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 (R