Optimization of the extraction process of Sanhuang Qingre Formula by integrating response surface methodology, grey correlation analysis, and machine learning.

This study aimed to optimize the ethanol reflux extraction process of the Sanhuang Qingre Formula (SHQRF) using response surface methodology (RSM), grey correlation analysis (GCA), and machine learning. A combination of single-factor experiments and a Box-Behnken design (BBD) was applied to optimize three key variables: ethanol concentration, reflux time, and liquid-solid ratio. During the experiments, the comprehensive score—calculated from the contents of 11 bioactive components (coptisine, epiberberine, berberine hydrochloride, palmatine, baicalin, chrysin-7-O-β-glucuronide, wogonoside, baicalein, wogonin, oroxylin A, atractylodin) and extraction yield—was used as the evaluation index. The combined weighting method of the fuzzy analytic hierarchy process and entropy weight method was employed to determine the comprehensive score. Results showed that the process optimized by RSM and the support vector machine (SVM) model achieved a higher comprehensive score of 56.08 compared with that of 52.67 obtained by GCA optimization. Consequently, the optimal extraction parameters for SHQRF were determined as follows: ethanol concentration of 55%, reflux time of 2 h per cycle, and a liquid-solid ratio of 12 mL/g. The optimized process significantly increased the individual and total contents of the 11 target components compared with the original process (the pre-optimization process). Furthermore, the original process, the GCA-optimized process, and the optimal processes derived from RSM and SVM were distinctly classified by hierarchical cluster analysis (HCA) and principal component analysis (PCA). This study provides an effective strategy to enhance the extraction efficiency and quality of SHQRF and offers new insights and approaches for optimizing the extraction of active components in traditional Chinese medicine.