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Nutritional Potential of Adzuki Bean Germplasm and Mining Nutri-Dense Accessions through Multivariate Analysis.

Deepika D DSiddhant Ranjan PadhiPadmavati G GoreKuldeep TripathiAshvinkumar KatralRahul ChandoraG J AbhishekVishal KondalRakesh SinghRakesh BharadwajKailash C BhattJai Chand RanaAmritbir Singh Riar
Published in: Foods (Basel, Switzerland) (2023)
The adzuki bean ( Vigna angularis ), known for its rich nutritional composition, holds significant promise in addressing food and nutritional security, particularly for low socioeconomic classes and the predominantly vegetarian and vegan populations worldwide. In this study, we assessed a total of 100 diverse adzuki bean accessions, analyzing essential nutritional compounds using AOAC's official analysis procedures and other widely accepted standard techniques. Our analysis of variance revealed significant genotype variations for all the traits studied. The variability range among different traits was as follows: moisture: 7.5-13.3 g/100 g, ash: 1.8-4.2 g/100 g, protein: 18.0-23.9 g/100 g, starch: 31.0-43.9 g/100 g, total soluble sugar: 3.0-8.2 g/100 g, phytic acid: 0.65-1.43 g/100 g, phenol: 0.01-0.59 g/100 g, antioxidant: 11.4-19.7 mg/100 g GAE. Noteworthy accessions included IC341955 and EC15256, exhibiting very high protein content, while IC341957 and IC341955 showed increased antioxidant activity. To understand intertrait relationships, we computed correlation coefficients between the traits. Principal Component Analysis (PCA) revealed that the first four principal components contributed to 63.6% of the variation. Further, hierarchical cluster analysis (HCA) identified nutri-dense accessions, such as IC360533, characterized by high ash (>4.2 g/100 g) and protein (>23.4 g/100 g) content and low phytic acid (0.652 g/100 g). These promising compositions provide practical support for the development of high-value food and feed varieties using effective breeding strategies, ultimately contributing to improved global food security.
Keyphrases
  • human health
  • oxidative stress
  • machine learning
  • public health
  • magnetic resonance imaging
  • climate change
  • artificial intelligence