Individualization of gasoline samples by covariance mapping and gas chromatography/mass spectrometry

A set of 10 fresh (unevaporated) gasoline samples from a single metropolitan area were differentiated based on a covariance mapping method combined with a t-test statistic. The covariance matrix for each sample was calculated from the retention time-ion abundance data set obtained by gas chromatography/mass spectrometry analysis. Distance metrics were calculated between the covariance matrices from replicate analyses of the same sample and between the replicate analyses of different samples. The distance metric for the same-sample comparisons were shown to constitute a population significantly different from the distance metric for the different-sample comparisons. A power analysis was performed to estimate the number of analyses needed to discriminate between two samples while maintaining a probability of type II error, beta, below 1%, e.g., a test power greater than 99%. Triplicate analyses of two gasoline samples was shown to be sufficient to discriminate between the two using a t-test, while keeping beta \u3c 0.01 at a significance level, alpha, of 0.05. Analysis of the 45 possible pairwise comparisons between samples found that 100% of the samples were statistically distinguishable, and no type II errors occurred. Blind tests were conducted wherein 2 of the 10 gasoline samples where presented as unknowns. One of the unknowns was found to be indistinguishable from the original source, and one unknown was determined to be statistically different from the original source, constituting a type I error. The effects of evaporation on sample comparison are not addressed in this paper. The results from this study demonstrate a statistically acceptable method of physical evidence comparison in forensic casework