Objective To achieve quality control during the production of Zhisou Qingguo Wan (ZQW, 止嗽青果丸), the pixel unmixing method from remote sensing was introduced into the uniformity evaluation of traditional Chinese medicine preparations, so as to visually analyze the mixing uniformity of medicinal powder and honey in ZQW. Methods Multiple preprocessing methods, including Savitzky-Golay (SG) smoothing, first derivative (1D), second derivative (2D), standard normal variate (SNV) and multiplicative scatter correction (MSC) were adopted to optimize spectral data and eliminate interferences such as background noise and spectral scattering. Based on pure substance spectra combined with the mixture tuned matched filtering (MTMF) algorithm, the abundance scores and mixture feasibility scores of target endmembers were integrated. Material abundance estimation and binarization at the pixel level of mixed samples were then performed, enabling the visualization of the spatial distribution of medicinal powders. Meanwhile, a residual network (ResNet) model was constructed for the prediction and pixel-level visualization of honey content. Results Compared with traditional quantitative models such as partial least squares regression (PLSR), multiple linear regression (MLR), ridge regression (RR), least absolute shrinkage and selection operator (Lasso), random forest (RF) and ElasticNet, the R² of the prediction set was significantly improved from 0.837 6 to 0.931 1 with the proposed model. Conclusion The method combining pixel unmixing and deep learning intuitively presents the spatial distribution of medicinal raw materials and excipients in the preparation. A novel insight for the refined production and quality control of traditional Chinese medicine preparations is provided.

Zhisou Qingguo Wan  /  hyperspectral  /  pixel unmixing  /  mixing uniformity  /  visualization