title: GC/MS based metabolomics: development of a data mining system for metabolite identification by using soft independent modeling of class analogy (SIMCA).
abstract: BACKGROUND
  The goal of metabolomics analyses is a comprehensive and systematic understanding of all metabolites in biological samples. Many useful platforms have been developed to achieve this goal. Gas chromatography coupled to mass spectrometry (GC/MS) is a well-established analytical method in metabolomics study, and 200 to 500 peaks are routinely observed with one biological sample. However, only ~100 metabolites can be identified, and the remaining peaks are left as "unknowns".
  
  
  RESULTS
  We present an algorithm that acquires more extensive metabolite information. Pearson's product-moment correlation coefficient and the Soft Independent Modeling of Class Analogy (SIMCA) method were combined to automatically identify and annotate unknown peaks, which tend to be missed in routine studies that employ manual processing.
  
  
  CONCLUSIONS
  Our data mining system can offer a wealth of metabolite information quickly and easily, and it provides new insights, particularly into food quality evaluation and prediction.
authors.affiliation: Department of Bioengineering, Graduate School of Engineering, Osaka University, Suita, Japan.
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Tsugawa
authors.forename: Yuki
authors.initials: Y
authors.lastname: Tsujimoto
authors.forename: Masanori
authors.initials: M
authors.lastname: Arita
authors.forename: Takeshi
authors.initials: T
authors.lastname: Bamba
authors.forename: Eiichiro
authors.initials: E
authors.lastname: Fukusaki
mesh_terms: D057225:Data Mining / Q000379:methods*; D030541:Databases, Genetic; D005524:Food Technology / Q000379:methods; D008401:Gas Chromatography-Mass Spectrometry; D007256:Information Systems; D055432:Metabolomics / Q000379:methods*; D008954:Models, Biological; D013662:Tea / Q000737:chemistry
chemical_list: D013662:Tea