Classification of weathered petroleum oils by multi-way analysis of gas chromatography-mass spectrometry data using PARAFAC2 parallel factor analysis

Diako Ebrahimi, Jianfeng Li, David Brynn Hibbert

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29 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)163-170
Number of pages8
JournalJournal of Chromatography A
Volume1166
Issue number1-2
DOIs
StatePublished - Sep 28 2007

Bibliographical note

Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.

Keywords

  • ANOVA-LSD variable selection
  • ASTM
  • Classification
  • Diesel oil
  • GC-MS
  • Multi-way analysis
  • Nordtest methodology
  • Oil spill identification
  • PARAFAC2
  • Petroleum oil
  • Photo-oxidation

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