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Research Article Open access CC BY 4.0

Characterization of Genetic Diversity in Fatty Acid and Nutritional Profiles Among Brassica Species

Achyuta Basak, Moumita Chakraborty, Shankar K. Bhujbal, Sanjay. J. Jambhulkar, Suvendu Kumar Roy, Saikat Das, Biplab Mitra, Rupsanatan Mandal, Lakshmi Hijam, Manoj Kanti Debnath, Md. Sabir Ahmed Mondol

Journal of Experimental Agriculture International · pp. 444–464 · Published 20 Dec 2024

10.9734/jeai/2024/v46i123151

Abstract

The present experiment was carried out to evaluate the nutritional profile of 10 advanced mutant lines of Indian mustard (Brassica juncea) along with 3 checks. Observation was recorded for 12 biochemical traits, and the analysis of variance (ANOVA) revealed significant variation among all characters under study. The findings demonstrate higher GCV and PCV in the case of oleic acid and arachidic acid which significantly impact these traits' inflow. Except for the total phenol content (mg GAE/g) and the total soluble protein content, all the other traits show high heritability and a significant genetic advance, indicating traits controlled by additive gene action. In the correlation study, it was found that the oleic acid, linoleic acid, linolenic acid, palmitic acid, eicosenoic acid and stearic acid are significantly negatively correlated with erucic acid. This indicates that improving these characteristics leads to decreased erucic acid content. The path diagram also indicates a high direct positive effect of total soluble protein (%) and stearic acid and a significant high direct negative effect of total phenol content and palmitic acid on total oil content. Three of the twelve PCs had eigenvalues greater than 1.0, explaining 83.36% of the variance. PC I accounted for 37.48% and PC II for 30.42% of the overall variance. Based on hierarchical cluster analysis, 13 Brassica genotypes were divided into 4 clusters. Genotypes TM-313, TM 305-1, TM 306-1, B9 show the most divergence and can be used in future breeding programs.

Brassica juncea genetic parameters path analysis cluster analysis PCA

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