Hyperspectral Imaging Combined With a Hybrid Deep Learning Framework for Simultaneous Monitoring of Multiple Quality Indicators in Frozen-thawed Beef

Abstract

Monitoring the quality of frozen meat is crucial for ensuring food safety. This study aimed to evaluate multiple quality indicators of frozen-thawed beef using hyperspectral imaging (HSI), including pH, total volatile basic nitrogen (TVB-N), thiobarbituric acid reactive substances (TBARS) and carbonyl content. Conventional hyperspectral analysis methods rely on complex manual feature engineering and require separate model training for different attributes, which is cumbersome and inefficient. To address these limitations, this study proposed a hybrid deep learning framework integrating computer vision processing, multi-task learning and hyperparameter adaptive optimization, enabling simultaneous assessment of multiple quality indicators. Comparative experiments demonstrated that the proposed method achieved optimal predictive performance for pH, TVB-N, TBARS and carbonyl content, with all coefficient of determination for prediction (R²_P) values exceeding 0.91 and ratio of performance deviation (RPD) values greater than 3.3. By applying the established model to each pixel of the hyperspectral images, chemical distribution maps for the four indicators were generated, providing a comprehensive and visual assessment of beef quality. The results indicated the great potential of integrating HSI with deep learning for simultaneously evaluating multiple quality indicators in frozen-thawed beef.