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.

The Indian subcontinent is the primary center of origin of rice where huge diversity is found in the Indian rice gene pool, including landraces. North Eastern States of India are home to thousands of rice landraces which are highly diverse and good sources of nutritional traits, but most of them remain nutritionally uncharacterized. Hence, nutritional profiling of 395 Assam landraces was done for total starch, amylose content (AC), total dietary fiber (TDF), total protein content (TPC), oil, phenol, and total phytic acid (TPA) using official AOAC and standard methods, where the mean content for the estimated traits were found to be 75.2 g/100g, 22.2 g/100g, 4.67 g/100g, 9.8 g/100g, 5.26%, 0.40 GAE g/100g, and 0.34 g/100g for respectively. The glycaemic index (GI) was estimated in 24 selected accessions, out of which 17 accessions were found to have low GI (<55). Among different traits, significant correlations were found that can facilitate the direct and indirect selection such as estimated glycemic index (EGI) and amylose content (−0.803). Multivariate analyses, including principal component analysis (PCA) and hierarchical clustering analysis (HCA), revealed the similarities/differences in the nutritional attributes. Four principal components (PC) i.e., PC1, PC2, PC3, and PC4 were identified through principal component analysis (PCA) which, contributed 81.6% of the variance, where maximum loadings were from protein, oil, starch, and phytic acid. Sixteen clusters were identified through hierarchical clustering analysis (HCA) from which the trait-specific and biochemically most distant accessions could be identified for use in cultivar development in breeding programs.

Cowpea (Vigna unguiculata L. (Walp)) is a multipurpose legume, which has good nutritional properties. Nutritional parameters assessed conventionally can be labour intensive, costly and time taking for germplasm screening. Near‐infrared reflectance spectroscopy (NIRS) is a rapid and nondestructive method, which can facilitate high‐throughput germplasm screening. In our study, estimation of amylose and sugars has been done using NIRS. Two preprocessing methods, that is, SNV‐DT (standard normal variate with detrending) and MSC (multiplicative scatter correction), were performed for optimization of the original spectra. Subsequently, MPLS (modified partial least square) regression method was employed to construct the prediction models. In amylose, the best RSQ_external (coefficient of determination) (0.962) was found in SNV‐DT with mathematical treatment 3,8,8,2. The same result was shown in sugar where the best RSQ_external (0.914) was found in SNV‐DT with mathematical treatment 3,4,4,1. Overall, in the case of amylose and sugars, SNV‐DT was found to be a good preprocessing treatment than MSC. Paired t‐test values in all the treatments for both the preprocessing methods were > 0.05 indicating their reliability. High RSQ_external values for both the traits imply the applicability of the prediction models. Thus, these models can facilitate high‐throughput germplasm screening in different national and international crop improvement programmes focusing on quality traits.

Potato is a globally significant crop, crucial for food security and nutrition. Assessing vital nutritional traits is pivotal for enhancing nutritional value. However, traditional wet lab methods for the screening of large germplasms are time- and resource-intensive. To address this challenge, we used near-infrared reflectance spectroscopy (NIRS) for rapid trait estimation in diverse potato germplasms. It employs molecular absorption principles that use near-infrared sections of the electromagnetic spectrum for the precise and rapid determination of biochemical parameters and is non-destructive, enabling trait monitoring without sample compromise. We focused on modified partial least squares (MPLS)-based NIRS prediction models to assess eight key nutritional traits. Various mathematical treatments were executed by permutation and combinations for model calibration. The external validation prediction accuracy was based on the coefficient of determination (RSQ_external), the ratio of performance to deviation (RPD), and the low standard error of performance (SEP). Higher RSQ_external values of 0.937, 0.892, and 0.759 were obtained for protein, dry matter, and total phenols, respectively. Higher RPD values were found for protein (3.982), followed by dry matter (3.041) and total phenolics (2.000), which indicates the excellent predictability of the models. A paired t-test confirmed that the differences between laboratory and predicted values are non-significant. This study presents the first multi-trait NIRS prediction model for Indian potato germplasm. The developed NIRS model effectively predicted the remaining genotypes in this study, demonstrating its broad applicability. This work highlights the rapid screening potential of NIRS for potato germplasm, a valuable tool for identifying trait variations and refining breeding strategies, to ensure sustainable potato production in the face of climate change.

Horsegram (Macrotyloma uniflorum [Lam.] Verdc.) is an underutilized pulse crop primarily cultivated in South Asian countries like India, Nepal, and Sri Lanka. It offers various nutraceutical properties and demonstrates remarkable resilience to both biotic and abiotic stresses. As a result, it has emerged as a promising crop for ensuring future food and nutritional security. The purpose of this study was to assess the nutritional profile of 139 horsegram germplasm lines obtained from 16 Indian states that were conserved at the National Gene Bank of India. Standard analytical methods, including those provided by the Association of Official Analytical Chemists (AOAC), were used for this investigation. The study revealed substantial variability in essential nutrients, such as protein (ranging from 21.8 to 26.7 g/100 g), starch (ranging from 26.2 to 33.0 g/100 g), total soluble sugars (TSSs) (ranging from 0.86 to 12.1 g/100 g), phenolics (ranging from 3.38 to 11.3 mg gallic acid equivalents (GAEs)/g), and phytic acid content (ranging from 1.07 to 21.2 mg/g). Noteworthy correlations were observed, including a strong positive correlation between sugars and phenols (r = 0.70) and a moderate negative correlation between protein and starch (r = −0.61) among the studied germplasm lines. Principal component analysis (PCA) highlighted that the first three principal components contributed to 88.32% of the total variability, with TSSs, phytates, and phenols emerging as the most significant contributors. The cluster analysis grouped the accessions into five clusters, with cluster III containing the accessions with the most desirable traits. The differential distribution of the accessions from north India into clusters I and III suggested a potential geographical influence on the adaptation and selection of genes. This study identified a panel of promising accessions exhibiting multiple desirable traits. These specific accessions could significantly aid quality breeding programs or be directly released as cultivars if they perform well agronomically.