Discovery of metabolite features for the modelling and analysis of high-resolution NMR spectra

Hyun Woo Cho, Seoung Bum Kim, Myong K. Jeong, Youngja Park, Nana Gletsu Miller, Thomas R. Ziegler, Dean P. Jones

Research output: Contribution to journalArticlepeer-review

67 Citations (Scopus)


This study presents three feature selection methods for identifying the metabolite features in nuclear magnetic resonance spectra that contribute to the distinction of samples among varying nutritional conditions. Principal component analysis, Fisher discriminant analysis, and Partial Least Square Discriminant Analysis (PLS-DA) were used to calculate the importance of individual metabolite feature in spectra. Moreover, an Orthogonal Signal Correction (OSC) filter was used to eliminate unnecessary variations in spectra. We evaluated the presented methods by comparing the ability of classification based on the features selected by each method. The result showed that the best classification was achieved from an OSC-PLS-DA model.

Original languageEnglish
Pages (from-to)176-192
Number of pages17
JournalInternational Journal of Data Mining and Bioinformatics
Issue number2
Publication statusPublished - 2008 Jun
Externally publishedYes


  • Bioinformatics
  • Data mining
  • Feature selection
  • Metabolomics
  • Multivariate statistical analysis
  • NMR
  • Nuclear magnetic resonance
  • OSC
  • Orthogonal signal correction

ASJC Scopus subject areas

  • Information Systems
  • Biochemistry, Genetics and Molecular Biology(all)
  • Library and Information Sciences


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