Explainable Deep-Shallow Feature Fusion of Two-Dimensional Encoded Vis-NIR Spectra and RGB Image Features for Chilled Lamb Freshness Assessment.

Quality deterioration of chilled lamb during storage poses a challenge to meat quality and safety control, making rapid and accurate freshness-grade classification essential. Existing methods based on either spectral information or RGB image information alone are insufficient to simultaneously characterize internal chemical changes and external appearance changes during lamb quality deterioration. To address this issue, this study developed a chilled lamb freshness-grade classification method by integrating deep features from two-dimensional visible-near-infrared (Vis-NIR) spectral encoding with RGB image features. In this method, one-dimensional Vis-NIR spectra were transformed into two-dimensional encoded images using Gramian angular difference field (GADF), Gramian angular summation field (GASF), Markov transition field (MTF), and recurrence plot (RP) to enhance the representation of inter-wavelength structural relationships in spectral sequences, thereby compensating for the limited ability of conventional one-dimensional spectral modeling to capture global correlations and local variation information. Meanwhile, recursive feature elimination (RFE)-selected spectral deep features were fused with Spearman-selected RGB image features to construct a deep-shallow classification model. The results showed that the fusion models outperformed the single-modality models, with GADF(10%)+Image-SVM achieving the best performance, yielding an accuracy, F1-score, and MCC of 0.966, 0.957, and 0.946, respectively. Shapley additive explanations (SHAP) analysis further indicated that GADF deep features were the primary contributors, while RGB image features provided effective complementary information, demonstrating the potential of the proposed method for rapid and nondestructive freshness-grade classification of chilled lamb.