Profiling of Nutraceuticals and Proximates in Peanut Genotypes Differing for Seed Coat Color and Seed Size.

Spurthi N Nayak,Hajisab L Nadaf,Pushpa Bharati,Viresh Hebbal,Gopalkrishna K Naidu,Ramesh S Bhat

doi:10.3389/fnut.2020.00045

Spurthi N Nayak, Hajisab L Nadaf + Show 4 more

Open Access

https://doi.org/10.3389/fnut.2020.00045

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Abstract

A total of 60 genotypes of peanut comprising 46 genotypes selected from ICRISAT mini core collection and 14 elite cultivars with differing kernel color and size were used to profile the nutritional parameters such as proximates (moisture, fat, ash, crude protein, crude fiber, carbohydrate content) and nutraceuticals (total polyphenol content and total antioxidant activity). The genotypes showed varied kernel color ranging from white to purple. Kernel skin color was quantified using colorimetry, and the color parameters were expressed as CIELAB color parameters. In total, nine morphological traits, six yield related traits, eight nutritional traits and eleven color parameters were observed across 60 genotypes. The sixty genotypes were grouped into ten clusters based on the color strength. Among them, Cluster-III with dark red seeds had the maximum fat content and total polyphenol content (TPC). Cluster-VI with light pink colored seeds had high antioxidant activity (AOA) and Cluster-X with white colored seeds had highest moisture and crude protein content. Color strength (K/S) was found to be positively correlated with TPC. Another color parameter, redness/greenness (a*) was found to be positively correlated with AOA. However, seed size was positively correlated with the crude protein content, but not with any other nutritional traits under study. The population studies based on the genotypic data indicated two distinct groups pertaining to botanical types of peanut. The marker-trait association (MTA) using single marker analysis indicated 75 major MTAs for most of the nutritional traits except for moisture content. The markers associated with nutritional parameters and other important yield related traits can further be utilized for genomics-assisted breeding for nutrient-rich peanuts.

Highlights

IntroductionPeanut has several functional components with variety of health benefits like coenzyme Q10 which helps the heart protection under low oxygen areas like high altitude and clogged arteries, rich arginine helps in blood circulation, resveratrol protects against cancer, cognitive disorder and Alzheimer’s disease, phytosterols of peanut butter, oil reduces the absorption of cholesterol from the diet, magnesium and dietary fiber reduce the diabetes, reduces LDL cholesterol and helps in weight management [reviewed by [5]]
Peanut (Arachis hypogaea L.) widely known as groundnut is a cultivated allotetraploid (2n = 4x = 40), valued for its protein content (28%), oil content (50%) and is rich source of calcium, phosphorus, potassium, magnesium, iron, zinc, and boron
The efforts toward elucidating the marker-trait association related to quality or nutritional parameters are limited to some crops like sorghum [19], wheat [20], rice, and barley [21]

Summary

Introduction

Peanut has several functional components with variety of health benefits like coenzyme Q10 which helps the heart protection under low oxygen areas like high altitude and clogged arteries, rich arginine helps in blood circulation, resveratrol protects against cancer, cognitive disorder and Alzheimer’s disease, phytosterols of peanut butter, oil reduces the absorption of cholesterol from the diet, magnesium and dietary fiber reduce the diabetes, reduces LDL cholesterol and helps in weight management [reviewed by [5]]. For selection of appropriate lines that retain the diversity persisting in the global collection becomes essential in utilizing in crop breeding programs In this regard, the “core collection” (i.e., 10% of the entire germplasm collection) presents a manageable and cost-effective entry point into germplasm collections for identifying candidate genotypes for various traits [11]. Evidence of metabotypephenotype linkage was explained by using parallel metabolite and phenotypic GWAS in traits such as grain color and size in rice and maize kernels [22]

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