| dc.description.abstract | Rapid and reliable techniques are necessary for the analysis of accelerants, including gasoline, from fire debris evidence in forensic arson investigations. Gasoline additives can be used as chemical attribute signatures (CAS) to distinguish between source locations due to the variation in additives used. Source attribution using CAS is needed in forensic chemistry, as the determination of a single gasoline source could be a potential investigation tool for law enforcement and other agencies conducting arson investigation. Direct analysis in real time-mass spectrometry (DART-MS) has had increasing popularity in the field of forensic chemistry for chemical analysis, and it has been applied to fire debris analysis. DART-MS has great capacity for gasoline source attribution due to its ionization technique and inclusion of higher molecular weight ions, which correspond to the CAS in gasoline.
To test the hypothesis of gasoline source attribution, 21 gasoline samples were collected across Massachusetts, New Hampshire, and Connecticut. DART-MS data were generated for each sample of gasoline in replicates of 10. The data were grouped based on geographical location and evaluated by Principal Component Analysis (PCA). PCA was used to evaluate the similarities and differences in gasoline DART-MS data by generating and classifying the gasoline sample groups formed. Leave-one-out cross-validation (LOOCV) was performed on each geographical group after PCA. LOOCV was used as
the validation technique to determine the validity of the model and asses its capability at classifying unknown gasoline samples.
DART-MS data across geographical groups was found to have varying levels of similarity and difference through visual inspection of the mass spectra. PCA showed distinct groupings of individual gasoline samples across all tested geographical groups, with 3 out of 6 geographical groups showing no overlap between gasoline sample classifications. Two groups showed minimal overlapping, while 1 group had overlapping between multiple gasoline sample classifications. Three groups had a LOOCV of 100% with no misclassifications. The other LOOCV were 98%, 96.67%, and 85%. The PCA and comparison of DART-MS data provides evidence of successful differentiation between gasoline samples of the same brand across Massachusetts, New Hampshire, and Connecticut. This research aims to provide an overview and understanding of chemometrics and DART-MS and how these techniques may be applied for forensic source attribution purposes. | en_US |
