Non-Destructive Assessment of Beef Freshness Using Visible and Near-Infrared Spectroscopy with Interpretable Machine Learning

Beef freshness is a critical indicator of meat quality and safety, and its rapid, non-destructive detection is of significant importance for ensuring consumer health and enhancing quality control throughout the meat industry chain. This study developed a novel methodology for non-destructive beef freshness assessment using visible and near-infrared (Vis-NIR) spectroscopy combined with machine learning, explainable artificial intelligence (xAI) techniques, and the SHapley Additive exPlanations (SHAP) framework. An improved hybrid heuristic method, particle swarm optimization-genetic algorithm (PSOGA), was used for feature selection, optimizing the wavelength subset for predicting beef quality indicators, including total volatile basic nitrogen (TVB-N) and color parameters (<i>L*</i>, <i>a*</i>, and <i>b*</i>). The eXtreme Gradient Boosting (XGBoost) was employed for regression modeling, and the results showed that PSOGA significantly outperforms traditional methods, with the PSOGA-XGBoost model achieving a satisfactory prediction accuracy (R²p values of 0.9504 for TVB-N, 0.9540 for <i>L*</i>, 0.8939 for <i>a*</i>, and 0.9416 for <i>b*</i>). The SHAP framework identified the key wavelengths as 1236 nm and 1316 nm for TVB-N, 728 nm for <i>L*</i>, 576 nm for <i>a*</i>, and 604 nm for <i>b*</i>, providing valuable insights into the determination of key wavelengths and enhancing the interpretability of the model. The results demonstrated the effectiveness of PSOGA and SHAP, providing a promising analytical method for monitoring beef freshness.