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Seed metabolite profiling of Vicia species from China via GC-MS.

Ann AbozeidJia LiuYanming MaYang LiuXiaorui GuoZhonghua Tang
Published in: Natural product research (2017)
In this study, we examined Vicia seeds using gas chromatography-mass spectrometry (GC-MS). The metabolic differences of seeds of twelve Vicia species were assessed. 184 metabolites were identified. Vicia species were classified via multivariate data analyses into four clusters. V. unijuga was most enriched in fatty acids and anthraquinones contents while highest levels of amino acids, alcohols and phenolic were in V. costata. Clustering analysis of biochemical profiles matched with the pervious phenotypic observation with all examined species from section Cracca grouped together under one sub-cluster, except for V. costata.
Keyphrases
  • gas chromatography mass spectrometry
  • fatty acid
  • ms ms
  • genetic diversity
  • electronic health record
  • data analysis
  • solid phase extraction
  • big data
  • mass spectrometry
  • machine learning
  • deep learning
  • water quality