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.

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.

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 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.

A GREEN METHOD FOR THE REMOVAL OF ZINC(II) IONS FROM WASTEWATER USING MODIFED BIOPOLYMERS

Zinc pollution in wastewater is a global problem because it is highly toxic. Zinc is commonly used in industries that transfer the water containing zinc directly into water sources, leading to pollution. Exposure to a high level of zinc causes major health problems. This study evaluated the adsorption of zinc ions from aqueous system using modified biopolymers of chitosan by crosslinking with sulphates using the batch adsorption method; the concentration was determined using atomic absorption spectrophotometry. The sulphate cross-linked chitosan (SCC) was characterised by several method. The effects of various experimental parameters such as pH, contact time, concentration, adsorbent dosage and temperature were investigated. Under the optimised conditions, the percentage efficiency for the removal of zinc(II) was up to 85%. Freundlich and Langmuir isotherms were used to analyse the equilibrium adsorption data along with kinetic studies. Various thermodynamic parameters have also been reported.

Comparative analysis of biodiesel produced by acidic transesterification of lipid extracted from oleaginous yeast Rhodosporidium toruloides

This study investigated the potential of oleaginous yeast Rhodosporidium toruloides strain (ATCC20409) for the sustainable production of microbial lipids as biodiesel feedstock and other economically important fatty acids in comparison to algal or plant-based biodiesel. The strain exhibited high lipid content (76% of dry cell weight biomass) through consolidated bioprocessing which was transesterified to produce biodiesel. Physico-chemical properties of the biodiesel produced showed that they were in accordance with ASTM standards, although few parameters such as acid value, calorific value and free fatty acid value differed to some extent, as also reported in plant-based/microalgal biodiesel. Fatty acid methyl esters analysis of biodiesel showed 50.18% unsaturated fatty acid and 49.81% saturated fatty acid. Total content of (monounsaturated fatty acid) MUFA was higher than (polyunsaturated fatty acid) PUFA, being 44.36% and 2.69%, respectively. Considering the yield and cost, lipid extracted from R. toruloides may become a promising alternative feed in biodiesel production.

Trace determination of dexamethasone sodium phosphate in pharmaceutical formulations by differential pulse polarography

A simple and rapid differential pulse polarographic method has been developed for the trace determination of dexamethasone sodium phosphate. A well-defined single peak with an Ep value of -1.14 V is obtained in acetate buffer (pH 5.0). The linearity is valid in the range 0.2-1.2 mg/25 mL ( r=0.9992) with minimum detection limit of 7.6x10(-6) M. The precision of the method developed is implied from the values of relative mean deviation, standard deviation and coefficient of variation, which are 2.44%, 0.014 and 3.5% respectively. Marketed formulations of dexamethasone sodium phosphate have been analysed by calibration and standard addition methods. Recovery experiments were found to be quantitative, and analysis to determine the mass per tablet was obtained within +/-0.2% of the expected market value. The studies have shown that the method is reproducible and accurate and can be used in the analysis of marketed formulations.