title: Classification of premium and regular gasoline by gas chromatography/mass spectrometry, principal component analysis and artificial neural networks.
abstract: 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.
authors.affiliation: Department of Chemistry, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia.
authors.forename: Philip
authors.initials: P
authors.lastname: Doble
authors.forename: Mark
authors.initials: M
authors.lastname: Sandercock
authors.forename: Eric
authors.initials: E
authors.lastname: Du Pasquier
authors.forename: Peter
authors.initials: P
authors.lastname: Petocz
authors.forename: Claude
authors.initials: C
authors.lastname: Roux
authors.forename: Michael
authors.initials: M
authors.lastname: Dawson