Development of a Computational Application to Aid with Chemometric and Forensic Analysis of Fire Debris Samples

Download or read book Development of a Computational Application to Aid with Chemometric and Forensic Analysis of Fire Debris Samples written by Michelle Anne Corbally and published by . This book was released on 2022 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fire debris analysis is a forensic science discipline that determines if an ignitable liquid residue is present or absent in a fire debris sample. Currently, fire debris analysis results in categorical statements based on qualitative data, not the quantitative evidentiary value of data. The purpose of this research was to develop a novel software application to aid fire debris analysts in the identification and classification of ignitable liquid residues that are found in fire debris samples. The developed application uses target factor analysis (TFA) and Pearson correlation for compound identification in gas chromatograms using mass spectral comparison and allows for visual comparison of unknown fire debris samples chromatograms to ignitable liquid references from the National Center for Forensic Science (NCFS) Ignitable Liquid Reference Collection (ILRC). Frequencies of occurrences were calculated for each of 295 compounds from the NCFS compound library through compound identification of ignitable liquid, substrate, and fire debris samples using the novel computer application. The log-likelihood ratios of compounds determined to be within an optimal subset of best chromosomes determined using a genetic algorithm were used for calculating Naïve Bayes log-likelihood ratios for fire debris samples. Finally, self-organizing feature maps (SOFM), trained with in-silico total ion spectra data, were used to classify ground truth fire debris samples into American Society for Testing and Materials (ASTM) E1618-19 classes. Pearson correlation was then used to compare the total ion chromatograms of the classified fire debris samples were then compared to the in-silico total ion chromatograms located within the assigned SOFM node. The performance and validation of these models are discussed further in this dissertation.