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

Publisher:
Elsevier Sci Ireland Ltd
Publication Type:
Journal Article
Citation:
Forensic Science International, 2003, 132 (1), pp. 26 - 39
Issue Date:
2003-01
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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 chromatographymass spectrometry (GCMS) spectral data obtained from gasoline sold in Canada over one calendar year. Depending upon the dataset used for training and tests, around 8093% of the samples were correctly classified as either premium or regular gasoline using the Mahalanobis distances calculated from the principal components scores. Only 4862% 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 GCMS 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.
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