## 3 Results and Discussion
### Response Surface Process Optimization
These data strongly suggested that the response surface models displayed excellent fitting to the measured (experimental) values. The 3D diagrams constructed based on the models depicted the effect of the process variables on the response (\(\mathrm{Y_{TP}}\)) and visualized the differences between the two solvents tested (Figure 5). Figure 4: Statistical information pertaining to response surface optimization of the 2-propanol-based organosolv treatment of PP. Plot (**A**) shows the desirability function, through which prediction of optimized variable values could be made, as well as the estimated maximum \(\mathrm{Y_{TP}}\). Plot (**B**) shows the correlation of predicted and measured \(\mathrm{Y_{TP}}\) values of each design point considered for the optimization. Values designated with different colors in the inset tables are statistically different. For the 1-propanol treatment, \(t\) (X\({}_{1}\)) was significant, but \(C_{\text{SuAc}}\) (X\({}_{2}\)) was not. However, the effect of \(C_{\text{SuAc}}\) was manifested by its cross term with \(t\) (X\({}_{1}\)X\({}_{2}\)), and its quadratic term (X\({}_{2}\)\({}^{2}\)). Nevertheless, quadratic effects of \(t\) were non-significant. By contrast, both X\({}_{1}\) and X\({}_{2}\) exhibited a significant effect on Y\({}_{\text{TP}}\) when the treatment was performed with 2-propanol, and so did the quadratic terms X\({}_{1}\)\({}^{2}\) and X\({}_{2}\)\({}^{2}\). However, no significant cross terms were seen in this case. By employing the desirability function (Figures 3A and 4A), the theoretical optimal values for both \(t\) and \(C_{\text{SuAc}}\) could be determined, along with the predicted maximum response for each treatment. In the case of 1-propanol, maximum Y\({}_{\text{TP}}\) (19.28 \(\pm\) 0.47 mg CGAE g\({}^{-1}\) DM) would require \(t\) = 60 min and \(C_{\text{SuAc}}\) = 1.5%. For the treatment with 2-propanol, maximum Y\({}_{\text{TP}}\) was estimated to be 15.56 \(\pm\) 0.66 mg CGAE g\({}^{-1}\) DM, with the optimum \(t\) and \(C_{\text{SuAc}}\) being 167 min and 1.36%, respectively. On the grounds of these data, it was made clear that the organosolv treatment with 1-propanol was more efficacious than that with 2-propanol, providing over 19% higher Y\({}_{\text{TP}}\). Furthermore, despite the comparable  
Figure 5: 3D graphs displaying the effect of variation of independent variables on the response (Y\({}_{\text{TP}}\)). Graph (**A,B**) correspond to treatments performed with 1- and 2-propanol. Where \(C_{\text{SuAc}}\) is the concentration of sulfuric acid in the solvent used for the treatment. \(C_{\text{SuAc}}\), the resident time required for the 1-propanol treatment was over than 4.5 times shorter than that determined for the 2-propanol treatment, meaning that the former was a less severe process. To bring out the role of acid catalysis in enhancing total polyphenol recovery from PP, treatments under optimal resident times but without sulfuric acid were also performed. For the treatment with 1-propanol, the presence of acid catalysts boosted Y\({}_{\text{TP}}\) by 87%, while in the 2-propanol treatment, the enhancement was 71% (Figure 6). This outcome highlighted the importance of the acid catalyst in developing the organosolv treatment. A critical appraisal of the Y\({}_{\text{TP}}\) levels attained by employing the 1-propanol/sulfuric acid treatment with those reported in several recent studies would illustrate the effectiveness of the process developed. Optimization of polyphenol extraction with hydroethanolic solutions has been reported to afford Y\({}_{\text{TP}}\) between 9.46 to 10.31 mg CGAE g\({}^{-1}\) DM [19]. With water/methanol solvent, ultrasound-assisted extraction was of equal performance, providing Y\({}_{\text{TP}}\) of 9.33 mg gallic acid equivalents g\({}^{-1}\) DM [27]. Combination of hydroethanolic solvent and microwave-assisted extraction was shown to give a level of 11 mg gallic acid equivalents g\({}^{-1}\) DM [28].