@article{ref1,
title="Classification of premium and regular gasoline by gas chromatography/mass spectrometry, principal component analysis and artificial neural networks",
journal="Forensic science international",
year="2003",
author="Doble, Philip and Sandercock, Mark and Du Pasquier, Eric and Petocz, Peter and Roux, Claude and Dawson, Michael",
volume="132",
number="1",
pages="26-39",
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.<p /><p>Language: en</p>",
language="en",
issn="0379-0738",
doi="",
url="http://dx.doi.org/"
}