Gas chromatography-mass spectrometry (GC-MS) is the method of choice for analyzing fire debris. Analysts perform a visual comparison between chromatograms of fire debris and ignitable liquid standards. The analysis is both complex and subjective due to evaporation of the liquid and interference compounds from the matrix as well as thermal degradation of both the matrix and liquid. This research investigates the use of unsupervised and supervised multivariate statistical procedures for simplifying the analysis and creating a more objective approach. Principal components analysis, an unsupervised technique, was used in conjunction with Pearson product moment correlation coefficients to successfully associate simulated fire debris to corresponding ignitable liquid standards. To do this, liquid standards of gasoline and kerosene were evaporated to different evaporation levels. The liquids were spiked onto unburned and burned wood that had been previously treated with Danish Oil. Additionally, simulated debris samples were generated by spiking the liquids onto the matrix prior to burning. The samples were extracted and analyzed by GC-MS. Soft independent modeling of class analogy, a supervised classification technique was applied to replicate chromatograms from a set of six ignitable liquid standards, different from those used above. The standards' chromatograms were split into training and test sets. The training set was used to generate models of each liquid to which the test set was classified. Classification of the liquids was successfully performed using the total ion chromatograms and extracted ion chromatograms.
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