A major gap in sexual assault casework is demonstrated when DNA is not recovered. Oftentimes, if DNA evidence is not present on the collection swabs, the sexual assault kit (SAK) is not further analyzed. Due to the "CSI effect," DNA is commonly understood as highly identifiable evidence, potentially leading to increased condom usage to eliminate or reduce DNA transfer during a sexual assault. Therefore, the analysis of condoms and sexual lubricants is pertinent. The purpose of this research is to develop analytical protocols to potentially connect unknown substances recovered in a SAK to known lubricant reference samples. Sexual lubricants were analyzed using Fourier transform infrared spectroscopy, gas chromatography-mass spectrometry, and direct analysis in real time-high resolution mass spectrometry. Analytical protocols were developed using 162 sexual lubricants comprised of bottled lubricants, condoms, and personal hygiene products. A statistical model was developed from 112 of the samples using hierarchical cluster analysis (HCA), principal component analysis (PCA), and linear discriminant analysis (LDA) to determine appropriate sample groupings that resulted in at least 97% accurate classification for each instrument. Assigned truth classes for the remaining 50 samples were developed using Pearson correlation coefficients (PCCs) to predict classification accuracy for unknown samples. The FTIR data resulted in a 96% accurate prediction, 54% for GC-MS, and 42% for DART-HRMS, showing the need for expansion of the sample set in future analysis. Potential storage conditions of SAK swabs were evaluated using PCCs to identify optimal swab storage conditions, which was determined to be a humidity-controlled environment around 22 °C. Then, post-coital swab samples from volunteers using an unknown condom were analyzed using the developed protocols. The data was analyzed using PCC, PCA, and LDA to compare the classification to the "ground truth" of the sample to determine potential applications of this research in SAK analysis.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2318 |
| Date | 01 January 2021 |
| Creators | Baumgarten, Brooke |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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