## 3 Results and Discussion
### Multivariate Analysis of Studied Parameters  
It was assumed that the parameters with large absolute values of their coordinates (>0.3) on the first two latent components of the PLS model determine the axes of the new coordinate system to the greatest extent. For pairs of such parameters, the correlation coefficients between them were calculated as cosines of the corresponding angles in order to express the strength of the bivariate associations. The \"corresponding angle\" is defined here as the angle subtended by the two lines connecting the origin with the coordinates of both parameters in the PLS loadings diagram. Two significant latent components of the PLS model were constructed with eigenvalues higher than 1 (equaled 8.56 and 1.27, respectively). They explained 89.3% of the variance in the predictive parameters, and 49.3% of response parameter variance. Figure 3 shows the loadings for the first two latent components in the PLS model. The first latent component in this model had positive weights predominantly for glycitein, biochanin, daidzin, and genistin, which formed the cluster of strongly correlated parameters (Table 3). The same latent component was loaded negatively by the group of antioxidant parameters: FRAP, DPPH, sinapic acid, and TRC, all also being strongly positively correlated (Table 2). The second latent component was loaded positively mainly  
Figure 3: Partial least squares model loadings (first and second latent components in PLS). The model correlated the predictive parameters (biochanin, chlorogenic acid, daidzin, genistein, genistin, glycitein, protocatechic acid, synapinic acid, DPPH, FRAP, TRC; depicted as blue diamonds) and response ones (cell viability tested in cell lines: DLD-1, HCT116, HT29; depicted as red dots). by daidzin, chlorogenic acid, TRC, and HT29, while was negatively loaded by HCT116 and genistein (HT29, HCT116--response parameters). The last pair of parameters was negatively correlated with the antioxidant parameters mentioned above, predominantly with TRC (Table 3). The score scatterplot of the PLS model, which points various groups of kale and lupine sprouts in the space determined by the first two latent components, is shown in Figure 4.  
The horizontal division, reflecting two different species of sprouts, is very clear. Compared to the plot of loadings of latent components in the PLS model, this confirms that both  
Figure 4: The projection of kale (K1...K8) and lupine (L1...L8) sprouts into the space defined by first two latent components of partial least squares model. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Pairs of Correlated Parameters**} & \multicolumn{1}{c}{**Correlation Coefficients**} \\ \hline Chlorogenic acid & HT29 & 1.000 \\ Biochanin & Glycitein & 0.999 \\ DPPH & FRAP & 0.999 \\ Genistein & HCT116 & 0.990 \\ FRAP & TRC & 0.974 \\ Biochanin & Genistin & 0.970 \\ Daidzin & Genistin & 0.962 \\ DPPH & Sinapic acid & 0.962 \\ Genistin & Glycitein & 0.961 \\ DPPH & TRC & 0.960 \\ Daidzin & HT29 & 0.949 \\ Daidzin & Chlorogenic acid & 0.948 \\ FRAP & Sinapic acid & 0.946 \\ Biochanin & Daidzin & 0.868 \\ Daidzin & Glycitein & 0.850 \\ TRC & Sinapic acid & 0.847 \\ Glycitein & Sinapic acid & \(-\)0.706 \\ TRC & HCT116 & \(-\)0.994 \\ TRC & Genistein & \(-\)1.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Correlation coefficients for the pairs of parameters with largest absolute values of their coordinates on the first two latent components of partial least squares model (correlation coefficients below absolute value 0.700 were not shown). species differed mainly in the values of parameters such as glycitein, biochanin, daidzin, genistin (all not detected in kale sprouts), FRAP (\(p\) = 0.000), DPPH (\(p\) = 0.000), sinapic acid (not detected in lupine sprouts), and TRC (\(p\) = 0.001). The comparison of two botanically unrelated plants in our study was intentional, as we selected species from different botanical families with significant relevance in global food production.