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

Wan N.S. Mat Desa, N. NicDaeid, Ismail Dzulkiflee, Kathleen Savage

Research output: Contribution to journalArticle

30 Citations (Scopus)
111 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)6395-6400
Number of pages6
JournalAnalytical Chemistry
Volume82
Issue number15
DOIs
Publication statusPublished - 1 Aug 2010

Keywords

  • lighter fuel samples
  • gas chromatography
  • mass spectrometry

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