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Application of unsupervised chemometric analysis and self-organising feature map (SOFM) for the classification of lighter fuels

Mat Desa, Wan N.S. and NicDaeid, N. and Dzulkiflee, Ismail and Savage, Kathleen (2010) Application of unsupervised chemometric analysis and self-organising feature map (SOFM) for the classification of lighter fuels. Analytical Chemistry, 82 (15). pp. 6395-6400.

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Abstract

A variety of lighter fuel samples from different manufacturers (both unevaporated and evaporated) were analysed using conventional gas chromatography-mass spectrometry (GC-MS) analysis. In total 51 characteristic peaks were selected as variables and subjected to data pre-processing prior to subsequent analysis using unsupervised chemometric analysis (PCA and HCA) and a SOFM artificial neural network. The results obtained revealed that SOFM acted as a powerful means of evaluating and linking degraded ignitable liquid sample data to their parent unevaporated liquids.