The escalating global demand for vegetable oils underscores the need to enhance the quality and yield of oilseed crops with Brassica species, due to their rich oil content and nutritional benefits. Traditional methods for assessing seed quality traits are often slow and destructive, limiting their scalability in breeding programs. This study presents Fourier transform near-infrared (FT-NIR) spectroscopy as a rapid, non-destructive alternative to evaluate these critical traits across 80 diverse Brassica genotypes, including three species, namely, Brassica juncea, Brassica napus , and Brassica rapa . By integrating FT-NIR with principal component analysis and partial least squares regression, we developed robust calibration models, achieving high predictive accuracy (R 2 0.85 for key fatty acids; R 2 = 0.92 for oil content) and low error rates (MAE 1.8). Our results revealed significant genetic variability, with oil content showing remarkable stability (CV = 0.68%) and erucic acid exhibiting the highest variation (CV = 9.18%), offering promising avenues for targeted breeding. PCA elucidated 68% of the total variance, spotlighting oleic acid, erucic acid, and oil content as key drivers of genetic differentiation. Pearson correlation analysis also revealed a strong inverse relationship between oleic acid and erucic acid, suggesting potential genetic linkages that could be exploited in breeding programs. The FT-NIR models demonstrated superior throughput and reliability compared to conventional wet chemistry. These findings not only streamline seed quality assessment but also pave the way for breeding Brassica cultivars with optimized nutritional profiles high in beneficial polyunsaturated fatty acids and low in anti-nutritional factors.
Sharma et al. (Tue,) studied this question.
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