Rice bean-adzuki bean multitrait near infrared reflectance spectroscopy prediction model: a rapid mining tool for trait-specific germplasm.

In the present era of climate change, underutilized crops such as rice beans and adzuki beans are gaining prominence to ensure food security due to their inherent potential to withstand extreme conditions and high nutritional value. These legumes are bestowed with higher nutritional attributes such as protein, fiber, vitamins, and minerals than other major legumes of the Vigna family. With the typical nutrient evaluation methods being expensive and time-consuming, non-invasive techniques such as near infrared reflectance spectroscopy (NIRS) combined with chemometrics have emerged as a better alternative. The present study aims to develop a combined NIRS prediction model for rice bean and adzuki bean flour samples to estimate total starch, protein, fat, sugars, phytate, dietary fiber, anthocyanin, minerals, and RGB value. We chose 20 morphometrically diverse accessions in each crop, of which fifteen were selected as the training set and five for validation of the NIRS prediction model. Each trait required a unique combination of derivatives, gaps, smoothening, and scatter correction techniques. The best-fit models were selected based on high RSQ and RPD values. High RSQ values of >0.9 were achieved for most of the studied parameters, indicating high-accuracy models except for minerals, fat, and phenol, which obtained RSQ <0.6 for the validation set. The generated models would facilitate the rapid nutritional exploitation of underutilized pulses such as adzuki and rice beans, showcasing their considerable potential to be functional foods for health promotion.

Development and optimization of NIRS prediction models for simultaneous multi-trait assessment in diverse cowpea germplasm.

Cowpea (Vigna unguiculata (L.) Walp.) is one such legume that can facilitate achieving sustainable nutrition and climate change goals. Assessing nutritional traits conventionally can be laborious and time-consuming. NIRS is a technique used to rapidly determine biochemical parameters for large germplasm. NIRS prediction models were developed to assess protein, starch, TDF, phenols, and phytic acid based on MPLS regression. Higher RSQexternal values such as 0.903, 0.997, 0.901, 0.706, and 0.955 were obtained for protein, starch, TDF, phenols, and phytic acid respectively. Models for all the traits displayed RPD values of >2.5 except phenols and low SEP indicating the excellent prediction of models. For all the traits worked, p-value ≥ 0.05 implied the accuracy and reliability score >0.8 (except phenol) ensured the applicability of the models. These prediction models will facilitate high throughput screening of large cowpea germplasm in a non-destructive way and the selection of desirable chemotypes in any genetic background with huge application in cowpea crop improvement programs across the world.

Intervarietal variations in various oxidative stress markers and antioxidant potential of finger millet (Eleusine coracana) subjected to drought stress.

Drought is a major form of abiotic stress leading to lower crop productivity. Experiment was carried out for selecting the most tolerant genotype among six different genotypes of finger millet under drought stress. Seeds of six finger millet genotypes were sown in pots and grown for 35 days. After this period, drought was induced by withholding watering for stressed plants while control plants were watered regularly for comparison. Among all six different varieties of finger millet screened (PR202, PES400, PRM6107, VL283, VL328 and VL149) under varying intensities of drought stress,PRM6107 and PR202 showed highest stress tolerance by limiting excessive accumulation of reactive oxygen species (ROS) through activation of ROS scavenging antioxidative enzymes. A 200% increase in ascorbate content was recorded in PRM6107 and PR202, while in other varieties limited increase in ascorbate content was observed. Maximum decrease in chlorophyll content was observed in VL328 (83%) while least drop was observed in VL149 (65%). Relative water content indicated that PR202 was able to retain maximum water content under stress, as it recorded least drop in relative water content (55%), contributing to its better survival under stress. In conclusion finger millet genotypes PRM6107 and PR202 possessed maximum drought tolerance potential and thus may be used for allele mining of drought tolerant genes, which can further be employed for the development of more drought stress tolerant staple crops using biotechnological approach.