Source attribution of trace gasoline additives by direct analysis in real time - mass spectrometry
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Citation
Abstract
Fire-setting is responsible for claiming the lives of so many each year as well as creating significant property damage. Arson is one of the easiest crimes in the world to commit, yet not every fire-setting event is deemed an arson event. A fire scene investigation must ensue before the charges of arson can be given, which has a fluctuating legal connotation depending on what was damaged. Therefore, extensive research is needed to aid in the process of a fire scene investigation. One main point of a fire scene investigation is to collect fire debris evidence with possible ignitable liquids present. Ignitable liquids are commonly used to initiate, propagate, or intensify the set fire by the arsonist and one of the most common ignitable liquids utilized is gasoline. Therefore, a rapid and reliable gasoline analysis method is needed for a forensic setting to aid with fire debris analysis. This research focuses on the instrumental technique of direct analysis in real time – mass spectrometry (DART-MS) to assess the feasibility to generate unique mass spectral data whereas prior gas chromatography – mass spectrometry (GC-MS) based efforts have been unable to.
Traditional GC-MS methods generate chromatograms and mass spectral data which the analyst is able to utilize to classify ignitable liquids. However, they are time consuming in terms of sample preparation of the fire debris evidence and the analysis of the potential ignitable liquid residues. GC-MS is unable to generate unique mass spectral data for gasoline samples; the methodology is capable of identifying gasoline but cannot identify brand or location which would be useful information in a fire scene investigation. DART-MS methodology requires little to no sample preparation and can generate mass spectral data almost instantaneously compared to GC-MS. Therefore, the remaining question is to assess if DART-MS is capable of producing unique mass spectral data where the data would vary by both brand and location.
Ten samples of gasoline were chosen to generate comparative GC-MS averaged mass spectral data for different brands from different locations. Evaporation series were created for two different gasoline samples and analyzed by GC-MS and DART-MS in order to compare across brands and techniques. Finally, thirty-one gasoline samples were analyzed by DART-MS. Of the thirty-one samples, nine different brands, multiple locations both within and outside of the Commonwealth of Massachusetts, and with differences in age of the gasoline samples were analyzed.
The mass spectral data was compared between different brands, locations within the same brand, and with evaporation series for both DART-MS generated data and GC-MS generated data. DART-MS generated unique mass spectral data with chemical attribute signatures based upon brand and location. The averaged mass spectral data generated by GC-MS analysis of selected samples was not able to differentiate gasoline samples by brand or by location. This allows for the possibility of source attribution to occur with DART-MS analysis of gasoline samples which is powerful from an evidentiary standpoint for forensic scientists.