Optimizing protein content prediction in rice bean (Vigna umbellata L.) by integrating near-infrared reflectance spectroscopy, MPLS, deep learning, and key wavelengths selection algorithms

Abstract

Rice bean, a potential protein-rich underutilized crop, can serve as a valuable source for food and feed, contributing to sustainable food and nutrition security. However, traditional methods for measuring protein content are laborious, tedious, and expensive. This study evaluates the performance of Modified Partial Least Squares (MPLS) and 1D Convolutional Neural Network (1D CNN) models in predicting protein content in rice bean using Near-Infrared Reflectance Spectroscopy (NIRS) data. Both models were validated with whole spectra, key wavelengths, and spectral segments derived from Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Competitive Adaptive Reweighted Sampling (CARS). The 1D CNN model demonstrated superior predictive performance, with an 11 % increase in R² and an 18 % increase in RPD using the whole spectra compared to the MPLS model. Specifically, the 1D CNN model with CARS-selected wavelengths achieved an R² of 0.82 and an RPD of 2.15, while the MPLS model achieved an R² of 0.84 and an RPD of 2.25. The segmented spectra approach further enhanced the 1D CNN model's performance, achieving the highest R² of 0.84 and an RPD of 2.28 with CARS-selected segments. These results highlight that the 1D CNN model with CARS-selected wavelengths provides the best predictive performance for protein content in rice bean. The optimized method can lead to rapid early-phase breeding practices and improved quality assessments in leguminous crops, emphasizing the importance of integrating advanced modeling techniques with NIRS for agricultural applications.