## 3 Results
### PCA Analysis  
From the PCA analysis performed to determine the parameters that have the greatest influence on the variability of the system, eighteen new variables were obtained, of which the first three principal components explain more than 50% of the variability of the system. Indeed, the first two principal components explain 40.16% of the variability of the system (PC1 in 24.95% and PC2 in 15.21%), and the parameters that are contained between the two circles have the greatest influence on the variability of the system. As a result of this procedure, fourteen parameters were determined from the forty parameters examined: C2, C3, C4, C5, L, IDF, TDF, bread yield, W, le, SH, P, P/L, and carbohydrates (Figure 3a). The remaining parameters have a weak influence on the variability of the system. Moreover, C2, C3, C4, C5, W, le, SH, P, and P/L are strongly and positively correlated. The same relationship was observed for IDF, TDF, and bread yield. A strong but negative correlation occurs between parameters C2, C3, C4, C5, W, le, SH, P, P/L, and L, while a strong and negative correlation was also determined for IDF, TDF, bread yield, and carbohydrates. There is no correlation between C2, C3, C4, C5, W, le, SH, P, P/L, and IDF, TDF, bread yield, and carbohydrates. There is also no correlation between L and IDF, TDF and bread yield, and between L and carbohydrates. Figure 3: PCA analysis: (**a**) projection of variables all parameters on the PC1 and PC2 loadings plot; (**b**) projection of modified wheat flours on the PC1 and PC2 scores plot. The PCA analysis also shows (Figure 3a,b) that HF, HFC, and HFCX are strongly and positively correlated with C2, C3, C4, C5, W, le, SH, P, and P/L, and strongly and negatively with L. In turn, F10, F20, TFC, and TFCX are strongly and positively correlated with L. In turn, K, FC, TF, and FCX are strongly and positively correlated with carbohydrates. The PCA analysis shows that the first principal component PC1 distinguishes hybrid thermal-enzymatic flour from control flour and thermal-enzymatic flour by 24.95% (Figure 3b). Positive values of the principal component PC1 describe the results of F and T and the control, and negative values of the principal component PC1 describe the results for H. The second principal component PC2 characterizes F, TF, and TFCX (positive values of PC2) and K, FC, and FCX (negative values of PC2) to 15.21%. The first and third principal components explain 36.99% of the variability of the system (PC1 to 24.95% and PC2 to 15.21%). The parameters that are contained between the two circles have the greatest influence on the variability of the system. As a result of the analysis, twelve parameters were determined from the forty parameters studied: C2, C3, C4, C5, bread yield, W, le, SH, P, P/L, specific volume, and bread volume (Figure 4a). The remaining parameters have a weak influence on the variability of the system. C2, C3, C4, C5, W, le, SH, P, and P/L are strongly and positively correlated. The same relationship was observed for specific volume and bread volume. A strong but negative correlation occurs between the parameters: specific volume, bread volume, and bread yield. There is no correlation between C2, C3, C4, C5, W, le, SH, P, P/L, and bread yield, specific volume, and bread volume. The PCA analysis also shows (Figure 4a,b) that HF, HFC, and HFCX are strongly and positively correlated with C2, C3, C4, C5, W, le, SH, P, and P/L. In turn, F10, F20, TFC, and TFCX are strongly and positively correlated with specific volume and bread volume. In turn, K, FC, TF, and FCX are strongly and positively correlated with bread yield. The PCA analysis shows that the first principal component PC1 distinguishes hybrid thermal-enzymatic flour from flour and thermal-enzymatic flour to 24.95% (Figure 4b).