## 3 Results and Discussion
### Regression Models and Response Surfaces  
When assessing the effect of individual factors for the daidzin extraction model, the linear factor of ethanol (A) was found to be non-significant (\(p>0.05\)), and the same was true for the BC (B-C) component (Table 3), which was not removed due to deterioration in model fit parameters (Table 4). In the model fit for daidzin and the analysis of variance explained by the model form and the lack of fit, the result for the model was highly significant (\(p=0.0000\)). In contrast, the model variance accounted for by the lack of fit was insignificant (\(p=0.07034\)). In evaluating the impact of individual factors for the daidzin extraction model, it should be noted that an interesting result was the linear factor (A), which was not significant, as evidenced by a \(p\)-value above 0.05 (Table 3). This finding indicates that the extraction process is contingent upon the interrelationship between the components. The findings suggest that the employed model demonstrated an adequate level of fit. This is corroborated by the determination coefficients of 0.98, indicating that the model developed accounts for 98% of the variability in the level of daidzin extraction (Table 4). The surface fit of the soybean seed extraction level was expressed using a triangular coordinate system (Figure 3) to evaluate all possible mixture combinations. In mixture plans, the input factors are the components of the mixture, meaning their values are not mutually independent. The configuration of the plan is constrained to a plane that assumes a triangular shape (Figure 3) due to the presence of three components. The regression model analyzing the effect of the extraction mixture composition on genistin levels shows that, in a manner analogous to daidzin, the linear component (A)--ethanol--is insignificant. Furthermore, the elimination of other quadratic and cubic components from the model enhanced its fit (Tables 3 and 4). The significance of the regression model for genistin was confirmed through its fit and the analysis of variance explained by the model (Table 4). However, the variance attributed to lack of fit remains statistically significant (\(p<0.05\)). The model's R\({}^{2}\) fit was 97% (Table 4). For genistin, despite efforts to identify the most suitable model components, the significant lack of fit may impair and restrict the model's predictive ability, which remains pertinent given its high fit rate. The surface fit of genistin extraction levels from seeds with varying combinations was utilized to evaluate the full range of mixture combinations. In the course of the analysis of the effect of individual factors on malonyldaidzin extraction, the effect of the linear factor (A)--ethanol--was found to be insignificant (Table 3 and Figure 3). The \(p\)-value for the model fit of 0.00000 indicates that the model provides a high degree of fit for malonyldaidzin. This assertion is substantiated by an R\({}^{2}\) of 99%, which is indicative of a high degree of statistical significance. To explore the effects of all mixture combinations, the fit of malonyldaidzin level plots with different mixtures is shown in Figure 3. For malonylglycitin extraction, the analysis revealed that the linear factor (A) was non-significant (Table 3, Figure 3). The model fit for malonylglycitin and the analysis of variance demonstrated that the model was a significant component, while the variance due to lack of fit was not significant. The model fit achieved an R\({}^{2}\) of 93%, as presented in Table 4. The impact of varying mixture combinations on malonylglycitin levels is illustrated in Figure 3.  
A thorough examination of the impact evaluation of the individual factors for the malonylgenistin extraction model revealed that the linear factor (A)--ethanol--was the non-significant component (Table 3, Figure 3). Both the model form and the lack of fit were significant components (\(p<0.05\)). The model fit R\({}^{2}\) was 99% (Table 3).