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Abstract

The application of multi-way parallel factor analysis (PARAFAC2) is described for the classification of different kinds of petroleum oils using GC–MS. Oils were subjected to controlled weathering for 2, 7 and 15 days and PARAFAC2 was applied to the three-way GC–MS data set (MS × GC × sample). The classification patterns visualized in scores plots and it was shown that fitting multi-way PARAFAC2 model to the natural three-way structure of GC–MS data can lead to the successful classification of weathered oils. The shift of chromatographic peaks was tackled using the specific structure of the PARAFAC2 model. A new preprocessing of spectra followed by a novel use of analysis of variance (ANOVA)-least significant difference (LSD) variable selection method were proposed as a supervised pattern recognition tool to improve classification among the highly similar diesel oils. This lead to the identification of diagnostic compounds in the studied diesel oil samples.

Item Type:	Article
Authors/Creators:	Ebrahimi, D and Li, J and Hibbert, DB
Keywords:	PARAFAC2; ANOVA-LSD variable selection; GC–MS; Petroleum oil; Classification; Photo-oxidation; Multi-way analysis; Oil spill identification; Diesel oil; Nordtest methodology; ASTM, Crude petroleum Data structures Diesel fuels Gas chromatography Mass spectrometry Mathematical models
Journal or Publication Title:	Journal of Chromatography A
ISSN:	0021-9673
DOI / ID Number:	10.1016/j.chroma.2007.07.085
Additional Information:	The definitive version is available at http://www.sciencedirect.com
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