High-throughput phenotyping of nutritional traits in rice bean (Vigna umbellata L.) flour using near infrared reflectance spectroscopy and chemometrics: An eco-friendly approach

Abstract

Rice bean ( Vigna umbellata L.) is an underutilized legume recognized for its superior nutritional profile, especially high starch, amylose, and protein content. However, mainstream adoption of nutritionally superior rice bean varieties remains limited due to significant bottlenecks in breeding programs, primarily the cumbersome nature of nutritional phenotyping. Traditional analytical methods used to evaluate nutritional traits are labor-intensive, time-consuming, costly, and environmentally unsustainable, thereby hindering breeding efforts aimed at improving nutritional quality. Addressing these constraints, this study developed robust and environmentally sustainable prediction models utilizing Near-Infrared Reflectance (NIR) spectroscopy coupled with Modified Partial Least Squares (MPLS) chemometric approaches. A diverse germplasm collection sourced from India's North Eastern Region was employed to establish high-throughput, rapid, and non-destructive MPLS-based models. These models exhibited excellent prediction accuracy, achieving high coefficient of determination (RSQ) values of 0.97 for starch0.92 for amylose, and 0.98 for protein content, along with strong Residual Prediction Deviation (RPD) values of 5.81, 3.62, and 9.99, respectively. Such rapid and reliable phenotyping methodologies hold promise not only in breeding programs but also in postharvest applications for enabling quick and non-destructive assessment of nutritional quality in harvested beans. Also, these techniques offer significant industrial value by supporting quality control in food processing, formulation of nutrient-rich products, and standardization of raw materials for nutraceutical and functional food industries. Ultimately, these innovative approaches enhance breeding efficiency, promote rice bean’s integration into sustainable agri-food systems, and contribute to global nutritional security.

Plant phenotyping relevance

NIR分光とMPLSケモメトリクスにより、イネマメの栄養形質を非破壊・高スループット推定するモデルを開発しており、形質取得手法が研究の中心です。

Code and data availability

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