## 3 Results
### Multivariate Analysis  
PCA is a tool to reduce multidimensional data, such as chemical fingerprinting, to lower dimensions with minimal loss of information. Many applications are often associated with chemical characterization, such as classification and clustering; PCA is a useful tool for identifying patterns and estimating missing data. These applications may improve traceability and quality control, as well as be used as preliminary tools for genetic analysis. In this study, the main aim was to use phytochemical data and create a multivariate statistical tool to be applied to unknown chestnuts to define their genotype (\"species\" level). A PCA for each chestnut group was performed (three repetitions per cultivar), as shown in Figure 9, to create a reliable final model. The five cultivars that were more clustered to the plot center were selected as the most representative of the group. Figure 7: Organic acids profile of the selected 26 chestnut cultivars. The lines to the right indicate different groups: “marrone-type” chestnut (MT); sweet chestnut (SC); Euro-Japanese hybrid (EH); Japanese chestnut (JC). Figure 8: Sugar profile of the 26 selected chestnut cultivars. The lines at the top indicate different groups: “marrone-type” chestnut (MT); sweet chestnut (SC); Euro-Japanese hybrid (EH); Japanese chestnut (JC). Figure 9: Principal component analysis (PCA) score plots of the four groups of analyzed chestnuts. Three repetitions were considered for each cultivar. From the left, on the top, Japanese chestnuts (total variance: 91.9%) and Euro-Japanese hybrids (total variance: 75.8%) are shown; on the bottom, “marrone-type” (total variance: 66.9%) and sweet chestnuts (total variance: 62.4%) of the European cultivars. Total variance was reported for eigenvalues greater than or equal to 1. The score plot of the Japanese chestnuts showed that the cultivars were rather clustered, indicating a low phytochemicalen variability. However, \"Ginyose\" cv was described by specific traits compared to the other ones, due to its high content of polyphenolic components, especially catechins and benzoic acids. The distribution of Euro-Japanese hybrids showed that \"Lusenta\" and \"Vignols\" cv were not related to other cultivars in the same group. For this reason, \"Bouche de Betizac\", \"Colossal\", \"Maridone\", \"Marigoule\", and \"Marissard\" cv were selected, as they are most closely clustered on the plot. The less representative cultivar for European sweet chestnuts was \"Pugenga\", maybe due to its higher sugar and cinnamic acid content than the other cultivars, whereas Portuguese chestnuts (\"Judia\" and \"Longal\" cv) presented a similar phytochemicalen profile. \"Contessa\", \"Gabiana\", \"Garrone Rosso\", \"Ruiana\", and \"Savoye\" cv were placed by the PCA in the most central part of the score plot. In the group of \"marrone-type\" cultivars, the varieties from Central Italy (\"Marrone di Marradi\" and \"Marrone di Castel del Rio\") were the most different. The French ones were mainly placed in the upper part of the distribution, except \"Marron Sauvage\" cv, which was more similar to \"Marrone della Val Pellice\" cv. The most representative cultivars were \"Marron Comballe\", \"Marron de Lyon\", \"Marron de Redon\", \"Marrone di Chiusa Pesio\", and \"Marrone della Val di Susa\". The phytochemicalenical data of the selected cultivars were then analyzed as variables by an overall PCA, building the final model to discriminate the different chestnut genotypes (Figure 10). The identified four principal components (PCs) represented 75% of the total variance with eigenvalues greater than or equal to 1 (from PC1 to PC4. the contribution was 27.1%, 21.3%, 14.3%, and 12.3%, respectively) (Figures 10 and 11). Then, the components were simplified to the main two PCs, maintaining almost 50% of the total variance; PC1  
Figure 10: Distribution of the most representative cultivars (three repetitions) for each group in the principal components plane.