Simultaneous detection of water content and ferulic acid content in Angelica sinensis using NIR-HSI and multi-task...

To achieve rapid, nondestructive, and intelligent quality detection of A. sinensis, this study combines NIR-HSI technology with deep learning. It proposes a multi-task convolutional neural network (MT-CNN) equipped with a multi-head hierarchical attention mechanism. First, a multi-task sample partitioning algorithm (MSPXY) was developed with two target variables to improve the representativeness and balance of sample division. Second, a multi-head hierarchical attention mechanism (4HSA + 8HSA) was added to the convolutional neural network to capture both local and global spectral features. Finally, an uncertainty-driven adaptive loss-balancing strategy (UW) was used to achieve dynamic collaborative optimization of multiple tasks. The model that combines these strategies (4HSA + 8HSA + 1DCNN(MSPXY) + UW) achieved the best performance in predicting the moisture and ferulic acid contents of Angelica sinensis. The R² values reached 0.9925 and 0.9880, and both RPD values exceeded 9. The model performed much better than traditional PLSR and single-task CNN models. This study demonstrates that the method can effectively and accurately predict both the moisture content and ferulic acid content of Angelica sinensis. It provides a rapid, convenient, and non-destructive testing approach for quality control in industrial production. Furthermore, this method offers an efficient and accurate new approach for quality testing in other food products.