Application of Unsupervised Chemometric Analysis and Self-organizing Feature Map (SOFM) for the Classification of Lighter Fuels

作者：Desa Wan N S Mat; Daeid Niamh Nic^*; Ismail Dzulkiflee; Savage Kathleen

来源：Analytical Chemistry, 2010, 82(15): 6395-6400.

DOI：10.1021/ac100381a

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.

• 出版日期2010-8-1