Research on rapid and non-destructive detection model for pork freshness based on dual-branch hyperspectral feature extraction network combined with machine learning

Traditional methods for assessing pork freshness, such as TVB-N and TVC measurement, are time-consuming and destructive. This study introduces a dual-branch hyperspectral feature extraction network (HybridFeatureExtractor). The designed feature extraction module consists of a spectral branch employing the SE attention mechanism, a spatial branch incorporating ASPP, and a gated fusion mechanism, effectively capturing spectral and spatial information across multiple scales. Together with machine learning regressors, the framework achieved excellent predictive performance, with R2 of 0.9786 (RMSE=2.4685) for TVB-N and R2 of 0.9597 (RMSE=0.3066) for TVC. Compared with chemometric approaches (e.g., SG+SPA, SNV+CARS), the proposed method shows superior accuracy and robustness. This hyperspectral modeling strategy may provide a robust and highly practical technical pathway for non-destructive, assessment of pork freshness. • A deep dual-branch hyperspectral feature extraction module was proposed for pork freshness detection. • Introduce the SE attention mechanism and the ASPP module to enhance the feature capture ability. • Modeling with PLSR and SVR has improved the prediction accuracy and generalization ability. • The RPD values for TVB-N and TVC reached as high as 7.1204 and 5.1831, respectively. • Ablation studies and attention analysis confirmed the model's robustness and interpretability.