Forensic analysis of evidence consists of the comparison of physical, spectroscopic, or chemical characteristics of a questioned sample to a set of knowns. Currently, decisions as to whether or not the questioned sample can be associated or grouped with the knowns are left up to the discretion of the forensic analyst. The implications of these outcomes are presented as evidence to a jury in a court of law to determine if a defendant is guilty of committing a crime or not. Leading up to, and since, the publication of the National Academy of Sciences (NAS) report entitled “Strengthening Forensic Science in the United States: A Path Forward,” the inadequacies of allowing potentially biased forensic opinion to carry such weight in the courtroom have been unmasked. This report exposed numerous shortcomings in many areas of forensic science, but also made recommendations on how to fortify the discipline. The main suggestions directed towards disciplines that analyze trace evidence include developing error rates for commonly employed practices and evaluating method reliability and validity. This research focuses on developing a statistical method of analysis for comparing visible absorption profiles collected from highly similarly colored textile fibers via microspectrophotometry (MSP). Several chemometric techniques were applied to spectral data and utilized to help discriminate fibers beyond the point where traditional methods of microscopical examination may fail. Because a dye's chemical structure dictates the shape of the absorption profile, two fibers dyed with chemically similar dyes can be very difficult to distinguish from one another using traditional fiber examination techniques. The application of chemometrics to multivariate spectral data may help elicit latent characteristics that may aid in fiber discrimination. The three sample sets analyzed include dyed fabric swatches (three pairs of fabrics were dyed with chemically similar dye pairs), commercially available blue yarns (100% acrylic), and denims fabrics (100% cotton). Custom dyed swatches were each dyed uniformly with a single dye whereas the dye formulation for both the yarns and denims is unknown. As a point for study, spectral comparisons were performed according to the guidelines published by the Standard Working Group for Materials Analysis (SWGMAT) Fiber Subgroup based on visual analysis only. In the next set of tests, principal components analysis (PCA) was utilized to reduce the dimensionality of the large multivariate data sets and to visualize the natural groupings of samples. Comparisons were performed using the resulting PCA scores where group membership of the questioned object was evaluated against the known objects using the score value as the distance metric. Score value is calculated using the score and orthogonal distances, the respective cutoff values based on a quantile percentage, and an optimization parameter, ?. Lastly, likelihood ratios (LR) were generated from density functions modelled from similarity values assessing comparisons between sample population data. R code was written in-house to execute all method of fiber comparisons described here. The SWGMAT method performed with 62.7% accuracy, the optimal accuracy rate for the score value method was 75.9%, and the accuracy rates for swatch-yarn and denim comparisons, respectively, are 97.7% and 67.1% when the LR method was applied.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-2342 |
| Date | 01 January 2015 |
| Creators | Flores, Alejandra |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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