## 3 Results and Discussion
### Optimisation of HS-SPME-GC-MS Conditions
All compounds together with their numbers are presented in Table A1 as Appendix A. PCA results show clear differentiation of samples. However, to determine the compounds responsible for it, further data analysis is required. Figure 2: Results of PCA analysis where (**a**) graph of scores, numbers 1–3 indicate three replicates of the same sample; (**b**) graph of loadings, numbers of loadings indicate compounds presented in Appendix A Table A1. Comparing Figure 2a,b a tendency can be seen. Figure 2a shows that earlier point samples (Los Velez and Huetor) have lower values (negative) for PC-1 along its axis. As the scale increases the later points have higher values for PC-1 scores which are on positive side of PC-1. There is a clear trend with maximum positive values for samples from Albarracin (Teruel) and Loarre (Huesca) collected in November. Analysis of both score and loading plot together allow one to link together the samples and variables. Samples that have low values for PC-1 score have relatively higher values for variables from negative side of PC-1 axis in loading plot. Those characteristic loadings are 58, 99 and 105 corresponding to benzoic acid, 5-pentyl-1,3-benzenediol and mequinol, respectively, compounds present in one or both samples AL and LO_N (Table A2 Appendix A). Moreover, mequinol (compound 105) is present in Figure 3 (which description has been presented below) as an important metabolite (red colour) for those two samples. In contrast for the variables in positive side of PC-1 the later points samples (Albarracin and Loarre collection in November) have relatively higher values for variables from positive side of PC-1 axis in loading plot. Those characteristic loadings are 50 and 104 corresponding to 1,3-bis(1,1-dimethylethyl)benzene and 5,6,7,7a-tetrahydro-4,4,7a-trimethyl-2(4\(H\))-benzofuranone. Those compounds are characterized by the highest relative area of peaks in samples AL and LO_N. Figure 3 shows PLS-DA analysis results, where the twenty most important metabolites are plotted according to the PLS-DA variable importance in projection (VIP) scores. VIP scores measure the importance and contribution of the variables to the PLS-DA model. It can be seen in Figure 3 that the five most important metabolites and, at the same time, the compounds with the highest relative area in samples collected from Albarracin are 1,3-bis(1,1-dimethylethyl)benzene, 3-ethyl-1,4-hexadiene, hexadecane, 2,3,6-trimethyldecane and 5,6,7,7a-tetrahydro-4,4,7a-trimethyl-2(4\(H\))-benzofuranone. On the other hand, the most abundant compounds in the samples collected in Pina de Montalgrao are 4-(1-hydroxally)-2-methoxyphenol, 1-ethylcyclohex, oxalic acid 6-ethyloct-3-yl ethyl ester and dihydromethylasmonate. While the most abundant compounds in samples collected in Loarre S are 1,3-bis(1,1-dimethylethyl)benzene, 2,4-heptadienal, hexadecane and 5,6,7,7a-tetrahydro-4,4,7a-trimethyl-2(4\(H\))-benzofuranone. These results are connected with the results of total phenolic content and different AOX measurements presented later. All the most essential compounds determined by PCA, and PLS-DA were selected, and Pearson correlation analysis was performed and presented in Figure 4. The figure  
Figure 3: Representation of the twenty most important metabolites ranked by PLS-DA VIP score. The mini heatmap on the right indicates variations of their relative intensities in 9 different samples of _Arctostayhylos uwa-ursi_. shows a positive correlation between the compounds obtained for compounds with r > 0.8 (marked in red colour). It means that an increase in the relative area of one compound increases the relative area of another correlated compound in the same sample. An example can be the correlation between one ester and one phenol, such as dihydro methyl jasmonate and 2,4-di-tert-butylphenol. When r = 0, there is no correlation. Finally, when r is lower than 0, there is a negative correlation between the compounds. Moreover, four main clusters can be observed in the HCA analysis.