# Exploring the Typicality, Sensory Space, and Chemical Composition of Swedish Solaris Wines
## 3 Results
### Volatile Composition  
Data for volatile compounds analysis were evaluated from two different perspectives. Firstly, data were analyzed using an unsupervised approach (PCA) considering the wines as individual products. Secondly, a supervised modelling approach was performed (OPLS-DA) with two defined groups: Solaris wines (Sol) and non-Solaris wines (non-Sol). The distribution of the wines based on their aroma profile was performed including all volatile compounds data. The total explained variance of the first two component of the PCA analysis was 46.1% (Figure 4). A trend separating between most of Sol and non-Sol wines (except for Sb-FR (1)) can be observed along PC1 (30.0%). The corresponding HCA analysis showed two clusters with a spread distribution of Sol wines (Supplementary Materials Figure S1). Sol-SW (2) and Sol-SW (5) grouped together with most of international wines, whereas the second cluster, on the right side of PC1, included Sol-SW (1), Sol-SW (3), Sol-SW (4), and Sb-FR (1) (Figure 4). An OPLS-DA model was built to evaluate the discriminant capacity of varietal compounds as a tool which could be a step forward in understanding the flavor potential of Solaris grapes. However, the OPLS-DA model reliability for the volatile composition was found not significant (CV-ANOVA \(p\)-value 0.729). This lack of significance was expected from the PCA (Figure 4) and the corresponding HCA. It has been shown in literature how model reliability is compromised when no group separation is observed on PCA scores [46]. This could be explained by a diversity in winemaking approaches  
Figure 4: PCA biplot showing the distribution of the wines according to their volatile composition. Solaris wines are colored in orange and non-Solaris in blue. within the Sol group. Indeed, OPLS-DA results showed that variability within each group was larger (F2-21.1%) than between the groups Sol and non-Sol (F1-16.5%) (Figure 5a). As expected, it was difficult to establish a cut-off from the corresponding S-plot (Figure 5B) to determine whether some compounds were discriminating between Sol and non-Solaris wines or not. However, information extracted from the VIP (Variable Importance for the Projection) combined with a Student's \(t\)-test approach [47] can help to establish a cut-off for the S-plot and determine the  
Figure 5: OPLS-DA score plot (**a**), VIP (Variable Importance for Projection) (**b**) and S-plot pair wise comparison (**c**) between Solaris (colored in orange) and non-Solaris wines (colored in blue) according to their volatile composition. Squares on the S-plot represent the significant variables for Sol and non-Sol based on the Student’s \(t\)-test. discriminant capacity between groups. Student's \(t\)-tests were carried out on variables with a VIP value \(>1\). The results showed that the levels of 4-MMP, ethyl propionate (fruit-like, pineapple aroma), ethyl 2-Methyl propanoate, and diethyl succinate were significantly higher in Sol wines (located on the upper right side of the S-plot); whereas, the levels of damascone were significantly higher in non-Sol wines (lower left side of the S-plot) (Figure 5c). The presence of varietal thiols (3-MH, 3 MHA and 4 MMP) was confirmed in all Sol wines. The occurrence of polyfunctional thiols has recently been reported for the first time in Solaris wines from Belgium [36]. Interestingly, 4-MMP was identified only in Sol wines in the set of wines analyzed (Supplementary Materials Table S2). This aroma compound has a characteristic smell of \"box tree\" and \"blackcurrant\", which can also contribute to the vegetal aroma of the wines and whose levels in the wines can also be influenced by fermentative conditions [48]. Damascone, an oxygenated C13-norisoprenoid, was found to be more abundant in non-Sol wines. Damascone has been associated with a characteristic \"rose\", \"tea-like\" and \"fruity\" (citrus and apple) aromas in other wine matrices [49].