Automated and explainable machine learning for monitoring lipid and protein oxidative damage in mutton using hyperspectral imaging

Current detection methods for lipid and protein oxidation using hyperspectral imaging (HSI) in conjunction with machine learning (ML) necessitate the involvement of data scientists and domain experts to adjust the model architecture and tune hyperparameters. Additionally, prediction models lack explainability in the predictive outcomes and decision-making process. In this study, ML, automated machine learning (AutoML) and automated deep learning (AutoDL) models were developed for visible near-infrared HSI of mutton samples treated with different freeze-thaw cycles to evaluate the feasibility of building prediction models for lipid and protein oxidation without manual intervention. SHapley Additive exPlanations (SHAP) were utilized to explain the prediction models. The results showed that the AutoDL attained the effective prediction models for lipid oxidation (R2p = 0.9021, RMSEP = 0.0542 mg/kg, RPD = 3.3624) and protein oxidation (R2p = 0.8805, RMSEP = 3.8065 nmol/mg, RPD = 3.0789). AutoML driven stacked ensembles further improved the generalization ability of the models, predicting lipid and protein oxidation with R2p of 0.9237 and 0.9347. The important wavelengths identified through SHAP closely align with the results obtained from spectral analysis, and the analysis also determined the magnitude and direction of the impact of these important wavelengths on the model outputs. Finally, changes in lipid and protein oxidation of mutton in different freeze-thaw cycles were visualized. The research indicated that the combination of HSI, AutoML and SHAP may generate high-quality explainable models without human assistance for monitoring lipid and protein oxidative damage in mutton.