## 3 Results
### Multivariate Metabolomics Data Analysis
Compounds under the detection threshold are represented in grey. They ranged from 38% of the total chemical compounds in Banana Hybrid to 46% in Gorilla CBD. In particular, the latter sample showed the lowest variety of detected compounds but the highest value percentages (in red) of \(\delta\)-9-cis-tetrahydrocannabinol and \(\beta\)-myrcene. After a preliminary sample's examination, a multivariate analysis investigation of _C. sativa_ obtained data, was performed. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) segregated the samples on the basis of metabolite levels in each sample. Specifically, PCA unsupervised algorithm is an orthogonal linear transformation of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PCs). This kind of application reveals group structure when within-group variation is sufficiently less than between-group variation. The PCA allows to determine the contribution of the original variables to the PC model. In this case, the performed PCA was based on the first two principal PCs scores: PC1 explained the 48.2%, and PC2 explained the 22.3%. The total variance amount was 70.5%. The PCA Score Plot (Figure 3A) showed a PC1 separation of Gorilla CBD in comparison with the other varieties. This was also confirmed by Hierarchical clustering (HCA). HCA was used as a complimentary data reduction and pattern recognition method for finding the underlying structure of objects through a repetitive process that associates (agglomerative methods) or dissociates (divisive methods) object by object until all are equally and completely processed [23; 24]. Automated HCA was performed on the data and the resulting dendrogram was calculated using Euclidean distance method. The HCA dendrogram (Figure 3C) showed descriptively similar results to those ones of PCA; in particular, Candy BUD and Green Poison followed by Banana Hybrid and V1 CBD were the most similar as the height of the link that joins them together was the smallest. The other cluster was represented by Gorilla CBD. The dendrogram showed that the metabolic composition of _C. sativa_ inflorescences of Gorilla CBD resulted to have the most characteristic and dissimilar profile in comparison to all the other samples (confirming PCA results). Additionally, supervised forms of discriminant analysis such as PLS-DA [25], that rely on the class membership of each observation, were also commonly applied in metabolic fingerprinting experiments [25; 26]. To identify the most important metabolites allowing discrimination between samples, next to PCA, we performed a supervised PLS-DA based on the variable importance in projection values (VIP). PLS-DA was conducted using the top 50 features. Thus, a metabolite with a VIP >1.5 is regarded as significantly discriminant. The findings suggested that the _C. sativa_ cultivars were characterized by a different volatile and non-volatile metabolic composition. To find out the metabolites responsible for such variation, a Biplot (Figure 3B) was generated from PCA model and VIP scores (Figure 3D) were found from PLS-DA model. In Biplot, it is observable that Gorilla CBD was mainly characterized by \(\beta\)-myrcene and \(\alpha\)-pinene in the volatile fraction and by \(\delta\)-9-cis-tetrahydrocannabinol in the non-volatile profile. 1,2-Benzenediol, 5-(3-(2,4-dihydroxypropyl)propyl)-3,4-bis(3-methyl-2-butenyl)- (or kazinol F) contributed to the explanation of variability of Candy BUD and Green Poison which are additionally characterized by the presence of selina-3,7(11)-diene and guaia-3,9-diene. Instead, \(\beta\)-caryophyllene mainly described Green Poison, Banana Hybrid and V1 CBD while it was at the opposite side of Gorilla CBD.