## 3 Results and Discussion
### Extraction Process Optimization
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Design Point** & \multicolumn{3}{c}{**Independent Variables**} & \multicolumn{3}{c}{**Polyphenols Yield (\(Y_{\text{T}p}\), mg GAE g\({}^{-1}\) dm)**} \\  & \(R_{\text{LLR}}\) (\(X_{\text{1}}\), mL g\({}^{-1}\)) & \multicolumn{3}{c}{CBS (\(X_{2}\), \% au/n)} & \multicolumn{3}{c}{\(T\) (\(X_{3}\), \% au/n)} & \multicolumn{3}{c}{GL-Lyss} \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\ \hline  & & & & & & & & & \\ \hline
1 & 50(\(-\)1) & 90(\(+\)1) & 60(0) & 124.58 & 123.65 & 78.59 & 79.84 & 80.66 & 78.68 \\
2 & 100(0) & 75(0) & 60(0) & 131.91 & 133.49 & 73.99 & 74.63 & 73.11 & 74.81 \\
3 & 50(\(-\)1) & 75(0) & 40(\(-\)1) & 101.50 & 100.40 & 61.87 & 62.28 & 70.10 & 72.61 \\
4 & 195(\({}^{\circ}\)1) & 75(0) & 80(\(+\)1) & 191.60 & 139.70 & 87.81 & 87.40 & 114.93 & 112.39 \\
5 & 50(\(-\)1) & 75(0) & 80(\(+\)1) & 146.70 & 144.40 & 79.21 & 77.80 & 95.53 & 96.93 \\
6 & 150(\(+\)1) & 90(\(+\)1) & 60(0) & 165.21 & 160.89 & 84.39 & 84.63 & 88.07 & 89.81 \\
7 & 50(\(-\)1) & 60(\(-\)1) & 60(0) & 92.37 & 96.70 & 67.45 & 67.20 & 75.96 & 74.23 \\
8 & 100(0) & 60(\(-\)1) & 80(\(+\)1) & 153.50 & 151.47 & 76.79 & 78.45 & 98.03 & 98.37 \\
9 & 150(\(+\)1) & 60(\(-\)1) & 60(0) & 146.00 & 146.93 & 78.86 & 77.61 & 74.91 & 77.10 \\
10 & 100(0) & 90(\(+\)1) & 40(\(-\)1) & 120.32 & 122.36 & 72.41 & 70.76 & 74.51 & 74.17 \\
11 & 100(0) & 60(\(-\)1) & 40(\(-\)1) & 114.00 & 110.77 & 59.77 & 99.61 & 67.40 & 66.60 \\
12 & 100(0) & 75(0) & 60(0) & 136.65 & 133.49 & 75.92 & 74.63 & 76.62 & 74.81 \\
13 & 150(\(+\)1) & 75(0) & 40(\(-\)1) & 136.26 & 138.60 & 66.48 & 67.89 & 77.62 & 71.14 \\
14 & 100(0) & 75(0) & 60(0) & 131.91 & 133.49 & 73.99 & 74.63 & 74.69 & 74.81 \\
15 & 100(0) & 90(\(+\)1) & 80(\(+\)1) & 177.54 & 180.79 & 86.80 & 86.96 & 107.16 & 107.96 \\ \hline \multicolumn{10}{c}{\({}^{*}\)  
On the basis of the Box-Behnken experimental design model, an empirical relationship expressed by a second-order-polynomial equation with interaction terms was fitted between the obtained experimental design and the input variables. The final equations in terms of coded significant variables and interactions are given in Table 4.  
It is obvious that the models explain 99%, 98%, and 99% in polyphenol extraction yield (r\({}^{2}\) = 0.99 for GL-Lys, r\({}^{2}\) = 0.98 for GL-Pro, and r\({}^{2}\) = 0.99 for GL-Arg) and have relatively small amounts of random error or root-mean-square error (RMSE); RMSE = 4.3927 for GL-Lys, 1.7598 for GL-Pro and 0.0001 for GL-Arg. This leads to the conclusion that the developed models were highly significant. The lack of fit test is another parameter to support mathematical model validity. This test is used to detect the existence of observations that have a poor fit to the model, even if the overall trend is highly significant [35]. The lack of fit is found to be insignificant when limited observations are outside of the shaded confidence interval region of the actual by-predicted plots. In this study, the models are found to be robust for all cases since all the observations were within the trend of the interval, and a maximum r\({}^{2}\) is reached for GL-Lys (r\({}^{2}\) = 0.9985, \(p\) = 0.2235); GL-Pro (r\({}^{2}\) = 0.9958, \(p\) = 0.5723); GL-Arg (r\({}^{2}\) = 0.9979, \(p\) = 0.2223). The coefficient of variation (CV) and the coefficient of correlation between the measured and the predicted values were determined to assess the goodness of the fit of the models. The results (CV, %, 3.13 GL-Lys, 2.35 GL-Pro, and 0.01 GL-Arg) indicated low CV, which suggests that the deviations between the experimental and the predicted values are low [36]. The low CV values along with the r\({}^{2}\) presented in Table 4 are strong indications that the developed models presented adequate reliability and precision. The measured experimental values under optimum conditions were compared with the predicted values (Table 5).