Design expert v 8

According to the single-factor experimental results of the extraction method, the ultrasonic time (X₁), the pH of the extraction solution (X₂), and the total volume of the extraction buffer (X₃) were selected as the optimized factors in the RSM experiments. The dependent variables (Y) here were the average recovery rates of SMZ and E2. Design-Expert.V.8.0.6 was used to design a 15-run Box-Behnken (BBD) experiment. Here, a wildly used quadratic regression model was selected [50 (link)], and the corresponding formula is as follows:
In the formula, Y is the predicted value obtained from the RSM method. A_i, A_ii, and A_ij refer to the linear coefficient, the quadratic term coefficient, and the coefficient of interactive terms, respectively. X_i and X_j represent the independent variables in the model. The obtained results from the RSM model were analyzed by Design-Expert V.8.0.6. (StatEase, Minneapolis, MN, USA) When the P value of the model was less than 0.05, it can be indicated that the obtained model is significant [51 (link)].

Based on the single-factor study, the optimized experimental conditions for maximum extraction of essential oil from C. citronella leaves using supercritical CO₂ were determined using the Design-expert V8.0.6. Given the influence of the above variables, the extraction processing of essential oil was optimized with a Box–Behnken design (BBD) and response surface methodology (RSM) [13 ]. For the BBD experiments of 4 factors, there were 24 experiments augmented and 3 replications at the center values (zero level). The levels of each factor and the design table are given in Tables 1 and 2.
Design-expert V8.0.6 was used for the experiment designs and subsequent regression analysis of the response data. Statistical analysis of the model was performed to evaluate the analysis of variance (ANOVA). The quality of the polynomial model equation was judged statistically using the coefficient of determination R² and adjustment R², and its statistical significance was determined by the F value and P value. The significance of the regression coefficients was tested by some parameters, such as coefficient of variation (CV) and adequate precision.

The experiment was repeated three times, and the results were expressed as $\overline{\mathrm{x}}\pm s$ . An analysis of variance and correlation analysis were performed using SPSS (version 16.0, Inc., Chicago, IL, USA) software. p < 0.05 indicated significant differences, and p < 0.01 indicated extremely significant differences. Origin Pro 9.1 64 Bit, Design-Expert. V8.0.6, and Excel 2010 were used to draw curves.