Optimization of processing technology of Hemsleya omeiensis by orthogonal design and central-composite design-response surface methodology / 中草药

Objective: To optimize the processing technology of Hemsleya omeiensis processed by licorice juice. Methods: HPLC was employed to determine the content of hemslecin A.Taking content of hemslecin A as index, the orthogonal test was adopted to optimize the covered moistening time, drying-time, and processing temperature. And the central composite design-response surface methodology (CCD-RSM) was adapted to optimize moistening time and drying-time further. Results: The optimum processing technology of H. omeiensis by the orthogonal test was covered moistening time of 7 h, drying-time of 12 h, and processing temperature at 80℃. The optimum processing technology by CCD-RSM was covered moistening time of 7.48-8.56 h, drying-time of 12.06-13.12 h, and processing temperature at 80℃. Conclusion: The experimental design method is precise and the data are reliable with the model. It is the first time that H. omeiensis is processed with licorice juice. Besides, it establishes the processing technology of H. omeiensis and provides a theoretical basis for the processing technology of H. omeiensis with licorice juice.

Optimization of preparation process of Zizyphi Spinosi Semen flavonoids dropping pills by central composite design-response surface methodology / 中国药学杂志

OBJECTIVE: To optimize the formulations of Zizyphi Spinosi Semen flavonoid dropping pills by central composite design-response surface methodology. METHODS: Central composite design-response surface methodology was applied to optimize the preparation process with the dropping distance, dropping temperature, and cooling temperature as independent variables, while dependent variables were the variance of weight, spherical degree, and dissolution time. SPSS software was used to fit multivariate linear equation and second-order polynomial equation for experimental data. Response surface and contour plot were delineated according to best-fit mathematic models by Origin software, and the optimum formulation was selected by response surface. RESULTS: Quadratic multinomial model was better than multivariate linear model, and the regression coefficient was 0.981. The bias between the observed and predicted values of the optimum process was negligible, indicating the high predictability of the model. CONCLUSION: The model established by central composite design-response surface methodology and SPSS software is accurate for prediction and can be used to optimize the preparation process of Zizyphi Spinosi Semen flavonoids dropping pills.