Amino Acid, Organic Acid, and Sugar Profiles of 3 Dry Bean (Phaseolus vulgaris L.) Varieties. Issue 12 (28th October 2015)

• Record Type: Journal Article
• Authors: John, K.M. Maria; Luthria, Devanand
• Is Part Of: Journal of food science. Volume 80:Issue 12(2015)
• Page Start: C2662
• Page End: C2669
• Publication Date: 2015-10-28
• Subjects: amino acid -- dry beans -- gas and ion chromatography mass spectrometry -- metabolic profiles -- sugars; Food -- Periodicals; Food -- Research -- Periodicals; Food -- Periodicals; Research -- Periodicals; Levensmiddelen; Voeding; 664
• DOI: 10.1111/1750-3841.13115
• Languages: English
• ISSNs: 0022-1147

Abstract

Abstract: In this study, we compared the amino acid, organic acid and sugar profiles of 3 different varieties of dry beans (black bean [BB], dark red bean [DRB], and cranberry bean [CB]). The efficiency of the 2 commonly used extraction solvents (water and methanol:chloroform:water [2.5:1:1, v/v/v/]) for cultivar differentiation based on their metabolic profile was also investigated. The results showed that the BB contained the highest concentration of amino acids followed by DRB and CB samples. Phenylalanine, a precursor for the biosynthesis of phenolic secondary metabolites was detected at low concentration in CB samples and correlated with the reduced anthocyanins content in CB extract as documented in the published literature. Comparing the extractability of 2 extraction solvents, methanol:chloroform:water (2.5:1:1, v/v/v/) showed higher recoveries of amino acids from 3 beans, whereas, sugars were extracted in higher concentration with water. Analytically, gas chromatography detected sugars (9), amino acids (11), and organic acids (3) in a single run after derivatization of the extracts. In comparison, ion chromatography detected only sugars in a single run without any derivatization step with the tested procedure. Bean samples are better differentiated by the sugar content extracted with water as compared to the aqueous organic solvent extracts using partial least‐square discriminant analysis.