GC-MS based metabolites profiling of nutrients and anti-nutrients in 10 Lathyrus seed genotypes: A prospect for phyto-equivalency and chemotaxonomy. (August 2021)

• Record Type: Journal Article
• Authors: Ibrahim, Nehal; Taleb, Mohamed; Heiss, Andreas G.; Kropf, Matthias; Farag, Mohamed A.
• Is Part Of: Food bioscience. Volume 42(2021)
• Publication Date: 2021-08
• Subjects: Lathyrus spp. -- Nutrients -- Anti-nutrients -- Silylated GC/MS -- Metabolomics -- Multivariate data analysis; Food -- Biotechnology -- Periodicals; Food -- Research -- Periodicals; Aliments -- Biotecnologia -- Revistes; Aliments -- Investigació -- Revistes; Food -- Biotechnology; Food -- Research; Revistes electròniques; Periodicals; 664.005
• DOI: 10.1016/j.fbio.2021.101183
• Languages: English
• ISSNs: 2212-4292

Abstract

Abstract: 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. Graphical abstract: Image 1