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 language | English (US) |
---|---|
Pages (from-to) | 163-170 |
Number of pages | 8 |
Journal | Journal of Chromatography A |
Volume | 1166 |
Issue number | 1-2 |
DOIs | |
State | Published - Sep 28 2007 |
Keywords
- ANOVA-LSD variable selection
- ASTM
- Classification
- Diesel oil
- GC-MS
- Multi-way analysis
- Nordtest methodology
- Oil spill identification
- PARAFAC2
- Petroleum oil
- Photo-oxidation