## 3 Results and Discussion
Low concentrations of \(\beta\)-farnesene has been reported in low concentrations in hop ([PERSON] et al., 2018; [PERSON] et al., 2018), and this can be one of the differentiating components of ENIG hop, while the presence of the monoterpene \(\beta\)-myrcene showed the highest concentration, followed by the two forms of \(\beta\)-ocimene. Figure 5: Biplot graph for the principal component analysis (PCA), representing (**A**) PC1\(\times\)PC2 and (**B**) PC1\(\times\)PC3. The circles represent the hop samples as AZAC (blue), CASC (pink), ENIG (yellow), LORA (green), and ZAPP (grey). LORA composition noted the esters isoamyl isobutanoate (**28**), ethyl heptanoate (**49**), hexyl isobutyrate (**63**), ethyl octanoate (**66**), _trans_-geranic acid methyl ester (**84**), isobutyric acid 1-methyl-octyl ester (**88**), and ethyl _cis_-4-decenoate (**92**). As reported, the major group responsible for the PCA separation was the esters, even in the specific compounds expressed only by the hop LORA. Nevertheless, compounds such as fenchol (**58**), \(\alpha\)-terpineol (**67**), perillaldehyde (**77**), and \(\alpha\)-citral (**75**) were important monoterpene alcohols and aldehydes for the chemical composition of this hop. Finally, ZAPP showed two compounds within the cluster of specific compounds: premyl isobutyrate (**38**) and methyl 6-methyl heptanoate (**46**); relations with the esters 2-methylbutyl acetate (**5**), isobutyl isobutyrate (**6**), isobutyl butyrate (**12**), 2-methylbutyl propionate (**16**), 2-methylbutyl isobutyrate (**29**), methyl 8-methylnonanoate (**78**), methyl decanoate (**83**), and methyl 3,6-dodecationoate (**119**); and the monoterpene hydrocarbons \(\gamma\)-terpinene (**42**), isoterpinolene (**45**), and \(\alpha\)-muurolene (**118**). ZAPP was similar to the LORA sample, where the ester class had the capacity to represent the separation of samples by PCA. Based on all the information described above and noting some patterns in the PCA (Figure 5), the classes of compounds were also studied according to PCA and HCA. For the PCA, the model was developed with the sum of the relative chromatographic areas (\(\%\)) of each class using the selection of autoscale as preprocessing and 3 PCs representing 90.58% of variance displaying the variance of 39.78% for PC1, 36.72% for PC2, and 14.08% for PC3. Regarding HCA, autoscale was used as preprocessing, [PERSON]'s method was applied as an algorithm, and PCA was used to choose three PCs. The PCA graphs are expressed as biplot graphs, as shown in Figure 6A (PC1\(\times\)PC2) and Figure 6B (PC1\(\times\)PC3), while HCA is represented in Figure 6C. The PCA for the classes of compounds separates the ZAPP sample by PC2, LORA by PC3, and ENIG by PC1. As explained previously, this represents that the sample ZAPP was  
Figure 6: Multivariate statistical analysis of PCA expressed by the biplot graph of (**A**) PC1\(\times\)PC2 and (**B**) PC1\(\times\)PC3, and (**C**) HCA for the hops AZAC, CASC, ENIG, LORA, and ZAPP regarding the classes of compounds. most influenced by esters, LORA by alcohols, and ENIG by monoterpene hydrocarbons, with the influence of ketones over the AZAC hop. HCA gave the highest similarity between classes of ZAPP and CASC, followed by LORA and AZAC, which means that compared to the results shown by PCA, the first two classes were largely influenced by esters, aldehydes, oxygenated monoterpenes, and sesquiterpenes, while the second two were more affected by sesquiterpene hydrocarbons and ketones.