Abstract

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.

Notes

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Graduation Date

2022

Semester

Spring

Advisor

Sigman, Michael

Degree

Doctor of Philosophy (Ph.D.)

College

College of Sciences

Department

Chemistry

Degree Program

Chemistry

Format

application/pdf

Identifier

CFE0008962; DP0026295

URL

https://purls.library.ucf.edu/go/DP0026295

Language

English

Release Date

May 2023

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Campus-only Access)

Restricted to the UCF community until May 2023; it will then be open access.

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