Mining nutri-dense accessions from rice landraces of Assam, India.

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.

Nutritional evaluation of some potential wild edible plants of North Eastern region of India.

Introduction: India's north-eastern hill region (NEH) is one of the biodiversity hotspots, inhabited by several tribal communities still maintaining their traditional food habits. Much of their food resources are drawn from wild sources. Materials and methods: Fourteen species of wild edible plants of high ethnic importance were collected from remote localities of Nagaland and Meghalaya states of the NEH region of India for nutritional profiling. Nutritional profiling of leaves of six species comprising Gynura cusimbua, Garcinia cowa, Herpetospermum operculatum, Plukenetia corniculata, Trichodesma khasianum, and Elatostemma sessile is conducted first time under present study. Samples were analyzed as per the Official Method of Analysis (AOAC) and standard methods. Results and discussion: The range of variation in proximate composition was observed for moisture (72-92%), protein (1.71-6.66%), fat (0.22-1.36%), dietary fibre (5.16-14.58%), sugar (0.30-3.41%), and starch (0.07-2.14%). The highest protein content (6.66%) was recorded in Herpetospermum operculatum, followed by Trichodesma khasianum (5.89%) and Plukenetia corniculata (5.27%). Incidentally, two of these also have high iron (>7.0 mg/100 g) and high zinc (>2.0 mg/100 g) contents, except Trichodesma khasianum, which has low zinc content. High antioxidant activities in terms of gallic acid equivalent (GAE) by the cupric ion reducing antioxidant capacity (CUPRAC) method ranged from 1.10 to 8.40 mg/100 g, and by the Fluorescence recovery after photobleaching (FRAP) method ranged from 0.10 to 1.9 mg/100 g, while phenol content ranged between 0.30 and 6.00 mg/100 g. These wild vegetables have high potential because of their nutritional properties and are fully capable of enhancing sustainability and improving ecosystem services. Efforts were also initiated to mainstream these resources, mainly for widening the food basket of native peoples.

Germplasm variability-assisted near infrared reflectance spectroscopy chemometrics to develop multi-trait robust prediction models in rice.

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 R 2 = 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.