The world's food demand is increasing rapidly due to fast population growth that has posed a challenge to meeting the requirements of nutritionally balanced diets. Pulses could play a major role in the human diet to combat these challenges and provide nutritional and physiological benefits. Pulses such as chickpeas, green gram, peas, horse gram, beans, lentils, black gram, etc., are rich sources of protein (190–260 g kg⁻¹), carbohydrates (600–630 g kg⁻¹⁾, dietary fibers, and bioactive compounds. There are many health benefits of phytochemicals present in pulses, like flavonoids, phenolics, tannins, phytates, saponins, lectins, oxalates, phytosterols peptides, and enzyme inhibitors. Some of them have anti-inflammatory, anti-ulcerative, anti-microbial, and anti-cancer effects. Along with these, pulses are also rich in vitamins and minerals. In this review, we highlight the potential role of pulses in global food systems and diets, their nutritional value, health benefits, and prospects for biofortification of major pulses. The food composition databases with respect to pulses, effect of processing techniques, and approaches for improvement of nutritional profile of pulses are elaborated.

Native germplasm resources are adapted to specific ecological niches. They have sustained over generations owing to the preference of local communities for their unique taste, the utility to particular dishes, and the low cost of cultivation. They may help eradicate malnutrition and act as a source for trait-linked genes. The present dataset comprises thirty-three native germplasm of maize collected from Rajasthan, Himachal Pradesh, and Andhra Pradesh states of India with an altitudinal variation of 386–2,028 m. They were evaluated for proximate composition, minerals, nutritional attributes, and antioxidant activity and compared with the standard values reported in the Indian Food Composition Table 2017 (IFCT2017). The nutritional profile showed moisture content in the range of 7.16–10.9%, ash 0.73–1.93%, crude protein 8.68–12.0%, crude fat 3.72–8.03%, dietary fiber 5.21–11.2%, and available carbohydrates 60.6–69.8%. Three accessions, namely, Malan 11 (7.06%), Malan 24 (7.20%), and Yellow Chamba Local 02 (8.03%) exhibited almost double the crude fat content as compared with the values notified in IFCT2017 (3.77). Total sugar content obtained was in the range of 5.00–11.3%, whereas the starch content was found between 50.9 and 64.9%. All the germplasm except Yellow Chamba Local reflected a higher protein content than reported values in IFCT2017 (8.80). Sathi, Safed Chamba Local, and Ragal Makka had nearly 12% protein content. Mineral malnutrition, mainly due to iron (Fe) deficiency, is a worldwide issue to science, humanity, and society. The mineral profile revealed that most germplasm had a higher iron content. Accessions with the iron content of nearly three times of IFCT2017 reported value were identified in germplasm belonging to three states. A negative relationship was observed between the altitude of the sample collection site and available carbohydrate content. In contrast, available carbohydrate showed inverse correlations with dietary fiber, protein, and fat content. The information generated in this study can be utilized to promote these germplasm as nutrifood, nutritional surveillance, labeling, and crop improvement programs.

Rice is a major staple food across the world in which wide variations in nutrient composition are reported. Rice improvement programs need germplasm accessions with extreme values for any nutritional trait. Near infrared reflectance spectroscopy (NIRS) uses electromagnetic radiations in the NIR region to rapidly measure the biochemical composition of food and agricultural products. NIRS prediction models provide a rapid assessment tool but their applicability is limited by the sample diversity, used for developing them. NIRS spectral variability was used to select a diverse sample set of 180 accessions, and reference data were generated using association of analytical chemists and standard methods. Different spectral pre-processing (up to fourth-order derivatization), scatter corrections (SNV-DT, MSC), and regression methods (partial least square, modified partial least square, and principle component regression) were employed for each trait. Best-fit models for total protein, starch, amylose, dietary fiber, and oil content were selected based on high RSQ, RPD with low SEP(C) in external validation. All the prediction models had ratio of prediction to deviation (RPD) > 2 amongst which the best models were obtained for dietary fiber and protein with R² = 0.945 and 0.917, SEP(C) = 0.069 and 0.329, and RPD = 3.62 and 3.46. A paired sample t-test at a 95% confidence interval was performed to ensure that the difference in predicted and laboratory values was non-significant.

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 RSQ_externalvalues 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.