## 3 Results and Discussion
### Comprehensive Summary of Results  
Principal component analysis (PCA) was used to comprehensively evaluate the bread supplemented with sugar beet pulp (SBP) without and with molasses based on the technological parameters, nutritional value, and antioxidant properties (Figure 3). The selection of the most appropriate number of components for the interpretation of variables was made based on [PERSON]'s scree plot, which shows the eigenvalues. Figure 3a shows that most of the information was captured by the first two principal components (the steep part of the curve), which explained 92.65% of the variability analysis. On the other hand, decreasing eigenvalues, arranged horizontally, indicate that the subsequent components extract only \"random noise\". Therefore, the loss of information in the analysis of variables was 12.35%. The PCA (Figure 3b) showed that the first component (PC1) explained 66.77% of the variability created by the indicators describing the baking quality of the bread, the lightness of the crumb color (L*), and shaping the nutritional value of the assessed samples. Visualization of the obtained results on the PCA load plot of the two principal components (Figure 3b) shows that bread samples with higher crumb moisture (MC), hardness (BCH), total dietary fiber, and fraction (TDF, IN-DF, S-DF) contents were characterized by a lower specific volume of bread (BV) and a darker color of the breadcrumb (L*). Moreover, PC1 was strongly negatively correlated with the protein (P), fat (F), and carbohydrate (CC) contents, determining the main caloric value (EV) of the bread. The second principal component (PC2) explained 25.94% of the variability. It was strongly negatively related to the parameters describing the antioxidant properties (TPC and DPPH), color parameter a* and acrylamide (AA), and ash (A) contents in the bread. The PC1/PC2 graph (Figure 3c) shows the distribution of the evaluated bread samples. It was shown that in the negative part of PC1, at relatively close distances from each other, there were samples of bread supplemented with 1% sugar beet pulp (SBP) and control wheat bread (WB). However, in the case of PC2, the control sample (WB) and the sample bread with 1% unmolassed SBP (1 umB) were located in the positive part of the graph, while the sample with molassed 1% SBP (1 mB) was located in the negative part of it. This indicates minor differences between the bread samples evaluated and their similarity. The evaluated bread samples supplemented with 1% SBP, both without and with molasses, were characterized by a larger specific volume (BV) of bread, lower crumb hardness (BCH),content of crumb moisture (MC) and its lighter color (L*). To a large extent, the values of the above quality parameters of the tested bread samples resulted from the lower content of TDE, as well as higher contents of P, F, and CC, which translated into a higher EV of the final products. Only the bread sample enriched with 1% molassed sugar beet pulp (1 mB) showed higher antioxidant potential compared to the control (WB) and 1% unmolasses sugar beet pulp (1 umB) samples. Figure 3c shows that the positive part of the PC1 contained bread samples fortified with SBP without molasses (umSBP)--an additive rich in total dietary fiber and its insoluble fraction. On the other hand, the negative part of the PC1 contained bread samples  
Figure 3: Principal component analysis (PCA) graphs considering similarities and differences of quality indices of bread samples: (**a**) PCA plot of the scree slope; (**b**) PCA loading plot of the principal components PC1 and PC2; (**c**) similarities between bread samples; (**d**) cluster analysis.