## 3 Results and Discussion
### Surface Response Graphs--Response Optimization Variable  
Response surface analysis is a statistical method that can be used to examine the relationship between several independent variables and a dependent variable in order to optimize a response. Before a contour plot (Figure 3) could be created, a model had to be created to relate these variables to each other using polynomial regression. All analyzed variables were subjected to regression analysis for model fitting. Not all variables analyzed in the sample were able to fit the model. Table 3 shows the models of the regression equations for the responses variable, along with the regression equation and the R\({}^{2}\) value. Model validation was conducted using an ANOVA test. Each response variable was fitted to a single model that best explained its behavior concerning the protein combination. As shown in Table 3, the lack of fit was not significant for any of the models (\(p\) > 0.05). The adjusted R\({}^{2}\) values, ranging from 0.7376 to 0.9988, indicate that the models can satisfactorily account for the variability in the data. The model was validated to ensure that the assumptions of the analysis, such as normality and homoscedasticity of the residuals, were met. However, response variables such as the stability index, mean particle size, and polydispersity index could not be adequately fitted to the model.  
After fitting the model, the variables that showed model fit are shown in Figure 3. The contour plots created for each response variable allow us to observe the interaction between the independent variables and the response and to visualize the maximum and minimum ranges of the dependent variable. This visualization helps us to identify the ideal conditions for optimizing the variables and to determine which protein is responsible for this behavior.  
To make the effect of the independent variables on the reaction of interest clear, contour diagrams for the variables of flocculation, coalescence stability, emulsification activity, centrifugal stability, surface charge (zeta potential), and HCT are shown in Figure 3. On the basis of the contour graph, the influence of the individual proteins or their combination on the most important reactions can be understood. It can be seen that pea protein has the strongest influence on the coalescence and flocculation stability and thermal stability (HCT). The bean protein was responsible for the accelerated stability when the sample was subjected to mechanical stress, and the pea protein influenced the emulsifying activity.  
It is possible to identify several experimentally untested regions that can assume optimal values for each tested variable. For coalescence and flocculation stability, the formulations can vary between pea and bean proteins, while the lowest values for surface charge are in the central region of the diagram, that is, when the three proteins are combined. The experimental values proved that for the zeta potential, T10 (combination of the three proteins in equal proportions) was the best formulation, so Table 4 shows experimental values of possible optimal formulations for the main tested variables.