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

TY - JOUR

T1 - Application of unsupervised chemometric analysis and self-organizing feature map (SOFM) for the classification of lighter fuels

AU - Mat Desa, Wan N. S.

AU - Nic Daéid , Niamh

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

PY - 2010/8/1

Y1 - 2010/8/1

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.

UR - https://www.scopus.com/pages/publications/77955143333

U2 - 10.1021/ac100381a

DO - 10.1021/ac100381a

M3 - Article

C2 - 20597464

AN - SCOPUS:77955143333

SN - 0003-2700

VL - 82

SP - 6395

EP - 6400

JO - Analytical Chemistry

JF - Analytical Chemistry

IS - 15

ER -