[Rapid prediction of phenolic acid content in Salviae Miltiorrhizae Radix et Rhizoma by hyperspectral imaging].

This study aims to establish a model for rapid non-destructive quantitation of main phenolic acids in Salviae Miltiorrhizae Radix et Rhizoma by hyperspectral imaging combined with chemometric methods. Hyperspectral imaging was performed for 420 Salviae Miltiorrhizae Radix et Rhizoma samples collected from Shandong, Hebei, Shanxi, Sichuan, and Anhui. Original spectral data(ORI) were preprocessed by first derivative(D1), second derivative(D2), savitzky-golay(SG) smoothing, multiplicative scattering correction(MSC), and standard normal variate(SNV) algorithms. Models for predicting the content of salvianolic acid B, lithospermic acid, danshensu, salvianolic acid Y, and rosmarinic acid were constructed via partial least squares regression(PLSR), backpropagation neural network(BPNN), and random forest regression(RFR). Additionally, iterative retained information variable(IRIV), successive projections algorithm(SPA), uninformative variables elimination(UVE), and variable iterative space shrinkage approach(VISSA) were employed for feature extraction to refine model efficiency. The results demonstrated that the PLSR model exhibited the best predictive performance. Significant differences in the number of wavelengths selected by different feature extraction methods directly impacted model performance. Therefore, preprocessing methods should be combined adaptively with appropriate feature extraction techniques to enhance model robustness. The optimal models for each component were as follows. VISSA-D1-PLSR models were selected for predicting salvianolic acid B, lithospermic acid, and salvianolic acid Y, with prediction set coefficient of determination(R_p~2) values of 0.942, 0.818, and 0.797 and residual predictive deviation(RPD) values of 4.158, 2.308, and 2.186, respectively. For danshensu and rosmarinic acid, the full-spectrum SG-PLSR models demonstrated the best performance, with R_p~2 values of 0.803 and 0.702 and RPD values of 2.162 and 1.782, respectively. This study demonstrates that HSI technology combined with chemometric methods can provide reliable technical support for predicting multiple phenolic acids in Salviae Miltiorrhizae Radix et Rhizoma and the real-time rapid evaluation of this medicinal material.