Statistical Analysis Of Visible Absorption Spectra And Mass Spectra Obtained From Dyed Textile Fibers

Transcription

1 University of Central Florida Electronic Theses and Dissertations Masters Thesis (Open Access) Statistical Analysis Of Visible Absorption Spectra And Mass Spectra Obtained From Dyed Textile Fibers 2010 Katie Margaret White University of Central Florida Find similar works at: University of Central Florida Libraries Part of the Chemistry Commons STARS Citation White, Katie Margaret, "Statistical Analysis Of Visible Absorption Spectra And Mass Spectra Obtained From Dyed Textile Fibers" (2010). Electronic Theses and Dissertations This Masters Thesis (Open Access) is brought to you for free and open access by STARS. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of STARS. For more information, please contact

2 STATISTICAL ANALYSIS OF VISIBLE ABSORPTION SPECTRA AND MASS SPECTRA OBTAINED FROM DYED TEXTILE FIBERS by KATIE MARGARET WHITE B.S. University of Central Florida, 2008 A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in the Department of Chemistry in the College of Sciences at the University of Central Florida Orlando, Florida Fall Term 2010 Major Professor: Michael E. Sigman

3 2010 Katie White ii

4 ABSTRACT The National Academy of Sciences recently published a report which calls for improvements to the field of forensic science. Their report criticized many forensic disciplines for failure to establish rigorously-tested methods of comparison, and encouraged more research in these areas to establish limitations and assess error rates. This study applies chemometric and statistical methods to current and developing analytical techniques in fiber analysis. In addition to analysis of commercially available dyed textile fibers, two pairs of dyes are selected based for custom fabric dyeing on the similarities of their absorbance spectra and dye molecular structures. Visible absorption spectra for all fiber samples are collected using microspectrophotometry (MSP) and mass spectra are collected using electrospray ionization (ESI) mass spectrometry. Statistical calculations are performed using commercial software packages and software written in-house. Levels of Type I and Type II error are examined for fiber discrimination based on hypothesis testing of visible absorbance spectra using a nonparametric permutation method. This work also explores evaluation of known and questioned fiber populations based on an assessment of p-value distributions from questioned-known fiber comparisons with those of known fiber self-comparisons. Results from the hypothesis testing are compared with principal components analysis (PCA) and discriminant analysis (DA) of visible absorption spectra, as well as PCA and DA of ESI mass spectra. The sensitivity of a statistical approach will also be discussed in terms of how instrumental parameters and sampling methods may influence error rates. iii

5 For Mom, Dad and Corie iv

6 ACKNOWLEDGMENTS I would like to thank my family for always supporting my academic pursuits, especially my Dad, who always encouraged my curiosity and love of learning; Dr. Michael Sigman for his devoted efforts as my graduate advisor and thesis committee chair; Drs. Andres Campiglia and Diego Diaz for serving on my thesis committee; Dr. Liqiang Ni for helpful suggestions on statistical matters; Mary Williams for providing research problem-solving, instrumentation guidance and protection from pigeons; My fellow graduate students for providing moral support, sharing many cups of tea and always picking restaurants that serve chicken; Erin McIntee for setting a good example and tolerating all my statistical questions; Kelly McHugh for being my partner in crime and restoring my sense of sanity, often at the expense of her own; Jennifer Lewis for accompanying me on countless frozen yogurt study breaks; Jenae Williamson for collecting a ridiculous number of spectra and still managing to remain cheerful; and Erynn Swigonski for being an awesome assistant and entertaining me with amusing anecdotes. I would also like to thank my non-science friends for always believing in me and even pretending to be interested whilst I shared my exciting research developments. Failure is only the opportunity to begin again, this time more wisely. -Henry Ford It is important that students bring a certain ragamuffin, barefoot, irreverence to their studies; they are not here to worship what is known, but to question it. - J. Bronowski, The Ascent of Man v

7 TABLE OF CONTENTS LIST OF FIGURES... ix LIST OF TABLES... xii LIST OF ABBREVIATIONS...xv CHAPTER 1: INTRODUCTION...1 Textile Fibers... 1 Importance as Evidence... 1 Classification... 4 Production of Man-made Fibers... 5 Yarn Construction... 9 Fiber Dyes Methods of Dye Application Analysis of Fibers Current Techniques Microscopic Comparison Examination of Dyes Spectroscopy Theory Current Challenges Use of Statistics Mass Spectral Dye Analysis CHAPTER 2: EXPERIMENTAL...29 Samples vi

8 Red Acrylic Yarns Red Cotton Samples Blue Acrylic Yarns Dyed Fabric Samples Instrumental Analysis Microspectrophotometry Fiber Optic Spectrometer Commercial Spectrometer Extracted Dye Analysis Flow cell Mass Spectrometry Data Analysis Pretreatment of the Data Hypothesis Testing Pearson Correlation and Fisher Transformation Parametric Student t-test Nonparametric Permutation test Wilcoxon Rank Sum test Multivariate Statistical Techniques Principal Component Analysis and Cluster Analysis Discriminant Analysis CHAPTER 3: RESULTS AND DISCUSSION...47 vii

9 Absorption Spectra Hypothesis Testing Red Acrylic Yarns Red Cotton Samples Blue Acrylic Yarns Dyed Fabric Samples Known vs Questioned Comparisons Multivariate Statistical Techniques Blue Acrylic Yarns Dyed Fabric Samples Mass Spectra Blue Acrylic Yarns Dyed Fabric Samples CHAPTER 4: CONCLUSIONS...94 Forensic Significance Future Work REFERENCES viii

10 LIST OF FIGURES Figure 1. Classification of textile fibers Figure 2. Construction of cord from ply, ply from yarn, and yarn from fiber... 9 Figure 3. Diagram of a typical microspectrophotometer Figure 4. Illustration of the Franck-Condon principle Figure 5. Chemical structures of Basic Green 1 (top) and Basic Green 4 (bottom) Figure 6. Chemical structures of Disperse Blue 3 (left) and Disperse Blue 14 (right) Figure 7. Microscope coupled with fiber-optic spectrometer (left) and with commercial spectrometer attachment (right) Figure 8. Diagram of aperture mirror in spectrometer head unit Figure 9. Diagram of flow cell interior Figure 10. Normalized plot of averaged spectra for all red acrylic sources, in air Figure 11. Normalized plot of averaged A fiber spectra, in air Figure 12. Normalized plot of averaged A fiber spectra Figure 13. Normalized plot of averaged spectra for all red acrylic sources Figure 14. Normalized plot of averaged spectra for all red cotton sources Figure 15. Normalized plot of averaged spectra for all blue acrylic sources Figure 16. Normalized plot of J3 segment spectra, flow cell Figure 17. Normalized plot of F4b spectra Figure 18. Surface plot illustrating random drift for spectra collected in sequential order Figure 19. Normalized plot of averaged spectra for select blue acrylic fibers from random collection sequence ix

11 Figure 20. Bar graph evaluating the p-value distributions for same fiber, same source and different source comparisons Figure 21. Example of box plots illustrating distributions of p-values from the nonparametric permutation test (left): same source A (in red), different source A with B (in orange) and same source B (in yellow); the location of these p-values within the discrimination matrix resulting from the nonparametric permutation test (right) Figure 22. Box plot of same source p-value distributions for source F Figure 23. Normalized plot of averaged spectra for Disperse Blue samples Figure 24. Normalized plot of averaged spectra for region B fibers of Disperse Blue Figure 25. Plot of undyed acetate fibers, delustered and lustrous Figure 26. Box plot of p-value distributions for Disperse Blue 3 against Disperse Blue 14 (K v Q, top) and Disperse Blue 3 with itself (K v K, bottom), excluding lustrous fiber comparisons Figure 27. Normalized plot of averaged spectra for Basic Green samples Figure 28. Illustration of KvK (in red) and KvQ (in green) populations within p-value matrix for Disperse Blue samples; inset below shows the difference between p-value in each population Figure 29. Box plot of PC1 scores for blue acrylic yarns MSP source averages from random collection sequence, nm Figure 30. Two-dimensional scores plot for blue acrylic yarns MSP fiber averages from random collection sequence, nm x

12 Figure 31. Three-dimensional scores plot for blue acrylic yarns MSP fiber averages from random collection sequence, nm Figure 32. Box plot of PC1 scores for Disperse Blue MSP source averages from random collection sequence, nm Figure 33. Two-dimensional scores plot for Disperse Blue MSP fiber averages from random collection sequence, nm Figure 34. Three-dimensional scores plot for Disperse Blue MSP fiber averages from random collection sequence, nm Figure 35. Box plot of PC1 scores for Basic Green MSP source averages from random collection sequence, nm Figure 36. Two-dimensional scores plot for Basic Green MSP fiber averages from random collection sequence, nm Figure 37. Mass spectra for blue acrylic yarn bulk extractions, 100V: source F (top left), source G (top right), source H (bottom left), source I (bottom middle) and source J (bottom right) Figure 38. Mass spectral comparisons for pairs of yarns produced by the same manufacturer: sources F and J (top), and sources G and I (bottom) Figure 39. Three dimensional scores plot for blue acrylic yarn MS: fibers at 60V (top left), fibers at 100V (top right), bulk at 60V (bottom left) and bulk at 100V (bottom right) Figure 40. Three-dimensional scores plot for blue acrylic yarns MS, m/z Figure 41. Mass spectra for extracts of Disperse Blue 3 (left) and Disperse Blue 14 (right) Figure 42. Mass spectra for extracts of Basic Green 1 (left) and Basic Green 4 (right) xi

13 LIST OF TABLES Table 1. Structures of some synthetic and naturally-occurring polymers... 7 Table 2. Color absorbed and color reflected in the visible spectrum Table 3. Common fiber types and their compatible dyes Table 4. Descriptive information from red yarn labels Table 5. Descriptive information from red cotton labels and garment tags Table 6. Descriptive information from blue yarn labels Table 7. Dye properties and selected fabric styles Table 8. Parametric discrimination matrix for red acrylic yarns (in air), nm, α = Table 9. Parametric discrimination summary for red acrylic yarns (in air), nm Table 10. Parametric discrimination matrix for red acrylic yarns, nm, α = Table 11. Parametric discrimination summary for red acrylic yarns, nm Table 12. Parametric discrimination matrix for red cotton samples, nm, α = Table 13. Parametric discrimination summary for red cotton samples, nm Table 14. Parametric discrimination summary for blue acrylic samples, nm Table 15. Parametric discrimination matrix for blue acrylic samples (flow cell), nm, α = Table 16. Parametric discrimination summary for blue acrylic samples (flow cell), nm58 Table 17. Nonparametric discrimination summary for blue acrylic samples, nm Table 18. Nonparametric discrimination summary for blue acrylic samples, nm Table 19. Nonparametric discrimination matrix for blue acrylic yarns from random collection sequence, nm, α = xii

14 Table 20. Nonparametric discrimination summary for blue acrylic samples from random collection sequence, nm Table 21. P-value distributions for blue acrylic samples from random collection sequence, nm Table 22. Wilcoxon rank sum p-values for blue acrylic yarn different source comparisons Table 23. Wilcoxon rank sum p-values for blue acrylic yarn same source comparisons Table 24. Nonparametric discrimination summary for Disperse Blue samples from random collection sequence, nm Table 25. Nonparametric discrimination summary for Disperse Blue samples from random collection sequence, nm, delustered and lustrous as different sources Table 26. Wilcoxon rank sum results for Disperse Blue samples, different source comparisons 72 Table 27. Wilcoxon rank sum results for Disperse Blue samples, same source comparisons Table 28. Nonparametric discrimination summary for Basic Green samples from random collection sequence, nm Table 29. Wilcoxon rank sum results for Basic Green samples, different source comparisons Table 30. Wilcoxon rank sum results for Basic Green samples, same source comparisons Table 31. Wilcoxon rank sum results for Disperse Blue samples, KvQ comparison Table 32. Wilcoxon rank sum results for Basic Green samples, KvQ comparison Table 33. DA classification matrix for blue acrylic yarns MSP, nm, α = Table 34. DA classification matrix for blue acrylic yarns MSP from random collection sequence, nm, α = Table 35. Ion fragments consistently present in mass spectra of blue acrylic yarns, 100V xiii

15 Table 36. Nonparametric discrimination matrix for blue acrylic samples MS, m/z, α = Table 37. DA classification matrix for blue acrylic yarns MS, m/z , α = xiv

16 LIST OF ABBREVIATIONS ASTM CI ESI ESI-MS FBI FTC FT-IR LC LC-MS MS MSP OPD PLM Py-GC RR SEM SWGMAT TLC American Society for Testing and Materials Color Index Electrospray ionization Electrospray ionization-mass spectrometry Federal Bureau of Investigation Federal Trade Commission Fourier transform infrared spectroscopy Liquid chromatograph Liquid chromatography-mass spectrometry Mass spectrometry, or mass spectrum Microspectrophotometry Optical path difference Polarized light microscope Pyrolysis-gas chromatography Relative retardation Scanning electron microscope Scientific Working Group for Materials Analysis Thin-layer chromatography xv

17 CHAPTER 1: INTRODUCTION Textile fibers are a very common form of trace evidence recovered at crime scenes. Fiber evidence has played a critical role in many notable cases through the years, with their presence not limited to the commission of any specific type of crime. A variety of analytical methods have been developed and validated for the examination of fiber samples, ranging from the evaluation of morphological characteristics to the classification of the fiber and its dyes. This research focuses on the discrimination of textile fiber dyes using the technique of microspectrophotometry (MSP) with an emphasis on statistical analysis of the data. Electrospray ionization-mass spectrometry (ESI-MS) is also explored as a method of fiber dye analysis. Textile Fibers Importance as Evidence Fiber evidence can make a considerable contribution to the investigation of criminal cases. Interpretation of fiber evidence can help investigators reconstruct the events of a crime by demonstrating strong associations. This potential imparts incredible value to fibers as forensic evidence. Fibers come from many different sources, including clothing, home and automobile carpeting or furnishings, and also from various bed and bath linens. Transfer fibers can also be retained by other non-fibrous surfaces, such as shoe soles, jewelry, body hair, weapon surfaces and building materials. 1, 2 Unlike some forms of forensic evidence, shedding of fibers is not limited to the commission of any particular type of crime. Such evidence has been recovered at the scene of assaults, homicides, kidnappings and various property crimes. According to Locard s exchange principal, fibers have the potential to be transferred between surfaces as a result of friction between them. 3, 4 Samples of unknown origin collected 1

18 from a crime scene, called questioned fibers, can be compared for similarity with known sources. Proof of fiber transfer can demonstrate a link between suspect and crime scene, suspect and victim, or victim and crime scene. 5 The quantity of fibers transferred will depend on several factors, such as the extent of contact between the surfaces, the ease with which fibers are shed from the donor and the recipient s ability to retain any transferred material. 3, 5 Garment age and wear can also influence fiber transfer. 6 The amount of transfer recovered will depend on the time elapsed between transfer and collection, as well as the conditions experienced by the recipient during this interval. 7, 8 The persistence of transfer is determined by many factors. For example, the probability of encountering transfer fibers from the clothing of a undisturbed body will be greater than the probability of obtaining transfer from a fleeing suspect. 5 The suspect s clothing is in motion, experiencing contact with secondary surfaces, and could possibly be laundered before the suspect is apprehended, all factors which reduce transfer persistence. 7 Despite being subject to the elements, the victim s body remained stationary and therefore had a greater ability to retain transfer. Although fiber transfer may not provide concrete association between individuals like biological evidence, it is greatly valued in the reconstruction of a crime. 5 Many publications have studied the transfer behaviors of textiles fibers. 1, 7-9 While these simulated transfer studies allow the forensic scientist to speculate about the likelihood of transfers in their case work, the complex nature of real transfer scenarios must be considered. 3, 10 Assessment of a transfer can indicate where contact was made between two surfaces, and may imply with how much force. 7 2

19 In addition to primary transfer, a secondary or indirect transfer can also be made when already 5, 7, 10 transferred fibers are transferred again to another surface. Because so many textiles are mass-produced, it becomes difficult to prove that fibers were contributed from one particular source. When analysis of questioned and known samples fails to discriminate, examiners can only state that the questioned fibers are consistent with originating from the source textile. 5 Still, investigators will weigh this association based on other contributing factors. Demonstration of cross-transfer, or a transfer of fibers from both interacting garments, challenges claims of coincidence. Similarly, the presence of multiple fiber types contributed from blended textile fabrics increases the significance of association between two surfaces. 3 The value of fiber evidence also increases when fibers with unique features are discovered. 5 Fiber examiners employ target fiber studies to determine how often a selected fiber may be expected to appear among a randomly sampled fiber populations. A study which examined fiber evidence submissions from 20 unrelated case files demonstrates that some fiber types and fiber colors are statistically more common than others. 11 This particular study found that nearly half of all submissions were cotton. Polyester and acrylic were the second and third most encountered fiber type, at 19.3 and 15.8% respectively. The study also reported fiber color frequencies of 33% for blue, 20% for brown, 20% for gray or black, 13% for green, 9% for red and 5% for all others. These kinds of distribution studies are important to fiber examination because the likelihood of encountering a questioned fiber will influence its evidentiary value. 12 For fibers commonly encountered in the general population, it becomes more difficult to demonstrate association; examples are white and blue denim cottons. 3

20 Classification There are two basic forms of textile fibers an analyst may encounter: natural and manmade fibers. Natural fibers are materials produced in nature, and may originate from animal hair and proteins, mineral materials or plant bast, leaves and seed. Examples of common natural fibers include cotton, hemp, silk and wool. Man-made fibers, also commonly termed synthetic fibers, are those fibers processed from fiber-forming polymers. Polymers used in these fibers are not strictly synthetic, as they may be formulated from regenerated natural polymers. 5 Examples of man-made fibers include polyamides, polyolefins and polyvinyl derivatives, like nylon, polypropylene, and acrylic. 3 Figure 1 further illustrates the possible classifications of textile fibers. 4

21 Figure 1. Classification of textile fibers 3 Production of Man-made Fibers Man-made fibers are generated from solid polymer formulations of synthesized or naturally occurring polymers. 3 Table 1 shows the repeating structural units for some of the most common fiber-forming polymers. Two categories of synthetic polymer exist, distinguished by their method of polymer formation; they are condensation and addition polymers. Addition polymerization occurs when monomers, one of which contains a double or triple bond, combine to form a linear aliphatic chain at the unsaturated site. 13 Examples include polyolefins and polyacrylonitriles. 3 Condensation polymerization involves a chemical reaction between the 5

22 monomers to form a linear polymer, resulting in loss of water or methanol. 13 They are less stable than addition polymers, as they contain accessible sites for cleavage. 3 Examples include polyamides, polyesters and polyurethanes. 6

23 Table 1. Structures of some synthetic and naturally-occurring polymers Synthetic Naturally-occurring Polymer Structure Polymer Structure Polyacrylonitrile Cellulose Nylon 6,6 Xylose Polyester Silk Fibroin Polypropylene Lignin 7

24 Extruded fibers can be produced through melt spinning or solution spinning. The method chosen for a particular polymer will depend on its inherent chemical properties 3. For melt spinning, pressure supplied from a pump forces the softened polymer through filters followed by a spinneret, a metal plate with numerous, die-cut micropores. 3, 13 Continuous fibers are then extruded into a stream of cold air to harden and collected onto rollers. 13 This method is common for polyamides, polyesters and polyolefins. 3 There are two forms of solution spinning: dry spinning and wet spinning. Both methods utilize a viscous solution of polymer. Dry spun filaments are extruded into a heated stream of air where solvent from the polymer solution is evaporated. 13 Typical dry spun fibers include polyacrylonitrile and other polyvinyl derivatives. 3 Wet spinning expels extruded filaments into a non-solvent bath for coagulation. Compared with melt spinning and dry spinning, the process is much slower; filaments cooled too quickly in the coagulation bath will form improperly. It is also not desired for fibers of round or oval cross-section, as the coagulating fibers often shrink and deform their cross-section shape. 13 Wet spinning is common for viscose rayon, a regenerated cellulose fiber. 3 As part of each production method, fibers must be stretched on rollers in a process known as drawing. This orients the polymers in a uniform direction and gives maximum strength to the fiber. 3 Because man-made filament fibers are formed by extrusion, their diameter and crosssection shape are determined by the pores on the spinneret, as well as the degree of stretching. 13 These processes produce smooth, lustrous fiber surfaces, which can be reduced to some extent by treatment with delustering agents. 14 8

25 Yarn Construction Fibers are used to construct yarns and they can be structured in many different ways. The American Society for Testing and Materials (ASTM) defines yarn as a generic term for a continuous strand of textile fibers, filaments, or material in a form suitable for intertwining to form a textile. 15 Because single strands of yarn are often spun or twisted together to form stronger yarns, such as plied yarn, cabled yarn, thread or cord, the term yarn is used to refer to any of these forms. 14 Figure 2 below illustrates the relation of these terms. The Federal Trade Commission (FTC) has also published clarified definitions in the Textile Products Identification Act [FTC 15 U.S.C. 70]. 16 Figure 2. Construction of cord from ply, ply from yarn, and yarn from fiber A single strand of yarn may be spun yarn or filament yarn; filament yarns come from long fiber filaments and are simpler to produce, while spun yarns are processed from shorter, staple-length fibers and require sufficient twisting to hold the short lengths together. 14 Most filament fibers are man-made, with the exception of silk. 9

26 Fiber Dyes As they exist in their original polymeric or natural form, fibers are colorless and opaque. 3 For aesthetic purposes, most commercially manufactured textiles are treated with colorants, either dyes or pigments. While pigments are applied to substrates with the aid of intermediary compounds, dyes are chemical compounds bound directly to a substance to impart color. 17 The property of color is related to the presence of chromophores in the structure, areas of the dye molecule distinguished by high electron density. 3 They function by absorption of specific wavelengths of visible light, so that color perceived by the observer results from the remaining 17, 18 wavelengths of unabsorbed light; thus, observed color is complementary to color absorbed. This principle is demonstrated in Table 2. While any organic molecule is capable of absorbing radiation, it must absorb in the visible region of the electromagnetic spectrum to be used as a colorant. 3 Table 2. Color absorbed and color reflected in the visible spectrum 18 Wavelength (nm) Absorbed color Observed color violet yellow-green blue yellow greenish-blue orange bluish-green red green red-purple yellow-green purple orange greenish-blue red blue-green In addition to the presence of chromophores, most dye compounds contain an auxochrome functional group which can influence the intensity of the dye color without significant variation in absorption maximum. 3 These groups can considerably affect the dye chemistry, however, altering solubility and reactivity with fiber substrates. For this reason, 10

27 certain dye classes are chosen for dyeing particular fiber types. Table 3 shows the common dye classes and the fiber types for which they are typically used. Table 3. Common fiber types and their compatible dyes 3 Fiber Types Acrylic Modified acrylic Cotton Nylon Polyester Silk Viscose Wool Compatible Dye Classes Basic, Disperse Basic, Mordant Azoic, Direct, Premetallized, Reactive, Sulfur, Vat Acid, Disperse, Mordant, Premetallized, Reactive Disperse Acid Azoic, Direct Acid, Mordant, Premetallized, Reactive, Sulfur, Vat Beyond simple observation of the hue reflected, dyes can be grouped based on features of their chemical structures, and further by their dyeing methods. 14 There are two main categories of dyes: those which are water soluble, and those which are water insoluble. Water soluble dyes can additionally be classified as either cationic or anionic. Dyes containing ammonium (NHR + 3 ), sulfonium (SR + 3 ) or oxonium (OR + 3 ) salts are the only cationic water soluble dyes; they are called basic dyes. Anionic water soluble dyes include acid, acid-mordant, direct and reactive dyes. Acid dyes bond best by forming salts with cationic regions of protein fibers; metallic mordants are frequently complexed with acid dyes to improve bonding with the fiber. 3 Direct dyes are dissolved in water for application to the fiber, and are thought to bond via hydrogen bonding. 3, 14 Reactive dyes form covalent bonds with fiber functional groups, and though structurally comparable to acid dyes, they are distinguished by the presence of a nucleophilic group. 3 11

28 Water insoluble dyes include disperse, vat, sulfur and solvent dyes. In truth, disperse dyes are not entirely insoluble in water, but their reduced solubility makes them unacceptable for dyeing directly to the fiber; in contrast, pigments have no water solubility. 17 Disperse dyes are applied using aqueous dispersing agents with finely ground colorant. Due to their limited solubility in water, the dyes will migrate from solution into the fiber, as they possess greater affinity for the fiber substrate. Vat dyes, named after the large vats used for dyeing, are modified chemically to dissolve in an alkaline dyebath. After application to the fiber, air oxidation restores the original dye compound, yielding dyed fibers with excellent colorfastness. Similarly, sulfur dyes are reduced with sulfur in an alkaline dyebath. Solvent dyes are dissolved in an organic solvent for fiber dyeing. There are several complementary styles of dye nomenclature. The color index number (CI) is a five digit code assigned to each dye based on its chemical structure class, e.g. nitro, azoic, acridine, quinoline, etc. Guidelines for classification are published in the Colour Index by the Society of Dyers and Colorists and the American Association of Textile Chemists and Colorists. 17 Generic names generally include the dye class and color, such as Basic Green 4 and Mordant Yellow 7. Most dyes have several accepted generic names, which may reflect specific applications. 3 Classical names are those titles assigned by manufacturers and thus, numerous designations may exist for the same dye. 17 It is important to remember that forensic dye analysis does not seek to identify specific dye molecules; it is not necessary to identify two dyes to effectively discriminate them. 3 However, when two dyed fibers possess similar features, techniques which provide greater structural information about the dyes will maximize discrimination potential. 12

29 Methods of Dye Application In addition to straightforward assessment of fiber type or principal color, there are many other characteristics that can be determined in the laboratory. Because so many of the features observed through analysis are imparted during the manufacture and processing of these textiles, it is essential to understand their production. The dyeing process involves distribution of dye from the dyebath onto the fiber substrate. When dye is distributed throughout the fiber composition (beneath the surface), the process is considered sorption. 17 Dye can be applied to textiles at various stages of their manufacture, as either fibers, yarns or completed fabrics. 5 To achieve the desired hue or shade, manufacturers often utilize mixed formulations of several dyes. 19, 20 Fiber stock may be dyed in large kiers, or dye vats, at atmospheric pressure or at controlled pressure, to avoid condition fluctuations. 21 Dyeing of fibers is more costly, but improves dye penetration. Depending on the manufacturer, fiber bundles may be continually fed through the dye bath and directly routed for rinsing. 14 For synthetic fibers, dye can be introduced to the polymer mixture before or after the fibers are formed; dye added before fiber extrusion is termed dope dyeing or gel dyeing. 21 In the case of yarn dyeing, dye is forced through many skeins of yarn at once. 14 This method produces yarns with solid color and dye penetration which is quite acceptable. Dyed yarns still allow the creation of garments with various colors. Piece dyeing of completed fabrics is typically preferred when solid color is desired for the entire garment. Although piece dyeing is the simplest and most economical method of dye application, there is a possibility that the dye will not uniformly permeate all fibers of the fabric; this will depend on fabric construction. Dye absorption rates will vary for different fiber types, 13

30 so it may be necessary to use multiple dyes for blended fabrics to ensure consistent color throughout. Some manufacturers may exploit this property in a technique called cross-dyeing, where different dye colors are applied to each fiber type, thus creating a pattern in the final garment. 14 Dye application methods are usually specific to the fiber and product type, and are tailored to attain the desired colorfastness. This process is governed by dyeing kinetics, or the rate of transfer of dye in solution from the dyebath into the substrate. To ensure dye molecules have sufficiently adhered to the fiber substrate, colorists must consider fiber-dye chemistry, as well as observe the mechanics of dyeing equilibria; that is, the position of sorption versus desorption after infinite time. Four distinct forces can influence this equilibrium; electrostatic forces, van der Waals forces, hydrogen bonding and hydrophobic interactions. 17 In addition to dyes, there are a variety of finishes and chemical treatments which may be applied to fibers. Examples include fixers for colorfastness, and optical brighteners, softeners, stain-resistant or fire retardant coatings, and water repellant agents. 14 Delustering agents, intended to reduce the luster of man-made fibers, can be added to the viscous polymer solution prior to spinning, or applied as an external coating to spun fibers. Depending on chosen methods for analysis, presence of these additives may or may not be detected. Analysis of Fibers Current Techniques According to the Forensic Fiber Examination Guidelines developed by the Scientific Working Group for Materials Analysis (SWGMAT), an analyst must perform a minimum of two analytical techniques for each of three categories: generic class, physical characteristics and 14

31 color. For generic class, tests include polarized light microscopy (PLM), Fourier-transform infrared (FTIR) spectroscopy, pyrolysis-gas chromatography (Py-GC), microscopic properties, solubility, and thermal analysis. Tests suggested for physical characteristics include stereomicroscopy, light microscopy and scanning electron microscopy (SEM). Color can be assessed using light microscopy, microspectrophotometry (MSP), thin-layer chromatography (TLC). It should be noted that some of the analytical techniques listed will provide greater discrimination than others. 22 The tests conducted in an individual laboratory will be limited by their available instrumentation. The number of questioned fibers available for comparison will be limited to those available from submission. When selecting comparison fibers from a known source, it is important that the known fibers are representative of the entire submitted material; 3 this means if variations in color exist within the source, known fibers should represent the complete range of fiber colors and dyeing depths represented in the known fabric, yarn, or other fiber source. 22 To account for any manufacturing or dyeing variations, it is common for analysts to examine 3, 19 numerous fibers, especially for natural fiber types. Microscopic Comparison Examination of fibers begins with viewing under a stereomicroscope. It is during this initial analysis that an analyst will record a description of the sample (e.g. cord, yarn, fiber, etc.) and its basic physical features, such as length and diameter, crimp or twist of fibers, damage or debris and color. 22 Next, some examiners may utilize a comparison compound light microscope for simultaneous viewing of the questioned and known samples. This side-by-side comparison allows identification of similar features and observation of differences. Illumination and 15

32 magnification must be the same for both samples, and the same mounting medium should be used to prepare mounts of each sample. 22 Selection of mounting media is left to the discretion of the analyst. While surface boundaries become more visible when the refractive index of the medium differs from that of the sample, dye color is best viewed in a medium of matching refractive index, where optical interference is minimal. Care should be taken to avoid mounting media which may damage the fiber or its dye. It is the unique optical properties belonging to each fiber type which allow them to be classified. Most fibers are anisotropic, meaning they have two refractive indices: one for light whose electromagnetic vector vibrates in a plane parallel to the fiber, n, and another for light whose electromagnetic vector vibrates in a plane perpendicular to the fiber, n. 23 The difference of these refractive indices is represented by birefringence, n, and can be determined utilizing techniques of polarized light microscopy. 3 Another feature of importance to trace analysts is optical path difference (OPD), or relative retardation (RR). The OPD will vary among fiber types and can be determined by use of a compensator which introduces a standard retardation into the optical path; this also assists in the determination of optic sign. Other microscopic features of interest include apparent cross-section shape, presence and characteristics of any surface finishes, extrusion marks, shape of any scales and relative scale counts. 3 The current nature of fiber examination does not allow standard thresholds for discrimination to be established; skills, training and experience dictate an examiner s ability to make this decision. Opinions rendered about the similarity of questioned and known fibers will be based on interpretation of all data and observations. 16

33 Examination of Dyes Analytical schemes have been developed for the determination of general dye class. These procedures are organized in a decision-tree format and involve sequential extraction in various solvent systems specific to the fiber type. Classification of questioned and known fiber dyes provides an additional feature for comparison and can also indicate the extraction system most likely to yield a successful extraction. Analysts may further employ thin layer chromatography for comparison of these extracted dyes to examine the relative affinity of each dye for a mobile, developing phase and a porous, stationary phase. 22 Although this method does not compare questioned and known dye color, interpretation of retention factors (R f values) offers information about dye chemistry and could provide a basis for exclusion. This technique is economical and simple to perform. Because analysis of extracted dyes compromises the original sample, this avenue may not be explored when sample size is limited. When no other characteristics are found to significantly differ between two fiber samples, dye color becomes an important feature, often providing the greatest opportunity for discrimination. 3 While it is certainly easy enough to differentiate fiber dyes of distinct color, the task becomes more complicated when fiber dyes appear to reflect similar hues. Because human perception limits any quantitative assessment of color, spectroscopic measurements of dye absorption are required. 17 Microspectrophotometry (MSP) is a technique for the measurement of light absorption accomplished using a visible spectrophotometer integrated with a light microscope. By collecting light that has passed through a sample, it is possible to determine its absorbance for wavelengths in the visible spectrum; this is illustrated by Beer s law, where absorbance, A, is related to path length, b, and concentration, c, by a molar absorptivity, ε (1-1). 17

34 Use of MSP for collecting absorption spectra of dyed fibers was reported by Amsler in Existing light microscopes can be easily upgraded to perform such measurements using a microscope-mounted spectrophotometer; Figure 3 illustrates the setup for a typical microspectrophotometer. This simple and affordable option is appealing to crime labs, making 3, 25, 26 MSP examination of fiber evidence very common. A = εbc (1-1) Figure 3. Diagram of a typical microspectrophotometer 27 18

35 MSP analysis is fairly straightforward; a fiber of interest must be properly focused in the microscope and positioned under the collection window of the spectrometer. After incident light is measured by a reference spectrum, light transmitted by the dyed fiber is collected. The final spectrum results from absorption properties of the fiber s dye molecules, and should not be influenced by fiber type or fiber features. To accurately characterize a sample, it is best to record spectra for several fibers, especially in the case of natural fibers; five to ten spectra is recommended. 3, 19 Visual comparison of overlaid spectra is best accomplished after normalization of their spectral areas. Similarities in spectral shape, as well as location of shoulders and absorption maxima will serve to strengthen evidence of a common source, but the only conclusions which can be made with certainty are exclusions. 3, 19 Presently, there are no guidelines which define a significant difference for MSP spectra. 26 Inspection of MSP spectrum cannot be used for unique identification of a dye. Changes in dye bath conditions, presence of additives and dye-solvent interactions can all influence the shape of a dye s spectral profile. 3, 19 Similarly, the spectral curve of a dye mixture cannot be expected to resemble a sum of the absorption curves for the individual dye components; unpredictable dye interactions could produce an altered shape. Further, the shape of the profile itself does not indicate the number of dye components present; dye formulations containing multiple components may only exhibit one absorption maximum and some single dyes are known to have more than one. 17, 19 For these reasons, absorption profiles can only be used for comparative purposes. 19

36 Spectroscopy Theory Spectroscopy is a study of the interaction of radiation and matter as a function of wavelength. The absorption of radiation in the UV and visible wavelength range promotes electronic transitions between a molecule s energy levels. These electronic energy levels also contain discrete rotational and vibrational levels. When a dye molecule absorbs radiation, valence electrons transition from ground state to excited state; the electronic transitions initiated by light absorption take place on a shorter time scale than molecular vibrations. Unlike line spectra produced by atomic absorption, molecular spectra contain many overlapping lines as a result of simultaneous electronic, rotational and vibrational transitions, and produce one continuous band of absorption. 28 The Franck-Condon principle describes the events of radiative processes. Classical potential energy curves define motion of molecules by the precise position of their nuclei and momentum at the time of transition. Transitions occur from the most probable nuclear configuration of the ground state. For a vibronic transition (combined electronic and vibrational transition) to occur, the vibrational wave functions of the initial and final states must overlap. The net positive overlap of these wave functions is given by the integral, χ i χ f, where χ i and χ f are the vibrational wave functions corresponding to the initial and final state vibrational quantum numbers, respectively. The Franck-Condon principle defines the probability of a given electronic transition and is governed by the square of this overlap integral; this is illustrated below in Figure 4. Larger differences in the vibrational quantum numbers, i and f, equate to greater difference between the initial and final state wave functions. This factor controls the 20

37 relative intensity of vibrational bands, and thus determines the shape of a molecule s electronic absorption spectrum. 29 Figure 4. Illustration of the Franck-Condon principle By this principle, it can be understood that minor changes to a molecule s chemical structure will not significantly affect these wave functions and will only introduce minor changes to the overall fine structure of the vibrational bands. Replacing a functional group substituent or even changing a functional group s position in the molecule can alter the absorbance maximum for dye molecules, 17 but the basic shape of the spectrum and the shift in the absorption maximum may be minimal. The vibrations, coupled to the electronic transition, will establish the Franck- Condon factor which governs spectral shape. As discussed previously, auxochromes have a nominal effect on absorption, so any changes to these groups will minimally influence the 21

38 spectral shape. The new vibrations introduced by small changes in molecular structure which introduce vibrations that do not strongly couple with the electronic transitions will not significantly influence the Franck-Condon factors. This is why it is possible for dyes with 3, 19, 20, 30 different chemical structures to exhibit very similar absorption profiles. Current Challenges Comparative analysis of questioned and known fibers is useful for identifying class characteristics, but these observations are only useful for the purposes of exclusion, not for individualization. Currently validated methodologies cannot be used to match two sources with the certainty of DNA profile matching. 31, 32 When all techniques fail to discriminate a questioned fiber from a known source, it can be concluded that the fibers match. 33 However, it cannot be stated with absolute certainty that the questioned sample originated from the known source. 34 Michael Grieve reminds that spectral similarity is not a guarantee of same identity: There is no certainty that two samples are from the same textile even if the spectral curves are very similar. We can only be sure they do not have a common origin if the spectra are different. 35 Still, the value of this evidence should not be understated; conclusions made in trace analysis can lend great support to a case. Current methods are limited in this respect, as they offer no means to quantitate and communicate the significance of these findings to the court. While a DNA database exists to determine allele frequencies within a population, there is no comprehensive library which details textile fiber frequencies within a chosen population; this gives defense attorneys cause to undermine expert opinion by suggesting such evidence could be encountered as a result of coincidence. One way to reduce the possibility of coincidental 22

39 matching would be to subject samples to more rigorous, statistical testing, as the significance of an occurrence of like properties (or match) depends to a great extent on how critically the two items are compared. 36 A study by Wiggins, et al. examined dye variation between consecutive batches of carpet samples and the discriminatory power of various analytical techniques. In cases where samples were notably different, obvious variation was noted for absorption spectra and TLCs, and often for microscopic observation, as well. But when the differences in the dye batch formulations were more subtle, TLC was able to identify differences not detected in evaluation of their visible spectra. This article also demonstrates the importance of microscopic comparison: in two cases, differences in color value (lightness) were observed between batches during microscopic comparison; both absorption profiles and TLC were not able to discriminate these samples. For visual comparison of absorption profiles, it is difficult to define what constitutes a significant spectral difference, especially when this assessment is based on human visual perceptions. 26 While some spectral variation is to be expected between fibers within a source, assigning a threshold for exclusion becomes difficult as the extent of variation may differ greatly among different sources. Many researchers have commented on the spectral variation occurring within a sample, as the discriminatory power of the analytical technique becomes more important for increasingly similar samples. 19, 26, 37 Variation between source fibers can occur as a result of irregular dye uptake, anomalies in the fiber surface or the presence of contaminants; 3 this can produce same source absorption spectra with varying peak ratios or different areas under the curve. 19 3, 19 23

40 These variations can make the task of spectral comparison more difficult, and when dye profiles are similar, the chances of making a false inclusion are greater. In forensic science, this severe Type II error could result in the incrimination of an innocent person. For these reasons, fiber examination would benefit from an objective, statistical method for the comparison of dye spectra. Use of Statistics Current interpretations of fiber evidence are based on inductive reasoning, educated assumptions based on careful examination and experience, and while this logic has served the discipline well thus far, it cannot provide absolutely unquestionable conclusions. While others criticize forensic identification sciences for not employing statistical methods, some argue that the lack of statistics does not mean forensics is less of a science and that statements of likelihood ratios would be ineffective in court. They believe that expectations for these disciplines cannot be based on comparisons to DNA analysis. Houck states: The tyranny of numbers, the trenchant belief that science is best expressed through mathematics, overshadows the potential explanatory power many disciplines have, simply because a mathematical value is expected but may not be possible. 31 While it is certainly inequitable to compare trace evidence with DNA analysis, the notion that a statistical approach for the evaluation of trace evidence findings may not be possible is misleading. Many researchers have proposed models for statistical evaluation of physical evidence and there has also been some interest in the use of Bayesian statistics for interpretation 24

41 of overall signficance. 3, 26, 34, 36 Although none of these methods has been universally accepted at this time, work in this area continues. Particularly in research, statistical analysis of spectral data has become very popular, and this interest has inevitably spread to research in many forensic disciplines. Research in forensic biology has utilized the Raman spectral signatures of known body fluids for matching to unknown samples, 38 while researchers in fire debris analysis have built a database of known ignitable liquid mass spectra for comparison to unknown samples. 39, 40 While application of statistical procedures provides greater discriminatory power, in-depth statistical analysis also presents an additional challenge: when such critical comparisons are made, there is a greater demand for high-quality spectral data. To ensure that the results of any statistical comparison represent true differences between samples, it is important to distinguish these differences from those contributed by spectral noise and baseline shift. This may require preprocessing in the form of smoothing, filtering, averaging and normalization to remove any random and systematic variations that might confound later interpretation. 41 Common preprocessing techniques include use of least squares smoothing procedures, baseline correction or spectral derivatives. 41 For the comparison of MSP spectra, a study by Macrae, et al. supposed that the areas under spectral curves might be suitable for comparison; they found that due to fluctuations in dye concentration, the variation in spectral area found among same source samples was nearly the same for different sources. 26 After normalizing the areas, they investigated the within-source variation by examining differences in absorption maxima, the sum of the squares of the absorbance differences, and differences in spectral slope at specified wavelengths. They found that parameters which compared features of spectral shape (i.e. sum of squares of difference of 25

42 absorbance and difference in slope) held greater discriminatory power than comparison of absorption maxima, which was found to differ by up to 5 nm for same-source samples. 26 Spectra not discriminated by a combination of cumulative squares and absorption maxima were found to be quite visually similar. A recent report published by the National Academy of Sciences was critical of many aspects of forensic science. 42 They encouraged research of objective, analytical methods which could provide more credible testimony, accompanied by error rates. Although some of the criticized disciplines may not have the ability to uniquely identify an individual, this evidence may still be able to provide accurate and useful information to help narrow the pool of possible suspects, weapons, or other sources when the results are presented effectively. 42, 43 In response to this report, the forensic science community must strive to attain higher standards for fiber examination and other comparison disciplines. Establishing statistical methods of spectral comparison can remove some of the subjectivity from fiber examination by introducing quantifiable error rates to convey uncertainty. Mass Spectral Dye Analysis While absorption spectroscopic methods provide information about fiber color properties, they offer no means to characterize chemical structures of dyes. 20 This can be especially important when multiple dyes are used in dye formulations, as it is possible for indistinguishable colors to be produced by different dye mixtures. 3 Mass spectrometry can provide a greater degree of structural information about fiber dyes and is complementary to absorption 20, spectroscopic methods. 26

43 There are many reasons why a protocol for mass spectral analysis of dyes has been slow to progress. Such analysis requires extraction of fiber dyes and may not be attempted when sample size is limited. 3 Further, there are many dyes which do not produce sufficient extract for analysis, and others still which are not extractable. 47 However, when extraction from the fiber is successful, analysis via mass spectrometry (MS) is a logical next step. TLC analysis has already proven to have greater discriminatory power than that of microscopic comparison and visual spectral comparison, 26, 47 but results are not highly reproducible and cannot provide information about the relative ratios of dye components. 3, 20 When two dyed fibers cannot be discriminated by other means, extracting the dyes and evaluating the similarity of their mass spectra could provide further confirmation of these findings, or uncover a difference not detected by other methods. 44 As mentioned previously, visible absorption spectra are broad curves and exhibit subtle changes when substituent groups are changed, but MS can provide structural information about the dye molecule and readily distinguish dyes with differing chemical structures. Protocols published by the Federal Bureau of Investigation (FBI) delineate extraction procedures for the classification of fiber dyes. 22 While this destructive extraction can provide information about dye class not afforded by microscopic or spectroscopic methods, following with mass spectral analysis has the potential to offer additional structural information. Research by Huang, et al. attempted to identify dyes by their MS, correlating ions observed with logical structure fragments. This work laid a foundation for extracted dye analysis with analytical parameters optimized for dye classes. 44 Beyond the ability to detect dye ions, mass spectra may present ions related to garment treatments, such as fluorescent brighteners, or even indicative of environmental conditions. 44, 48 This could be of significance for dyed fibers exhibiting similar 27

44 absorption profiles; although the dyes are chemically identical, discovery of distinct ions contributed by other dyeing agents could provide opportunity for discrimination

45 CHAPTER 2: EXPERIMENTAL Samples Many samples were investigated in relation to this project. Originally, the research topic focused on the ability to discriminate dyed samples with very similar dye structures. While ordering of these samples was coordinated, dyed acrylic yarn samples were utilized to refine the sampling and statistical methods. Analysis of these samples led to additional research questions, prompting further study. Red Acrylic Yarns Five skeins of red acrylic plied yarn had been purchased previously and were already available in the laboratory. Initial microscopic evaluation of the yarns suggested that all samples were similar in color. Information recorded from the yarn s packaging is shown in Table 4. Of the five samples, two pairs of yarn (sources A and C, and sources B and E) came from the same manufacturer, but had different shade names. No identifying information about the yarn dyes was known. All yarns were found to be constructed of filament fibers between 8 to 12 cm in length. 29

46 Table 4. Descriptive information from red yarn labels Sample Designation Brand and Style Fiber Material Color/Shade Code Yarn Weight A Caron Simply Soft 100% acrylic Red Med B Lion Brand Microspun 100% acrylic Cherry Red # Fine C Caron Wintuk 100% acrylic Christmas Red Med D Red Heart Sport 100% acrylic 0912 Cherry Red 3 - Light E Lion Brand Jiffy 100% acrylic True Red # Bulky Red Cotton Samples Ten red cotton samples were analyzed. These samples had previously been examined in Huang, et al. They were of interest for reexamination because there were some samples deemed indistinguishable by the absorbance profiles. Identifying information for these samples is shown below in Table 5. Table 5. Descriptive information from red cotton labels and garment tags Sample Number Item Description Color/Shade Bar code # Other 1 Woven 1yd x 45 in Maroon Cloth ribbed 2 Cross woven 1yd x 45 in Red Cloth open weave 3 Girl top (Jordan) Red Ribbed knitted 4 Girl top (Honduras) Maroon Ribbed knitted 5 Cap sleeve top (Turkmenistan) China red Ribbed knitted 6 Crochet thread (Hungary) Victory red Spool 350 yds/10 7 DMC floss (France) x 2 Scarlet m 8 DMC floss (France) Red m 9 DMC floss (France) Scarlet m 10 DMC floss (France) Red m Blue Acrylic Yarns Five skeins of dark blue acrylic plied yarns were obtained from local craft stores. Samples were chosen to represent yarns that in the bulk were considered visually 30

47 indistinguishable by color. Relevant information found on the yarn labels is presented in Table 6. Of the five samples, two pairs of yarns (sources F and G, and sources G and I) came from the same manufacturer, but had different shade names. No identifying information about the yarn dyes was known. All yarns were composed of filament fibers with lengths of 8 to 12 cm. Table 6. Descriptive information from blue yarn labels Sample Yarn Brand and Style Fiber Material Color/Shade Code Designation Weight F Bernat Satin 100% acrylic Admiral Med G Caron Simply Soft 100% acrylic Navy Super Quick Bulky H Red Heart Super Saver 100% acrylic 0387 Soft Navy 4 Med I Caron Simply Soft 100% acrylic DK Country Blue Med J Bernat Satin Sport 100% acrylic Marina Light Dyed Fabric Samples For these samples, two pairs of dyes were chosen based on the similarities of their absorbance spectra and dye structures. The powdered dyes were ordered from chemical suppliers and then shipped to a company specializing in custom fabric dyeing (Test Fabrics, Inc.). Table 7 shows the identifying information for each of the dyes selected. Figure 5 and Figure 6 display the structures of each dye compound. Length of fibers collected from the fabrics was dependent on fiber type, but all were found to be constructed from staple fibers measuring between 2 to 4 cm in length. 31

48 Table 7. Dye properties and selected fabric styles Dye Name Molecular Weight (g/mol) Dye Purity CAS Number λ max (nm) Fabric Style/Code Basic Green [385.53]* 90% #864 - Spun Basic Green [329.43]* 96% Acrylic Type 75 Disperse Blue % #105B - Acetate Disperse Blue % Satin *This refers to the molecular weight of the dye component only; see structures for clarification Figure 5. Chemical structures of Basic Green 1 (top) and Basic Green 4 (bottom) 32

49 Figure 6. Chemical structures of Disperse Blue 3 (left) and Disperse Blue 14 (right) Instrumental Analysis Microspectrophotometry All fiber samples were placed on glass microscope slides under coverslips. The microscope used for this project was a Nikon Eclipse E600 POL with a Nikon C-CU universal system condenser. The heat-absorbing filter (transmittance range of nm) located in the base of the microscope was later removed to improve intensity of the incident beam at shorter wavelengths. A 40X objective from the Nikon Plan Fluor Series was used to image samples. Specifications for the objective include flat field and fluorite aberration correction, numerical aperture of 0.75, working distance of 0.72 mm, infinity-corrected tube length and correction for standard 0.17 mm-thick coverslips. With the additional 10X magnification provided by the eyepiece, this resulted in 400X total magnification. Visible absorption measurements were collected by coupling the microscope to a fiber optic spectrometer and also to a commercial spectrometer attachment. Instrumental assembly for both spectrometers is illustrated in Figure 7. Although both spectrometers permitted collection of spectra in ultraviolet region of the electromagnetic spectrum, all optical lenses in the microscope were constructed of glass and prevented transmission of radiation in this range. Both microscope 33

50 lamp and spectrometers were warmed up for 30 minutes prior to the collection of measurements. Adjustments to the condenser, stage height and aperture iris, were made between collection measurements as needed. Measurement positions within a fiber were chosen to avoid inherent fiber deformities and obvious color irregularities. Figure 7. Microscope coupled with fiber-optic spectrometer (left) and with commercial spectrometer attachment (right) Fiber Optic Spectrometer An Ocean Optics USB4000-UV-VIS miniature fiber optic spectrometer was used to collect UV-VIS absorption measurements. The spectrometer was equipped with a Toshiba TCD1304AP linear CCD array detector, with a useable range of nm, and a grating of 600 lines/mm blazed at 300 nm. The spectrometer was coupled to the microscope using an Ocean Optics premium-grade UV/SR-VIS optical fiber assembly, designed to work in the range 34

51 of nm. The fiber s optical diameter was 600 µm with an acceptance angle of 25.4, and the cable was terminated at both ends with SMA 905 fiber optic connectors. The other end of the fiber optic cable was connected to a standard C-mount attached to a Diagnostic Instruments 1X relay lens; this relay lens was nested in the microscope phototube. Spectra were collected using the SpectraSuite software sold by Ocean Optics for use with the spectrometer. Integration time for absorption measurements was determined using the autointegration function, which selected the time that delivered an intensity value closest to the recommended value of 55,704 counts. Smoothing was accomplished by averaging several scans per spectrum, and the number averaged varied by experiment. Commercial Spectrometer Additionally, a CRAIC Technologies QDI 302 microscope spectrophotometer was used for collection of UV-VIS absorption measurements. The spectrometer was equipped with a thermoelectric-cooled Sony ILX511 CCD array detector, with a useable range of nm, and a grating of 600 lines/mm blazed at 500 nm. A significant advantage to this instrument was the incorporation of a 1.3 megapixel digital imaging system with FireWire CCD color camera, model DFK 41AF02. This feature made it possible to collect spectra from specific locations on the fiber sample, and to know the exact boundaries of collection. The area of the image appearing as a dark box was diverted to the spectrometer for collection using aperture mirrors oriented at 45 with respect to the image plane. The head unit was equipped with six aperture mirrors, with dimensions A x B, ranging from 85 x 60 µm to 1416 x 1000 µm (sizes reported 35

52 refer to actual mirror dimensions, where a mirror size A x B would yield a collection area of B x B and where A cos45 = B). An illustration of the aperture mirror is provided in Figure 8. Figure 8. Diagram of aperture mirror in spectrometer head unit Spectra were collected using the CRAIC MSP Data Acquisition software sold by CRAIC Technologies for use with the spectrometer. Settings remained consistent throughout all experiments, with integration time of 4.0 ms, reduced lamp intensity and mirror dimensions of 354 x 250 µm (collection area of 250 x 250 µm equates to 6.5 µm x 6.5 µm under 40X magnification). Smoothing was accomplished by averaging 50 scans per spectrum. Extracted Dye Analysis Based on the knowledge that all yarn samples (sources A-E, red, and sources F-J, blue) were comprised of 100% acrylic fibers, extraction was attempted using the preferred extraction system suggested by SWGMAT for acrylic fibers: formic acid/water (1:1) at 100 C for 20 minutes. Dye was successfully extracted from the fibers, but resulted in change of dye color and absorption maximum for some samples. Because this project intended to analyze extracted dyes 36

53 using mass spectrometry, a method for dye extraction was sought that would not chemically alter the dye molecules. This was achieved using an extraction system described by Burns and McGuigan for use with acrylic fibers, where pyridine, acetic acid and water were mixed in a ratio of 20:5:75 (v/v). 49 Fiber segments intended for extraction were cut into short (1 mm) lengths and placed in the bottom of 200 µl conical vial inserts with 1 µl dead volume. Using a micropipette, enough of the extraction system was added to cover the fibers (roughly 40 µl) and the inserts were added to 2 ml crimp-top target vials. Bulk yarn samples intended for extraction were added directly to 2 ml crimp-top target vials and filled with enough of the extraction system to cover the sample (roughly 1 ml). Blank extraction samples were prepared as controls for both fiber and bulk analysis, and were treated under the same conditions. Vials were crimp-sealed for extraction, but the septa were punctured to allow release of pressure during heating. Samples were heated at 100 C for 60 minutes. After sufficient cooling, the extraction system was evaporated from the vials in a 95 C dry bath under a modest flow of nitrogen gas. For fiber samples, evaporation took only 15 minutes, while bulk samples took roughly 1 hour. After all traces of the extraction system had been eliminated, samples were resolvated with methanol; fiber samples with 20m µl and bulk samples with 0.5 ml. This extraction procedure was followed for dyed fabric samples, as well. Flow cell Absorbance measurements for extracted dyes were collected using a FIAlab Instruments Fiber Optic SMA Z-Flow Cell, constructed of high-grade stainless steel with a 1 cm pathlength. 37

54 The unit was designed with fused silica windows at either end of the path, sandwiched between two Teflon seals and held in place by a tightened SMA fitting. Syringe injections were made into PEEK tubing mounted in the unit by Upchurch fittings. Each sample injection contained the extract volume followed by 10 µl of air needed to position the extract volume in the path without air bubbles. Figure 9 illustrates the interior construction of the flow cell, where the red bracket indicates the optical path. Figure 9. Diagram of flow cell interior Mass Spectrometry All extracts were analyzed on an Agilent 1100 series quadrupole mass spectrometer with electrospray ionization (ESI) source. Drying gas flow rate was 12 L/min at 350 C and nebulizer pressure was kept at 30 psig. An Agilent 1100 series binary liquid chromatograph (LC) pump supplied a 0.2 ml/min flow of solvent: LCMS-grade methanol for blue and red acrylic yarns, and a 1:1 mixture of epure filtered water and LCMS-grade methanol for dyed fabric samples. Samples were directly introduced through an injection port with 5 µl sample loop positioned before the ESI interface; no chromatographic separation was performed. A solution of 4.1% acetic acid post-additive was delivered to the flow by syringe pump at 300 µl/hr. Each dye 38

55 extract was preceded by a solvent blank and control extraction before progressing to the next sample, with each extract in the analytical run separated by a blank and control injection. Spectra were collected over a mass range of m/z in the positive mode, using fragmentor voltages of either 60 or 100V. Data Analysis All raw spectral data was exported in the comma separated values (CSV) file format using the software with which it was collected. Pretreatment of the Data Prior to analysis, raw data was treated to improve graphical comparisons of spectra. These adjustments did not alter the data or change the results of statistical analysis; linear transformations introduced by pretreatment had no effect on correlation. For visible absorption spectra, the area under each spectral curve for the wavelength range of interest was normalized to an area of one. Also, the last absorbance value of each spectrum (corresponding to 700 nm) was subtracted from the remaining absorbance values. This gave all spectra an absorbance of zero at the final wavelength and ensured that any baseline shift would not interfere with visual evaluation of similarity. For mass spectra, blank and control extract spectra were subtracted from the sample mass spectrum. This process removed ions contributed by the extraction system and produced a spectrum comprised only of sample analyte ions. The sum of the mass spectral intensities for each spectrum was normalized to one. Also, any missing data values over the mass-to-charge range were restored with an intensity of zero, giving each mass spectrum the same number of data points. 39

56 Hypothesis Testing Hypothesis testing is a statistical decision-making process used to determine if two analytical samples can be discriminated based on an operator-defined level of statistical significance, α. In hypothesis testing, the null hypothesis asserts that two samples are the same, according to a specified parameter known as the test statistic, while the alternative hypothesis states that they are different; failure to accept the null hypothesis means that the alternative hypothesis is true. 40, 50 The decision to accept or reject the null hypothesis is governed by the p- value, which is the probability of observing a particular test statistic if the null hypothesis is assumed to be true, and whether that p-value exceeds the significance level. 50 When hypothesis testing leads to an incorrect conclusion, a statistical error has occurred. Rejection of the null hypothesis when it is true is considered a Type I error, while failing to reject the null hypothesis 40, 50 when it is false indicates a Type II error. Pearson Correlation and Fisher Transformation The Pearson s product-moment correlation coefficient, known as the Pearson s correlation, represents the dependence of two variables, x and y, and can be calculated by dividing their covariance by the product of their individual standard deviations (2-1). 51 r = (x i x ) (y i y ) (x i x ) 2 (y i y ) 2 (2-1) In the case of the experiments considered here, the two variables being compared are two spectra, either absorbance spectra or mass spectra. In the equation, x i represents the ith value in the spectrum of x, y i represents the ith value in the spectrum of y and the terms and 40

57 correspond to the averaged values in the spectra of x and y, respectively. The value of the coefficient, r, may range from -1 to +1. The greater the absolute value of the coefficient, the stronger the linear relationship of the two variables. 52 Because Pearson s correlation values are not guaranteed to have a normal sampling distribution, they are unsuitable for statistical comparisons which assume normal distribution about the mean. The Fisher transformation can be used to convert these correlation coefficients 51, 53 to normally distributed z-values (2-2). z = 1 2 ln (1 + r) (1 r) (2-2) Parametric Student t-test A two-tailed Student t-test is a method for comparing two sample distributions. While the null hypothesis asserts that the sample averages are the same, the alternative hypothesis states that the averages are different. When the sample distributions are found to overlap by more than the specified significance level, α, the null hypothesis is accepted; when there is no significant overlap, the null is rejected in favor of the alternative hypothesis. Because this test is parametric, it assumes normal distribution of the test statistic. In cases where normality of the data is unknown, use of Fisher s transformations of the Pearson correlation coefficients will satisfy this requirement. For the purposes of this research, the two distributions being compared were the averages of z-values (Fisher transformations of the Pearson correlation coefficients) from same sample comparisons ( SS) and from different sample comparisons ( DS). Each same sample average represents an average of the z-values for all unique spectral comparisons within a file; when two different files are compared, their 41

58 respective same sample z-values are summed and averaged according to the combined number of comparisons. Each different sample average represents an average of the z-values for all spectral comparisons between two files; when the two files are the same, as they would be for cells along the diagonal of a matrix, this average is equivalent to the same sample average. For each file comparison, the calculated test statistic is equal to the absolute value of the difference between SS and DS, divided by the pooled variance for unequal sample size and unequal variance (2-3). 54 t calc = x DS x SS σ DS 2 + σ 2 SS n DS n SS (2-3) Nonparametric Permutation test Whereas the parametric test assumes normal distribution of the test statistic, the nonparametric test guarantees the specified significance level without this distribution restriction. Equation (2-4) shows the calculation for the test statistic, W 0, where W 1 and W 2 are summations of the Fisher transformation of their Pearson correlation coefficients for m spectra from sample 1 (S 1 ) and n spectra from sample 2 (S 2 ). W 3 is the summation of the Fisher transformation of their Pearson correlation coefficients for spectra between the samples. For a total of (m+n) spectra, there will be k = (m n)! / (m!n!) different partitions which lead to k different value of W. Under the null hypothesis, all k values follow the same distribution. The large value of W 0 is in favor of the alternative hypothesis. The p-value is the proportion of k values that are no less than the observed W 0. At a significance level of 5% (α = 0.05), any sample comparison yielding a p-value 42

59 less than α will be considered a discrimination. 53 This test was performed using an software program developed in-house. W 1 = f ij ; (i,j) S 1 W 2 = f ij ; (i,j) S 2 W 3 = f ij, i S 1 j S 2 (2-4) W 0 = W 1 + W 2 m(m 1) + n(n 1) W 3 mn Wilcoxon Rank Sum test Also called the Mann-Whitney U-test, the Wilcoxon Rank Sum test is another nonparametric test used to assess probability distributions of two independent populations based on their sample medians. The null hypothesis asserts that the two sample distributions are the same, and the alternative hypothesis states that the medians are different. This test can be used to compare samples with an unequal number of observations, where n 1 n 2. To perform the calculations, data values for both samples are combined and ranked in increasing order. Ranks are assigned to each data value, and the ranks for each sample are summed to give R 1 and R 2. When data values are tied for rank, they are assigned a rank value equivalent to the average of the ranks which they would fill. U values for each sample are calculated as shown in Equation (2-5). The smaller U value is used as the test statistic. 50 U 1 = R 1 n 1(n 1 + 1) 2 U 2 = R 2 n 2(n 2 + 1) 2 (2-5) When the combined sample size, N, does not exceed 60, significance tables containing critical values of U can be consulted for decision-making. When N is larger, U is said to follow a 43

60 normal distribution and thus, a normalized approximation is used. If the computed value of U is found to be less than or equal to the critical value of U, the null hypothesis is rejected and it can be said that the two distributions are significantly different. 50 This test was performed using code written for the free statistical computation software, R. Multivariate Statistical Techniques This section discusses common techniques for multivariate analysis. Such techniques are used to identify underlying patterns within a set of analytical data. These variables may be intangible, the result of minor fluctuations within the data, which could be difficult to recognize otherwise, especially for large, complex data sets. Observing the covariance, or relationship of these variables, among different analytical samples can be a useful tool for discrimination. 41 In multivariate analysis of spectral data, each of n sample spectra is treated as a vector having p dimensions; the number of variables, p, is determined by the number of measured values, which could be wavelengths, mass-to-charge ratios, etc. This provides a data matrix, [D] with dimensions n p. Principal Component Analysis and Cluster Analysis Principal component analysis (PCA) is a data dimensionality-reduction technique which separates the data into components based on the variance within the data set. The technique is unsupervised, meaning that it does not require prior knowledge of sample classifications. 41 For this analysis, the data matrix [D] is premultiplied by its transpose [D] T to yield the covariance matrix, [Z] with dimensions p p. This matrix is then decomposed into a set of eigenvalues and eigenvectors. Each orthogonal vector within the eigenvector matrix, [C], represents a latent 44

61 variable identified within the data set; eigenvectors are also referred to as principal components. The eigenvalues can then be used to compute the scores matrix, [R], which defines the relative contribution of each principal component to a given sample. 55 The first principal component represents the greatest variance in the data, while each subsequent component accounts for a less significant portion of the total variance. To accurately reproduce the original data set, all principal components are needed. However, by retaining only the components which contribute significantly to the variance, it is possible to reproduce the data without noise and achieve a more fundamental comparison of the spectra. 41 While these mathematical solutions are abstract, they can be plotted to visualize the spatial separation of the samples and locate significant clusters. 55 Discriminant Analysis Discriminant analysis (DA) is a supervised technique which analyzes the data for linear or quadratic functions that maximize between-class variance and minimize within-class variance for prescribed data classes. These functions, termed canonical variates or CVs, can then be used as a model to predict separation for subsequent data sets. 41 The ratio of the between-class variance to within-class variance is known as the Fisher ratio. The canonical variates are eigenvectors derived from the matrix of [W] -1 [B], where [B] is the between-class sum of squares and cross-products matrix and [W] -1 is the inverse of the pooled within-class sum of squares and cross-products matrix. For any data set, the number of canonical variates can never exceed either the number of groups minus one, or the number of variables. This analysis also requires that the number of samples exceed the number of variables, which is a problem when examining data of high dimensionality, e.g. chromatograms 45

62 and absorbance spectra. One way to overcome this difficulty is to reduce the dimensionality of the data set before DA is performed; using the scores calculated from PCA greatly reduces the number of variables

63 CHAPTER 3: RESULTS AND DISCUSSION Absorption Spectra Hypothesis Testing Red Acrylic Yarns For the first analysis of the red acrylic yarn samples, five fibers were selected from each source (denoted by source code and fiber number as A1, A2, A3, etc.) and ten measurements were taken along the length of each fiber. Measurements were collected using the fiber optic spectrometer and each spectrum was the result of 8 averaged scans. With plans for subsequent dye extraction, it was considered that use of any mounting medium may interfere with ESI-MS analysis; for this reason, sample spectra were collected in air. Results of the parametric t-test showed correct discrimination of all different sources, with the exception of sources B and C; these sources were not produced by the same manufacturer. Furthermore, several comparisons between fibers within a source yielded discrimination. The parametric discrimination matrix is shown in Table 8 and this data is summarized in Table 9. Visible inspection of the spectra confirmed that sources B and C exhibited similar profiles (Figure 10), and also demonstrated slight variation between spectra within a source (Figure 11). 47

64 Table 8. Parametric discrimination matrix for red acrylic yarns (in air), nm, α = 0.05 A1 A2 A3 A4 A5 B1 B2 B3 B4 B5 C1 C2 A A A A A B B B B B C C C C C D D D D D E E E E E C3 C4 C5 D1 D2 D3 D4 D5 E1 E2 E3 E4 E5 48

65 Table 9. Parametric discrimination summary for red acrylic yarns (in air), nm Total Not Type I Type II Discriminated comparisons discriminated error error Same source % --- Different source % Figure 10. Normalized plot of averaged spectra for all red acrylic sources, in air 49

66 Figure 11. Normalized plot of averaged A fiber spectra, in air Examination of individual spectral measurements revealed a strong, interference pattern superimposed on the spectra. This was particularly evident in the spectra of B and C, and even noticeable in their averaged spectra. The cause of this phenomenon was initially unclear; it was speculated that variations in the fiber cross section shape or extrusion marks on the fibers may have contributed to this pattern. Absorbance measurements from a well-plate containing an extracted dye did not appear to exhibit this behavior, supporting the theory that the fiber was responsible. Attempts were made to collect spectra from rolled fibers, but flattening the fibers reduced the path length, resulting in lower absorbance. It was then considered that use of water as a mounting medium would reduce refraction of light without contaminating fibers intended for dye extraction and mass spectral analysis; this is because the refractive index of water (n = 1.33) is closer to the refractive index of the acrylic fibers (n = 1.51) than that of air (n = 1.00). 50

67 Testing this theory, another set of measurements was collected using the same parameters used in the previous experiment, with water as a mounting medium; only sources A, B and C were examined. As water was found to evaporate quickly from under the coverslips, the water was applied to slides just before collection of spectra. It was observed that spectra collected from fibers mounted in water did not exhibit the interference wave pattern; this can be seen in the spectra of source A (Figure 12). For this reason, water was used as a mounting medium for all subsequent measurements. The parametric t-test correctly discriminated different sources, with the exception of one pairwise comparison between sources B and C, but there was a high percentage of same source discriminations; Type I error was very high. The parametric discrimination matrix is shown in Table 10 and this data is summarized in Table 11. Comparisons of spectral profiles are shown in Figure 13. Figure 12. Normalized plot of averaged A fiber spectra 51

68 Table 10. Parametric discrimination matrix for red acrylic yarns, nm, α = 0.05 A1 A2 A3 A4 A5 B1 B2 B3 B4 B5 C1 C2 C3 C4 C5 A A A A A B B B B B C C C C C Table 11. Parametric discrimination summary for red acrylic yarns, nm Total Not Type I Type II Discriminated comparisons discriminated error error Same source % --- Different source % Figure 13. Normalized plot of averaged spectra for all red acrylic sources 52

69 Although the test demonstrated an ability to discriminate different sources, the frequent discrimination of samples from the same source was unfavorable. Red Cotton Samples Spectra from the red cotton samples had previously been collected using a SEE 2100 microspectrophotometer, and this work demonstrated that some dyed cotton samples produced similar absorption profiles. 30 Beyond interest in statistical analysis of such spectra, replicate analysis of these samples was desired to demonstrate that spectra collected using the fiber optic spectrometer were comparable to those collected on a commercial instrument. Three fibers were selected from each source (denoted by source code and fiber number as 01A, 01B, 01C, 02A, etc.) and five measurements were taken along the length of each fiber. Measurements were collected using the fiber optic spectrometer and each spectrum was the result of 8 averaged scans. Water was added under each coverslip prior to the collection of spectra. Results of the parametric t-test showed correct discrimination of most different sources; sources 2 and 4 were only discriminated in 2 of 9 comparisons, while source 3 with 6, and 4 with 7 were both discriminated in only 8 of 9 comparisons. As with the red acrylic samples, same source discriminations were very high. The parametric discrimination matrix is shown in Table 12, along with a summary in Table 13. A comparison of absorption profiles is shown in Figure 14, demonstrating the degree of similarity observed for spectral sets. 53

70 Table 12. Parametric discrimination matrix for red cotton samples, nm, α = A 01B 01C 02A 02B 02C 03A 03B 03C 04A 04B 04C 05A 05B 05C 06A 06B 06C 07A 07B 07C 08A 08B 08C 09A 09B 09C 10A 10B 10C 01A B C A B C A B C A B C A B C A B C A B C A B C A B C A B C

71 Table 13. Parametric discrimination summary for red cotton samples, nm Total Not Type I Type II Discriminated comparisons discriminated error error Same source % --- Different source % Figure 14. Normalized plot of averaged spectra for all red cotton sources Blue Acrylic Yarns For the first analysis of the blue acrylic yarn samples, five fibers were selected from each source (denoted by source code and fiber number as F1, F2, F3, etc.). In an effort to determine whether same source discriminations were the result of instrumental error or actual variations in the samples themselves, a new level of comparison was introduced: comparisons of segments within a fiber. Each fiber was divided into three equal segments and ten measurements were taken along the length of each segment. Water was added under each coverslip prior to the collection of spectra. After MSP analysis, these same fiber segments were extracted and injected into the flow cell; one measurement was obtained for each segment extract. Both MSP and flow 55

72 cell measurements were collected using the fiber optic spectrometer and each spectrum was the result of 8 averaged scans. A summary of results from the parametric t-test for MSP spectra is shown in Table 14 (Assuming segments originating from the same fiber should not be discriminated, any discriminations reported for these comparisons are referred to as Type I error. All other same source discriminations are termed apparent Type I error.); the complete discrimination matrix is included in Appendix A. The test correctly discriminated all different sources, with the exception of sources F and J, and G and I; these two pairs correspond to the sources produced by the same manufacturer. Similarities between their spectra can be seen in Figure 15. For comparisons within a source, percent discriminations were exceptionally high, especially between fibers of the same source. Table 14. Parametric discrimination summary for blue acrylic samples, nm Total comparisons Discriminated Not discriminated Type I error Apparent Type I error As for the absorbance data collected from the flow cell, measurements were very noisy and difficult to compare visually, as shown in Figure 16. Because each segment only yielded one extract, comparisons were only possible between fibers, with each file containing three representative segment spectra. Overall, the parametric t-test showed less frequent discrimination within same sources, only 32%, compared with 83.1% from the MSP same source discriminations; results are displayed in Table 15 along with a summary in Table 16. On the Type II error Same fiber % --- Same source % --- Different source % 56

73 other hand, the flow cell gave much higher Type II error. So although variation among same source spectra was limited for flow cell measurements and contributed to lower Type I errors, this same feature made it more difficult to distinguish different source spectra, negatively impacting Type II error. Figure 15. Normalized plot of averaged spectra for all blue acrylic sources Figure 16. Normalized plot of J3 segment spectra, flow cell 57

74 Table 15. Parametric discrimination matrix for blue acrylic samples (flow cell), nm, α = 0.05 F1 F2 F3 F4 F5 G1 G2 G3 G4 G5 H1 H2 F F F F F G G G G G H H H H H I I I I I J J J J J H3 H4 H5 I1 I2 I3 I4 I5 J1 J2 J3 J4 J5 Table 16. Parametric discrimination summary for blue acrylic samples (flow cell), nm Total Not Type I Type II Discriminated comparisons discriminated error error Same source % --- Different source % 58

75 It was considered that the correlations for this data may not be normally distributed and would therefore be better evaluated using a nonparametric test. The permutation test had proven useful in the discrimination of laser-induced breakdown spectroscopy data for paint samples, having successfully held the Type I error at 5%. 53 Results of the nonparametric permutation test for MSP spectra are summarized in Table 17; the complete discrimination matrix is included in Appendix B. As with the parametric test, correct discrimination was achieved for all different sources, with the exception of sources F and J, and G and I; however, Type II error was slightly higher for the nonparametric test (2.2% compared with 1.1% for the parametric test). For comparisons between fibers within a source, same fiber discrimination increased slightly, but same source discrimination decreased from 83.1% to 78.0%. Table 17. Nonparametric discrimination summary for blue acrylic samples, nm Total comparisons Discriminated Not discriminated Type I error Apparent Type I error As stated previously, this test guarantees that α will hold the Type I error at 5%. Because the Type I error still remained high after implementing the nonparametric test (even for segments from the same fiber), individual MSP spectra were closely examined to determine what spectral variations might be contributing to these same source discriminations. As there seemed to be a great deal of variation and noise below 450 nm (see Figure 17 for an example), the comparison was repeated using spectra renormalized over the wavelength range of nm. This Type II error Same fiber % --- Same source % --- Different source % 59

76 comparison gave a slightly improved Type I and Type II error; a summary of these results is displayed in Table 18 and the complete discrimination matrix is included in Appendix C. Figure 17. Normalized plot of F4b spectra Table 18. Nonparametric discrimination summary for blue acrylic samples, nm Total comparisons Discriminated Not discriminated Type I error Apparent Type I error Otherwise, visual comparison did not reveal obvious differences between discriminated and non-discriminated same source spectra. Based on the assumption that no fiber should be discriminated from itself, interpretation of these results suggested that there must be an underlying instrumental drift affecting our discriminations. To understand how the baseline Type II error Same fiber % --- Same source % --- Different source % varied from one measurement to the next, a surface plot was generated for the ten measurements 60

77 from J1a after subtracting the first measurement (Figure 18). This showed fluctuation between measurements, which could be caused by drift in the spectrometer. Figure 18. Surface plot illustrating random drift for spectra collected in sequential order In order to determine whether such instrumental variation was truly altering the discrimination results, spectra were collected from a set of fibers in both sequential and random order. For this experiment, one fiber was chosen from sources H and J, and two fibers were chosen from source F (this would serve to evaluate the effect of random collection on same source discriminations). Each fiber was divided into two segments and six measurements were taken along the length of each fiber. Sequential measurements were collected beginning with F1a1, F1a2, etc., and ending with J1b6. For random collection, a string of numbers from a 61

78 random number generator was used to reorder the entire sequence; for example, the first measurement might be from H2a, followed by a measurement from F1b, etc., until all measurements were collected. Comparison of the discrimination results from these separate collections, shown in Table 19, demonstrates smaller Type I error for the randomized set. It appeared that randomizing the collection sequence decreased the effect of the instrumental drift by distributing the variation randomly among the samples. These results suggested that random collection may be effective for obtaining a Type I error closer to the guaranteed 5%. Conducting an experiment with a greater number of samples would provide more same source comparisons from which to better assess the effect on Type II error. Additionally, both collections indicated complete discrimination of the same source fibers from F; Figure 19 demonstrates the difference in absorbance for fibers F1 and F2 near 625 nm. Repeating these measurements with multiple new fibers taken from source F found fibers with profiles similar to both F1 and F2, demonstrating the presence of a secondary population within this source. Table 19. Nonparametric discrimination matrix for blue acrylic yarns from random collection sequence, nm, α = 0.05 F1a F1b F2a F2b H1a H1b J1a J1b F1a F1b F2a F2b H1a H1b J1a J1b F1a F1a F1b F1b F2a F2a F2b F2b H1a H1a H1b H1b J1a J1a J1b J1b

79 Figure 19. Normalized plot of averaged spectra for select blue acrylic fibers from random collection sequence Another set of blue acrylic yarns samples were chosen for measurement. This time, fourteen fibers from each source were selected, with each fiber divided into three segments and six measurements taken along the length of each segment (Fourteen fibers were chosen so that enough MS data would be generated to perform a nonparametric test.). For this and all remaining experiments, the commercial CRAIC spectrometer was used with measurements collected in random sequence and each spectrum the result of 50 averaged scans. The discrimination matrix for this comparison is too large to include here or in the appendix, but a summary is provided in Table 20. Compared to previous experiments in which spectra were collected sequentially, the Type I error for both same fiber and same source saw a dramatic decrease for random collection. For same fiber, the error dropped to 7.1%, not far from the expected 5%. For same source comparisons, error was still greater than 5%, but much improved from previous MSP 63

80 experiments, where error often exceeded 70%. Even though the rate of same source discriminations has been reduced, an error of 30.5% suggests that some variation remains within same sources that cannot be controlled. Table 20. Nonparametric discrimination summary for blue acrylic samples from random collection sequence, nm When reviewing the discrimination results, the question was posed: to what extent are discriminated samples actually different? Although the discrimination matrix assigned 1s and 0s based on the relation of the p-value to the specified α cut-off, there was a wide distribution of p- values among discriminated samples. While different source comparisons had very low p-values (most were , the lowest attainable for 12 choose 6 permutations), the majority of same source p-values were not so low; despite being below the α level, same source p-values were generally higher than those of different source comparisons. Table 21 separates these p-values into four ranges: , (0.0022, 0.01], (0.01, 0.05) and [0.05]; p-values from every grouping except the last one (p 0.05) are considered a discrimination. The difference between p-value distributions for same source and different source comparisons is also presented graphically in Figure 20. Total comparisons Discriminated Not discriminated Type I error Apparent Type I error Type II error Same fiber % --- Same source % --- Different source % 64

81 Table 21. P-value distributions for blue acrylic samples from random collection sequence, nm p = < p < p < 0.05 p 0.05 Same fiber 0 0% 0 0% % % Same source % % % % Different source % % % % Figure 20. Bar graph evaluating the p-value distributions for same fiber, same source and different source comparisons Based on the knowledge that different source p-value distributions were quite different from the distributions for same source and same fiber, the nonparametric Wilcoxon rank sum test was selected as a method to evaluate these distributions. In principle, if two samples being compared (a questioned source and a known source) are truly different, the medians of their p- values coming from the nonparametric permutation test will be significantly different. If the samples are the same, the test should find that the medians of their p-value distributions are not significantly different. 54 For each comparison, one source was selected as the known and one as the questioned. The test was then repeated with their roles reversed. This accomplished a test of 65

82 the different source p-value distribution against both of the same source p-value distributions (same source does not include same fiber p-values). Figure 21 displays an example: with source A as the known, the red and orange box plot distributions would be compared; with source B as the known, the yellow and orange box plots would be compared. Note that this differs from the calculation of the test statistic for the nonparametric permutation test (2-4), which combines the same source Fisher transformations of the Pearson correlation coefficients for both sources in the first term (W 1 +W 2 ). Figure 21. Example of box plots illustrating distributions of p-values from the nonparametric permutation test (left): same source A (in red), different source A with B (in orange) and same source B (in yellow); the location of these p-values within the discrimination matrix resulting from the nonparametric permutation test (right) For all comparisons of different sources, very low p-values were determined, as shown in Table 22. Of particular interest were the p-values calculated for comparison of distributions for source F with J and source G with I. Both comparisons of source F with J were found to differ 66

83 significantly from their self comparisons; each returned a p-value of < 2.2 e- 16, the same value found for comparisons of sources with visibly different spectra. The p-values for comparisons of source G with I were slightly higher, but still implied an unlikely probability of same source. Table 22. Wilcoxon rank sum p-values for blue acrylic yarn different source comparisons Known Questioned p-value Known Questioned p-value F G < G F < F H < H F < F I < I F < F J < J F < G H < H G < G I I G G J < J G < H I < I H < H J < J H < I J < J I < To be an effective test, it must also be demonstrated that distributions of p-values for same sources will not be distinguished. As a check on the test, each source was then evaluated against itself, with fibers 1-7 treated as the known and 8-14 as the questioned; Figure 22 displays a box plot for same source p-value distributions of source F. Roles were then reversed. Results of these tests were varied, as shown in Table 23. While some same source comparisons gave p- values exceeding 0.05, this was not the case for sources F and J. There was some variation between p-values reported when the known and questioned roles were reversed; source H showed the greatest difference, increasing from to when fibers 8-14 became the known sample. 67

84 Table 23. Wilcoxon rank sum p-values for blue acrylic yarn same source comparisons Known Questioned p-value Known Questioned p-value F 1-7 F F 8-14 F G 1-7 G G 8-14 G H 1-7 H H 8-14 H I 1-7 I I 8-14 I J 1-7 J J 8-14 J Figure 22. Box plot of same source p-value distributions for source F With 0.05 selected as α, this test was able to discriminate all different sources successfully, but incorrectly discriminated two of the same sources. Although source F and J were discriminated from all other different sources, these results have no significance because the sources were also discriminated from themselves. Dyed Fabric Samples For both pairs of dyed fabric samples, each cloth was divided into five regions: (A) top left, (B) top right, (C) middle, (D) bottom left and (E) bottom right. Three fibers were selected from each region and twelve measurements were taken along the length of each fiber. As lack of 68

85 significant variation was seen among segments of the same fiber for the blue acrylic yarns, fibers were not divided into segments for these experiments; one self-comparison would be possible for each fiber by separating the twelve measurements into two files of six spectra. Water was added under the coverslips prior to measurement and spectra were again collected in random sequence. The nonparametric discrimination matrix for the comparison of Disperse Blue 3 and Disperse Blue 14 is included in Appendix D, and a summary of the discrimination results is shown in Table 24. The different sources were discriminated for all comparisons; an overlay of their averaged spectra is shown in Figure 23. It was discovered during measurement collection that these acetate fabrics were constructed from both delustered and lustrous fiber types; the type depended on whether fibers were pulled from warp or weft threads. The lack of delusterants influenced the shape of the normalized absorption profile and thus affected the results of discrimination; this can be seen in Figure 24, where fiber B3 was a lustrous fiber. At first glance, the Type I error seems rather high, but when delustered and lustrous fibers are treated as different sources, shown in Table 25, the error improves considerably. A discrimination matrix modified to present these samples as different sources is also shown in Appendix E. Table 24. Nonparametric discrimination summary for Disperse Blue samples from random collection sequence, nm Total comparisons Discriminated Not discriminated Type I error Apparent Type I error Type II error Same fiber % --- Same source % --- Different source % 69

86 Figure 23. Normalized plot of averaged spectra for Disperse Blue samples Figure 24. Normalized plot of averaged spectra for region B fibers of Disperse Blue 14 70

87 Table 25. Nonparametric discrimination summary for Disperse Blue samples from random collection sequence, nm, delustered and lustrous as different sources Despite this improvement, same source discriminations were still higher than expected. With the lustrous fibers excluded, it was unlikely that any secondary fiber population existed in these sources. So why did the Type I error remain higher than 0.05? Fluctuations in the instrument had been reduced by random collection of spectra, and it was known that these fabric samples had been vat dyed under controlled conditions. It was considered that contributions from the fiber itself, contributions not significant enough to influence visual spectral comparisons, were possibly affecting the sensitive statistical comparison. Since undyed fabric samples were also furnished by the company performing the dyeings, spectra were collected from these fibers to determine the extent of spectral variation contributed by the fibers alone. Ten measurements each were collected along the length of one lustrous (L) and one delustered (DL) fiber. Figure 25 displays these spectra, without normalizing or zeroing. The degree of spectral variation presented by the fibers themselves provides a possible explanation for the persistence of Type I error and supports the idea that the nonparametric test may be too sensitive for this spectral data. Rather than assessing discrimination based on individual p-values, a comparison of the p-value distributions, with the Wilcoxon Rank Sum test, would not be so strongly influenced by these spectral variations. Total comparisons Discriminated Not discriminated Type I error Apparent Type I error Type II error Same fiber % --- Same source % --- Different source % 71

88 Figure 25. Plot of undyed acetate fibers, delustered and lustrous For the Wilcoxon rank sum test, one comparison was performed with Disperse Blue 3 as the known and another with Disperse Blue 14 as the known (see Table 26); the lustrous fibers were regarded as different sources and not included in this comparison. These comparisons found negligible overlap between the distributions, demonstrated by a box plot in Figure 26. Table 26. Wilcoxon rank sum results for Disperse Blue samples, different source comparisons Known Questioned p-value Known Questioned p-value DB03DL DB14DL < DB14DL DB03DL <

89 Figure 26. Box plot of p-value distributions for Disperse Blue 3 against Disperse Blue 14 (K v Q, top) and Disperse Blue 3 with itself (K v K, bottom), excluding lustrous fiber comparisons As with the blue acrylic yarns, a check on the test was conducted to determine the results of same source comparisons. To test each source against itself, fibers from regions A and B were treated as the known and fibers from regions D and E were treated as the questioned. Roles were then reversed. Results of this test are shown in Table 27. P-values greater than 0.05 were returned for both self-comparisons of Disperse Blue 3. For Disperse Blue 14, both p-values were less than 0.05, but still greater than those returned for different source distributions. Table 27. Wilcoxon rank sum results for Disperse Blue samples, same source comparisons Known Questioned p-value Known Questioned p-value DB03DL_AB DB03DL_DE DB03DL_DE DB03DL_AB DB14DL_AB DB14DL_DE DB14DL_DE DB14DL_AB The nonparametric discrimination matrix for the comparison of Basic Green 1 and Basic Green 4 is included in Appendix F, and a summary of the discrimination results in shown in Table 28. The different sources were discriminated for all comparisons; an overlay of their 73

90 averaged spectra is shown in Figure 27. Unlike the previous fabric samples, all fibers from these sources were found to be delustered. As with the Disperse Blue samples, Type I error was still higher than 5%. Table 28. Nonparametric discrimination summary for Basic Green samples from random collection sequence, nm Total comparisons Discriminated Not discriminated Type I error Apparent Type I error For the Wilcoxon rank sum test, one comparison was performed with Disperse Blue 3 as the known and another with Disperse Blue 14 as the known. These comparisons both yielded very low p-values, as seen in Table 29. Same source comparisons were performed as before and the p-values are displayed in Table 30; all but one of the comparisons gave p-values greater than 0.05, indicating that the different source discriminations are reliable for this dyed fabric pair. Type II error Same fiber % --- Same source % --- Different source % 74

91 Figure 27. Normalized plot of averaged spectra for Basic Green samples Table 29. Wilcoxon rank sum results for Basic Green samples, different source comparisons Known Questioned p-value Known Questioned p-value BG1 BG4 < BG4 BG1 < Table 30. Wilcoxon rank sum results for Basic Green samples, same source comparisons Known Questioned p-value Known Questioned p-value BG1_AB BG1_DE BG1_DE BG1_AB BG4_AB BG4_DE BG4_DE BG4_AB Known vs Questioned Comparisons Beyond their utility for large scale experimental comparisons, these statistical methods would also lend themselves to use in trace evidence cases where only one questioned fiber is recovered. To demonstrate the application of these methods for such a scenario, one source of the dye pair was treated as the known source and one fiber from the second source was treated as the questioned source. To increase the number of p-values for the different source distribution (comparisons between the known source and questioned fiber are limited) and provide a more 75

92 accurate distribution, each fiber s twelve spectra were combined into one file and six spectra were randomly chosen for three repetitions. For Disperse Blue samples, Disperse Blue 14 fiber A1 (delustered) was chosen as the questioned fiber and all eleven delustered Disperse Blue 3 fibers represented the known. This provided a known same source (KvK) distribution of 495 p-values with a different source (KvQ) distribution of 99 p-values; as mentioned previously, same source p-value distributions exclude comparisons between the same fiber. Figure 28 illustrates the two populations of p-values selected for comparison. The Wilcoxon rank sum test yielded a p-value of less than , indicating a very low probability that these samples originated from the same source (Table 31). The fact that same source comparisons between regions of Disperse Blue 3 yielded p-values exceeding 0.05 (see Table 27) strengthens this conclusion. Table 31. Wilcoxon rank sum results for Disperse Blue samples, KvQ comparison Known Questioned p-value DB03_DL DB14A1 < For Basic Green samples, Basic Green 1 fiber A1 was chosen as the questioned fiber and all fifteen Basic Green 4 fibers represented the known source. This provided a known same source (KvK) distribution of 945 p-values with a different source (KvQ) distribution of 135 p- values. The Wilcoxon rank sum test yielded a p-value of less than , indicating a very low probability that these samples originated from the same source (Table 32). Table 32. Wilcoxon rank sum results for Basic Green samples, KvQ comparison Known Questioned p-value BG4 BG1A1 <

93 Figure 28. Illustration of KvK (in red) and KvQ (in green) populations within p-value matrix for Disperse Blue samples; inset below shows the difference between p-value in each population 77

94 Multivariate Statistical Techniques Blue Acrylic Yarns Normalized MSP data for the blue acrylic yarns was subjected to principal components analysis (PCA). Due to the similarity of their spectral shapes, the majority of the variance was contained in the first principal component. Figure 29 displays the separation possible when only one principal component is retained, accounting for 99.5% of the variance. Figure 30 demonstrates how separation improves when two principal components are retained, accounting for a total of 99.8% of the variance. Although the first principal component contained such a large percentage of the variance, the decision was made to retain the first three principal components as this achieved the best spatial separation; Figure 31 displays the three-dimensional scores plot for averaged fibers from random collection. No significant differences were observed for sequential collection. This plot illustrates separation for different sources, with the exception of sources F and J, and sources G and I. Although these pairs of yarns produced by the same manufacturer have clustered together, it is interesting to note that the yarns of each pair appear polarized to one side of the cluster. This feature is present in the scores plot for spectra collected in both sequential and random sequence, suggesting that these differences are not a result of instrumental drift; it is likely they reflect true differences in the samples. This contrasts with the increase seen in Type II error for the nonparametric test when measurements were collected in random order (from 1.2% for sequential collection to 10.7%). 78

95 Figure 29. Box plot of PC1 scores for blue acrylic yarns MSP source averages from random collection sequence, nm Figure 30. Two-dimensional scores plot for blue acrylic yarns MSP fiber averages from random collection sequence, nm 79

96 Figure 31. Three-dimensional scores plot for blue acrylic yarns MSP fiber averages from random collection sequence, nm Following PCA, the scores from the first three principal components of each experiment (sequential and random) were used for discriminant analysis (DA); Table 33 displays the classification matrix for sequentially collected spectra and Table 34 for randomly collected spectra. Similar to the changes observed for results of the nonparametric test, incorrect classification of yarns produced by the same manufacturer increased for spectra collected in random sequence. 80

97 Table 33. DA classification matrix for blue acrylic yarns MSP, nm, α = 0.05 Test Class Assigned Class F G H I J %Correct F G H I J Total Table 34. DA classification matrix for blue acrylic yarns MSP from random collection sequence, nm, α = 0.05 Test Class Assigned Class F G H I J %Correct F G H I J Total Dyed Fabric Samples The MSP absorbance spectra for the Disperse Blue and Basic Green samples were analyzed using PCA. As with the blue acrylic yarns, a high percentage of variance was contained in the first principal component as a result of similarity among absorption profiles within the spectral sets. Separation for Disperse Blue samples with one and two retained principal components is shown in Figure 32 and Figure 33, respectively. The best separation is achieved with retention of the first three principal components; Figure 34 displays a three-dimensional plot of the component scores for the Disperse Blue samples. Separation of each source is evident and, spectra of lustrous fibers can also be seen to have clustered apart from the delustered fibers. 81

98 Figure 32. Box plot of PC1 scores for Disperse Blue MSP source averages from random collection sequence, nm Figure 33. Two-dimensional scores plot for Disperse Blue MSP fiber averages from random collection sequence, nm 82

99 Figure 34. Three-dimensional scores plot for Disperse Blue MSP fiber averages from random collection sequence, nm For the same reasons, two principal components were retained for the Basic Green samples, despite the large percentage of the variance contained in the first principal component (Figure 35). Figure 36 displays a two-dimensional plot of the component scores for the Basic Green samples; separation of each source is clear. Figure 35. Box plot of PC1 scores for Basic Green MSP source averages from random collection sequence, nm 83

100 Figure 36. Two-dimensional scores plot for Basic Green MSP fiber averages from random collection sequence, nm Mass Spectra As the red acrylic yarns and red cotton samples were only analyzed to optimize MSP measurement collection and test the statistical method, they were not subjected to MS analysis. Blue Acrylic Yarns Following initial MSP analysis of the blue acrylic yarns, five 12 cm fibers from each source were selected for extraction and analysis via MS; these were not the same samples analyzed for MSP. Bulk extracts of each yarn were also analyzed and examples of these MS are given in Figure 37. Most different sources could be differentiated based on ions present; this is summarized in Table 35. Although sharing many of the ions present in sources G and I, source H was distinguished by the presence of an ion at m/z 304. As was the case for MSP spectra, it was difficult to discriminate pairs of sources F and J, and sources G and I. The fiber extracts, which were less concentrated than the bulk samples, exhibited more variability. Although presence of 84

101 ions was generally consistent for fiber extracts from the same source, ion intensities were not always reproducible. This was problematic, since the ratios of key ions were the only feature that differed between bulk samples of yarns produced by the same manufacturer. 85

102 Figure 37. Mass spectra for blue acrylic yarn bulk extractions, 100V: source F (top left), source G (top right), source H (bottom left), source I (bottom middle) and source J (bottom right) 86

103 Table 35. Ion fragments consistently present in mass spectra of blue acrylic yarns, 100V Ions present (m/z) Yarn F X X X X G X X X X H X X X X I X X X X J X X X X Though these MS gave no information about dye class or molecular structure, they did provide some insight: the spectra of yarn sources produced by the same manufacturer, sources F and J, and sources G and I, contain the same ions (see Figure 38). This suggests that these source were dyed with the same dyes, possibly in slightly varied ratios to impart different shades. 20 After bulk and fiber extracts were each analyzed by MS at 60 and 100V, PCA was performed on each data set. Three-dimensional scatter plots of the sample scores show the clustering of different sources (Figure 39). Separation was achieved for all different sources, with the exception of sources F and J, and sources G and I. Because the higher voltage produced more fragment ions, 100V provided slightly better separation for the fiber samples and was chosen as the voltage for future MS analysis of the blue acrylic yarns. 87

104 Figure 38. Mass spectral comparisons for pairs of yarns produced by the same manufacturer: sources F and J (top), and sources G and I (bottom) 88

105 Figure 39. Three dimensional scores plot for blue acrylic yarn MS: fibers at 60V (top left), fibers at 100V (top right), bulk at 60V (bottom left) and bulk at 100V (bottom right) 89

106 Based on the second MSP analysis of the blue acrylic yarns, which suggested the presence of secondary fiber populations within some sources, it was desirable to collect MS data from the same fibers analyzed by MSP; this would make it possible to determine whether fibers with discriminated MSP spectra would also exhibit differences in their MS. As mentioned previously, fourteen fibers were chosen for this experiment to provide sufficient MS data to perform a nonparametric test and allow for one same source comparison per source. Following MSP, these fibers were extracted and analyzed by MS at 100V. For each source, the twelve spectra (of fourteen) with the greatest S:N were chosen and grouped into two files. The results of this nonparametric comparison are shown in Table 36. The Type I error for this test was less than 5%, and most different sources were discriminated; MS did not provide sufficient information to completely discriminate different sources produced by the same manufacturer, sources F and J, and sources G and I. 90

107 Table 36. Nonparametric discrimination matrix for blue acrylic samples MS, m/z, α = 0.05 F.1 F.2 G.1 G.2 H.1 H.2 I.1 I.2 J.1 J.2 F F G G H H I I J J This data set was also subjected to PCA, shown in Figure 40, and the scores from the first three principal components were evaluated using DA, summarized in Table 37. These statistical methods did not provide superior discrimination compared to the results from MSP data. Figure 40. Three-dimensional scores plot for blue acrylic yarns MS, m/z

108 Table 37. DA classification matrix for blue acrylic yarns MS, m/z , α = 0.05 Test Class Assigned Class F G H I J %Correct F G H I J Total Dyed Fabric Samples Because the colorants used to dye these samples were known to have different molecular weights, and thus different molecular ions, statistical evaluation of these spectra was not necessary for discrimination. As shown in Figure 41 and Figure 42, visual comparison of these spectra is sufficient to discriminate. Figure 41. Mass spectra for extracts of Disperse Blue 3 (left) and Disperse Blue 14 (right) 92

109 Figure 42. Mass spectra for extracts of Basic Green 1 (left) and Basic Green 4 (right) In the case of the Disperse Blue samples, it was found that although some ions from Disperse Blue 3 are also present in the spectra of Disperse Blue 14, namely m/z 252 and 261, ions at m/z 319 and 297 were only found in the spectra of Disperse Blue 3. These spectra contain similar ions, which is expected considering that these dyes share so many structural features. For the Basic Green samples, the basic dyes were easily ionized to form their respective molecular ions, m/z 385 for Basic Green 1 and m/z 329 for Basic Green 4. 93

110 CHAPTER 4: CONCLUSIONS One goal of this research was to investigate the ability to discriminate textile fibers treated with structurally-similar dyes based on their absorption and mass spectra, while also offering a statistical context for these findings. Although visual comparisons of these absorption spectra may result in successful discrimination, such conclusions are subjective and fail to provide an indication of significance. Analysis of the dyed fabric pairs demonstrates that successful discrimination is possible for such samples through visual evaluation of extracted dye mass spectra. Statistical comparison of absorption spectra resulted in complete discrimination of these different sources, but Type I error remained high. Because high rates of Type I error devalue the reliability of different source discriminations, efforts were made to identify possible sources of error. Implementing a random collection sequence was shown to reduce Type I error for intraand inter-fiber comparisons of the blue acrylic yarns by more than half. Type I error for segments of the same fiber was determined to be much closer to the expected α level when measurements were collected in random sequence. The difference between these errors suggests additional spectral variation within these sources that remains uncontrolled. For the dyed fabric samples, the difference between these errors was slightly reduced. It is possible that this change could be a result of the controlled dyeing conditions for the custom fabric dyeing. Knowing that most textile fibers are dyed commercially, it is important that any statistical method applied to fiber examination are not easily influenced by within-source variations resulting from dyeing conditions. 94

111 Through the course of these experiments, it became clear that the need for quality spectral data becomes increasingly important as comparisons of greater scrutiny are made. Effects of instrumental drift would likely not introduce noticeable differences in spectra subjected to visual comparison, but these fluctuations were shown to significantly influence error rates of statistical comparisons. For a statistical test to effectively discriminate spectra of different sources, especially those not distinguished by visual comparison, sensitivity to subtle differences in the spectra is crucial. However, a sensitive test will be more influenced by variation within a source and could therefore increase the Type I error. Homogeneity of a source cannot be assumed and the extent of variation will differ among textiles; this also emphasizes the importance of collecting multiple spectra, in order to accurately characterize variation within a source. 19 With same source p-values falling below α at a rate greater than 5%, the distribution of these p-values was compared to those from different source comparisons and found to be quite different. Though it was considered that changing the α level would improve Type I error, doing so would also negatively impact the Type II error. So the Wilcoxon rank sum test was examined for comparison of p-value distributions from the nonparametric permutation test, a method that would also lend itself to comparisons of a known source with a questioned fiber. Although comparisons of p-value distributions for different sources yielded very low p-values, comparisons of p-value distributions for same sources did not yield consistently high p-values. While it is significant that p-values for same source distribution comparisons exceeded those of different sources, more research is needed to understand these results and determine the best interpretation. 95

112 Overall, ESI-MS was shown to produce spectra with unacceptable reproducibility in peak intensities, making them unsuitable for statistical comparisons. Although they offer the advantage of dye structural information, mass spectra were not as reproducible as absorption spectra. Compared to the cost and time required for sample preparation, analysis of extracted dyes did not provide superior discriminatory power for the blue acrylic yarn sources produced by the same manufacturer. As these sources were suspected to be dyed with different formulations of the same dye components, poor instrument reproducibility reduced the ability to detect subtle differences in ion ratios that were observed for bulk extractions. In the case of structurallysimilar dyes, presence of distinct ions enabled discrimination of different source spectra without the need for statistical comparison. Forensic Significance Recent scrutiny of forensic science has raised the bar, demanding more reliable methods and better ways to communicate the significance of scientific findings in court. This work does not seek to replace existing methodologies; rather it aims to better equip the forensic scientist for discussion of their conclusions in the courtroom. For fiber examination, it is recognized that the discriminatory power of microscopic comparison followed by complementary microspectrophotometry and thin-layer chromatography can often suffice for exclusion. Target fiber studies have been performed in the past, where visibly similar fibers were compared and successfully discriminated using this analytical scheme. 11, 34, 47 These studies are helpful, but they often dismiss the possibility of coincidentally encountering indistinguishable fibers as extremely unlikely; research by Huang, et al., which found samples of red cotton to exhibit indistinguishable MSP spectra, seems to contradict this 96

113 speculation. 30 The National Academy of Sciences report calls forensic scientists to hold their work to a higher standard, and in doing so, they must entertain these possibilities to ensure that the methods used to analyze fiber evidence can withstand the rigors of a worst case scenario. More importantly, they must be able to effectively translate this assessment into terms of certainty. Two ideas are proposed in this work: that a standard, statistical method needs to be established for the comparison of spectral data, and that analysis of extracted dyes via MS may hold greater discriminatory potential than MSP alone. While it is true that analysts are trained to interpret differences between spectral profiles, use of a statistical approach would make this a much less subjective determination; findings would also be presented with a statistical confidence. Profiles of an obviously different shape are easy enough to distinguish, but as profiles shapes become increasingly similar and spectral variation within a source increases, such conclusions can no longer be reached with certainty. There are many advantages to applying a statistical method to the existing MSP technique: it would remain nondestructive, would require minimal sample and could be used for all dyed fibers. If sources of error can be identified to control Type I error, the nonparametric permutation test holds great promise for comparison of these visible absorption spectra. This work demonstrates the use of mass spectrometry for straightforward discrimination of textile fiber dyes with similar molecular structures and absorption profiles. For cases like the dyed fabric samples, where microscopic and spectral visual comparisons fail to discriminate samples, presence of MS ions can be sufficient to discriminate. But while MS offers great discriminatory power, ESI-MS is a technique plagued with ion intensity reproducibility 97

114 problems. This complicates discrimination of sources treated with similar dye formulations where ion ratios may be the only differing factor. It is also difficult to predict the analytical conditions under which unknown dye extracts will exhibit the best signal, such as ion mode and fragmentor voltage. Resolution of these issues is needed for realistic applications of MS analysis to fiber examination. Future Work The persistence of Type I error is the biggest obstacle for the nonparametric permutation test. Although the test effectively discriminates different sources, high rates of Type I error diminish the significance of these discriminations. Before moving forward with this method, a greater study of the variation within sources is needed. It would be beneficial to monitor these error rates with regard to sampling conditions, such as number of spectral scans averaged and chosen mounting medium for fiber samples. If this variation cannot be controlled or corrected for statistical comparison, it may be necessary to modify the sensitivity of the test. Once these methods are optimized, it would also be useful to administer large-scale blind tests to determine the relative success of the statistical approach when compared with traditional visual comparisons. As demonstrated by analysis of the blue acrylic yarns, reproducibility of ESI mass spectra significantly affected the ability to differentiate spectra from sources containing similar ions. Due to the thermal stability of dye compounds, ESI-MS has been the chosen method for their analysis. But as observed in this work, reproducibility for fiber samples is poor and dye extraction may not be feasible for small sample sizes. One possible alternative would be to utilize nanospray techniques to improve ionization efficiency. 98

115 It would also be helpful to know the limitations this reproducibility imposes for dye analysis; to what extent can this technique discriminate different formulations of the same dyes? Because manufacturers often use mixtures of several dyes to achieve the desired shade, it would be useful to study the impact on discriminations of absorption and mass spectra when dye ratios are varied. As past researchers have examined variations in absorption spectra for samples of different dye batches, it would also be helpful to study the ability of MS to differentiate these samples. 37 While many manufacturers monitor their dye formulations to minimize variations from batch to bath, their standards for quality may not be enough to prevent discrimination under the scrutiny of MS. 99

116 APPENDIX A. PARAMETRIC DISCRIMINATION MATRIX FOR BLUE ACRYLIC YARNS (IN WATER), nm, α =

117 101

118 APPENDIX B. NONPARAMETRIC DISCRIMINATION MATRIX FOR BLUE ACRYLIC YARNS (IN WATER), nm, α =

119 103

120 APPENDIX C. NONPARAMETRIC DISCRIMINATION MATRIX FOR BLUE ACRYLIC YARNS (IN WATER), nm, α =

121 105

122 APPENDIX D. NONPARAMETRIC DISCRIMINATION MATRIX FOR DISPERSE BLUE SAMPLES (IN WATER), nm, α =

123 107