Molecular characterization and genetic diversity among 82 soybean accessions was carried out by using 44 simple sequence repeat (SSR) markers. Of the 44 SSR markers used, 40 markers were found polymorphic among 82 soybean accessions. These 40 polymorphic markers produced a total of 119 alleles, of which five were unique alleles and four alleles were rare. The allele number for each SSR locus varied between two to four with an average of 2.97 alleles per marker. Polymorphic information content values of SSRs ranged from 0.101 to 0.742 with an average of 0.477. Jaccard's similarity coefficient was employed to study the molecular diversity of 82 soybean accessions. The pairwise genetic similarity among 82 soybean accessions varied from 0.28 to 0.90. The dendrogram constructed based on genetic similarities among 82 soybean accessions identified three major clusters. The majority of genotypes including four improved cultivars were grouped in a single subcluster IIIa of cluster III, indicating high genetic resemblance among soybean germplasm collection in India.

Soybean is a leading oilseed crop in India, which contains about 40% of protein and 20% of oil. Core collection will accelerate the management and utilization of soybean genetic resources in breeding programmes. In the present study, eight agromorphological traits of 3443 soybean germplasm were analysed for the development of core collection using the principal component score (PCS) strategy and the power core method. The PCS strategy yielded core collection (CC1) of 576 accessions, which accounted for 16.72% of the entire collection (EC). The analysis based on the power core programme resulted in CC2 of 402 accessions, which accounted for 11.67% of the EC. Statistical analysis showed similar trends for the mean and range estimated in both core collections and EC. In addition, the variance, standard deviation and coefficient of variance were in general higher in core collections than in the EC. The correlations observed in the EC in general were preserved in core collections. A total of 311 and 137 unique accessions were found in CC1 and CC2 in addition to 265 accessions that were found to be common in both core collections. These 265 common accessions were the most diverse core sets, which accounted for 7.64% of the EC. We proposed to constitute an integrated core collection (ICC) by integrating both common and unique accessions. The ICC comprised 713 accessions, which accounted for about 20.62% of the EC. Statistical analysis indicated that the ICC captured maximum variation than CC1 and CC2. Therefore, the ICC can be extensively evaluated for a large number of economically important traits for the identification of desirable genotypes and for the development of mini core collection in soybean.

Colors are added to food items to make them more attractive and appealing. Food colorants therefore, have an impressive market due to the requirements of food industries. A variety of synthetic coloring agents approved as food additives are available and being used in different types of food prepared or manufactured worldwide. However, there is a growing concern that the use of synthetic colors may exert a negative impact on human health and environment in the long run. The natural pigments obtained from animals, plants, and microorganisms are a promising alternative to synthetic food colorants. Compared to animal and plant sources, microorganisms offer many advantages such as no seasonal impact on the quality and quantity of the pigment, ease of handling and genetic manipulation, amenability to large scale production with little or no impact on biodiversity etc. Among the microorganisms algae, fungi and bacteria are being used to produce pigments as food colorants. This review describes the types of microbial food pigments in use, their benefits, production strategies, and associated challenges.

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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.

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