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

@article{Desa2010ApplicationOU,
  title={Application of unsupervised chemometric analysis and self-organizing feature map (SOFM) for the classification of lighter fuels.},
  author={Wan Nur Syuhaila Mat Desa and Niamh Nic Daeid and Dzulkiflee Ismail and Kathleen A Savage},
  journal={Analytical chemistry},
  year={2010},
  volume={82 15},
  pages={6395-400}
}
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… CONTINUE READING

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