NIRS features and multi-model optimization fusion enabled comprehensive method for quantitative and qualitative assessment of lamb meat quality

作者：Liu J, Zhang B, Li X, Teng L, Wang Z, Bao J, Wang Y, Liu P, Xie X, Li J, Ma X, Zhang M, Luo H · 发表于：Food research international (Ottawa, Ont.) · 年份：2026 · DOI：10.1016/j.foodres.2026.119670 · 研究领域：Red Meat、Food Quality、Animals、Spectroscopy, Near-Infrared、Support Vector Machine、Convolutional Neural Networks、Algorithms、Sheep、Least-Squares Analysis、Soft Computing、Neural Networks, Computer

To address the limitations in near-infrared spectroscopy (NIRS) for lamb meat quality (LMQ) assessment, specifically incomplete index coverage, constrained model accuracy, and the lack of a multi-index-based evaluation method, this study presents a comprehensive method for quantitative and qualitative assessment of LMQ. By combining NIRS features with multi-model optimization and fusion, the proposed method enables precise LMQ control, supports targeted marketing, and promotes maximized economic value. Quantitative models for key quality indices (KQIs) were constructed using a multi-model optimization fusion algorithm based on a convolutional neural network, the Rime-ice-based optimizer, and least squares support vector machine (CNN-RIME-LSSVM). Building on this, this study constructed a KQIs-based LMQ grading method using a multi-method approach integrating Analytic Hierarchy Process (AHP), Entropy Weighting Method (EWM), membership function, Ward's hierarchical clustering, and K-means. The grading information obtained from this grading method is associated with NIRS features to develop a qualitative model for quality grades. This qualitative model was developed using a composite algorithm, namely the Generalized Regression Neural Network optimized by the Caterpillar Fungus Optimizer (CFO-GRNN), thereby achieving rapid and intelligent grading of LMQ. Both the quantitative model (with R2P = 0.9792-0.9932 and RMSEP = 0.0155-0.0283) and the qualitative model (with Accuracy = 99...