Optimization of Vacuum Drying Process for Hugan Jiedu Extract Using Box-Behnken Design-Response Surface Methodology Combined with Artificial Neural Network

OBJECTIVE To optimize the vacuum drying process of Hugan Jiedu extract and determine the optimal parameters.

METHODS The contents of gentiopicroside, chlorogenic acid, paeoniflorin, berberine hydrochloride, and schisandrin A in the extract were determined by HPLC. The overall desirability (OD) value, derived from the five component contents and the drying rate, served as the comprehensive evaluation index. Following single-factor experiments on relative density, drying thickness, and temperature , the process was optimized using a hybrid AHP-CRITIC weighting method combined with Box-Behnken design-response surface methodology (BBD-RSM). An artificial neural network (ANN) was further employed to validate the predictive accuracy of the established model.

RESULTS The optimal parameters were identified as relative density 1.25, drying thickness 8 mm and drying temperature 66 ℃, yielding a predicted OD value of 0.821 1. In three parallel verification trials under these conditions, the average contents of gentiopicroside, chlorogenic acid, paeoniflorin, berberine hydrochloride, and schisandrin were 5.97, 4.75, 5.89, 1.83, and 0.47 mg·g^-1, respectively, with an average drying rate of 2.73%·h^-1. The composite OD values were 0.7538, 0.8012, and 0.7765, respectively, yielding an average measured OD of 0.777 2 with an RSD of 3.05% (< 5%),which demonstrated high consistency with the predicted value.

CONCLUSION The optimized vacuum drying process for Hugan Jiedu extract is stable and reproducible, providing a scientific basis for the standardized production and quality control of the extract.