## 3 Results and Discussion
### Experimental Design and Analysis of Variance  
#### 3.2.1 Building Response Surface Model  
Experimental design and observed responses were presented in Table 2. The whole design consisted of 29 experimental points and the data were analyzed by Design-Expert software. As a result, the extraction yield value could be expressed by the following second order polynomial equations:  
\[\begin{split} Y=3.19+0.38A-0.14B+0.034C+0.096D+0.50 AB-0.12 AC+0.14 AD+ 0.13 BC-\\ 0.20 BD+0.06 CD-0.70A^{2}-0.70B^{2}-0.085C^{2}-0.68D^{2}\end{split} \tag{2}\]  
where \(Y\) was the polysaccharide yield, \(A\), \(B\), \(C\), and \(D\) were extraction time, extraction temperature, enzyme concentration, and pH, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{\(A\) **(Extraction Time, min)**} & \multirow{2}{*}{\(B\) **(Extraction Temperature, \({}^{\circ}\)C)**} & \multirow{2}{*}{\(C\) **(Enzyme Concentration, \%)**} & \multirow{2}{*}{\(D\) **(pH)**} & \multirow{2}{*}{**Yield of PSP (\(\gamma\)) \%**} \\ \hline
1 & 40 & 70 & 3.0 & 6.0 & 2.31 \\
2 & 40 & 60 & 2.0 & 5.0 & 2.47 \\
3 & 50 & 60 & 2.0 & 6.0 & 2.78 \\
4 & 40 & 60 & 3.0 & 5.0 &  
The statistical significance of the regression equation was checked by _F_-value and _p_-value, and the analysis of variance (ANOVA) for the response surface quadratic model was summarized in Table 3. The high model _F_-value (63.31) and low _p_-value (_p_! 0.0001) showed that the model was highly significant. The lack of fit _F_-value with the value of 4.23 indicated the suitability of the model to predict the variations. The determination coefficient (\(R^{2}\) = 0.9845) of the quadratic regression model exhibited that only 1.55% of the total variations could not be explained by the model. The adjusted determination coefficient (\(R^{2}_{\text{adj}}\) = 0.9161) was also very high, showing the high degree of correlation between the experimental and predicted values. At the same time, the coefficient of the variation (_C.V._) with the low value of 4.75 clearly stated the dispersion of data points was around the mean and had a good reliability.  
Table 3 also showed that the linear coefficients (\(A\), \(B\), \(D\)), the quadratic term coefficients (\(A^{2}\), \(B^{2}\), \(D^{2}\)), and the cross product coefficients (\(AB\), \(AC\), \(AD\), \(BC\), \(BD\)) significantly affected the yield (\(p\)! 0.05 or \(p\)! 0.01). In addition, extraction time was the most significant single parameter followed by extraction temperature and pH.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & \begin{tabular}{c} \(A\) **(Extraction** \\ **Time, min)** \\ \end{tabular} & \begin{tabular}{c} \(B\) **(Extraction** \\ **Temperature, \({}^{\circ}\)C)** \\ \end{tabular} & \
\begin{tabular}{c} \(C\) **(Enzyme** \\ **Concentration, \%)** \\ \end{tabular} &
\begin{tabular}{c} \(D\) **(pH)** \\ \end{tabular} & **Yield of PSP (Y) \%** \\ \hline
23 & 50 & 60 & 3.0 & 6.0 & 2.77 \\
24 & 30 & 60 & 2.0 & 6.0 & 1.79 \\
25 & 40 & 60 & 3.0 & 7.0 & 2.54 \\
26 & 30 & 60 & 2.5 & 5.0 & 1.41 \\
27 & 40 & 50 & 3.0 & 6.0 & 2.50 \\
28 & 40 & 50 & 2.5 & 7.0 & 2.27 \\
29 & 30 & 70 & 2.