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 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.