Mat Desa, Wan N.S. and NicDaeid, N. and Dzulkiflee, Ismail and Savage, Kathleen (2010) Application of unsupervised chemometric analysis and self-organising feature map (SOFM) for the classification of lighter fuels. Analytical Chemistry, 82 (15). pp. 6395-6400.
Download (43kB) | Preview
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.
|Notes:||The authors wish to acknowledge the Ministry of Higher Education of Malaysia (SLAB Grant) which provided studentship to W. Mat-Desa and D. Ismail.|
|Keywords:||lighter fuel samples, gas chromatography, mass spectrometry, Science (General), Chemistry, Analytical Chemistry|
|Subjects:||Science > Science (General)
Science > Chemistry
|Department:||Faculty of Science > Pure and Applied Chemistry|
|Depositing user:||Ms Lorraine Stewart|
|Date Deposited:||12 May 2011 13:55|
|Last modified:||06 Jan 2017 14:35|