Citation

Doble P, Sandercock M, Du Pasquier E, Petocz P, Roux C, Dawson M. Forensic Sci. Int. 2003; 132(1): 26-39.

Affiliation

Department of Chemistry, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia.

Copyright

(Copyright © 2003, Elsevier Publishing)

DOI

unavailable

PMID

12689748

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.

Language: en