Near infrared reflectance spectroscopy-driven chemometric modeling for predicting key quality traits in lablab bean (Lablab purpureus L.) Germplasm

Lablab bean (Lablab purpureus L.) is a multipurpose crop, commonly used for food, feed, and fodder, and its potential as a plant-based meat alternative. Its nutritional diversity, including high protein, starch, and phenolic content, makes it a suitable candidate for nutritional profiling, which is essential for developing nutritionally enhanced varieties. Traditional methods for analyzing its nutritional parameters are labor-intensive, timeconsuming, and expensive. This study employs Near-Infrared Reflectance Spectroscopy (NIRS) as a rapid, nondestructive alternative to evaluate 112 Lablab bean genotypes. We developed prediction models for starch, amylose, protein, fat, and phenols using a Modified Partial Least Squares (MPLS) approach, with spectral preprocessing using Standard Normal Variate (SNV) to remove scatter effects and Detrending (DT) to reduce baseline shifts and noise. The models were optimized for derivatives, gap selection, and smoothing, and evaluated using independent test data and key performance metrics including coefficient of determination (R2), bias, and Residual Prediction Deviation (RPD). The best-performing models were: starch (R2 = 0.959, RPD = 4.57), amylose (R2 = 0.737, RPD = 1.76), protein (R2 = 0.911, RPD = 3.09), fat (R2 = 0.894, RPD = 2.92), and phenols (R2 = 0.816, RPD = 2.36). Statistical tests, including paired t-tests, correlation, and reliability analysis, confirmed the robustness of these models. This study presents a first report offering rapid, multi-trait assessment method for evaluating Lablab bean germplasm, demonstrating high predictive accuracy for pre-breeding practices. It has broad applications in developing nutritionally enhanced varieties, supporting plant-based protein alternatives, and optimizing food production processes to meet the growing demand for healthier, sustainable foods.