## 3 Results
### Multivariate Metabolomics Data Analysis
VIP-plot (Figure 3D) analyzed ten metabolites with VIP score between 1.5 and 3.5 and carried metabolites such as \(\delta\)-9-cis-tetrahydrocannabinol characterized by high levels (in red and orange) in Gorilla CBD and Green Poison with an opposite trend of clovanemagnool which instead particularly high in V1 CBD and Banana Hybrid. Cannabigerolic acid (CBGA), precursor of cannabinoids, was another non-volatile compound mainly found  
Figure 3: (**A**). Principal component Analysis (PCA) Score Plot. Two dimensional 2D PCA scores plot demonstrates statistical clustering for _C. sativa_ infloresences cultivars. (**B**). Principal component Analysis (PCA) BiPlot. A biplot provides information on both metabolites and samples of a data matrix to be displayed graphically. Metabolites grouped at the origin of the graph do not contribute to samples variability. (**C**). Hierarchical clustering (HCA). It shows the dendrogram based on Euclidean distance. (**D**). PLS-DA and Variable importance in projection (VIP) plot. It displays the top 10 most important metabolite features identified by PLS-DA. Colored boxes on right indicate relative mean percentage of corresponding metabolite for _C. sativa_ infloresences. VIP is a weighted sum of squares of the PLS-DA loadings considering the amount of explained Y-variable in each dimension. in Banana Hybrid and Candy BUD inversely to the 5-hydroxy-7-[4-hydroxy-2-methoxy-5-(3-methyl-2-butenyl)phenyl]-2,2-dimethyl-7,8-dihydro-2H,6H-pyrano[3,2-g]chromen-6-one (or 2\({}^{\prime}\)-O-methylcajanone) dominant in Gorilla CBD and in V1 CBD. Among the top 10 most characteristic metabolites identified by PLS-DA (Figure 3D), two volatile compounds such as \(\alpha\)-pinene and selina-3,7(11)-diene showed an opposite trend. Further, Pearson's correlation analysis was performed among various metabolites of _C. sativa_ infloresances. In Figure 4, it is shown a correlation matrix, a table representing correlation coefficients between variables. In this case, it is based on Pearson Correlation Coefficient which measures the linear correlation between two datasets defined as the ratio between the covariance of two variables and the product of their standard deviations. The results range from \(-1\) (indicating a strong inverse correlation) to 1 (indicating a strong direct correlation). The dendrogram next to the correlation matrix clustered compounds is sharing a similar correlation trend; for instance, the cluster composed of y-langene, \(\beta\)-caryophyllene and humulene or the other group including alloaromadendrene, \(\alpha\)-santalene, \(\alpha\)-pinene, camphene and \(\beta\)-myrccene. Regarding non-volatile compounds, it is noticeable the subset formed by clovanemagnolol, cannabilactone and ananolignan J.  
Figure 4: Correlation matrix. Correlation matrix based on Pearson Correlation coefficient among different volatile and non-volatile compounds of _C. sativa_ infloresances with dendrogram. Specifically, in Figure 5 is described the analysis of the top 25 correlated compounds based on Pearson Correlation Coefficient for \(\delta\)-9-cis-tetrahydrocannabinol, \(\alpha\)-pinene, \(\beta\)-caryophyllene, \(\beta\)-myrccene which are some of the metabolites emerging from Biplot and VIP analysis. \(\delta\)-9-cis-tetrahydrocannabinol, particularly high in Gorilla CBD, resulted inversely correlated with the non-volatile compounds cannflavin B, cannabilactone, clovanemagnolol, ananolignan J. Regarding volatile metabolites, a strong inverse correlation arose with \(\beta\)-bisabolene which instead was directly correlated with \(\alpha\)-pinene. This latter metabolite and \(\delta\)-9-cis-tetrahydrocannabinol shared a positive correlation with cannabinoid acid, showing high levels in Gorilla CBD. \(\alpha\)-Pinene revealed an inverse correlation trend with many other compounds such as \(\beta\)-caryophyllene, guaia-3,9-diene, guaia-1(10),11-diene, \(\beta\)-bisabolene, selina-3,7(11)-diene and cis-\(\alpha\)-bisabolene.