## 3 Results and Discussion  
### Liquefaction and Saccharification  
By applying multiple regression analysis on the experimentally determined data, the regression coefficients were estimated and the following second-order polynomial equation (Equation (2)) was obtained using Minitab software:  
\[Y=117.901-1.359X_{1}\ -33.643X_{2}\ -1.559X_{3}-\ 33.708\ X_{2}^{2}\ -0.219X_{3}^{2} +8.742X_{2}X_{3} \tag{2}\]  
The lowest loss in non-dissolved solids of waste potato mash observed at the combination of 50 \({}^{\circ}\)C, 0.6 mL of \(\alpha\)-amylase, and 10 g dry waste potato mash with a 43.7% loss. On the other hand, the maximum loss in non-dissolved solids of waste potato mash observed at the combination of 95 \({}^{\circ}\)C, 0.6 mL of \(\alpha\)-amylase, and 1 g dry waste potato mash with a 79% loss (Table 3). Also, %loss is compared for 1 and 10 g dry waste potato mash while temperature and enzyme concentrations were kept same to determine the enzyme-substrate relation. From counterplots, it can be seen that higher dry waste potato mash require higher levels of enzymes (Data not shown). When counterplots are interpreted, it can be seen that increasing temperature (more than 95 \({}^{\circ}\)C) might have resulted higher % loss, but at that time enzyme would not be active. By using the results of Box-Behnken design, Box-Behnken optimizer was used to determine optimum conditions of liquefaction, and then an optimum parameter combination suggested as 95 \({}^{\circ}\)C, 1 mL of \(\alpha\)-amylase, and 4.04 g dry weight of waste potato mash per 100 mL of deionized water, with a prediction of 68.8% loss in dry weight. Verification for this optimum combination was performed with three replications and 74 \(\pm\) 0.05% of loss in non-dissolved components was obtained, which is very close the estimated value by the RSM optimizer. Waste potato mash slurry, 4.04 g dry weight of waste potato mash per 100 mL of deionized water, was liquefied at 95 \({}^{\circ}\)C after addition of 1 mL of \(\alpha\)-amylase solution, which was determined as optimum combination for liquefaction process. This liquefied slurry obtained at the optimum liquefaction conditions was used for saccharification. By applying multiple regression analysis on the experimentally determined data, the regression coefficients were estimated as shown in the following second-order polynomial equation (Equation (3)) was obtained using Minitab software:  
\[Y=31.0208-0.8X_{1}\,+16.375X_{2}\,-0.0278X_{3}+\,1.9792\,X_{2}^{2}-0.2417X_{1}X _{2}-0.1667X_{2}X_{3} \tag{3}\]  
Results of saccharification were summarized as glucose concentration, because saccharification is a conversion of liquefied starch to glucose (Table 4). Levels of temperature were 30, 45, and 60 \({}^{\circ}\)C, whereas 0.2, 0.6, and 1 mL of amyloglucidase solutions were compared to find out optimum combination for saccharification. Moreover, time was another factor, which also had three levels; 24, 48, and 72 h. Minimum glucose (19.2 g/L) obtained at 30 \({}^{\circ}\)C, 0.2 mL of amyloglucosidase, and 48 h combination. Maximum glucose concentration was found at 60 \({}^{\circ}\)C, 72 h and 0.6 mL of amyloglucidase with 30.7 g/L glucose concentration. Although longer saccharification might result  
\begin{table}
\begin{tabular}{l l l l l l} \hline \multirow{2}{*}{**Run**} & \multicolumn{3}{c}{**Factors**} & \multicolumn{3}{c}{**\% Loss**} \\ \cline{2-6}  & \(\mathbf{X_{1}}\) & \(\mathbf{X_{2}}\) & \(\mathbf{X_{3}}\) & **Experimental** & **Predicted** \\ \hline
1 & 50 & 0.2 & 5.5 & 52.1 & 60.95 \\
2 & 72.5 & 0.2 & 10 & 58.07 & 39.68 \\
3 & 72.5 & 0.6 & 5.5 & 60.09 & 57.48 \\
4 & 95 & 0.6 & 10 & 52.1 & 62.39 \\
5 & 95 & 1 & 5.5 & 65.13 &  
in higher glucose conversion, this wasn't recommended due to high energy costs.