Simultaneous monitoring of two comprehensive quality evaluation indexes of frozen-thawed beef meatballs using hyperspectral imaging and multi-task convolutional neural network

The quality of beef meatballs during repeated freeze-thaw (F-T) cycles was assessed by multiple indicators. This study introduced a novel quality evaluation method using hyperspectral imaging (HSI) and multi-task learning. Seventeen quality indicators were analyzed to assess the impact of F-T cycles. Subsequently, a comprehensive quality index (CQI) and a comprehensive weight index (CWI) were constructed from 11 key indicators via factor analysis. By integrating HSI data from 150 samples with multi-task convolutional neural network (MT-CNN), the feasibility of simultaneous monitoring of CQI and CWI of the beef meatballs was explored. The results demonstrated that MT-CNN achieved superior predictions for CQI (RMSE_p = 1.24, R² = 0.94) and CWI (RMSE_p = 20.436, R² = 0.94) compared to traditional machine learning and single-task CNN approaches. Furthermore, the deterioration trends of beef meatballs during multiple F-T cycles were effectively visualized. Thus, the integration of HSI and MT-CNN enabled efficient prediction of comprehensive evaluation indexes for beef meatballs, contributing to their quality control.