## 3 Results and Discussion
### Optimization of the Enzymatic Hydrolysis Process  
A Box-Behnken design (BBD) was used to analyze the experimental data. BBD belongs to Response Surface Methodology (RSM), which is a collection of statistical and mathematical techniques useful for process development, improvement, and optimization [28]. In general, RSM has two main types of designs--the Box-Behnken design (BBD) and the central composite design (CCD). BBDs differ from CCDs in that they use fewer series and only three levels, compared to CCD's five [29]. For this reason, BBD was preferred in the present study. All experiments were repeated three times and the means and standard deviations were calculated. Experimental data were evaluated statistically using Design-Expert software (version 13.0.5.0). Table 3 presents the results of the studies on the radical scavenging activity of the obtained hydrolysates, against DPPH and ABTS. DPPH and ABTS tests are some of the most widely used methods to assess antioxidant activity. The radical scavenging activity to DPPH in the existing LPH sample varied from 49.48 to 77.08%. Under the conditions of the analysis, the concentration of the standard antioxidant Trolox, at which 50% inhibition of the DPPH radical is reached, is 13.07 ug/mL. When analyzed with ABTS, the RSA% values of the hydrolysates ranged from 20.49 to 42.54%. Trolox inhibited 50% of the ABTS radical at a concentration of 64.70 ug/mL. As can be seen from Table 3, all tested LPH samples inhibited both radicals, but to a different extent depending on the applied enzymatic hydrolysis parameters. Table 4 summarizes the models obtained in this study. These models were evaluated based on the values of a lack of fit and coefficient of determination (R\({}^{2}\)). The significance of each coefficient in the model was determined using an F-test obtained from the analysis of variance (ANOVA) generated. Numerical optimization was conducted to obtain the optimal conditions for the enzymatic hydrolysis process. The software product Design-Expert defines the dependence of DPPH on the input variables as quadratic (Table 5), with statistically insignificant coefficients in front of the added members. The Model F-value of 5.30 implies the model is significant. There is only a 0.60% chance that an F-value this large could occur due to noise. _P_-values less than 0.0500 indicate model terms are significant. In this case, A, AB, BC, and B\({}^{2}\) are significant model terms. \begin{table}
\begin{tabular}{c c} \hline
**Composition** & **Content (g/100 g)** \\ \hline Moisture & 4.65 \(\pm\) 0.08 \\ Fat & 12.76 \(\pm\) 0.02 \\ Protein & 46.98 \(\pm\) 0.30 \\ Ash & 5.65 \(\pm\) 0.61 \\ \hline \end{tabular} Values represent means \(\pm\) standard deviations (n = 3).  
\end{table} 
Table 2: Physicochemical analysis of CPC. The fit statistics for the obtained model are presented in Table 6. The values of R2 and adjusted R\({}^{2}\) indicate a good explanation of the variability by the selected model for DPPH. The Adeq Precision value measures the signal-to-noise ratio and it must be greater than 4. The current ratio of 7.993 indicates an adequate signal. Therefore, this model can be used to navigate the design space. \begin{table}
\begin{tabular}{c c c c} \hline
**Dependent Variables** & **Symbols** & **Model** \\ \hline DPPH & \(Y_{1}\) & \(Y_{1}=56.36-2.26X_{1}-3.94X_{1}X_{2}-8.80X_{2}X_{3}+5.71X_{2}^{2}\) \\ ABTS & \(Y_{2}\) & \(Y_{2}=37.05-2.38X_{1}-1.98X_{2}-2.58X_{3}-3.09X_{2}X_{3}\) \\ \hline \end{tabular}
\end{table} 
Table 4: Coded dependent variables used in the RSM design.