A method of finding optimal weight factors for compound identification in gas chromatography-mass spectrometry

Seongho Kim, Imhoi Koo, Xiaoli Wei, Xiang Zhang

Research output: Contribution to journalArticle

40 Scopus citations

Abstract

Motivation: The compound identification in gas chromatography-mass spectrometry (GC-MS) is achieved by matching the experimental mass spectrum to the mass spectra in a spectral library. It is known that the intensities with higher m/z value in the GC-MS mass spectrum are the most diagnostic. Therefore, to increase the relative significance of peak intensities of higher m/z value, the intensities and m/z values are usually transformed with a set of weight factors. A poor quality of weight factors can significantly decrease the accuracy of compound identification. With the significant enrichment of the mass spectral database and the broad application of GC-MS, it is important to re-visit the methods of discovering the optimal weight factors for high confident compound identification.Results: We developed a novel approach to finding the optimal weight factors only through a reference library for high accuracy compound identification. The developed approach first calculates the ratio of skewness to kurtosis of the mass spectral similarity scores among spectra (compounds) in a reference library and then considers a weight factor with the maximum ratio as the optimal weight factor. We examined our approach by comparing the accuracy of compound identification using the mass spectral library maintained by the National Institute of Standards and Technology. The results demonstrate that the optimal weight factors for fragment ion peak intensity and m/z value found by the developed approach outperform the current weight factors for compound identification.

Original languageEnglish (US)
Article numberbts083
Pages (from-to)1158-1163
Number of pages6
JournalBioinformatics
Volume28
Issue number8
DOIs
StatePublished - Apr 1 2012

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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