Rapid and non-destructive pork quality prediction using an interpretable deep learning-ensemble model

Pork is among the most widely consumed meats worldwide, rendering objective, high-throughput quality assessment essential for food safety, supply chain efficiency, and consumer trust. To address this need, we introduce a rapid, non-destructive analytical framework that integrates visible–near-infrared hyperspectral imaging (HSI, 400–1100 nm) with an interpretable hybrid deep learning architecture for the quantitative prediction of five critical pork quality indicators: lightness ( L* ), pH, protein content, moisture content, and fat content. At its core lies BO-1D-CNN-XGB-Reg—a novel, purpose-built model that synergistically combines a one-dimensional convolutional neural network (1D-CNN) for hierarchical spectral feature extraction, an XGBoost regressor for robust, nonlinear prediction, and Bayesian optimization for principled, global hyperparameter tuning. Evaluated on a rigorously curated dataset of 400 pork samples exhibiting substantial heterogeneity in breed, cut, and post-mortem aging, the model consistently outperformed established benchmark algorithms—including PLSR, SVR, and standalone CNN—achieving high predictive performance, as quantified by coefficient of determination (R²p): L* (0.94), pH (0.93), protein (0.94), moisture (0.97), and fat (0.94). Crucially, to ensure scientific interpretability beyond predictive accuracy, we applied SHAP (SHapley Additive exPlanations) to quantify feature attribution across the full spectral range; this analysis robustly linked spectrally influential wavelengths to well-documented biochemical and biophysical mechanisms—including myoglobin redox transitions, protein secondary structure changes, water–protein interactions, and lipid oxidation products. Collectively, this work delivers a validated, end-to-end proof-of-concept that bridges deep learning and explainable AI (XAI), establishing a transparent, high-fidelity framework for next-generation meat quality analysis.