# Characterization of Freisa Wines from Piedmont (Italy) by Aroma and Element Profile
## 3 Results and Discussion
### Multivariate Data Analysis  
A multivariate study on the analytical data was carried out to obtain a visual representation of the metal distribution in the Freisa wine samples, to find out similarities and correlations among variables (metal contents), and to identify possible clusters among objects (wine samples) which would be more difficult to identify only by a simple comparison of results on chemical composition. Total metal concentrations in wines are useful for characterizing them and classifying them according to geographical origin and assessment of authenticity. Indeed, it is known that the information on metal contents in finished wines is useful for their \"fingerprinting\", mostly because metals are stable and represent factors affecting wine composition. In particular, several literature studies report that Ba, Ca, Cr, Mg, Mn, Na, Rb, and Sr concentrations are the most discriminatory variables to distinguish the geographical origin of a particular wine and its authenticity [33; 35; 42; 43; 44; 45]. Figure 4a shows the combined plot of PC1 vs. PC2 obtained by PCA. In general, even if no net grouping is present, it is possible to evidence that (i) most CTF samples (83%) show negative scores on PC1 and positive scores on PC2; (ii) most FA samples (71%) show positive scores on PC1 and negative scores on PC2; (iii) most LF samples (69%) are characterized by negative scores on PC1 and PC2; and (iv) MF and FC samples are dispersed along PC1 and assume both positive and negative values on PC2. It is interesting to note the opposite positions occupied by CTF and FA samples, characterized by higher concentrations of Sr, Cr, Ti, and Zn and by P, Cu, and Mn, respectively. In particular, FA samples are aligned with the direction of the Cu and P vectors due to their higher concentration in these samples. Instead, the LF samples are not strongly influenced by the variables identified by the vectors to indicate how these are characterized by a low content of all the determined elements. Overall, except for the CTF samples, the high similarity found for all the other samples allows us to hypothesize that Freisa wine has similar content of elements regardless of the Piedmont collection area, and this might represent a simplification for evaluating its authenticity compared to other Italian and non-Italian red wines. The results of the CA are reported as a dendogram in Figure 4b. At a dissimilarity level of 74%, it is possible to identify three clusters: (i) the first on the right composed by  
Figure 4: Chemometric processing. (a) Biplot on PC1 and PC2 obtained considering Freisa wine samples. (b) Dendrogram obtained by HCA in R-mode (i.e., studying the clustering of samples). all the CTF samples and most of the LF samples (77%); (ii) the second at the center, larger in size and composed by most of the FA and FC samples (86% and 89% respectively), and finally (iii) the third on the left in which three Freisa wine samples are grouped, belonging to the MF, LF, and CTF sites and characterized by a higher content of elements such as Ni, Ba, Fe, Na, and Mg, as it is also evident from their separate position in the PCA plot shown in Figure 4a. At a lower dissimilarity level, the first cluster shows a further separation between CTF and LF samples. Finally, MF samples are equally distributed among the three clusters (four in the first, four in the second, and one in the third). Therefore, the CA confirms the results obtained by PCA, and in particular allows to distinguish the CTF and LF samples from FA and FC samples. To evaluate if the element composition of the Freisa wines is useful for their characterization and classification according to geographical origin, the dataset obtained considering the mean element concentrations determined in the Freisa wines (this study) and in the red Italian wines reported in Table S4 was treated by PCA. From the PCA plot (Figure 5), it is apparent how most wine samples taken from the same geographical area tend to group in well-defined clusters.