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• Seed-glucosinolate chemotypes of 641 Brassica juncea genotypes were analyzed. • PCA of glucosinolates data showed the distinctiveness of four populations used. • A comprehensive Vis-NIRS model developed using MPLS regression analysis. • The model has high predictability for 8 glucosinolates of B. juncea. • The Vis-NIRS model also facilitates cross-species glucosinolates quantification. The globally cultivated Brassica crops contain high deliverable concentrations of health-promoting glucosinolates. Development of a Visible-Near InfraRed Spectroscopy (Vis-NIRS) calibration to profile different glucosinolate components from 641 diverse Brassica juncea chemotypes was attempted in this study. Principal component analysis of HPLC-determined glucosinolates established the distinctiveness of four B. juncea populations used. Subsequently, modified partial least square regression based population-specific and combined Vis-NIRS models were developed, wherein the combined model exhibited higher coefficient of determination (R2; 0.81–0.97) for eight glucosinolates and higher ratio of prediction determination (RPD; 2.42–5.35) for seven glucosinolates in B. juncea populations. Furthermore, range error ratio (RER > 4) for twelve and RER > 10 for eight glucosinolates make the combined model acceptable for screening and quality control. The model also provided excellent prediction for aliphatic glucosinolates in four oilseed Brassica species. Overall, our work highlights the potential of Vis-NIR spectroscopy in estimating glucosinolate content in the economically important Brassica oilseeds. [ABSTRACT FROM AUTHOR]