## 3 Results
### Optimization of the Processing Parameters  
[PERSON]'s prediction tool was applied to compute the maximum value of polyphenols, antioxidant activity by the DPPH test, antioxidant activity by the FRAP test, and minimum value of unit energy consumption. The optimal conditions for each response were listed in (Table 5).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **df** & **Mean Square** & **F-Value** & **\(p\)-Value** & **Coefficient** & \\ \hline Model & 7.17 & 7 & 1.02 & 36.16 & \(<\)0.0001 & significant \\ \(X_{1}\) & 0.7689 & 1 & 0.7689 & 27.15 & 0.0012 & \(-\)1.83239 \\ \(X_{2}\) & 2.21 & 1 & 2.21 & 77.87 & \(<\)0.0001 & \(-\)0.213000 \\ \(X_{3}\) & 2.84 & 1 & 2.84 & 100.29 & \(<\)0.0001 & \(-\)0.155604 \\ \(X_{1}X_{3}\) & 0.2493 & 1 & 0.2493 & 8.80 & 0.0209 & \(+\)0.142643 \\ \(X_{2}X_{3}\) & 0.5869 & 1 & 0.5869 & 20.72 & 0.0026 & \(+\)0.021888 \\ \(X_{1}{}^{2}\) & 0.3070 & 1 & 0.3070 & 10.84 & 0.0133 & \(+\)1.14998 \\ \(X_{2}{}^{2}\) & 0.2472 & 1 & 0.2472 & 8.73 & 0.0213 & \(+\)0.010320 \\ \hline Residual & 0.1983 & 7 & 0.0283 & & & \\ \hline Lack of Fit & 0.1908 & 5 & 0.0382 & 10.25 & 0.0912 & not significant \\ \hline Pure Error & 0.0074 & 2 & 0.0037 & & & \\ \hline Cor Total & 7.37 & 14 & & & & \\ \hline \hline \end{tabular} \(\text{R}^{2}=0.9731.\text{adj-R}^{2}=0.9462.\text{CV}=14.02.\text{Adeq}\) Precision 18.2411.  
\end{table}
Table 4: ANOVA results for response: unit energy consumption.  
The predictive capacity of our models was computed as the relationship of the empirical values to the predicted values that were obtained at the optimal parameters for each response. In all cases, the experimental values of the total phenolic content, the antioxidant activity estimated by the DPPH and FRAP test and unit energy consumption were slightly lower than the predicted values from developed models. The best predictive capacity of the model was obtained for the polyphenols and the worst for the FRAP test. This may be due to the different number of coefficients included in individual models. For FRAP, a much larger number of parameters were removed from the model, which turned out to be statistically insignificant, and thus the coefficient of determination was much lower than in the case of the model for polyphenols However, for each of the response, the differences between experimental and predictive values are less than 10 percent, which can be considered a satisfactory result.  
The analyzed process parameters (particle size of the raw material, processing time and ultrasound intensity) had a different effect on the improvement of the extraction efficiency for different optimization criteria (low energy consumption-high efficiency). The greatest convergence of extraction parameters for the analyzed criteria was observed in the case of the particle size, and the lowest in the case of the intensity of ultrasounds. Taking into account the extraction yield and the antioxidant activity of the obtained extracts, the optimal conditions for the process were as follows: particle size approx. 0.6 mm, extraction time 13 to 15 min, and the ultrasound intensity 7 to 8.6 W\(\cdot\)cm\({}^{-2}\). The optimal conditions for unit energy consumption were as follows: particle size approx. 0.5 mm, extraction time 7.7 min and ultrasound intensity 1.8 W\(\cdot\)cm\({}^{-2}\). Under these conditions, the amount of predicted total phenolic content was 10.14 mg GAE\(\cdot\)g\({}^{-1}\), which is 66.53% of the maximum yield. The value of unit energy consumption in optimal conditions for obtaining polyphenols was 1.61 kJ\(\cdot\)mg\({}^{-1}\) GAE\(\cdot\)g\({}^{-1}\) dry matter, which accounts for as much as 432% of the minimum energy consumption.  
The obtained findings clearly show that after reaching the optimum for unit energy inputs, further increase of the extraction efficiency results in a very fast increase in energy consumption.