4.7 Article

GC-MS based metabolites profiling of nutrients and anti-nutrients in 10 Lathyrus seed genotypes: A prospect for phyto-equivalency and chemotaxonomy

期刊

FOOD BIOSCIENCE
卷 42, 期 -, 页码 -

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ELSEVIER
DOI: 10.1016/j.fbio.2021.101183

关键词

Lathyrus spp.; Nutrients; Anti-nutrients; Silylated GC/MS; Metabolomics; Multivariate data analysis

资金

  1. Alexander von Humboldt Foundation, Germany

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The chemical composition of 10 Lathyrus species was assessed via GC/MS-based metabolomics, identifying significant differences in fatty acids, sugars, and amino acids among different species. Some species like L. annuus were found to be rich in amino acids, suitable for nutritional use, while others enriched with fatty acids and sugars like L. pannonicus, L. annuus, and L. odoratus showed high potential for food or fodder.
Despite their favourable nutritional, agricultural and ecological traits, Lathyrus species are among the under-recognized legume crops meriting further exploitation as nutrient-rich seeds. Holistic chemical characterization of nutrients/anti-nutrients in this genus is an important prerequisite to select high quality seeds for breeding programs. Herein, the chemical composition of 10 Lathyrus species was assessed via GC/MS-based metabolomics. Seventy-nine metabolites were identified comprising fatty acids, sugars, organic acids, and amino acids. Richest in amino-acids, L. annuus appeared the most suited for nutritional use. Furthermore, taxa enriched with fatty acids and sugars (L. pannonicus, L. annuus and L. odoratus) also show high potential as food or fodder. The richness of organic acids in L. sativus and L. tingitanus can override the anti-nutritive effect of phytic acid. Comparison to extant phylogenetic studies of the genus illustrates to what extent infrageneric classification is also reflected by the chemical variation observable via metabolomics, which has not been done before: Segregate sectional clustering among accessions was demonstrated along with several relationships at both infrasectional (e.g. L. sylvestris and L. latifolius) and interspecific level (e.g. L. venetus and L. niger) using multivariate data analysis.

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