Combined Hyperspectral Imaging with Wavelet Domain Multivariate Feature Fusion Network for Bioactive Compound Prediction of Astragalus membranaceus var. mongholicus

The pharmacological quality of Astragalus membranaceus var. mongholicus (AMM) is determined by its bioactive compounds, and developing a rapid prediction method is essential for quality assessment. This study proposes a predictive model for AMM bioactive compounds using hyperspectral imaging (HSI) and wavelet domain multivariate features. The model employs techniques such as the first-order derivative (FD) algorithm and the continuum removal (CR) algorithm for initial feature extraction. Unlike existing models that primarily focus on a single-feature extraction algorithm, the proposed tree-structured feature extraction module based on discrete wavelet transform and one-dimensional convolutional neural network (1D-CNN) integrates FD and CR, enabling robust multivariate feature extraction. Subsequently, the multivariate feature cross-fusion module is introduced to implement multivariate feature interaction, facilitating mutual enhancement between high- and low-frequency features through hierarchical recombination. Additionally, a multi-objective prediction mechanism is proposed to simultaneously predict the contents of flavonoids, saponins, and polysaccharides in AMM, effectively leveraging the enhanced, recombined spectral features. During testing, the model achieved excellent predictive performance with R 2 values of 0.981 for flavonoids, 0.992 for saponins, and 0.992 for polysaccharides. The corresponding RMSE values were 0.37, 0.04, and 0.86; RPD values reached 7.30, 10.97, and 11.16; while MAE values were 0.14, 0.02, and 0.38, respectively. These results demonstrate that integrating multivariate features extracted through diverse methods with 1D-CNN enables efficient prediction of AMM bioactive compounds using HSI.