Application of unsupervised chemometric analysis and self-organizing feature map (SOFM) for the classification of lighter fuels.
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.
Desa
Wan N S Mat
WN
Centre for Forensic Science, Department of Pure and Applied Chemistry, University of Strathclyde, 204 George Street, Glasgow G1 1WX.
Nic Daéid
Niamh
N
Ismail
Dzulkiflee
D
Savage
Kathleen
K