Ignitable Liquid Classification and Identification Using the Summed-Ion Mass Spectrum

In some cases there are hidden correlations in a highly fluctuating signal, but these are lost in a conventional averaging procedure. Covariance mapping allows these correlations to be revealed unambiguously. As an example of the applicability of this technique, the dynamics of fragmentation of molecules ionized by an intense picosecond laser are analyzed.

The comparison of two or more samples of liquid gasoline (petrol) to establish a common origin is a difficult problem in the forensic investigation of arsons and suspicious fires. A total of 35 randomly collected samples of unevaporated gasoline, covering three different grades (regular unleaded, premium unleaded and lead replacement), were examined. The high-boiling fraction of the gasoline was targeted with a view to apply the techniques described herein to evaporated gasoline samples in the future.A novel micro solid phase extraction (SPE) technique using activated alumina was developed to isolate the polar compounds and the polycyclic aromatic hydrocarbons (PAHs) from a 200microl sample of gasoline. Samples were analysed using full-scan gas chromatography-mass spectrometry (GC-MS) and potential target compounds identified. Samples were then re-analysed directly, without prior treatment, using GC-MS in selected ion monitoring (SIM) mode for target compounds that exhibited variation between gasoline samples. Principal component analysis (PCA) was applied to the chromatographic data. The first two principal components (PCs) accounted for 91.5% of the variation in the data. Linear discriminant analysis (LDA) performed on the PCA results showed that the 35 samples tested could be classified into 32 different groups.

Detection and correct classification of gasoline is important for both arson and fuel spill investigation. Principal component analysis (PCA) was used to classify premium and regular gasolines from gas chromatography-mass spectrometry (GC-MS) spectral data obtained from gasoline sold in Canada over one calendar year. Depending upon the dataset used for training and tests, around 80-93% of the samples were correctly classified as either premium or regular gasoline using the Mahalanobis distances calculated from the principal components scores. Only 48-62% of the samples were correctly classified when the premium and regular gasoline samples were divided further into their winter/summer sub-groups. Artificial neural networks (ANNs) were trained to recognise premium and regular gasolines from the same GC-MS data. The best-performing ANN correctly identified all samples as either a premium or regular grade. Approximately 97% of the premium and regular samples were correctly classified according to their winter or summer sub-group.