## 3 Results and Discussion  
RMS is a collection of statistical and mathematical techniques useful for developing, improving, and optimizing processes [22]. RMS is preferred because it minimizes the number of experiments for a specific number of factors and their levels [23]. In general, RMS has two main types of designs--the Box-Behnken design (BBD) and the central-composite design (CCD). The BBDs differ from the CCDs in that they use fewer runs and only three levels, compared to the five in CCD. BBDs were initiated to limit the sample size as the number of parameters increased. The BBDs pair well with the green approach for the extraction of bioactive compounds, using the fewest resources possible. For this reason, the BBD was preferred in the present study. Table 2 summarizes the obtained models of this study. The software product Design-Expert defines the dependence of TPC on the input variables as linear (Table 3) with statistically insignificant coefficients in front of the added members. Three factors (hydro module, duration, and temperature) influence the TPC values (Table 4). Other temperature-dependent studies on the topic of phenolic compounds are available in the literature [24]. Duration is also reported as critical to phenolic content availability and possible damage and degradation [25]. The Extractant's effect was not statistically significant. However, many papers reveal that different extracts of the same plant result in different levels of phenolic compounds [26]. The F-value of the model itself is 10.08 and the \(p\)-value is \(<\)0.0001, indicating that the model is significant. The coefficient of determination is R\({}^{2}\) = 0.63, while adjusted R\({}^{2}\) = 0.56. The main idea of the \"adjusted\" R-squared statistic is to \"penalize\" the addition of terms that do not add statistical value. In this case, the only statistically insignificant term is the D-Extractant factor. Its inclusion in the TPC model caused the R-squared to decrease to a value of 0.56. In addition to the coefficient of determination, the standard error S is also applied as a measure of goodness-of-fit in the regression analysis. S is in the units of the dependent variable. In general, a higher coefficient of determination is usually associated with a smaller standard error of the regression, since a higher fit of the model to the data usually leads to smaller prediction errors. On the other hand, the standard error of the regression may decrease with a larger range of values of the independent variables or with a larger sample size, regardless of the value of the coefficient of determination. The standard error here is 5.4, meaning that the TPC data differ from the regression line by an average of 5.4. Therefore, when estimating the TPC value, an average error of less \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dependent** & **Symbols** & **R\({}^{2}\)** & **Model** \\ \hline TPC & \(Y_{1}\) & 0.63 & \(Y_{1}=90.11+9.26X_{1}-4.93X_{2}+6.95X_{3}\) \\  & \(Y_{2}\) & 0.82 & \(Y_{2}=15.34+3.17X_{1}+2.49X_{2}-4.49X_{4}-6.59X_{1}X_{4}+4.14X_{2}X_{3}+7.36X_ {2}X_{4}+8.5X_{3}X_{4}\) \\  & & & \(Y_{3}=288.82+23.13X_{3}+18.85X_{4}-72.77X_{1}X_{2}+42.96X_{2}X_{3}+20.81X_{3}X_ {4}-19.4X_{1}^{2}-19.38+352X_{4}^{2}\) \\  & & & \(Y_{5}=99.82-5.98X_{1}+3.02X_{2}-5.1X_{1}X_{3}+13.68X_{2}X_{3}-15.66X_{2}X_{4}+11.25X_{3}X_{4}-12.63X_{2}^{2}-6.48X_{2}^{2}\) \\  & & & \(Y_{6}=238.93-28.45X_{2}+35.71X_{3}+123.95X_{1}X_{3}-58.88X_{1}X_{4}+85.91X_{2}X_ {4}+53.69X_{2}^{2}-53.5X_{2}^{2}-33.74X_{4}^{2}\) \\ \(\alpha\)-Glucosidase & \(Y_{7}\) & 0.97 & \(Y_{7}=0.04-0.02X_{4}+0.016X_{1}X_{4}+0.37+0.057X_{2}^{2}-0.013X_{3}^{2}\) \\  & & & \(Y_{8}=0.24-0.064X_{1}+0.08X_{2}+0.05X_{3}-0.12X_{4}+0.07X_{1}X_{2}+0.05X_{1}X_ {3}+0.19X_{2}X_{3}-0.054X_{2}X_{4}+0.26+0.25X_{2}^{2}-0.082X_{3}^{2}-0.076X_{4}^ {2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Coded dependent variables used in the RSM design. than 5.4 mg GAE/100 g fw can be expected. Figure 1 reveals some of the most important model informational graphs.