## Results and Discussion
### Generalized Procrustes Analysis  
In order to analyze jointly both the wheat and beer VOCs datasets without losing useful information, improving data visualization, and reducing block-variance inequalities due to a strong difference in terms of number of variables, a multitude statistical approach was chosen. Thus, a GPA was carried out by using wheat kernel (6 samples \(\times\) 2 replicates \(\times\) 149 variables) and wheat beer (6 samples \(\times\) 2 replicates \(\times\) 31 variables) datasets as configurations to obtain a common volatile profile as much alike as possible. GPA results are shown graphically through the consensus and correlation maps interpretable similarly to PCA scores and the loadings plot, respectively [26]. In Figure 2a, the consensus coordinates are shown along the first two components (accounting for the 83% of the total consensus variance), whereas in Figure 2b, the third and fourth components are shown (accounting for the 12% of the total consensus variance). The kernel and the beer samples VF3, SF1, SF3, and VF2 (highlighted in figure by polygonal dashed lines) are clearly separated among them in the first two components of the consensus space, while the third and fourth components classify the sample SF2. Thus, the combination of wheat variety and cultivation site generated a distinguishable and unique volatile profile at least for five samples, of which differences are reflected from kernels' to beers' VOCs. In Figure 3, the correlations between variables (VOCs) and factors (F1 and F2) are shown. To improve the readability of the graph, only the VOCs with a correlation higher than 0.7 are shown. Interestingly, Linalool oxide found in kernels resulted in being correlated to \(\beta\)-Linalool found in beers (negatively correlated to the first component). Indeed, [PERSON] et al. [27] suggested that the final concentration of Linalool in beer may depend on the amount of linalool glycosides hydrolyzed by Linalool oxide in fermentation. Even if not all the samples generated consensus individually, by looking at the centroids in Figure 4, it was possible to observe a clear separation between groups, in particular between Solina and Vittorio wheat. As a consequence, the consensus scores suggest that the two analyses are interchangeable for discrimination purposes on the basis of wheat varieties. \begin{table}
\begin{tabular}{l l c c c c c c c c c c c} \hline \multicolumn{1}{c}{} & \multicolumn{6}{c}{**Wheat Kernel**} & \multicolumn{6}{c}{**Wheat Beer**} \\ \hline \hline
**Chemical** & **VOCs** & **SF1** & **VF1** & **SF2** & **VF2** & **SF3** & **VF3** & **SF1** & **VF1** & **SF2** & **VF2** & **SF3** & **VF3** \\ \hline \multirow{2}{*}{ \begin{tabular}{l} Alcohol \\ Brunze \\ Organite \\ Acid \\ \end{tabular}  } & 2-Methyl-1 & 0.077 \(\pm\) & 0.000 \(\pm\) & 0.016 \(\pm\) & 0.100 \(\pm\) & 0.181 \(\pm\) & 0.286 \(\pm\) & 0.664 \(\pm\) & 0.855 \(\pm\) & 0.753 \(\pm\) & 0.772 \(\pm\) & 0.763  
Figure 3: GPA correlations between dimensions in the initial consensus configuration and the first two factors by using GC-MS datasets. Figure 2: Consensus map, presented by configuration (wheat kernels and wheat beers), obtained by GPA along the F1 and F2 components (**a**); F3 and F4 components (**b**). Score nomenclature: (V) Vittorio wheat; (S) Solina wheat; F1: 70 m a.s.l. ; F2: 500 m a.s.l. ; F3: 1200 m a.s.l. Thus, genotype, was considerable the first root of consensus between the GC-MS analyses conducted on wheat kernels first and then on wheat beers. These findings are in accordance with the results of [PERSON] et al. [11]. [PERSON] et al., in [11] analyzing the VOCs of wheat kernels for two consecutive years, reported a greater effect on VOCs profile due to genetical factors rather than cultivation altitude. In particular, as shown in Figure 3, Solina wheat was characterized by \(\beta\)-Linalool and 2-Phenethylacetate in beer whereas it was in kernel by Phenol and Linalool oxide.