Latent print evidence collected from crime scenes are analyzed and compared by examiners using the ACE-V protocol (Analysis, Comparison, Evaluation-Verification) to identify potential suspects. However, smudged and poor-quality prints, usually deemed inconclusive, could be utilized for source association or discrimination based on the chemistry of the oils and sweat that comprises the latent print residue (LPR). For this research, LPRs were collected from various places on the face and analyzed via two-dimensional gas chromatography-mass spectrometry (2D GC-MS), one-dimensional (1D) GC-MS, and direct analysis in real time-high resolution mass spectrometry (DART-HRMS). LPR recovery with two common sample preparation methods, derivatization and non-derivatization, was examined when collected on a porous and non-porous substrate. The method that provided high correlation and low relative standard deviation for each substrate was used for subsequent studies. Then, the LPRs were investigated to observe if association to a source can be achieved in either 1D or 2D GC. Comparison methods of Pearson correlation coefficient (PCC), principal component analysis (PCA), hierarchical cluster analysis (HCA), and receiving operating characteristic (ROC) curves were used to test source association. An aging study was performed to analyze the change in latent print chemistry when simulating evidence storage for up to three months via 1D GC and DART-HRMS. During this time, a longevity study was conducted by collecting LPRs every two weeks to test the intra- and inter-variability of the recovered chemistry. Correlation and similarity metrics such as PCC, Spearman's rank correlation, and Euclidean distance were used to compare the monthly and weekly changes of the LPRs.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2590 |
| Date | 01 January 2023 |
| Creators | Kindell, Jessica |
| 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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