## 3 Results and Discussion
### Multifactorial Experiment
#### 3.2.2 Regression Model Deff, Sp, dE  
Function of Moisture Diffusion Coefficient Deff (\(\times 10^{-10}\) m\({}^{2}\)/s)  
The relationship between the moisture diffusion coefficient Deff and T, P and A is represented by the following regression function, based on the regression function (8) and Figures 13-16:  
\[ \begin{array}{l}\text{Deff}=-\;12.7671+0.48175\text{T}+0.04025\text{P}+5.9229 2\text{A}-0.00341667\text{T}^{2}-0.0675\text{TA}-\\ 0.000133073\text{P}^{2}-8.51042\text{A}^{2}\end{array}  \tag{8}\]  
Conditional function: \(40\leq T\leq 50;80\leq P\leq 120;0\leq A\leq 0.4\) (R\({}^{2}\) = 0.9917)  
The moisture diffusion coefficient Deff is affected by the first- and second-order parameters of drying temperature T, ultrasonic power P, intermittency ratio A, and also simultaneously affected by both the temperature and intermittency ratio. The influence level and order of parameters are shown in Figure 13.  
In the experimental area, temperature has the strongest influence on moisture diffusion coefficient Deff. The higher the temperature T, the higher the moisture diffusion coefficient Deff. The next level of influence is the ultrasonic power P and the intermittency ratio A. It is necessary to choose the appropriate power level and intermittency ratio for the optimum value of the moisture diffusion coefficient. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{3}{c}{**Input Parameter**} & \multicolumn{3}{c}{**Output Parameter**} \\ \cline{2-7}  & **T (\({}^{\circ}\)C)** & **P (W)** & **A** & **Sp (\%)** & **Deff (\(\times 10^{-10}\) m\({}^{2}\)/s)** & **dE** \\ \hline
1 & 45 & 120 & 0.2 & 96.4 & 5.17 & 5.38 \\
2 & 45 & 120 & 0.2 & 94.6 & 5.14 & 5.3 \\
3 & 45 & 120 & 0.2 & 96.4 & 5.12 & 5.42 \\
4 & 45 & 160 & 0.4 & 85.7 & 4.85 & 9.05 Figure 16: Effect of P and A on moisture diffusion coefficient Deff. Figure 14: Effect of T and P on moisture diffusion coefficient Deff. Figure 13: Effect of regression coefficients on moisture diffusion coefficient Deff. Figure 15: Effect of T and A on moisture diffusion coefficient Deff. Function of Saponin Content Sp (%)  
The relationship between saponin content Sp and T, P and A is represented by the following regression equation, based on the regression function (9) and Figures 17-20:  
\[ \begin{array}{ll}\text{Sp}=-603.693+31.7698\text{T}&-\ 0.123125\text{P}+294.048 \text{A}-\ 0.355692\text{T}^{2}-6.325\text{TA}+\\ &\ 0.615625\text{PA}-\ 285.433\text{A}^{2}\end{array}  \tag{9}\]  
Conditional function: \(40\leq T\leq 50;80\leq P\leq 120;0\leq A\leq 0.4\) (R\({}^{2}\) = 0.9553)  
The saponin content Sp is affected by the first- and second-order parameters of drying temperature T, intermittency ratio A and first-order parameter of ultrasonic power P, and also simultaneously affected by both the temperature and intermittency ratio, as well as power and intermittency ratio. The influence level and order of parameters are shown in Figure 17.  
Conditional function: \(40\leq T\leq 50;80\leq P\leq 120;0\leq A\leq 0.4\) (R\({}^{2}\) = 0.9990)  
The color changes dE is influenced by the first- and second-order parameters of drying temperature T, intermittency ratio A and ultrasonic power, and also simultaneously influenced by both temperature and power, as well as power and intermittency ratio. The influence level and order of parameters are shown in Figure 21.  
In the experimental area, temperature T has the most significant influence on color change dE. When the temperature T increases, the color changes dE decreases due to reducing drying time. However, when T exceeds a certain level, the color change will be substantial. This can be observed in Figures 22 and 23. As the intermittency ratio increases, the color change decreases. However, when A exceeds a certain level, the color change becomes considerable. This is shown in Figures 23 and 24. As the ultrasonic power increases, the color change decreases. However, when P exceeds a certain level, the color change becomes considerable. This is shown in Figures 22 and 24.