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

Wan N. S. Mat Desa, Niamh Nic Daéid (Lead / Corresponding author), Dzulkiflee Ismail, Kathleen Savage

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)

Abstract

A variety of lighter fuel samples from different manufacturers (both unevaporated and evaporated) were analyzed using conventional gas chromatography-mass spectrometry (GC-MS) analysis. In total 51 characteristic peaks were selected as variables and subjected to data preprocessing 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

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