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; Dzulkiflee Ismail; Kathleen Savage

doi:10.1021/ac100381a

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

Leverhulme Research Centre for Forensic Science

Research output: Contribution to journal › Article › peer-review

35 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 language	English
Pages (from-to)	6395-6400
Number of pages	6
Journal	Analytical Chemistry
Volume	82
Issue number	15
DOIs	https://doi.org/10.1021/ac100381a
Publication status	Published - 1 Aug 2010

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

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.",

author = "{Mat Desa}, {Wan N. S.} and {Nic Da{\'e}id}, Niamh and Dzulkiflee Ismail and Kathleen Savage",

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AU - Ismail, Dzulkiflee

AU - Savage, Kathleen

N1 - The authors wish to acknowledge the Ministry of Higher Education of Malaysia (SLAB Grant) which provided studentship to W. Mat-Desa and D. Ismail

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N2 - 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.

AB - 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.

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

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