.
(189) Mirabella F.M. Internal reflection spectroscopy: Theory and Applications; Marcel Dekker, New York, 1993.
(190) Fredericks P.M. Chemistry in Australia 1999, 8-10.
(191) Do T., Celina M., Fredericks P.M., Polymer Degradation and Stability 2002, 77, 417-422.
(192) Fahrenfort, J., Molecular Spectroscopy, 1962, 701.
(193) Harrick N.J., Internal Reflection Spectroscopy, Harrick Scientific Corp, Ossining, NY, 1979.
(194) Standard Practices for Internal Reflection Spectroscopy, ASTM: E 573-581; American Society for Testing Materials: Philadelphia, PA, 1981.
(195) Lyman D.J., Murray-Wijelath J., Fueghelman M., Applied Spectroscopy 2001, 55, 552-554.
(196) Coates J., Spectroscopy 1999, 14, 34-45.
(197) Diz M., Jocic D., Infante M.R., Erra P. Textile Research Journal 1997, 67, 486- 493.
(198) Woodhead A.L., Harrigan F.J., Church J.S., Vibrational Spectroscopy 1997, 15, 179-189.
(199) Erra P., Gomez N., Dolce L.M., Julia M.R., Lewis D.M., Willoughby J.H. Textile Research Journal 1997, 67, 397-401.
(200) Church J.S., Journal of Applied Polymer Science 1995, 57, 1585-1594.
(201) D.J. Carlsson, Canadian Textile Journal 1970, 87, 73-76, 78, 84.
(202) Tatsumi D., Yamauchi T., Journal of Applied Polymer Science 1998, 69, 461- 468.
(203) Burkinshaw S.M., Lu J.G., Dyes and Pigments 1994, 24, 59-73.
(204) Jain A.K., Gupta V.B., Journal of Applied Polymer Science 1990, 41, 2931- 2939.
(205) McEwen D.J, Cheever G.D., Journal Coat. Technology 1993, 65, 35.
(206) Merrill R.A., Bartick E.G., Journal of Forensic Sciences, 2000, 45, 93-98.
(207) Muller G., Riedel C., Applied Spectroscopy 1999, 53, 1551-1555.
292 (208) Ishida H., Journal of the Adhesive and Sealant Council 1996, 1, 521-533.
(209) Ferrer N., Mikrochimica Acta 1997, 14, 329-332 Suppl.
(210) Heise H.M. Journal of Molecular Structure 2003, 651-653, 127-132.
(211) Iconomidou V.A., Chryssikos G.D., Gionis V., Willis J.H., Hamodrakas, S. J. Insect Biochemistry and Molecular Biology 2001, 31, 877-885.
(212) Nagle D., Infrared spectroscopic investigation of the degradation of various commercial rubbers, Master‟s Thesis, Queensland University of Technology, Brisbane, Queensland, Australia 2004.
(213) Gardella J.A., Grobe G.L., Hopson W.L., Eyring E.M. Analytical Chemistry, 1984, 56, 1169-1177.
(214) Head T., Rydzak J. American Pharmaceutical Review 2003, 6, 78-80, 82-84.
(215) Baulsir C.F., Simler R.J., Advanced Drug Delivery Reviews 1996, 21, 191-203.
(216) Katon J.E., Micron. 1996, 27, 303-314.
(217) Harrick N.J., Journal Optical Society America 1965, 55, 851.
(218) Nunn S., Nishikida K., Thermo Fisher Scientific Inc, 2008.
(219) www.thermo.com/com/cda/resources/resources_detail/1,2166,13230,00.html. Accessed 15/12/2008
(220) Griffiths P.R., deHasethy, J., Fourier Transform Spectrometry; Wiley, New York, 1986.
(221) Stamm R.F., Garcia M.L., Fuchs J.J., Journal Society Cosmetic Chemistry, 1977, 28, 571-600.
(222) Queensland Health Scientific Services, Brisbane QLD, Inside Forensic Science Forum, 2004.
(223) Rosencwaig A. Photoacoustics and Photoacoustic Spectroscopy; John Wiley and Sons, New York, 1980.
(224) Fredericks P.M., Queensland University of Technology, Brisbane, QLD , Australia, Personal Communication, 2004.
(225) Dymond E.G., Proceedings Cambridge Philosophy Society 1923/25, 22, 405.
(226) Bridge T.P., Fell A.F., Wardman, R.H., Journal Society Dyers and Colourists 1987, 103, 17-27.
(227) Jones R. N., Canadian Journal of Chemistry 1963, 41, 750.
(228) Fell A.F, U.v. Group Bulletin 1980, 8, 5.
293 (229) O'Haver T.C, Green G.L. Analytical Chemistry, 1976, 48, 312.
(230) Savitzky A, Golay M.J.E., Analytical Chemistry, 1964, 36, 1627-1639.
(231) Steinier J., Termonia Y., Deltour, J., Analytical Chemistry, 1972, 44, 1906.
(232) Cargnello J.A., Powell J.J., Thompson P.H., Crocker P.R., Watt, F. Analyst 1995, 120, 783-787.
(233) Jui-Lee Y., Buried Hair Fibres, PCB 604 Undergraduate Project, Queensland University of Technology, 2005.
(234) Sullivan R.W., PCB 604 Undergraduate Project, Queensland University of Technology, 2005.
(235) Kuzuhara A., Biopolymers 2005, 77, 335-344.
(236) Kuzuhara A., Biopolymers 2006, 81, 506-514.
(237) Arnberg R., Grung B., Kvalheim, O.M. Sirius 6.0. Software for Multivariate Analysis and Experimental Design. User Guide; Pattern Recognition Systems AS, Bergen, 1998.
(238) Meloun M., Militky J., Forina, M. Chemometrics for Analytical Chemistry. Volume 1: PC-Aided Statistical Data Analysis; Ellis Horwood Limited: West Sussex, England, 1992.
(239) Albano C., Blomqvist G., Coomans D., Dunn III W.J., Edlund U., Eliasson B., Hellberg S., Johansson E., Norden B., Johnels D., Sjostrom M., Soderstrom B., Wold, S.W., In Proceedings Symb. On Applied Statistics, Copenhagen, 1981.
(240) Derde M.P., Coomans D., Massart D.L. Analytica Chimica Acta 1982, 141, 187- 192.
(241) Massart D.L., Vandeginste B.G.M., Deming S.N., Michotte Y., Kaufman, L., Chemometrics: A textbook, Elsevier Science Publishing Company Inc, New York, NY 10010, U.S.A., 1988.
(242) Hopke P.K., Analytica Chimica Acta 2003, 500, 365-377.
(243) Einax J.W., Zwanziger H.W., Geiß S. Chemometrics in Environmental Analysis; VCH Verlagsgesellschaft mbH, D-69451 Weinheim (Federal Republic of Germany), 1997.
(244) Brereton R.G., Analyst 1987, 112, 1635-1657.
(245) Kowalski K.R., Chemometrics: Theory and Application; American Chemical Society, Washington, DC, 1977.
(246) Frank I.E., Kowalski B.R., Analytical Chemistry, 1982, 54, 232R.
294 (247) Brereton R.G., Chemometrics. Data Analysis for the Laboratory and Chemical Plant, John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England, 2003.
(248) Beebe K.R., Pell R.J., Seaholtz M.B., Chemometrics, A Practical Guide, John Wiley & Sons, Inc., 1998.
(249) Analysis Technology, Buhler FT-NIR Spectrometer NIRVIS System Operators Manual, Section 5: Chemometry, Buhler Ltd., 1994.
(250) Wold S. Pattern Recognition 1976, 8, 127.
(251) Wold S. Technometrics 1978, 20, 397.
(252) Chatfield C., Introduction to Multivariate Analysis, University Press, Cambridge, 1980.
(253) Brereton R.G., Chemometrics: Applications of Mathematical and Statistics to Laboratory Systems; Ellis Horwood Ltd., London, 1990.
(254) Kokot S., Grigg M., Panayiotou H., Phuong T., Electroanalysis 1998, 10, 1-8.
(255) Otto M., Chemometrics and Intelligent Laboratory Systems 1988, 4, 101.
(256) Adams M.J., Chemometrics in Analytical Spectroscopy; The Royal Society of Chemistry: Cambridge, 1995.
(257) Brans J.P., European Journal of Operational Research 2002, 136, 340-352.
(258) Keller H.R., Massart D.L., Brans J.P.. Chemometrics and Intelligent Laboratory Systems 1991, 11, 175-189.
(259) Bracewell J.M., Journal of Soil Science 1984, 35, 549-558.
(260) Brans J.P., Vincke Ph., Mareschal B,. European Journal of Operational Research 1986, 24, 228-238.
(261) Geldermann J., Zhang K., Journal of Multi-Criteria Decision Analysis, 2001, 10, 317-323.
(262) Decision Lab 2000. Executive Edition. Getting Started Guide, Visual Decision Inc, Montreal, Canada, 2003.
(263) Mareschal B., Brans J.P., European Journal of Operations Research 1988, 34, 6-77.
(264) Chatt A, Katz S.A, Hair Analysis: Applications in the Biomedical and Environmental Sciences, VCH Publishers, New York, 1988.
(265) Signori V., Lewis D.M., Macromolecular Symposium 1997, 119, 240-253.
295 (266) Barton P., FT-IR Spectroscopic Studies of Hydrogen Bonding in Cotton- Cellulose with the Aid of Chemometrics, Undergraduate Project, Queensland University of Technology, 2003.
(267) Sandercock P.M.L., Pasquier E.D., Forensic Science International 2004, 140, 43-59.
(268) Watanabe A., Morita S., Kokot S., Matsubara M., Fukai K., Ozaki Y. Journal of Molecular Structure 2006, Article in Press.
(269) Royds D, Australian Federal Police Force, Canberra, ACT, Personal Communication, 2004.
(270) Milczarek P., Zielinski M. Garcia M. L., Colloid & Polymer Science 1992, 270, 1106-1115.
(271) Monteiro V.F., Maciel A.P., Longo E., Journal of Thermal Analysis and Calorimetry 2005, 79, 289-293.
(272) Ruetsch S.B., Kamath Y.K., Journal Cosmetic Science, 2004, 55, 13-27.
(273) Tu A.T., Raman Spectroscopy in Biology: Principles and Applications; John Wiley and Sons, 1982.
(274) Solomons T.W.G., Organic Chemistry, 6th Edition, John Wiley and Sons, Inc., New York, 1996.
(275) Fraser R.D.B., MacRae T.P., Rogers G.E., Keratins: Their Composition, Structure and Biosynthesis, 1972.
(276) Tensmeyer L.G., Kauffman W., Protein Structure as Revealed by Nonresonance Raman Spectroscopy, VCH Publishers, Chapter 5.
(277) Jakobsen R.J., Strand S.W., Internal Reflection Spectroscopy: Theory and Applications, Marcel Dekker Inc, New York, 1993.
(278) Kuzuhara, A. Biopolymers 2006, 85, 274-283.
(279) Hilterhaus S., Zahn, H., In Proceedings of the 7th International Wool Textile Research Conference Tokyo, Vol. 4, 1985,
(280) Pielesz A., Wlochowicz A., Binias W, Spectrochimica Acta Part A 2000, 56, 1409-1420.
(281) Millington K.R., Colorists Technology 2006, 122, 169-186.
(282) White A., Handler P., Smith E.L., Principles of Biochemistry, McGraw-Hill, New York, 1959.
(283) Brown K., Journal Society Cosmetic Chemistry, 1982, 33, 375-383.
296 (284) Lin-Vien D., Colthup N.B., Fateley W.G., Grasselli J.G., The Handbook of Infrared and Raman Characteristic Frequencies of Organic Molecules; Academic Press, San Diego, 1991.
(285) Rintoul L, Queensland University of Technology, Brisbane, QLD, Personal Communication, 2004.
(286) Wilson P.D., Polo S.R., Journal Optical Society of America, 1981, 71, 599- 603.(287) Bradbury J.H., Ley K.F., Australian Journal of Biological Science, 1972, 25, 1235-1247.
(288) Bradbury J.H., Anfinsen C.B., Edsall J.T., Richards F.M., Advances in Protein Chemistry; Academic Press, New York, 1973, Vol. 27.
(289) Nishikawa N., Tanizawa Y., Tanaka S., Horiguchi Y., Asakura T., Polymer 1998, 39, 3835-3840.
(290) Ogawa S., Tokuda S., Fujii K., Kaneyama K., Joko K., Sen'I Gakkaishi 2004, 60, 35-42.
(291) Astbury W.T., Street A., General Trans. Royal Society, 1931, 230A, 75-101.
(292) Astbury W.T., Woods H.J., Philosophy Trans. Royal Society London A. 1933, 232, 333-394.
(293) Bendit E.G., Nature 1957, 179, 535
(294) Bendit E.G., Biopolymers 1966, 4, 539-559.
(295) Frushour B.G., Koenig J.L., Advances in Infrared and Raman Spectroscopy London, 1975, Vol. 1.
(296) Kreplak L., Doucet J., Dumas P., Briki F., Biophysical Journal 2004, 87, 640- 647.
(297) Kuzuhara A., Fujiwara N., Hori T., Biopolymers, 2007, 87, 134-140.
(298) Hordern R., PCB 604 Undergraduate Project, Queensland University of Technology, Brisbane, Queensland, Australia, 1998.
(299) Inglis A.S., Leaver I.H., Textile Research Journal 1967, 37, 995-997.
(300) Purcell D.E., Leonard G.J., O'Shea M.G., Kokot S., Chemometrics and Intelligent Laboratory Systems 2004, Article in Press.
(301) Decision Lab 2000, Visual Decision Computer Program, 1999.
(302) Graham A. Statistics, Hodder Education, London, 2008.
297 (303) Wolfram L.J., The Reactivity of Human Hair. A Review, in Hair Research. Status and Future Aspects, Springer-Verlag, Germany, 1981.
(304) Porter C.E., Diridollou S., Barbosa V.H., International Journal of Dermatology 2005, 44, 4-5.
(305) Garcia M.L., Diaz J., J. Soc. Cosmet. Chem. 1976, 27, 379-398.
(306) Kamath Y.K., Hornby S., Weigmann, H.D., Journal Society Cosmetic Chemistry, 1984, 35, 21-43.
(307) Lindelof B., Forslind B., Hedblad M.A., Kaveus U., Archaeological. Dermatology, 1988, 124, 1359-1363.
(308) http://www.fbi.gov/hq/cjisd/iafis.htm. Accessed 18/06/2010.
(309) http://www.sigmaaldrich.com/analytical-chromatography/analytical- reagents/amino-acid-analysis.html. Accessed 10/05/2010
298 Appendix I – Data on Subjects - Forensic Protocol
ETHNICITY GENDER AGE COSMETIC SUN SWIMMING TREATMENS EXPOSURE Semi-Permanent 1. Asian (A) Female 42 Dye, Hair Spray Average None 2. Asian Female 21 None Minimum Average 3. Asian Female 42 Semi-Permanent Medium None Dye 4. Asian Female 21 None Minimum Average 5. Asian Male 23 Hair Gel, Wax Maximum Average 6. Asian Male 21 None Medium None 7. Asian Female 35 None Minimum None 8. Asian Female 35 None Minimum None 9. Asian Male 30 Mustard Oil, Hair Minimum Minimum Gel 10. Asian Male 22 None Minimum None 11. Asian Female 21 Semi-Permanent Medium Minimum Dye 12. Asian Male 19 Fixation Gel Minimum Minimum 13. Asian Male 31 Hair Gel Medium Minimum 14. Asian Male 22 Hair Gel Minimum None 15. Asian Male 26 Herbal Hair Oil Minimum Minimum Permanent and Semi-Permanent 16. Asian Female 20 Dye, Frosting Minimum None
17. Asian Female 53 None Minimum None 18. Asian Female 23 None Minimum None 19. Asian Male 22 Hair Tonic Average Minimum 20. Asian Male 23 None Medium Minimum 21. Asian Female 25 Moisturiser Average Nil Tinged Hairspray 22. Asian Female 21 Wax Average Average 1. Caucasian (C) Female 22 None Average None 2.Caucasian Female 23 None Average None 3. Caucasian Male 19 None Minimum Minimum 4. Caucasian Male 23 None Maximum None
299 5. Caucasian Male 54 None Medium None (Greying) 6. Caucasian Male 51 None Minimum None (Greying) 7.Caucasian Male 54 None Minimum None (Greying) Bleached Semi-Permanent 9. Caucasian Female 53 Dye Maximum Minimum 10. Caucasian Female 53 Bleached, Dyed Minimum Maximum 11.Caucasian Female 21 Semi-Permanently Medium Medium Dyed 12. Caucasian Female 18 Permanently Medium None Dyed, Hair Spray and Wax Bleached, Semi and Permanently 13. Caucasian Female 23 Dyed Average None 14. Caucasian Female 22 Foils, Semi- Medium Minimum Permanently Dyed 15. Caucasian Female 21 Foils Minimum Minimum 16. Caucasian Female 74 Mousse Minimum Minimum 17. Caucasian Female 21 Permanently Medium Medium Dyed, Bleached, Gel, Hair Spray, Wax 18. Caucasian Female 21 Foils Average None 19. Caucasian Male 18 Hair Gel Medium Medium
20. Caucasian Female 18 Perm, Hair Gel Average Minimum Bleached Permanent Dye 21. Caucasian Hair Gel Male 20 Hair spray Average Minimum 1. African-type (N) Male 24 None Maximum None 2. African-type Male 22 None Minimum Minimum
300 3. African-type Male 22 None Minimal Minimal Straightened, Hair 4. African-type Female 24 Spray, Moisturiser Average None
Perm, Permanent 5. African-type Female 29 Dye, Hairspray Minimum None 6. African-type Male 18 Moisturiser Minimum None Permanent Dye, 7. African-type Male 36 Hair Gel Average Minimum 8. African-type Male 22 None Minimum Minimum 9. African-type Permanently Dyed Male 46 Hair Cream Minimum Minimum 10. African-type Male 10 Hair Cream Minimum Medium 11. African-type Male 46 None Maximum Minimum 12. African-type Male 48 None Minimum Minimum 13. African-type Male 13 Relaxed Average Medium 14. African-type Male 48 None Minimum Minimum 15. African-type Male 38 None Minimum Minimum 16. African-type Male 42 None Average Minimum 17. African-type Male 15 None Average Medium Permanently Waved 18. African-type Relaxed Female 41 Hair Cream Minimum Minimum 19. African-type Semi-Permanently Female 47 Dyed Average Minimum 20. African-type Female 37 None Average Minimum 21. African-type
Female 6 None Average Medium Permanently Waved 22. African-type Relaxed Female 14 Hair Cream Minimum Medium 23. African-type Female 16 Relaxed Average Medium
301 Appendix I (Continued) - Hair Profile Survey for Forensic Investigation on the Effects of Environmental Stress on Single α- Keratin Human Hair Fibres, PhD Thesis, Queensland University of Technology
(Please provide at least 10 strands of Hair, Thank You)
(Please circle or fill in appropriate space provided)
(General information below is needed as it may aid in the interpretation process)
Gender: Male Female
Age: ______
Ethnicity/Origin: ______
Chemical Treatment/s (e.g. Bleached, Highlights, Tinged, Semi or Permanently Dyed, Waved, Permanently Waved, Gels, Wax, Hair Spray, etc.):
Level of Sun Exposure: Minimal Moderate Maximum
Swimming Frequency: Minimal Moderate Maximum
Smoker: Yes No
Medication/s (Do not list): Yes No
302 Appendix II – Fuzzy Clustering (p = 1.2) 3-cluster model 1750-800 cm-1
Class Class Class Class Class Class Spectra 1 2 3 Spectra 1 2 3 AAF121 0.032 0.968 0 ACM2131 0.935 0.065 0 AAF122 0.997 0.003 0 ACM2132 1 0 0 AAF123 0.046 0 0.954 ACM2191 0 1 0 AAF171 0.999 0.001 0 ACM2201 1 0 0 AAF172 1 0 0 ACM2202 0.001 0 0.999 AAF173 0.908 0.092 0 ACM3101 0.001 0.999 0 AAF2121 0.998 0.002 0 ACM3191 0 1 0 AAF2171 0.542 0.458 0 ACM4101 0.979 0.003 0.018 AAF2172 0.999 0 0.001 AF111 0 0 1 AAF3121 0.557 0.443 0 AF112 0.374 0 0.626 ACF11 0.259 0.741 0 AF113 0.856 0 0.144 ACF111 0.988 0.012 0 AF2111 0.981 0 0.019 ACF12 0.903 0.097 0 AF2112 1 0 0 ACF21 0.997 0.001 0.002 AF2203 0.005 0 0.994 ACF211 0 0 1 AF2281 0.371 0 0.628 ACF2111 0 1 0 AF2282 0 1 0 ACF212 0 0 1 AF2283 1 0 0 ACF213 0.978 0.022 0 AF2284 0 0 1 ACF22 0.892 0.108 0 AF241 0.001 0 0.999
ACF221 0.02 0 0.98 AF242 0.001 0 0.999 ACF222 0.985 0.015 0 AF243 0 0 1 ACF23 0.002 0 0.998 AF244 0 0 1 ACF241 0 0 1 AF245 0.002 0 0.998 ACF242 0 0 1 AF281 0.835 0 0.165 ACF261 0 0 1 AF282 1 0 0 ACF262 1 0 0 AF283 0.055 0 0.945 ACF291 1 0 0 AF284 0.989 0 0.011 ACF292 1 0 0 AF3281 0.001 0.999 0 ACF3111 1 0 0 AF3282 0.019 0.981 0 ACF41 0 0 1 AF3283 0 1 0 ACF4111 0.177 0.822 0.001 AM151 0 0 1 ACF42 0 1 0 AM152 0 0 1
ACF43 0 0 1 AM153 1 0 0 ACF5111 0.873 0.127 0 AM201 0.001 0.999 0 ACF61 0 0 1 AM2201 0 1 0 ACF62 0 0 1 AM2251 0.931 0.069 0 ACF63 1 0 0 AM2252 0.011 0 0.989 ACF91 0.013 0.987 0 AM2253 1 0 0 ACF92 1 0 0 AM2261 1 0 0 ACF93 0.002 0 0.998 AM2262 0.995 0.005 0 ACM101 0.005 0.995 0 AM2263 0.997 0.003 0 ACM102 0.995 0.001 0.004 AM2271 0.999 0 0.001 ACM131 0.999 0.001 0 AM2272 1 0 0 ACM132 1 0 0 AM2273 1 0 0 ACM133 0.042 0.958 0 AM2281 1 0 0 ACM191 0.992 0.001 0.007 AM2282 1 0 0 ACM192 0 1 0 AM2283 1 0 0 ACM201 0 0 1 AM2284 1 0 0 ACM202 0.861 0 0.139 AM251 0.034 0 0.966 ACM203 1 0 0 AM252 0.991 0 0.008 ACM2101 0.998 0.001 0 AM253 1 0 0
303 Appendix II - Continued
Class Class Class Class Class Class Spectra 1 2 3 Spectra 1 2 3 AM261 0.998 0 0.002 CF2305 0.089 0 0.911 AM262 1 0 0 CF2311 0.996 0.004 0 AM263 0.998 0.002 0 CF2312 0 0 1 AM271 0.006 0.994 0 CF2313 0.084 0.916 0 AM281 1 0 0 CF2314 1 0 0 AM282 1 0 0 CF2315 1 0 0 AM283 0 0 1 CF2321 0 1 0 AM284 0.839 0 0.16 CF2322 0.999 0.001 0 AM285 1 0 0 CF241 0.262 0.735 0.002 AM3201 0 1 0 CF242 0.001 0.999 0 AM3202 0.085 0.915 0 CF243 0.599 0.218 0.183 AM3251 0 0 1 CF244 0.593 0.362 0.045 AM3252 1 0 0 CF245 0.662 0.297 0.04 AM3253 0.78 0 0.22 CF11 0 0 1 AM3261 0.001 0.999 0 CF12 0 0 1 AM3262 1 0 0 CF13 0 0 1 AM3263 1 0 0 CF14 0 0 1 AM3271 1 0 0 CF15 0 0 1 AM3272 0 1 0 CF291 1 0 0 AM3273 1 0 0 CF292 0.999 0.001 0 AM4201 1 0 0 CF293 0.067 0 0.933 AM4251 1 0 0 CF294 0.924 0.076 0 AM4252 1 0 0 CF295 1 0 0 AM4253 1 0 0 CF301 1 0 0 AM4271 1 0 0 CF302 0.002 0 0.998 AM4272 0.005 0 0.995 CF303 1 0 0 AM4273 0 0 1 CF304 0.98 0 0.02 CF161 0 0 1 CF305 1 0 0 CF162 0.001 0 0.999 CF311 0 0 1 CF211 0.024 0 0.976 CF312 0.001 0 0.999 CF212 1 0 0 CF313 0 0 1 CF213 0 0 1 CF314 0.992 0 0.008 CF2161 0 0 1 CF315 1 0 0 CF2162 0 0 1 CF321 0 1 0 CF2163 0 0 1 CF322 0 1 0
CF2211 0.001 0.999 0 CF3321 0.088 0.912 0 CF2212 1 0 0 CF3322 0.001 0.999 0 CF16 0 0 1 CF41 0.058 0.941 0 CF17 0.001 0 0.999 CF42 0.05 0.949 0.001 CF18 0 0 1 CF43 0.319 0.658 0.023 CF18 0 0 1 CF44 0.063 0.937 0.001 CF110 0 0 1 CF45 0.002 0.998 0 CF2291 1 0 0 CF101 0.055 0.945 0 CF2292 1 0 0 CF102 0.999 0.001 0 CF2293 0.775 0 0.225 CF103 1 0 0 CF2294 0.01 0 0.99 CF104 0.001 0.999 0 CF2295 0.686 0.001 0.313 CF105 0.186 0.814 0 CF2301 0.128 0 0.872 CF106 0.983 0.017 0 CF2302 0 1 0 CF107 0 1 0 CF2303 0 0 1 CF108 0 1 0 CF2304 0.025 0 0.975 CF109 0 1 0
304 Appendix II - Continued
Class Class Class Class Class Class Spectra 1 2 3 Spectra 1 2 3 CF1010 0.982 0.018 0 AIF311 0 1 0 CF1011 0.997 0.003 0 AIF312 0 1 0 CFA251 1 0 0 AIF313 0.982 0.018 0 CFA252 1 0 0 AIF315 0.997 0.003 0 CFA253 0.255 0.745 0 AIF51 1 0 0 CFA254 0.016 0.984 0 AIF52 1 0 0 CM121 1 0 0 AIF53 0.255 0.745 0 CM122 0.905 0.095 0 AIM211 0.016 0.984 0 CM131 0.992 0 0.008 AIM212 1 0 0 CM132 0.001 0.999 0 AIM221 0.905 0.095 0 CM133 0.872 0.128 0 AIM2211 0.992 0 0.008 CM2121 1 0 0 AIM2212 0.001 0.999 0 CM2122 1 0 0 AIM222 0.872 0.128 0 CM2131 0.999 0 0.001 AIM2221 1 0 0 CM2132 1 0 0 AIM2222 1 0 0 CM231 0.989 0.011 0 AIM2223 0.999 0 0.001 CM232 1 0 0 AIM223 1 0 0 CM241 1 0 0 AIM2291 0.989 0.011 0 CM251 0 0 1 AIM2292 1 0 0 CM252 0.98 0 0.02 AIM2293 1 0 0 CM253 0.984 0.016 0 AIM2311 0 0 1 CM31 0 1 0 AIM2312 0.98 0 0.02 CM3121 1 0 0 AIM2313 0.984 0.016 0 CM32 1 0 0 AIM2314 0 1 0 CM33 0 1 0 AIM2315 1 0 0 CM341 0 1 0 AIM291 1 0 0 CM342 0.969 0 0.031 AIM292 0 1 0 CM351 1 0 0 AIM293 0 1 0 CM352 0.999 0 0.001 AIM311 0.969 0 0.031 CM353 0 1 0 AIM312 1 0 0 CM41 0.001 0.999 0 AIM313 0.999 0 0.001 CM42 0.91 0.09 0 AIM314 0 1 0 CM441 0 1 0 AIM3222 0.001 0.999 0 CM51 0.998 0.002 0 AIM3223 0.91 0.09 0 CM52 0.996 0.004 0 AIM3291 0 1 0 CM53 0.995 0.005 0 AIM3292 0.998 0.002 0 CM541 0 1 0 AIM3293 0.996 0.004 0 CM542 0 1 0 AI3221 0.001 0.999 0 AIF171 0.058 0.941 0 Legend AIF172 0.05 0.949 0.001 AIF173 0.319 0.658 0.023 A = Asian AIF174 0.063 0.937 0.001 C = Caucasian AIF175 0.002 0.998 0 F = Female AIF2171 0.055 0.945 0 N = African-type AIF2172 0.999 0.001 0 M = Male AIF2173 1 0 0 AIF2174 0.001 0.999 0 Untreated = Blue AIF2175 0.186 0.814 0 Mildly Treated = Green AIF251 0.983 0.017 0 Chemically Treated = Pink AIF252 0 1 0 Fuzzy Objects = Blank Mild Physical = Turquoise 305 Appendix III–Fuzzy Clustering (p = 1.2) 4-cluster Model 1750-800 cm-1
Class Class Class Class Class Class Class Class Spectra 1 2 3 4 Spectra 1 2 3 4 AAF121 0 0 0 1 ACM2131 0 0 0 0.999 AAF122 0.001 0 0 0.999 ACM2132 0.974 0 0 0.026 AAF123 0.999 0 0.001 0 ACM2191 0 1 0 0 AAF171 0.022 0 0 0.977 ACM2201 0.156 0 0 0.844 AAF172 0.762 0 0 0.238 ACM2202 0.829 0 0.17 0 AAF173 0.002 0.003 0 0.995 ACM3101 0 0.999 0 0.001 AAF2121 0 0 0 1 ACM3191 0 1 0 0
AAF2171 0 0 0 1 ACM4101 0.848 0.001 0.007 0.144 AAF2172 0.989 0 0 0.011 AF111 0.008 0 0.992 0 AAF3121 0 0 0 1 AF112 1 0 0 0 ACF11 0 0.002 0 0.998 AF113 1 0 0 0 ACF111 0.001 0 0 0.998 AF2111 0.996 0 0.001 0.004 ACF12 0 0 0 1 AF2112 0.999 0 0 0.001 ACF21 0.87 0 0 0.13 AF2203 0.023 0 0.975 0.002 ACF211 0.002 0 0.998 0 AF2281 1 0 0 0 ACF2111 0.001 0.822 0 0.177 AF2282 0 1 0 0 ACF212 0.001 0 0.999 0 AF2283 0.999 0 0 0.001 ACF213 0 0 0 1 AF2284 0 0 1 0 ACF22 0.001 0 0 0.999 AF241 0.001 0 0.999 0 ACF221 0.858 0 0.138 0.004 AF242 0.001 0 0.999 0 ACF222 0.002 0 0 0.998 AF243 0 0 1 0 ACF23 0.179 0 0.819 0.002 AF244 0 0 1 0 ACF241 0 0 1 0 AF245 0.003 0 0.997 0 ACF242 0 0 1 0 AF281 1 0 0 0 ACF261 0 0 1 0 AF282 0.987 0 0 0.013 ACF262 0.016 0 0 0.984 AF283 0.998 0 0.002 0 ACF291 0.005 0 0 0.995 AF284 0.998 0 0 0.002 ACF292 0.199 0 0 0.801 AF3281 0 0.775 0 0.225 ACF3111 0.397 0 0 0.603 AF3282 0 0.004 0 0.995 ACF41 0.121 0 0.879 0 AF3283 0 1 0 0 ACF4111 0.08 0.302 0.002 0.617 AM151 0.008 0 0.992 0 ACF42 0 0.994 0 0.006 AM152 0.002 0 0.998 0 ACF43 0.005 0 0.995 0 AM153 0.037 0 0 0.963 ACF5111 0.001 0 0 0.999 AM201 0 0.999 0 0.001 ACF61 0 0 1 0 AM2201 0 0.999 0 0.001 ACF62 0 0 1 0 AM2251 0.001 0.001 0 0.998 ACF63 0.724 0 0 0.276 AM2252 0.607 0 0.39 0.003 ACF91 0 0.068 0 0.932 AM2253 0.597 0 0 0.403 ACF92 0.997 0 0 0.003 AM2261 1 0 0 0 ACF93 0.945 0 0.055 0 AM2262 0.006 0.001 0 0.993 ACM101 0.002 0.106 0 0.892 AM2263 0.001 0 0 0.999 ACM102 0.884 0.001 0.002 0.113 AM2271 1 0 0 0 ACM131 0.001 0 0 0.999 AM2272 0.09 0 0 0.91 ACM132 0.005 0 0 0.995 AM2273 0.9 0 0 0.1 ACM133 0 0.058 0 0.942 AM2281 0.555 0 0 0.445 ACM191 0.961 0 0 0.039 AM2282 0.554 0 0 0.446 ACM192 0 0.97 0 0.03 AM2283 0.164 0 0 0.836 ACM201 0.005 0 0.995 0 AM2284 0.009 0 0 0.991 ACM202 1 0 0 0 AM251 0.952 0 0.047 0.001 ACM203 1 0 0 0 AM252 0.994 0 0 0.005 ACM2101 0.48 0.004 0.001 0.515 AM253 0.2 0.001 0 0.8 306 Appendix III – Continued Class Class Class Class Class Class Class Class Spectra 1 2 3 4 Spectra 1 2 3 4 AM261 1 0 0 0 CF2305 0.999 0 0.001 0 AM262 0 0 0 1 CF2311 0.006 0.001 0 0.993 AM263 0.002 0 0 0.998 CF2312 0.301 0 0.699 0 AM271 0 0.609 0 0.39 CF2313 0 0.076 0 0.923 AM281 1 0 0 0 CF2314 1 0 0 0 AM282 1 0 0 0 CF2315 0.939 0 0 0.061 AM283 0.163 0 0.836 0 CF2321 0 1 0 0 AM284 0.998 0 0.001 0.001 CF2322 0 0 0 1 AM285 0.008 0 0 0.992 CF241 0.072 0.098 0.001 0.829 AM3201 0 1 0 0 CF242 0.001 0.972 0 0.028 AM3202 0 0.036 0 0.964 CF243 0.456 0.045 0.062 0.437 AM3251 0.086 0 0.913 0 CF244 0.3 0.062 0.014 0.624 AM3252 0.184 0 0 0.815 CF245 0.305 0.043 0.011 0.641 AM3253 0.996 0 0.002 0.002 CF11 0.001 0 0.999 0 AM3261 0 0.986 0 0.014 CF12 0 0 1 0 AM3262 0.008 0 0 0.992 CF13 0 0 1 0 AM3263 0.051 0 0 0.949 CF14 0 0 1 0 AM3271 0.975 0 0 0.025 CF15 0 0 1 0 AM3272 0 1 0 0 CF291 0.965 0 0 0.035 AM3273 0.871 0 0 0.129 CF292 0 0 0 1 AM4201 1 0 0 0 CF293 0.967 0 0.031 0.002 AM4251 0.993 0 0 0.007 CF294 0 0 0 1 AM4252 0.971 0 0 0.029 CF295 0.018 0 0 0.982 AM4253 0.788 0 0 0.212 CF301 0 0 0 1 AM4271 0.996 0 0 0.004 CF302 0.938 0 0.061 0 AM4272 0.807 0 0.193 0.001 CF303 0.002 0 0 0.998 AM4273 0.002 0 0.998 0 CF304 1 0 0 0 CF161 0 0 1 0 CF305 0.035 0 0 0.965 CF162 0.011 0 0.989 0 CF311 0 0 1 0 CF211 0.997 0 0.003 0 CF312 0.001 0 0.999 0 CF212 1 0 0 0 CF313 0 0 1 0 CF213 0.285 0 0.715 0 CF314 1 0 0 0 CF2161 0.004 0 0.996 0 CF315 1 0 0 0 CF2162 0 0 1 0 CF321 0 0.964 0 0.036 CF2163 0.057 0 0.942 0 CF322 0 0.993 0 0.007 CF2211 0 0.86 0 0.14 CF3321 0 0.012 0 0.988 CF2212 0.54 0 0 0.46 CF3322 0 0.807 0 0.192 CF16 0 0 1 0 CF41 0.033 0.29 0 0.676 CF17 0.834 0 0.166 0 CF42 0.03 0.484 0.001 0.485 CF18 0 0 1 0 CF43 0.181 0.194 0.011 0.613 CF19 0 0 1 0 CF44 0.034 0.377 0.001 0.588 CF110 0.238 0 0.762 0 CF45 0 0.989 0 0.011 CF2291 0.282 0 0 0.718 CF101 0 0 0 0.999 CF2292 0.324 0 0 0.676 CF102 0.011 0 0 0.989 CF2293 0.999 0 0 0 CF103 0.023 0 0 0.977 CF2294 0.802 0 0.196 0.002 CF104 0 0.012 0 0.988 CF2295 0.996 0 0.002 0.002 CF105 0 0 0 1 CF2301 1 0 0 0 CF106 0 0 0 1 CF2302 0 1 0 0 CF107 0 1 0 0 CF2303 0 0 1 0 CF108 0 1 0 0 CF2304 0.993 0 0.007 0 CF109 0 1 0 0
307 Appendix III - Continued
Class Class Class Class Class Class Class Class Spectra 1 2 3 4 Spectra 1 2 3 4 CF1010 0 0 0 1 AF311 0 1 0 0
CF1011 0 0 0 1 AF312 0 1 0 0 CFA251 0.936 0 0 0.064 AF313 0 0 0 1 CFA252 0.987 0 0 0.013 AF315 0 0 0 1 CFA253 0.004 0.009 0 0.987 AF51 0.936 0 0 0.064 CFA254 0.006 0.244 0 0.75 AF52 0.987 0 0 0.013 CM121 0 0 0 1 AF53 0.004 0.009 0 0.987 CM122 0.001 0.002 0 0.998 AM211 0.006 0.244 0 0.75 CM131 1 0 0 0 AM212 0 0 0 1 CM132 0 0.526 0 0.474 AM221 0.001 0.002 0 0.998 CM133 0.013 0.027 0 0.959 AM2211 1 0 0 0 CM2121 0.935 0 0 0.065 AM2212 0 0.526 0 0.474 CM2122 0.991 0 0 0.009 AM222 0.013 0.027 0 0.959 CM2131 1 0 0 0 AM2221 0.935 0 0 0.065 CM2132 1 0 0 0 AM2222 0.991 0 0 0.009 CM231 0.001 0 0 0.999 AM2223 1 0 0 0 CM232 1 0 0 0 AM223 1 0 0 0 CM241 0.002 0 0 0.998 AM2291 0.001 0 0 0.999 CM251 0 0 1 0 AM2292 1 0 0 0 CM252 1 0 0 0 AM2293 0.002 0 0 0.998 CM253 0 0 0 1 AM2311 0 0 1 0 CM31 0 0.993 0 0.007 AM2312 1 0 0 0 CM3121 1 0 0 0 AM2313 0 0 0 1 CM32 0.001 0 0 0.999 AM2314 0 0.993 0 0.007 CM33 0 0.991 0 0.009 AM2315 1 0 0 0 CM341 0 1 0 0 AM291 0.001 0 0 0.999 CM342 1 0 0 0 AM292 0 0.991 0 0.009 CM351 0.006 0 0 0.994 AM293 0 1 0 0 CM352 1 0 0 0 AM311 1 0 0 0 CM353 0 1 0 0 AM312 0.006 0 0 0.994 CM41 0 1 0 0 AM313 1 0 0 0 CM42 0.001 0.002 0 0.997 AM314 0 1 0 0 CM441 0 0.998 0 0.002 AM3222 0 1 0 0 CM51 0 0 0 1 AM3223 0.001 0.002 0 0.997 CM52 0 0 0 1 AM3291 0 0.998 0 0.002 CM53 0 0 0 1 AM3292 0 0 0 1 CM541 0 0.991 0 0.009 AM3293 0 0 0 1 CM542 0 1 0 0 AI3221 0 0.807 0 0.192 AF171 0.033 0.29 0 0.676 AF172 0.03 0.484 0.001 0.485 AF173 0.181 0.194 0.011 0.613 AF174 0.034 0.377 0.001 0.588 AF175 0 0.989 0 0.011 AF2171 0 0 0 0.999 AF2172 0.011 0 0 0.989 AF2173 0.023 0 0 0.977 AF2174 0 0.012 0 0.988 AF2175 0 0 0 1 AF251 0 0 0 1 AF252 0 1 0 0
308 Appendix IV–Fuzzy Clustering (p = 1.2) 3 Cluster 1690-1200 cm-1 Class Class Class Class Class Class Spectra 1 2 3 Spectra 1 2 3 AAF121 1 0 0 ACM2201 0 0 1 AAF122 0 0 1 ACM2202 0 0.425 0.575 AAF123 0 0.035 0.965 ACM3101 0.999 0 0.001 AAF171 0.667 0 0.333 ACM3191 1 0 0 AAF172 0.028 0 0.972 ACM4101 0.001 0.166 0.832 AAF173 0.998 0 0.002 AF111 0 0.653 0.346 AAF2121 0.995 0 0.005 AF112 0 0.001 0.999
AAF2171 0.977 0 0.023 AF113 0 0 1 AAF2172 0.01 0.003 0.987 AF2111 0.017 0 0.983 AAF3121 1 0 0 AF2112 0 0 1 ACF11 1 0 0 AF2203 0.041 0.761 0.198 ACF111 0.977 0 0.023 AF2281 0 0.001 0.999 ACF12 0.996 0 0.004 AF2282 1 0 0 ACF21 0 0 1 AF2283 0 0 1 ACF211 0 0.999 0.001 AF2284 0 1 0 ACF2111 1 0 0 AF241 0 1 0 ACF212 0 0.998 0.002 AF242 0 0.999 0.001 ACF213 1 0 0 AF243 0 1 0 ACF22 0.365 0 0.635 AF244 0 1 0 ACF221 0 0.103 0.897 AF245 0 0.997 0.003
ACF222 0.005 0 0.995 AF281 0 0.002 0.998 ACF23 0 0.993 0.007 AF282 0 0 1 ACF241 0 1 0 AF283 0 0.142 0.858 ACF242 0 1 0 AF284 0 0.004 0.996 ACF261 0 1 0 AF3281 1 0 0 ACF262 0.028 0 0.972 AF3282 1 0 0 ACF291 0.011 0 0.989 AF3283 1 0 0 ACF292 0 0 1 A3221 1 0 0 ACF3111 0.041 0 0.959 AF171 0 0 1 ACF41 0 0.994 0.006 AF172 0 0.448 0.552 ACF4111 0.982 0 0.018 AF173 0 0.003 0.997 ACF42 1 0 0 AF174 0 0 1 ACF43 0 1 0 AF175 0 0 1
ACF5111 0.566 0 0.434 AF2171 0 0 1 ACF61 0 1 0 AF2172 0 0.161 0.839 ACF62 0 1 0 AF2173 0 0 1 ACF63 0 0 1 AF2174 1 0 0 ACF91 1 0 0 AF2175 0.004 0 0.996 ACF92 0 0 1 AF251 0 0.027 0.973 ACF93 0 0.023 0.977 AF252 0 1 0 ACM101 0.045 0 0.955 AF311 0 0.999 0.001 ACM102 0 0.012 0.987 AF312 0 1 0 ACM131 0.086 0 0.914 AF313 0.032 0 0.968 ACM132 0.136 0 0.864 AF315 0 1 0 ACM133 0.999 0 0.001 AF51 0 1 0 ACM191 0 0 1 AF52 0 0 1 ACM192 0.997 0 0.003 AF53 0 0 1 ACM201 0 0.98 0.02 AM211 1 0 0 ACM202 0 0.001 0.999 AM212 1 0 0 ACM203 0 0 1 AM221 1 0 0 ACM2101 0.002 0.042 0.956 AM2211 1 0 0 ACM2131 0.986 0 0.014 AM2212 1 0 0 ACM2132 0.001 0 0.999 AM222 1 0 0 ACM2191 1 0 0 AM2221 1 0 0 309
Appendix IV - Continued Class Class Class Class Class Class Spectra 1 2 3 Spectra 1 2 3 AM2222 1 0 0 AM3201 1 0 0 AM2223 0.999 0 0.001 AM3202 0.983 0 0.017 AM223 1 0 0 AM3251 0 0.998 0.002 AM2291 1 0 0 AM3252 0.01 0 0.99 AM2292 1 0 0 AM3253 0.001 0.002 0.997 AM2293 0.289 0 0.711 AM3261 1 0 0 AM2311 0 0.822 0.178 AM3262 0.984 0 0.016 AM2312 0 0.996 0.004 AM3263 0.965 0 0.035 AM2313 0 0.999 0.001 AM3271 0 0 1 AM2314 0 0 1 AM3272 1 0 0 AM2315 0 1 0 AM3273 0.001 0 0.999 AM291 0.991 0 0.009 AM4201 0 0 1 AM292 0.996 0 0.003 AM4251 0 0 1 AM293 0.999 0 0.001 AM4252 0 0 1 AM311 0 0.01 0.989 AM4253 0.055 0 0.945 AM312 0.897 0 0.103 AM4271 0 0 1 AM313 0 0 1 AM4272 0 0.193 0.807 AM314 0.037 0 0.963 AM4273 0 0.998 0.002 AM3222 1 0 0 CF161 0 0.988 0.012 AM3223 1 0 0 CF162 0.004 0.935 0.061 AM3291 0 0 1 CF211 0 0 1 AM3292 0 0.155 0.845 CF212 0 0 1 AM3293 1 0 0 CF213 0 0.119 0.881 AM151 0 0.982 0.018 CF2161 0.011 0.731 0.258 AM152 0 0.994 0.006 CF2162 0.002 0.971 0.028 AM153 0 0 1 CF2163 0 0.982 0.017 AM201 1 0 0 CF2211 1 0 0 AM2201 1 0 0 CF2212 0 0 1 AM2251 0.937 0 0.063 CF11 0 1 0 AM2252 0.001 0.718 0.281 CF12 0 0.809 0.191 AM2253 0.001 0 0.999 CF13 0 0.999 0.001 AM2261 0.002 0 0.998 CF14 0 0.999 0.001 AM2262 1 0 0 CF15 0 0.84 0.16 AM2263 0.999 0 0.001 CF2291 0 0 1 AM2271 0 0 1 CF2292 0 0 1 AM2272 0 0 1 CF2293 0 0.002 0.998 AM2273 0 0 1 CF2294 0 0.432 0.568 AM2281 0.15 0 0.85 CF2295 0 0 1 AM2282 0.139 0 0.861 CF2301 0 0.001 0.999 AM2283 0.811 0 0.189 CF2302 1 0 0 AM2284 0.987 0 0.013 CF2303 0 0.999 0.001 AM251 0 0.015 0.985 CF2304 0 0.067 0.933 AM252 0 0.004 0.996 CF2305 0 0.011 0.989 AM253 0.001 0 0.999 CF2311 0.971 0 0.029 AM261 0 0 1 CF2312 0 0.223 0.777 AM262 0.97 0 0.03 CF2313 1 0 0 AM263 0.999 0 0.001 CF2314 0 0 1 AM271 1 0 0 CF2315 0.059 0 0.941 AM281 0 0 1 CF2321 1 0 0 AM282 0 0 1 CF2322 0.022 0 0.978 AM283 0 0.053 0.947 CF241 0.031 0 0.969 AM284 0.001 0 0.999 CF242 1 0 0 AM285 0.998 0 0.002 CF243 0.001 0.034 0.965 CF244 0.002 0.002 0.996 310
Appendix IV - Continued Class Class Class Class Class Class Spectra 1 2 3 Spectra 1 2 3 CF245 0.002 0.003 0.996 CM231 1 0 0 CF16 0 1 0 CM232 0 0 1 CF17 0 1 0 CM241 0 0 1 CF18 0 1 0 CM251 0 0.999 0.001 CF19 0 1 0 CM252 0 0 1 CF110 0 1 0 CM253 0.999 0 0.001 CF291 0 0 1 CM31 1 0 0 CF292 0.083 0 0.917 CM3121 0 0 1 CF293 0 0.084 0.915 CM32 0.067 0 0.933 CF294 1 0 0 CM33 1 0 0 CF295 0.01 0 0.99 CM341 1 0 0 CF301 0.779 0 0.221 CM342 0 0.001 0.999 CF302 0 0.019 0.98 CM351 0.035 0 0.965 CF303 0.724 0 0.276 CM352 0 0 1 CF304 0 0 1 CM353 1 0 0 CF305 0.339 0 0.661 CM41 1 0 0 CF311 0 1 0 CM42 0.991 0 0.009 CF312 0 1 0 CM441 1 0 0 CF313 0 1 0 CM51 0.014 0 0.986 CF314 0 0 1 CM52 0.541 0 0.459 CF315 0 0 1 CM53 0.961 0 0.039 CF321 1 0 0 CM541 1 0 0 CF322 1 0 0 CM542 1 0 0 CF3321 0.991 0 0.009 CF3322 1 0 0 CF41 0.988 0 0.012 CF42 0.305 0 0.695 CF43 0.011 0.001 0.988 CF44 0.275 0 0.725 CF45 0.999 0 0.001 CF101 1 0 0 CF102 0 0 1 CF103 0.002 0 0.998 CF104 1 0 0 CF105 0.998 0 0.002 CF106 0.997 0 0.003 CF107 1 0 0 CF108 1 0 0 CF109 1 0 0 CF1010 0.004 0 0.996 CF1011 0.163 0 0.837 CFA251 0 0 1 CFA252 0.002 0 0.998 CFA253 0.208 0 0.792 CFA254 0.376 0.001 0.623 CM121 0.013 0 0.987 CM122 1 0 0 CM131 0 0 1 CM132 1 0 0 CM133 1 0 0 CM2121 0 0 1 CM2122 0 0 1 CM2131 0 0 1 CM2132 0 0 1 311
Appendix V–Fuzzy Clustering (p = 1.2) 3-cluster Model 1690-1500 cm-1
Class Class Class Class Class Class Spectra 1 2 3 Spectra 1 2 3 AAF121 1 0 0 ACM2131 0.052 0 0.948 AAF122 0.014 0 0.986 ACM2132 0 0 1 AAF123 0 0.012 0.988 ACM2191 1 0 0 AAF171 0.978 0 0.021 ACM2201 0 0 1 AAF172 0.614 0.002 0.384 ACM2202 0 0.913 0.087 AAF173 0.999 0 0.001 ACM3101 0.999 0 0.001 AAF2121 0.998 0 0.002 ACM3191 1 0 0 AAF2171 0.986 0 0.014 ACM4101 0.001 0.752 0.247 AAF2172 0.152 0.042 0.806 AF111 0 0.627 0.373 AAF3121 1 0 0 AF112 0 0.004 0.996 ACF11 1 0 0 AF113 0 0 1 ACF111 0.988 0 0.012 AF2111 0.001 0 0.999 ACF12 0.999 0 0.001 AF2112 0 0 1 ACF21 0 0 1 AF2203 0.077 0.76 0.163 ACF211 0 1 0 AF2281 0 0 1 ACF2111 0.999 0 0.001 AF2282 1 0 0 ACF212 0 0.998 0.002 AF2283 0.004 0 0.996 ACF213 1 0 0 AF2284 0 0.996 0.004 ACF22 0.941 0 0.059 AF241 0 1 0
ACF221 0 0.082 0.918 AF242 0 1 0 ACF222 0.094 0 0.906 AF243 0 1 0 ACF23 0 0.946 0.054 AF244 0 1 0 ACF241 0 1 0 AF245 0 0.998 0.002 ACF242 0 1 0 AF281 0 0 1 ACF261 0 1 0 AF282 0.006 0 0.994 ACF262 0.674 0 0.326 AF283 0 0 1 ACF291 0.565 0 0.435 AF284 0 0 1 ACF292 0.04 0 0.96 AF3281 1 0 0 ACF3111 0.054 0 0.946 AF3282 1 0 0 ACF41 0 0.999 0.001 AF3283 1 0 0 ACF4111 0.964 0 0.036 A3221 1 0 0 ACF42 1 0 0 AF171 0 0 1
ACF43 0 1 0 AF172 0 0.315 0.685 ACF5111 0.839 0 0.161 AF173 0 0.002 0.998 ACF61 0 1 0 AF174 0 0 1 ACF62 0 1 0 AF175 0 0 1 ACF63 0.025 0 0.975 AF2171 0 0 1 ACF91 1 0 0 AF2172 0.001 0.06 0.939 ACF92 0 0 1 AF2173 0 0 1 ACF93 0 0.036 0.964 AF2174 1 0 0 ACM101 0.216 0.004 0.78 AF2175 0.008 0 0.992 ACM102 0.002 0.252 0.746 AF251 0.005 0.326 0.669 ACM131 0.012 0 0.988 AF252 0 1 0 ACM132 0.006 0 0.994 AF311 0 0.998 0.002 ACM133 0.701 0 0.299 AF312 0 1 0 ACM191 0 0 1 AF313 0.248 0 0.752 ACM192 0.907 0 0.093 AF315 0 1 0 ACM201 0 0.997 0.003 AF51 0 1 0 ACM202 0 0.029 0.971 AF52 0.021 0.001 0.979 ACM203 0 0 1 AF53 0.003 0 0.996 ACM2101 0.024 0.231 0.745 AM211 1 0 0
312 Appendix V - Continued
Class Class Class Class Class Class Spectra 1 2 3 Spectra 1 2 3 AM212 1 0 0 AM262 0.09 0 0.91 AM221 0.999 0 0.001 AM263 0.626 0 0.374 AM2211 1 0 0 AM271 0.994 0 0.006 AM2212 1 0 0 AM281 0 0 1 AM222 1 0 0 AM282 0 0 1
AM2221 1 0 0 AM283 0 0.561 0.439 AM2222 1 0 0 AM284 0 0 1 AM2223 0.999 0 0.001 AM285 0.97 0 0.03 AM223 1 0 0 AM3201 1 0 0 AM2291 0.947 0 0.053 AM3202 1 0 0 AM2292 1 0 0 AM3251 0 0.996 0.004 AM2293 0.001 0 0.999 AM3252 0 0 1 AM2311 0 0.965 0.035 AM3253 0.001 0.025 0.975 AM2312 0 0.994 0.006 AM3261 1 0 0 AM2313 0 0.995 0.005 AM3262 0.06 0 0.94 AM2314 0 0.006 0.994 AM3263 0.043 0 0.957 AM2315 0 0.998 0.002 AM3271 0 0 1 AM291 0.993 0 0.007 AM3272 1 0 0 AM292 0.998 0 0.002 AM3273 0 0 1 AM293 0.999 0 0.001 AM4201 0.003 0 0.997 AM311 0.001 0.045 0.954 AM4251 0 0 1 AM312 0.831 0 0.169 AM4252 0 0 1 AM313 0 0.008 0.992 AM4253 0 0 1 AM314 0.021 0 0.979 AM4271 0 0 1 AM3222 1 0 0 AM4272 0 0.657 0.342 AM3223 1 0 0 AM4273 0 0.978 0.022 AM3291 0 0 1 CF161 0.005 0.961 0.033 AM3292 0 0.5 0.5 CF162 0.027 0.887 0.086 AM3293 1 0 0 CF211 0 0 1 AM151 0 0.859 0.14 CF212 0 0 1 AM152 0 0.935 0.065 CF213 0 0.006 0.994 AM153 0 0 1 CF2161 0.021 0.85 0.129 AM201 1 0 0 CF2162 0.004 0.965 0.031 AM2201 1 0 0 CF2163 0 0.991 0.009 AM2251 0.014 0 0.986 CF2211 1 0 0 AM2252 0 0.947 0.052 CF2212 0 0 1 AM2253 0 0 1 CF16 0 1 0 AM2261 0 0 1 CF17 0 0.835 0.165 AM2262 0.954 0 0.046 CF18 0.001 0.934 0.065 AM2263 0.286 0 0.714 CF19 0 0.942 0.058 AM2271 0 0 1 CF110 0.011 0.298 0.691 AM2272 0 0 1 CF2291 0 0 1 AM2273 0 0 1 CF2292 0 0 1 AM2281 0.001 0 0.999 CF2293 0 0.007 0.993 AM2282 0 0 1 CF2294 0 0.16 0.839 AM2283 0.009 0 0.991 CF2295 0 0.004 0.996 AM2284 0.112 0 0.888 CF2301 0 0.002 0.998 AM251 0 0.118 0.882 CF2302 1 0 0 AM252 0 0.054 0.946 CF2303 0 0.993 0.007 AM253 0 0 1 CF2304 0 0.145 0.855 AM261 0 0 1 CF2305 0 0.026 0.974
313 Appendix V - Continued
Class Class Class Class Class Class Spectra 1 2 3 Spectra 1 2 3 CF2311 0.098 0 0.902 CF1011 0.047 0 0.953 CF2312 0 0.661 0.339 CFA251 0.002 0 0.998 CF2313 1 0 0 CFA252 0.353 0 0.646 CF2314 0 0 1 CFA253 0.986 0 0.014 CF2315 0.001 0 0.999 CFA254 0.986 0 0.014 CF2321 1 0 0 CM121 0.003 0 0.997 CF2322 0.015 0 0.985 CM122 0.998 0 0.002 CF241 0.987 0 0.013 CM131 0 0 1 CF242 1 0 0 CM132 1 0 0 CF243 0 0 1 CM133 1 0 0 CF244 0.004 0 0.996 CM2121 0.001 0 0.999 CF245 0.012 0 0.988 CM2122 0.002 0.002 0.997 CF11 0 0.999 0.001 CM2131 0 0 1 CF12 0 1 0 CM2132 0 0 1 CF13 0 1 0 CM231 0.976 0 0.024 CF14 0 1 0 CM232 0 0 1 CF15 0 1 0 CM241 0.001 0 0.999 CF291 0 0 1 CM251 0 0.98 0.02 CF292 0 0 1 CM252 0 0 1 CF293 0 0.446 0.554 CM253 0.831 0 0.169 CF294 0.944 0 0.056 CM31 1 0 0 CF295 0 0 1 CM3121 0 0 1 CF301 0.788 0 0.212 CM32 0.566 0 0.434 CF302 0 0.023 0.977 CM33 1 0 0 CF303 0.912 0 0.088 CM341 1 0 0 CF304 0 0 1 CM342 0 0 1 CF305 0.217 0 0.783 CM351 0.004 0 0.996 CF311 0 0.999 0.001 CM352 0 0 1 CF312 0 0.998 0.002 CM353 1 0 0 CF313 0 1 0 CM41 1 0 0 CF314 0 0 1 CM42 0.954 0 0.046 CF315 0 0 1 CM441 1 0 0 CF321 1 0 0 CM51 0.004 0 0.996 CF322 1 0 0 CM52 0.681 0 0.319 CF3321 0.963 0 0.037 CM53 0.833 0 0.167 CF3322 1 0 0 CM541 1 0 0 CF41 1 0 0 CM542 1 0 0 CF42 0.999 0 0.001 CF43 0.459 0 0.541 CF44 1 0 0 CF45 1 0 0 CF101 1 0 0 CF102 0.001 0 0.999 CF103 0.057 0 0.943 CF104 1 0 0 CF105 1 0 0 CF106 0.989 0 0.011
CF107 1 0 0 CF108 1 0 0 CF109 1 0 0 CF1010 0.019 0 0.981
314 Appendix VI – FC (p=1.2) African-type Hair Fibres 1750-800 cm-1
Class Class Class Class Class Class Spectra 1 2 3 Spectra 1 2 3 NF4021 0.036 0 0.964 NF4425 0.091 0.909 0 NF4022 0 0 1 NF443 0.001 0 0.999 NF4023 0 0 1 NF4431 0.761 0.239 0 NF4024 0.001 0 0.999 NF4432 1 0 0 NF4025 0 0 1 NF4433 0.999 0 0.001 NF4041 0.022 0 0.978 NF444 0 0 1 NF4042 0.266 0 0.733 NF4441 0.94 0.001 0.06 NF4043 1 0 0 NF4442 0.712 0 0.288 NF4044 0.939 0 0.061 NF4443 0.085 0.915 0 NF4045 0.005 0 0.995 NF4444 0.021 0 0.979 NF411 0.422 0.577 0.001 NF445 0.993 0 0.007 NF412 0.998 0.002 0.001 NF446 0 0 1 NF4121 0.736 0.263 0 NF451 1 0 0 NF4122 0.909 0.091 0.001 NF452 0.017 0.983 0 NF4123 0.001 0.999 0 NF4521 0.357 0 0.643 NF4124 0.283 0 0.717 NF4522 1 0 0 NF4125 0.872 0 0.128 NF4523 0.996 0 0.004 NF413 0.128 0.872 0 NF4524 0.994 0 0.006 NF4131 0.488 0 0.512 NF4525 0.547 0 0.453 NF4132 0.954 0 0.046 NF453 1 0 0 NF4133 0.996 0.001 0.004 NF4531 0.978 0 0.022 NF4134 0.998 0 0.002 NF4532 1 0 0 NF4135 0.001 0 0.999 NF4533 1 0 0 NF414 0.999 0.001 0.001 NF4534 1 0 0 NF415 0.001 0 0.999 NF4535 1 0 0 NF421 0 0 1 NF454 1 0 0 NF422 0.166 0 0.833 NF455 0.01 0.99 0 NF4221 0.137 0 0.863 NF71 0.004 0.996 0 NF4222 0.002 0 0.998 NM181 1 0 0 NF4223 0.001 0 0.999 NM182 0.055 0 0.945 NF4224 0.002 0 0.998 NM183 1 0 0 NF4225 0 0 1 NM211 0.999 0 0.001 NF423 0.28 0 0.72 NM212 1 0 0 NF424 0.118 0.882 0 NM213 0.975 0.025 0 NF425 0.114 0 0.886 NM2181 0.13 0.87 0 NF431 0 0 1 NM2182 0.134 0.866 0 NF432 0.998 0 0.002 NM221 1 0 0 NF4321 0.999 0 0.001 NM222 1 0 0 NF4322 0.999 0.001 0 NM223 0 1 0 NF4323 0 0 1 NM2231 0.076 0.924 0 NF4324 0 0 1 NM2232 0.012 0.987 0 NF4325 0.001 0 0.999 NM2241 1 0 0 NF433 0 0 1 NM2301 0 1 0 NF434 0.002 0 0.998 NM2302 0 1 0 NF435 0 0 1 NM2303 0 1 0 NF441 0.168 0 0.832 NM231 0.013 0.987 0 NF442 0 0 1 NM2311 1 0 0 NF4421 0.999 0 0.001 NM2312 0.017 0 0.983 NF4422 0.006 0.994 0 NM2313 0.997 0.003 0 NF4423 0.001 0.999 0 NM2314 0 0 1 NF4424 0.002 0.997 0 NM2315 0.004 0.996 0
315 Appendix VI - Continued
Class Class Class Class Class Class Spectra 1 2 3 Spectra 1 2 3 NM232 0.876 0.124 0 NM4231 0 1 0 NM241 0.002 0.998 0 NM4232 0.053 0.946 0 NM242 0.001 0.999 0 NM4233 0.017 0.983 0 NM243 0 1 0 NM4234 0.971 0 0.028 NM244 0 1 0 NM4235 0.992 0.008 0 NM281 0.06 0.937 0.002 NM4241 0.996 0 0.004 NM301 0 1 0 NM431 0 1 0 NM302 0.001 0.999 0 NM4322 0.91 0.089 0.001 NM303 0 1 0 NM442 0.654 0.341 0.004 NM304 0.002 0.998 0 NM4421 0 0 1 NM311 0.001 0.999 0 NM4422 0 0 1 NM312 0.002 0.998 0 NM4423 0 0 1 NM313 0 1 0 NM443 0.992 0.008 0 NM314 0.049 0.951 0 NM4431 0.619 0.381 0 NM315 0.003 0.997 0 NM4432 0.004 0 0.996 NM321 0 1 0 NM4433 0 0 1 NM322 0 1 0 NM444 0.986 0 0.014 NM323 0 1 0 NM445 0 0 1 NM3231 0.013 0.987 0 NM451 0.999 0 0.001 NM3232 0.009 0.991 0 NM4521 0 0 1 NM3241 1 0 0 NM4522 0 0 1 NM3242 0.056 0.944 0 NM4523 0 0 1 NM3243 0 1 0 NM4524 1 0 0 NM3244 1 0 0 NM4525 0.798 0 0.202 NM3301 0 1 0 NM4621 0 0 1 NM3302 0.016 0.984 0 NM4622 0.991 0.008 0.002 NM3303 0.972 0.028 0 NM4623 0 0 1 NM331 1 0 0 NM4631 0 0 1 NM401 0.004 0 0.996 NM4632 0.996 0 0.004 NM402 0.001 0 0.999 NM4633 0.126 0.874 0 NM4021 0 0 1 NM472 0.002 0 0.998 NM4022 0.001 0.999 0 NM4721 0 0 1 NM403 0 0 1 NM4722 1 0 0 NM4031 0.996 0 0.003 NM4723 1 0 0 NM4032 0 0 1 NM473 0 1 0 NM4033 1 0 0 NM4731 0.001 0 0.999 NM411 0.998 0.002 0 NM4732 0.976 0.024 0 NM412 0.981 0.019 0 NM4733 0.096 0 0.904 NM4121 0.009 0.991 0 NM4741 0.992 0 0.008 NM4122 0.28 0 0.72 NM4742 0.995 0.005 0 NM4123 0 0 1 NM4743 0 0 1 NM413 0.166 0 0.834 NM4821 0.985 0.015 0 NM4131 0 0 1 NM4822 1 0 0 NM4132 0 0 1 NM4823 1 0 0 NM4133 0 0 1 NM483 0.001 0 0.999 NM421 0 0 1 NM4831 1 0 0 NM422 0 0 1 NM4832 0.305 0 0.695 NM4221 0.999 0 0.001 NM4833 0 1 0 NM4222 1 0 0 NM4841 1 0 0 NM4223 0 0 1 NM4842 1 0 0 NM423 0.004 0 0.996 NM4843 0.96 0 0.04 NM581 0.763 0.225 0.012 316 Appendix VII – FC (p = 1.2) African-type Hair Fibres 1690-1500 cm-1
Class Class Class Class Class Class Spectra 1 2 3 Spectra 1 2 3 NF271 0.999 0 0.001 NF433 1 0 0 NF272 0.989 0 0.011 NF434 1 0 0 NF273 0.021 0 0.979 NF435 1 0 0 NF274 0.001 0.075 0.924 NF441 0.903 0 0.097 NF341 0 0 1 NF442 1 0 0 NF371 0 1 0 NF4421 0 0 1 NF372 0 1 0 NF4422 0 0.994 0.006 NF373 0 1 0 NF4423 0 0.992 0.008 NF374 0 0.999 0.001 NF4424 0 0.998 0.002 NF4021 0.979 0 0.021 NF4425 0 0.836 0.164 NF4022 1 0 0 NF443 1 0 0 NF4023 1 0 0 NF4431 0.001 0.082 0.916 NF4024 1 0 0 NF4432 0 0 1 NF4025 1 0 0 NF4433 0.001 0 0.999 NF4041 1 0 0 NF444 0.993 0 0.007 NF4042 1 0 0 NF4441 0 0 1 NF4043 0.166 0 0.834 NF4442 0.001 0 0.999 NF4044 0.91 0 0.09 NF4443 0 0.999 0.001 NF4045 0.998 0 0.002 NF4444 0.252 0 0.748 NF411 0.111 0.016 0.874 NF445 0.01 0.008 0.982 NF412 0.293 0.003 0.704 NF446 0.999 0 0.001 NF4121 0.017 0.004 0.979 NF451 0 0 1 NF4122 0.019 0.002 0.979 NF452 0 0.954 0.045 NF4123 0.004 0.203 0.793 NF4521 0.894 0 0.106 NF4124 1 0 0 NF4522 0.002 0 0.998 NF4125 0.984 0 0.016 NF4523 0.301 0 0.699 NF413 0.029 0.02 0.951 NF4524 0.387 0 0.613 NF4131 1 0 0 NF4525 0.446 0 0.554 NF4132 0.995 0 0.005 NF453 0 0 1 NF4133 0.989 0 0.011 NF4531 0.01 0 0.99 NF4134 0.911 0 0.089 NF4532 0 0 1 NF4135 1 0 0 NF4533 0 0 1 NF414 0.63 0.001 0.369 NF4534 0.057 0 0.943 NF415 0.999 0 0.001 NF4535 0 0 1 NF421 1 0 0 NF454 0 0 1 NF422 0.993 0 0.007 NF455 0 0.998 0.002 NF4221 0.981 0 0.019 NF71 0 0.988 0.012 NF4222 1 0 0 NM181 0 0 1 NF4223 1 0 0 NM182 1 0 0 NF4224 1 0 0 NM183 0.001 0 0.999 NF4225 1 0 0 NM211 0.122 0 0.878 NF423 0.982 0 0.018 NM212 0 0 1 NF424 0.001 0.027 0.973 NM213 0 0 1 NF425 0.997 0 0.003 NM2181 0 0 1 NF431 1 0 0 NM2182 0 0 1 NF432 0.134 0 0.866 NM221 0 0 1 NF4321 0.084 0 0.916 NM222 0 0 1 NF4322 0 0 1 NM223 0 0.273 0.727 NF4323 1 0 0 NM2231 0 0 1 NF4324 1 0 0 NM2232 0 0 1 NF4325 1 0 0 NM2241 0.025 0 0.975
317 Appendix VII - Continued
Class Class Class Class Class Class Spectra 1 2 3 Spectra 1 2 3 NM2301 0 1 0 NM4131 1 0 0 NM2302 0 0.999 0.001 NM4132 1 0 0 NM2303 0 1 0 NM4133 1 0 0 NM231 0 0 1 NM421 1 0 0 NM2311 0.146 0 0.854 NM422 1 0 0 NM2312 1 0 0 NM4221 0.175 0 0.825 NM2313 0 0 1 NM4222 0.003 0 0.997 NM2314 1 0 0 NM4223 1 0 0 NM2315 0.003 0.264 0.733 NM423 0.995 0 0.005 NM232 0.002 0 0.998 NM4231 0 0.13 0.87 NM241 0 0.001 0.999 NM4232 0 0 1 NM242 0 1 0 NM4233 0 0 1 NM243 0 0.006 0.994 NM4234 0.905 0 0.095 NM244 0 0.004 0.996 NM4235 0 0 1 NM281 0 0.921 0.079 NM4241 0.961 0 0.039 NM301 0 0.993 0.007 NM431 0.004 0.181 0.815 NM302 0 1 0 NM4322 0.002 0 0.997 NM303 0 1 0 NM442 0.044 0.089 0.867 NM304 0 1 0 NM4421 1 0 0 NM311 0 0 1 NM4422 1 0 0 NM312 0 0 1 NM4423 1 0 0 NM313 0 0.005 0.995 NM443 0.009 0.013 0.979 NM314 0 0 1 NM4431 0 0 1 NM315 0 0 1 NM4432 1 0 0 NM321 0 0 1 NM4433 1 0 0 NM322 0 0.301 0.699 NM444 0.559 0 0.441 NM323 0 0.324 0.676 NM445 1 0 0 NM3231 0.02 0.016 0.964 NM451 0 0 1 NM3232 0 0.992 0.008 NM4521 1 0 0 NM3241 0.111 0 0.889 NM4522 0.999 0 0.001 NM3242 0 0 0.999 NM4523 1 0 0 NM3243 0 0.216 0.784 NM4524 0 0 1 NM3244 0.001 0 0.999 NM4525 0.011 0 0.989 NM3301 0 0.91 0.09 NM4621 1 0 0 NM3302 0 0.006 0.994 NM4622 0.002 0.004 0.995 NM3303 0 0 1 NM4623 1 0 0 NM331 0 0 1 NM4631 1 0 0 NM401 1 0 0 NM4632 0.226 0 0.774 NM402 1 0 0 NM4633 0.001 0 0.999 NM4021 1 0 0 NM472 0.999 0 0.001 NM4022 0 0.995 0.005 NM4721 1 0 0 NM403 1 0 0 NM4722 0 0 1 NM4031 0.243 0 0.757 NM4723 0 0 1 NM4032 1 0 0 NM473 0 0.067 0.932 NM4033 0 0 1 NM4731 1 0 0 NM411 0 0 0.999 NM4732 0 0 1 NM412 0 0.001 0.998 NM4733 0.898 0 0.102 NM4121 0.001 0.038 0.962 NM4741 0.009 0 0.991 NM4122 0.943 0 0.057 NM4742 0 0 0.999 NM4123 1 0 0 NM4743 1 0 0 NM413 0.319 0 0.681 NM482 0 1 0
318 Appendix VII - Continued
Class Class Class Spectra 1 2 3 NM4821 0 0 1 NM4822 0 0 1 NM4823 0 0 1 NM483 1 0 0 NM4831 0 0 1 NM4832 0.428 0 0.572 NM4833 0 0.903 0.097 NM4841 0 0 1 NM4842 0.018 0 0.982 NM4843 0.018 0 0.982 NM581 0 0 1
319 Appendix VIII – FC (p = 1.2) Mildly Treated Database 1690-1500 cm-1
Class Class Class Class Class Class Class Class Spectra 1 2 3 4 Spectra 1 2 3 4 ACF291 0.42 0.025 0 0.554 AM262 0.006 0.618 0 0.377 ACF292 0.948 0.014 0 0.038 AM263 0 0.002 0 0.998 ACF91 0 0 0 1 AM271 0 0.002 0 0.998 ACF92 1 0 0 0 AM281 0.017 0.982 0.001 0 ACF93 0.008 0 0.992 0 AM282 0.125 0.872 0.003 0 ACM101 0.188 0.712 0.008 0.092 AM283 0 0 1 0 ACM102 0.006 0.031 0.962 0 AM284 0.177 0.812 0.01 0 ACM131 0.01 0.984 0 0.005 AM285 0 0.002 0 0.998 ACM132 0.003 0.996 0 0.002 AM3251 0.008 0.002 0.99 0 ACM133 0.01 0.434 0 0.556 AM3252 0 1 0 0 ACM201 0 0 1 0 AM3253 0.015 0.135 0.85 0 ACM202 0 0 1 0 AM3261 0.001 0.004 0 0.995 ACM203 0.901 0.094 0.005 0 AM3262 0.003 0.544 0 0.453 ACM2101 0.121 0.453 0.416 0.01 AM3263 0.003 0.686 0 0.311 ACM2131 0.012 0.322 0 0.666 AM3271 0.003 0.997 0 0 ACM2132 1 0 0 0 AM3272 0.001 0.002 0 0.998 ACM2201 0 1 0 0 AM3273 0.002 0.998 0 0 ACM2202 0 0 1 0 AM4201 0.991 0.007 0 0.002 AF111 0 0 1 0 AM4251 0.002 0.998 0 0 AF112 0.008 0 0.992 0 AM4252 0 1 0 0 AF113 1 0 0 0 AM4253 0.002 0.996 0 0.002 AF2111 1 0 0 0 AM4271 0.059 0.941 0 0 AF2112 0.998 0.002 0 0 AM4272 0 0 1 0 AF171 0.031 0.969 0 0 AM4273 0.004 0.002 0.994 0 AF172 0 0 1 0 CF211 0.982 0.011 0.007 0 AF173 0.033 0.007 0.96 0 CF212 0.999 0.001 0 0 AF174 0.575 0.419 0.006 0 CF213 0.715 0.005 0.279 0 AF175 0 1 0 0 CF2211 0 0 0 1 AF2171 0.924 0.076 0 0 CF2212 0.904 0.096 0 0 AF2172 0.113 0.003 0.883 0 CF2291 0 1 0 0 AF2173 0.837 0.162 0 0.001 CF2292 0 1 0 0 AF2174 0 0.001 0 0.998 CF2293 0.005 0.015 0.98 0 AF2175 0.01 0.974 0 0.016 CF2294 0 0.001 0.999 0 AM2251 0.001 0.946 0 0.053 CF2295 0.032 0.8 0.168 0 AM2252 0.001 0.001 0.999 0 CF2301 0.527 0.008 0.465 0 AM2253 0 1 0 0 CF2302 0.001 0.002 0 0.997 AM2261 0 1 0 0 CF2303 0.001 0 0.999 0 AM2262 0 0 0 0.999 CF2304 0.011 0 0.988 0 AM2263 0.001 0.05 0 0.95 CF2305 0.053 0.001 0.946 0 AM2271 0.536 0.453 0.01 0 CF291 0 1 0 0 AM2272 0.004 0.996 0 0 CF292 0 0.999 0 0.001 AM2273 0.02 0.979 0 0.001 CF293 0 0 0.999 0 AM2281 0.001 0.998 0 0 CF294 0 0 0 1 AM2282 0.072 0.92 0 0.008 CF295 0 1 0 0 AM2283 0.004 0.899 0 0.098 CF301 0.001 0 0 0.998 AM2284 0.003 0.093 0 0.904 CF302 0.037 0.002 0.961 0 AM251 0 0 1 0 CF303 0 0 0 1 AM252 0 0 0.999 0 CF304 1 0 0 0 AM253 0 1 0 0 CF305 0.015 0.002 0 0.983 AM261 0.844 0.156 0 0 CM121 0.003 0.981 0 0.016
320 Appendix VIII - Continued
Class Class Class Class Class Class Class Class Spectra 1 2 3 4 Spectra 1 2 3 4 CM122 0 0 0 1 NM2313 0.99 0.01 0 0 CM2121 0.19 0.799 0.006 0.005 NM2314 0.004 0.005 0 0.99 CM2122 0.1 0.856 0.039 0.005 NM2315 0.63 0.052 0.316 0.002 CM3121 0.974 0.026 0 0 NM232 0.948 0.036 0 0.017 NF451 0 1 0 0 NM241 0.999 0.001 0 0 NF452 0.01 0.081 0.908 0 NM242 0 0 1 0 NF4521 0 0.001 0 0.998 NM243 0.987 0.006 0.007 0 NF4522 0.11 0.758 0 0.132 NM244 0.986 0.007 0.006 0 NF4523 0.001 0.003 0 0.996 NM311 1 0 0 0 NF4524 0.007 0.029 0 0.965 NM312 1 0 0 0 NF4525 0 0.001 0 0.999 NM313 0.998 0.001 0.001 0 NF453 0.003 0.997 0 0 NM314 0.996 0.002 0 0.002 NF4531 0 0 0 1 NM315 1 0 0 0 NF4532 0.019 0.981 0 0 NM321 1 0 0 0 NF4533 0.009 0.991 0 0 NM322 0.36 0.002 0.637 0 NF4534 0.007 0.013 0 0.979 NM323 0.074 0.003 0.923 0 NF4535 0.242 0.424 0 0.335 NM3231 0.902 0.055 0.014 0.029 NF454 0.002 0.998 0 0 NM3232 0.017 0.004 0.979 0 NF455 0.002 0.003 0.995 0 NM3241 0.006 0.003 0 0.991 NM181 0.859 0.133 0 0.008 NM3242 0.955 0.039 0.001 0.005 NM182 0 0 0 1 NM3243 0.361 0.023 0.616 0 NM183 0.039 0.01 0 0.951 NM3244 0.456 0.133 0 0.411 NM211 0 0 0 1 NM331 0.983 0.006 0 0.012 NM212 0.996 0.004 0 0.001 NM482 0.002 0.001 0.997 0 NM213 1 0 0 0 NM4821 0.998 0.002 0 0 NM2181 1 0 0 0 NM4822 0.063 0.008 0 0.929 NM2182 1 0 0 0 NM4823 0.986 0.013 0 0.001 NM221 0.999 0.001 0 0 NM483 0 0 0 1 NM222 0.193 0.024 0 0.783 NM4831 0.983 0.016 0 0.001 NM223 0.304 0.003 0.694 0 NM4832 0.001 0 0 0.999 NM2231 0.997 0.003 0 0 NM4833 0.015 0.003 0.982 0 NM2232 0.999 0.001 0 0 NM4841 0.744 0.048 0 0.208 NM2241 0.016 0.002 0 0.982 NM4842 0.001 0 0 0.998 NM231 0.993 0.007 0 0 NM4843 0.003 0.001 0 0.996 NM2311 0.019 0.022 0 0.959 NM581 0.03 0.97 0 0 NM2312 0 0 0 1
321 Appendix IX – FC (p =1.2) Treated Hair Database 1690-1500 cm-1
Spectra Class 1 Class 2 Class 3 Spectra Class 1 Class 2 Class 3 AAF171 0.135 0.862 0.002 CF243 0 1 0 AAF172 0.011 0.989 0 CF244 0 1 0 AAF173 0.756 0.173 0.071 CF245 0 1 0 AAF2171 0.332 0.638 0.03 CF321 1 0 0 AAF2172 0.005 0.995 0 CF322 1 0 0 ACF111 0.088 0.912 0 CF3321 0.94 0.06 0 ACF2111 0.743 0.149 0.109 CF3322 1 0 0 ACF4111 0.196 0.793 0.011 CF41 1 0 0 ACF5111 0.003 0.997 0 CF42 0.906 0.094 0 ACM191 0.004 0.996 0 CF43 0.011 0.989 0 ACM192 0.727 0.156 0.117 CF44 0.992 0.008 0 ACM2191 0.081 0 0.919 CF45 0 0 1 ACM3191 0 0 1 CF81 1 0 0 A3221 0.983 0 0.017 CF821 0 1 0 AM211 1 0 0 CF822 0 1 0 AM212 0.208 0 0.792 CF823 1 0 0 AM221 1 0 0 CF824 0.898 0.102 0 AM2211 0.999 0 0.001 CF825 0.901 0.099 0 AM2212 0 0 1 CF831 0 0 1 AM222 0 0 1 CF832 0 0 1 AM2221 0.058 0 0.942 CF833 0.017 0 0.983 AM2222 1 0 0 CF834 0.006 0.994 0 AM2223 0 0 1 CF835 0.066 0.934 0 AM223 0.939 0 0.061 CM231 1 0 0 AM2291 0.983 0.016 0 CM232 0.008 0.992 0 AM2292 0.733 0 0.267 CM31 0.978 0 0.022 AM2293 0.001 0.999 0 CM32 0.943 0.057 0.001 AM291 0.802 0.198 0 CM33 1 0 0 AM292 0.002 0 0.998 CM341 0 0 1 AM293 0 0 1 CM342 0.002 0.998 0 AM3222 1 0 0 CM41 0 0 1 AM3223 1 0 0 CM42 0.86 0.14 0 AM201 0 0 1 CM441 0.999 0 0.001 AM2201 0.012 0 0.988 CM541 1 0 0 AM3201 0 0 1 CM542 0 0 1 AM3202 0.999 0 0 NF4021 0.558 0.01 0.431 CF2321 0.997 0 0.003 NF4022 0.014 0 0.986 CF2322 0.007 0.993 0 NF4023 0.999 0 0.001 CF241 0.817 0.183 0 NF4024 0.999 0 0.001 CF242 0.04 0 0.96 NF4025 0.952 0 0.048
322 Appendix IX - Continued
Spectra Class 1 Class 2 Class 3 NF4041 0.977 0 0.023 NF4042 1 0 0 NF4043 0.898 0.102 0 NF4044 0.998 0.002 0 NF4045 1 0 0 NF421 0.301 0 0.699 NF422 0.999 0.001 0 NF4221 0.993 0.004 0.003 NF4222 0.935 0.001 0.065 NF4223 0.887 0 0.113 NF4224 0.988 0 0.012 NF4225 0.007 0 0.993 NF423 1 0 0 NF424 0.001 0.999 0 NF425 0.998 0.001 0.001 NF431 0.998 0 0.002 NF432 0.794 0.206 0 NF4321 0.84 0.159 0 NF4322 0 1 0 NF4323 0.002 0 0.998 NF4324 1 0 0 NF4325 1 0 0 NF433 0.015 0 0.985 NF434 1 0 0 NF435 0.336 0 0.664 NM401 1 0 0 NM402 0.003 0 0.997 NM4021 0.999 0 0.001 NM4022 0.032 0.966 0.002 NM403 0.114 0 0.886 NM4031 0.898 0.101 0.001 NM4032 0.873 0 0.127 NM4033 0.039 0.961 0 NM442 0.006 0.994 0 NM4421 0 0 1 NM4422 0.029 0 0.971 NM4423 0.98 0 0.02 NM443 0 1 0 NM4431 0 1 0 NM4432 1 0 0 NM4433 0.995 0 0.005 NM444 0.622 0.377 0.001 NM445 0 0 1
323 Appendix X – Alternative Spectral Regions for the Proposed Forensic Protocol (Continued from Chapter 4)
4.2.3.1 Chemometric Analysis of Single Human Hair Fibres using Alternative Spectral Regions - 1690-1360 cm-1
Untreated Chemically Treated Mildly Treated
10
Mildly Treated 5
0
CFTR 10 -5
PC2 PC2 (12.2%) Chemically Treated CFUN 1 -10 Untreated
-15 Increase in Physical/Chemical Treatment -20 -30 -20 -10 0 10 20 30 PC1 (77.8%)
Figure 4.19 - PCA scores plot of PC1 (77.8 %) vs. PC2 (12.2 %) of the untreated fibres (blue), the chemically treated fibres (pink)and the mildly treated fibres (green) using the alternate spectral region between 1690-1360 cm-1.
324
Figure 4.20 - PC1 Loadings plot of the chemically treated fibres (positive loadings) and the untreated and mildly treated fibres (negative loadings) between 1690-1360 cm-1.
Figure 4.21– PC2 Loadings plot of the mildly treated fibres (positive loadings) and the untreated and chemically treated fibres (negative loadings) between 1690-1360 cm-1.
325 Table 4.9 – PROMETHEE II Net Flows of the 1690-1360 cm-1 Database Net φ Net φ Rank Object Index Rank Object Index
1 UN 0.951 56 CFTR102 0.147 2 CFUN18 0.911 57 TR 0.129 3 UN 0.892 58 MTR 0.110 4 CFUN19 0.89 59 CFTR103 0.100 5 UN 0.875 60 MTR 0.080 6 UN 0.872 61 MTR 0.077 7 UN 0.865 62 MTR 0.076 8 UN 0.853 63 MTR 0.072 9 UN 0.85 64 MTR 0.069 10 CFUN110 0.847 65 MTR 0.069 11 UN 0.825 66 UN 0.063 12 CFUN17 0.812 67 MTR 0.061 13 MTR 0.793 68 TR 0.059 14 CFUN13 0.777 69 MTR 0.051 15 CFUN16 0.76 70 MTR 0.043 16 UN 0.748 71 MTR 0.039 17 UN 0.733 72 CFTR105 0.035 18 UN 0.726 73 MTR 0.033 19 UN 0.716 74 MTR 0.032 20 CFUN14 0.710 75 MTR 0.029 21 CFUN15 0.691 76 MTR 0.019 22 MTR 0.643 77 MTR 0.01 23 CFUN11 0.633 78 MTR 0.008 24 UN 0.632 79 MTR -0.001 25 UN 0.631 80 MTR -0.006 26 MTR 0.621 81 MTR -0.009 27 UN 0.616 82 MTR -0.012 28 CFUN12 0.594 83 MTR -0.014 29 UN 0.582 84 TR -0.042 30 TR 0.549 85 MTR -0.049 31 MTR 0.479 86 CFTR1010 -0.049 32 UN 0.477 87 MTR -0.053 33 MTR 0.422 88 MTR -0.056 34 MTR 0.408 89 MTR -0.057 35 MTR 0.396 90 MTR -0.064 36 UN 0.393 91 MTR -0.065 37 UN 0.381 92 TR -0.070 38 TR 0.372 93 MTR -0.072 39 MTR 0.342 94 MTR -0.078 40 MTR 0.326 95 MTR -0.084 41 MTR 0.324 96 CFTR101 -0.088 42 UN 0.318 97 MTR -0.089 43 UN 0.305 98 MTR -0.089 44 UN 0.258 99 MTR -0.097 45 MTR 0.238 100 MTR -0.103 46 TR 0.235 101 CFTR104 -0.106 47 MTR 0.234 102 TR -0.115 48 TR 0.227 103 MTR -0.117 49 MTR 0.219 104 TR -0.117 50 MTR 0.186 105 MTR -0.117 51 UN 0.177 106 MTR -0.125 52 TR 0.176 107 TR -0.133 53 TR 0.171 108 MTR -0.136 54 MTR 0.154 109 CFTR106 -0.137 55 TR 0.150 110 MTR -0.139 326
Table 4.9 - Continued Net φ Net φ Rank Object Index Rank Object Index 111 MTR -0.144 166 MTR -0.33 112 MTR -0.145 167 TR -0.336 113 MTR -0.147 168 TR -0.348 114 MTR -0.150 169 TR -0.352 115 MTR -0.164 170 TR -0.358 116 MTR -0.171 171 TR -0.362 117 MTR -0.175 172 MTR -0.367 118 MTR -0.177 173 TR -0.367 119 MTR -0.177 174 MTR -0.369 120 MTR -0.183 175 MTR -0.372 121 CFTR109 -0.190 176 MTR -0.379 122 MTR -0.191 177 MTR -0.386 123 TR -0.191 178 TR -0.391 124 CFTR1011 -0.200 179 TR -0.396 125 MTR -0.206 180 MTR -0.398 126 MTR -0.212 181 TR -0.399 127 TR -0.215 182 TR -0.405 128 MTR -0.216 183 MTR -0.412 129 TR -0.217 184 MTR -0.414 130 TR -0.221 185 TR -0.417 131 CFTR107 -0.221 186 TR -0.417 132 MTR -0.223 187 TR -0.454 133 MTR -0.224 188 TR -0.456 134 MTR -0.231 189 TR -0.458 135 MTR -0.234 190 TR -0.485 136 MTR -0.236 191 MTR -0.495 137 MTR -0.242 192 MTR -0.512 138 MTR -0.245 193 TR -0.512 139 TR -0.246 194 TR -0.514 140 TR -0.248 195 TR -0.519 141 TR -0.252 196 TR -0.523 142 MTR -0.255 197 MTR -0.525 143 CFTR108 -0.257 198 TR -0.528 144 MTR -0.259 199 TR -0.537 145 TR -0.263 200 TR -0.559 146 TR -0.264 201 TR -0.601 147 TR -0.268
148 MTR -0.269 149 TR -0.27 150 MTR -0.271 151 MTR -0.276 152 MTR -0.279 153 MTR -0.284 154 TR -0.288
155 MTR -0.292 156 MTR -0.292 157 MTR -0.297 158 TR -0.303 159 MTR -0.312 160 TR -0.319 161 MTR -0.319 162 MTR -0.32 163 TR -0.320 164 TR -0.323 165 MTR -0.326 327
Mild Treatment
Chemically Treated
Untreated
Δ 100 %
Figure 4.22 – GAIA analysis of the 201 spectra for the 1690-1360 cm-1 hair fibre database; ▲ untreated fibres, ■ chemically treated fibres, ■ mildly treated hair fibres, ● pi (Π) decision-making axis, and ■ PC1 and PC2 criterion variables using a Gaussian preference function.
328 4.2.3.2 Second Derivative Keratin FTIR-ATR Spectra 1750-800 cm-1 Region
Untreated Chemically Treated Mildly Treated
35
30 Mildly Treated 25
20
15 Chemically Treated 10 CFTR 10 5
PC2 PC2 (15.5%) 0 CFUN 1 -5
-10 Increase in Untreated -15 Physical/Chemical Treatment -20 -30 -20 -10 0 10 20 30 40 PC1 (27.5%)
Figure 4.23 - PCA scores plot of PC1 (27.5 %) vs. PC2 (15.5 %) of the untreated fibres (blue), mildly treated fibres (green) and the chemically treated fibres (pink) of second derivative spectra between 1750-800 cm-1.
Unfortunately with second derivative spectra, the variables (loadings) that give rise to the separation of the spectra cannot be the used as second derivative spectra consist of minima and maxima peaks. Only the minima peaks are used for characterisation of the spectra. Hence, the PC1 and PC2 loadings plots are complex because it is too difficult to ascertain whether the loadings correlate to the minima or maxima peaks.
329 Table 4.10 - PROMETHEE II Net Flows 2nd Derivative 1750-1800 cm-1 Database
Net φ Net φ Rank Object Index Rank Object Index 1 Un 0.965 51 Tr 0.238 2 Un 0.933 52 Un 0.223 3 Un 0.862 53 Tr 0.199 4 Un 0.783 54 CF13 0.185 5 Un 0.777 55 Tr 0.180 6 Un 0.747 56 Un 0.176 7 Tr 0.734 57 CF19 0.171 8 Un 0.707 58 Un 0.167 9 Un 0.685 59 MT 0.162 10 Un 0.675 60 MT 0.162 11 Un 0.651 61 MT 0.159 12 Un 0.631 62 Tr 0.147 13 Un 0.631 63 MT 0.145 14 Un 0.616 64 Tr 0.141 15 Tr 0.605 65 Un 0.129 16 Un 0.589 66 CF15 0.122 17 Tr 0.571 67 MT 0.114 18 Un 0.555 68 Tr 0.106 19 Un 0.54 69 Tr 0.103 20 Un 0.529 70 Tr 0.100 21 Tr 0.520 71 MT 0.099 22 Tr 0.517 72 Tr 0.092 23 Tr 0.512 73 Tr 0.091 24 Un 0.511 74 Tr 0.089 25 Un 0.510 75 Tr 0.087 26 Un 0.509 76 Tr 0.086 27 Un 0.507 77 Tr 0.078 28 Tr 0.487 78 Tr 0.075 29 Un 0.437 79 Un 0.068 30 CF17 0.407 80 MT 0.043 31 Un 0.388 81 Tr 0.041 32 Tr 0.382 82 Tr 0.037 33 Un 0.379 83 MT 0.033 34 Un 0.372 84 Tr -0.015 35 Un 0.372 85 Tr -0.02 36 Tr 0.370 86 Tr -0.020 37 Tr 0.352 87 CF102 -0.024 38 Tr 0.349 88 MT -0.026 39 CF11 0.346 89 Tr -0.038 40 CF18 0.326 90 MT -0.042 41 Un 0.315 91 Tr -0.051 42 CF12 0.304 92 Tr -0.051 43 CF14 0.301 93 MT -0.052 44 CF110 0.293 94 MT -0.052 45 Tr 0.287 95 Tr -0.056 46 Un 0.269 96 MT -0.073 47 Tr 0.264 97 MT -0.076 48 Un 0.247 98 MT -0.076 49 Un 0.246 99 Tr -0.082 50 CF16 0.24 100 MT -0.084
330 Table 4.10 - Continued
Net φ Net φ Rank Object Index Rank Object Index 101 Tr -0.084 139 Tr -0.301 102 Tr -0.091 140 MT -0.301 103 MT -0.091 141 CF105 -0.332 104 Tr -0.099 142 Tr -0.334 105 Tr -0.111 143 MT -0.335
106 Tr -0.115 144 MT -0.346 107 CF103 -0.141 145 Tr -0.361 108 Tr -0.160 146 Tr -0.365 109 Tr -0.162 147 Tr -0.394 110 Tr -0.167 148 CF104 -0.397 111 Tr -0.169 149 Tr -0.402 112 Tr -0.169 150 Tr -0.419 113 MT -0.171 151 Tr -0.425 114 Tr -0.173 152 Tr -0.45 115 CF106 -0.183 153 MT -0.455 116 Tr -0.194 154 MT -0.464 117 Tr -0.197 155 Tr -0.480 118 MT -0.218 156 MT -0.481
119 Tr -0.230 157 Tr -0.517 120 MT -0.232 158 MT -0.537 121 Tr -0.237 159 Tr -0.556 122 MT -0.245 160 MT -0.559 123 Tr -0.252 161 Un -0.572 124 Tr -0.254 162 MT -0.576 125 MT -0.255 163 Tr -0.603 126 CF1011 -0.257 164 Un -0.614 127 CF1010 -0.258 165 Tr -0.624 128 MT -0.261 166 Tr -0.637 129 Tr -0.267 167 Tr -0.675 130 Tr -0.270 168 Tr -0.702 131 Tr -0.279 169 Tr -0.703
132 Tr -0.281 170 CF109 -0.706 133 CF101 -0.281 171 Tr -0.707 134 MT -0.282 172 CF107 -0.725 135 MT -0.285 173 CF108 -0.764 136 MT -0.295 174 MT -0.767 137 Tr -0.297 175 MT -0.810 138 MT -0.298 176 MT -0.899
331
Mild Treatment
Chemically Treated Untreated
Δ 100 %
Figure 4.24 - GAIA analysis of the 176 second derivative spectra for the 1750-800 cm-1 hair fibre database; ▲ untreated fibres, ■ chemically treated fibres, ■ mildly treated hair fibres, ● pi (Π) decision-making axis, and ■ PC1 and PC2 criterion variables using a Gaussian preference function.
332 4.2.3.3 Second Derivative Keratin FTIR-ATR Spectra 1690-1500 cm-1 Region
Untreated Chemically Treated Mildly Treated
25
Increase in 20 Physical/Chemical Treatment 15 Chemically Treated Untreated 10 Mildly Treated
5 PC2 PC2 (21.9%)
0 CFTR 10
-5 CFUN 1
-10 -15 -10 -5 0 5 10 15 20 PC1 (47.9%)
Figure 4.25 - PCA scores plot of PC1 (47.9 %) vs. PC2 (21.9 %) of the untreated fibres (blue), mildly treated fibres (green) and the chemically treated fibres (pink) of second derivative spectra between 1690-1500 cm-1.
333 Table 4.11 - PROMETHEE II Net Flows 2nd Derivative 1690-1500 cm-1 Database
Net φ Net φ Rank Object Index Rank Object Index 1 Un 0.969 51 Tr 0.210 2 Un 0.919 52 Tr 0.202 3 Un 0.917 53 Tr 0.186 4 Un 0.909 54 Un 0.178 5 Un 0.893 55 CF110 0.178 6 MT 0.867 56 CF102 0.176 7 Un 0.804 57 Tr 0.173 8 Un 0.774 58 MT 0.171 9 MT 0.718 59 Tr 0.163 10 Un 0.699 60 MT 0.156 11 Un 0.690 61 Tr 0.156 12 Un 0.679 62 Tr 0.152 13 Tr 0.663 63 MT 0.152 14 MT 0.606 64 MT 0.149 15 Tr 0.575 65 MT 0.148 16 MT 0.568 66 MT 0.140 17 CF13 0.564 67 MT 0.140 18 MT 0.543 68 Tr 0.136 19 CF18 0.539 69 Tr 0.133 20 MT 0.524 70 Tr 0.130 21 CF11 0.510 71 MT 0.121 22 Tr 0.497 72 MT 0.120 23 MT 0.485 73 MT 0.115 24 Un 0.461 74 MT 0.107 25 MT 0.445 75 Un 0.096 26 MT 0.439 76 Tr 0.092 27 Un 0.421 77 MT 0.084 28 MT 0.414 78 MT 0.072 29 Tr 0.407 79 MT 0.064 30 Tr 0.397 80 MT 0.058 31 Tr 0.363 81 MT 0.055 32 Tr 0.355 82 Tr 0.054 33 MT 0.351 83 MT 0.046 34 Un 0.340 84 Tr 0.042 35 Tr 0.338 85 MT 0.039 36 MT 0.333 86 MT 0.032 37 MT 0.324 87 MT 0.025 38 Tr 0.279 88 0.024 39 CF16 0.269 89 MT 0.023 40 Un 0.266 90 MT 0.016 41 Tr 0.262 91 MT 0.015 42 Un 0.258 92 MT 0.013 43 MT 0.256 93 Tr 0.012 44 MT 0.255 94 MT 0.010 45 Un 0.253 95 MT 0.010 46 MT 0.249 96 MT 0.008 47 CF19 0.236 97 Tr -0.002 48 CF103 0.227 98 Tr -0.006 49 Tr 0.220 99 Un -0.029 50 CF17 0.215 100 MT -0.033
334 Table 4.11 - Continued
Net φ Net φ Rank Object Index Rank Object Index 101 Tr -0.034 151 MT -0.263 102 Tr -0.035 152 MT -0.263 103 CF105 -0.053 153 Tr -0.266 104 MT -0.055 154 Tr -0.279 105 MT -0.056 155 MT -0.280 106 Tr -0.061 156 MT -0.280 107 MT -0.085 157 Tr -0.280 108 CF14 -0.085 158 Tr -0.282 109 Tr -0.086 159 Tr -0.282 110 Tr -0.086 160 MT -0.286 111 CF1011 -0.087 161 Tr -0.288 112 MT -0.093 162 Tr -0.295 113 MT -0.099 163 MT -0.296 114 CF12 -0.111 164 Tr -0.308 115 Tr -0.113 165 Tr -0.317 116 MT -0.115 166 Tr -0.325 117 CF15 -0.116 167 CF109 -0.328 118 Tr -0.117 168 Tr -0.331 119 MT -0.125 169 MT -0.331 120 MT -0.126 170 MT -0.357 121 MT -0.126 171 MT -0.366 122 Tr -0.131 172 Tr -0.371 123 MT -0.140 173 Tr -0.386 124 Tr -0.156 174 MT -0.391 125 Tr -0.157 175 Tr -0.393 126 MT -0.157 176 Tr -0.395 127 MT -0.160 177 CF1010 -0.399 128 Tr -0.163 178 CF106 -0.404 129 MT -0.174 179 MT -0.408 130 MT -0.179 180 Tr -0.411 131 MT -0.181 181 Tr -0.411 132 Tr -0.189 182 MT -0.412 133 MT -0.196 183 MT -0.42 134 Tr -0.196 184 Tr -0.425 135 MT -0.197 185 MT -0.430 136 Tr -0.214 186 CF108 -0.434 137 Tr -0.221 187 Tr -0.496 138 Tr -0.227 188 Tr -0.515 139 CF104 -0.233 189 MT -0.515 140 MT -0.234 190 MT -0.522 141 MT -0.236 191 Tr -0.534 142 MT -0.239 192 Tr -0.540 143 Tr -0.240 193 MT -0.595 144 Tr -0.241 194 CF107 -0.619 145 Tr -0.242 195 Tr -0.638 146 CF101 -0.246 196 Tr -0.706 147 Tr -0.258 197 MT -0.717 148 MT -0.258 198 Tr -0.757 149 MT -0.259 199 Tr -0.757 150 MT -0.261 200 MT -0.905
335
Chemically Treated Untreated
Mild Treatment
Δ 100 %
Figure 4.22 - GAIA analysis of the 200 second derivative spectra for the 1690-1500 cm-1 hair fibre database; ▲ untreated fibres, ■ chemically treated fibres, ■ mildly treated hair fibres, ● pi (Π) decision-making axis, and ■ PC1 and PC2 criterion variables using a Gaussian preference function.
336
337