## 2 Results and Discussion
### Statistical Analysis of the Obtained Results  
To investigate the variation in VOCs among two genetic groups of _C. suecica_ (groups 1 and 2) revealed on the basis of chloroplast DNA [9], a set of 107 detected compounds were subjected to multivariate statistical analyses. First, a PCA was conducted, which is an unsupervised analysis used to reduce the dimensions of a large data set and to extract and visualize the hidden structure in the analyzed data. The explanatory and predictive abilities of the PCA model are evaluated on the basis of two parameters: R\({}^{2}\)X and Q\({}^{2}\). The closer R\({}^{2}\)X and Q\({}^{2}\) are to 1, the better the fitness of the model is. In the analysis of _C. suecica_ samples, the model included  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**Collection Place**} & \multicolumn{1}{c}{**Mean of**} & \multicolumn{1}{c}{**\(t\)-Test for**} \\ \cline{4-9}
**No. ** & **Compounds** & **RI \({}^{\text{a}}\)** & **Bieszczady** & **Beskid** & **Pieniny Mts** & **Group 2** & **Group 2** & **Group 2** \\  & & & **Mts** & **Sqdecki Mts** & **Pieniny Mts** & **Group 2** & **\(p\) Value** \\ \hline
66 & 218[M+](24) 148(100) 133(63) & 1532 & 17.33 (1.24) & 11.53 (0.43) & 10.73 (0.50) & 11.64 (2.16) & 0.000 \\
67 & 220[M+](8) 85(100) 135(89) & 1545 & 0.09 (0.03) & 0.67 (0.08) & 0.10 (0.05) & 0.22 (0.25) & 0.045 \\
68 & 218[M+](4) 135(100) 107(42) & 1548 & 0.10 (0.02) & 0.16 (0.05) & 0.26 (0.10) & 0.22 (0.11) & 0.000 \\
69 & 202[M+](8) 131(100) 91(81) & 1551 & - & - & - & - & - & 0.000 \\
70 & 218[M+](6) 191(100) 157(90) & 1554 & 0.12 (0.01) & 2.35 (0.25) & 2.27 (0.28) & 2.05 (0.75) & 0.000 \\
71 & 218[M+](25) 145(100) 147(97) & 1561 & 0.22 (0.05) & 0.26 (0.06) & 0.20 (0.05) & 0.22 (0.05) & 0.000 \\
72 & 218[M+](4) 93(100) 43(75) & 1568 & 0.60 (0.07) & 0.54 (0.28) & 0.63 (0.24) & 0.61 (0.23) & 0.000 \\
73 & 4,5-dehydrovririidirol & 1572 & 0.96 (0.08) & 1.17 (0.85) & 0.52 (0.50) & 0.72 (0.61) & 0.940 \\
74 & 222[M+](3) 43(100) 81(53) & 1578 & 3.21 (0.09) & 0.08 (0.03) & 0.52 (0.14) & 0.72 (0.93) & 0.048 \\
75 & 218[M+](6) 43(100) 93(57) & 1579 & 0.48 (0.07) & 0.67 (0.16) & 1.25 (0.23) & 1.04 (0.38) & 0.000 \\
76 & 218[M+](7) 43(100) 91(67) & 1581 & 0.61 (0.02) & 1.49 (0.11) & 0.48 (0.25) & 0.72 (0.47) & 0.000 \\
77 & 220[M+](14) 79(100) 93(93) & 1584 & 6.41 (0.17) & 0.14 (0.09) & - & - & 0.74 (2.06) & 0.263 \\
78 & 220[M+](1) 94(100) 79(43) & 1589 & - & - & - & - & 0.000 \\
79 & 218[M+](25) 145(100) 147(87) & 1594 & 9.87 (1.50) & 16.49 (0.46) & 17.11 (1.13) & 16.17 (2.51) & 0.000 \\
80 & bischaola-2,10-dieme & 1[1,9]oxide & 1596 & 3.48 (0.22) & 3.69 (1.57) & 3.00 (1.16) & 3.20 (1.12) & 0.000 \\  
five statistically significant components that explained 83.8% of the variation (R\({}^{2}\)X) and 75.7% of the predicted ability (Q\({}^{2}\)). However, the first two principal components, PC1 and PC2, explained as much as 63.0% of the total variance (R\({}^{2}\)X), with values of 48.7 and 14.3%, respectively. The PCA revealed a clear separation of the studied samples. The 2D scatterplot shows that the main differentiation between the studied samples, corresponding to their genetic group affiliation (group 1 and group 2), occurred along the PC1 axis, which explained 48.7% and predicted 44.0% of the total variation (Figure 3). The analysis of factor loadings indicates that the samples of group 1 and 2 differed primarily in the content of compounds **40**, **48**, **52**, **60**, **63**, **66**, **71**, **79**, **80**, **91**, and **106**, which made the largest contribution to the PC1 axis. Variables **40**, **48**, **60**, **63**, **66**, **71**, **79**, and **91** had high (>0.90) positive loading, whereas **52**, **80**, and **106** had high negative (>\(-\)0.90) loading (Figure S3).