Classification of premium and regular gasoline by gas chromatography/mass spectrometry, principal component analysis and artificial neural networks

Publication Type:
Journal Article
Citation:
Forensic Science International, 2003, 132 (1), pp. 26 - 39
Issue Date:
2003-03-12
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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. © 2003 Elsevier Science Ireland Ltd. All rights reserved.
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