## 3 Results and Discussion
### Principal Component Analysis  
The relationship between the quality parameters measured in nine winter wheat cultivars fertilised with variable nitrogen dosage has been assessed by e principal component analysis (PCA) in order to estimate the source of variability. Among the twenty-one tested parameters, for the purpose of analysis eight extracted principal components were selected having eigenvalues higher than the average. The eigenvalues for the first component was 3.73, and the percentage of the explained variance was 46.56%. The second component explains 26.12% of the variance, and its own value was 2.09 (Table 5). The first two components transfer 72.69% of the variability of the original data. In order to determine the number of main components, the criterion of eigenvalue greater than the unity of the so-called Kaiser criterion was used [46]. It is therefore possible to approximate the original data set only in two dimensions. A scree plot confirms the significance of the first two components, which allows the reduction of the 8-dimensional space to two components (Figure 2). Figure 3a shows the projection of variables on the factor plane. The first principal component transfers the information contained in positively correlated variables, degree of softening, dough yield and negatively correlated values, dough stability, quality number and grain vitreousness (Table 6). The variable quality number is positively correlated with dough stability (r = 0.96, \(p<0.05\)), while the variable dough yield is positively correlated with the variable dough degree of softening (r = 0.58, \(p<0.05\)), which indicates the position of the vectors in close proximity. The second principal component comprises mostly of positively correlated variables, Dallmann porosity index of crumb, gluten index \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Value Number**} & \multicolumn{4}{c}{**Eigenvalues (Correlations), Related Statistics**} \\ \cline{2-5}  & **Eigenvalue** & **\% of Total Variance** & **Cumulative Eigenvalue** & **Cumulative\%** \\ \hline
1 & **3.725399** & **46.56749** & **3.725399** & **46.5675** \\
2 & **2.089800** & **26.12250** & **5.815199** & **72.6900** \\
3 & 0.792959 & 9.91199 & 6.608158 & 82.6020 \\
4 & 0.618540 & 7.73174 & 7.226698 & 90.3337 \\
5 & 0.439531 & 5.49414 & 7.666229 & 95.8279 \\
6 & 0.250196 & 3.12745 & 7.916425 & 98.9553 \\
7 & 0.062311 & 0.77889 & 7.978736 & 99.7342 \\
8 & 0.021264 & 0.26580 & 8.000000 & 100.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Individual values of correlation matrix. Figure 2: Scree plot for individual values of the matrix of correlation. and bread yield. The variables of bread yield and Dallmann porosity index of crumb are positively correlated with the gluten index variable (r = 0.48 and r = 0.49, \(p\) < 0.05). The vectors of the degree of softening, dough quality number and grain vitreousness variables are longer and located closer to the circle, which indicates that these variables contribute the most information. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Factor Coordinates of Variables,**} \\ \cline{2-3}  & **Based on Correlation** \\ \cline{2-3}  & **Factor 1** & **Factor 2** \\ \hline dough degree of softening & 0.935932 & \(-\)0.147075 \\ dough stability & 0.651097 & \(-\)0.107195 \\ Dallmann porosity index of crumb & 0.433313 & 0.771696 \\ gluten index & 0.024504 & 0.797857 \\ bread yield & \(-\)0.222052 & 0.788339 \\ dough stability & \(-\)0.940939 & 0.117195 \\ quality number & \(-\)0.951258 & 0.129446 \\ vitreousness & \(-\)0.630539 & \(-\)0.415473 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Value of principal components coefficients. Figure 3: Principal components analysis of (**a**) distribution of the analyzed parameters (**b**) distribution of wheat cultivars and nitrogen fertilisation. 1—Hybery. 2—Hyena. 3—Hyfi. 4—Hyking. 5—Hymalaya. 6—Hypocamp. 7—Hyvento. 8—Belissa. 9—Honda. N1—110 kg/ha. N2—150 kg/ha.