# Exploring the Typicality, Sensory Space, and Chemical Composition of Swedish Solaris Wines
## 3 Results  
### Typicality and Quality of Swedish Solaris Wines  
Agreement between experts concerning typicality and quality was explored by means of PCA (Figure 1). A lack of consensus was found among the judges regarding what they perceived as a typical Solaris (Figure 1a). This lack of agreement resulted in a non-significant ANOVA result and a very close (within a range of 41-57) Solaris typicality means between samples (Supplementary Materials Table S1).  
A similar result was found concerning quality ratings (Figure 1b). However, in this case the ANOVA was significant although the only significant differences were observed only between the two extreme wines.  
Figure 1: (**a**) Principal Component Analysis (PCA) Biplot representing the consensus between judges (J1-J16) according to their typicality scores. (**b**) PCA Biplot representing the consensus between judges (J1-J16) according to their quality scores.  
In order to segment the panel into consensual clusters, HCA was performed on PC1 and PC2 coordinates of the typicality PCA. The HCA analysis unveiled three clusters based on the configuration of the typicality scores, typicality cluster 1 (\(n=6\), in green), typicality cluster 2 (\(n=4\), in red), and typicality cluster 3 (\(n=6\), in blue) (Figure 1a). A segmentation of the data was performed and the analysis of variance for the typicality scores was analyzed for each of the clusters. Significant differences between the wines were only found in typicality cluster 1 (Table 2). However, this cluster considered Sol-SW (1) as the most typical Solaris wine and Sol-SW (2) as the least typical. It was therefore necessary to have a look at the sensory space of these wines to have a better understanding of what was driving cluster 1 typicality scores.  
As previously mentioned, significant differences were found for the quality scores of the wines, (F = 2.17; \(p=0.018\)). AL-SP (1) was perceived as the wine with the highest quality (Table S1). This score was significantly higher than Sol-SW (5), wine which had the lowest quality score. The rest of the Solaris wines (Sol) were spread in the lower part of the quality rating, although no significant differences were found between these wines and the highest and lowest scoring wines. The main interest of this work was to understand the perception of Solaris wines as a group and, therefore, when looking into the wines grouped as Solaris (Sol) versus international wines (non-Sol), the latter were perceived with a significantly higher quality (t = 2.49; \(p=0.031\)). As it occurred with the typicality of the wines, a disagreement between judges was also found for the perception of the wine quality (quality cluster 1, n= 10 and quality cluster 2, \(n=6\), Figure 1b). Analysis of variance of the quality was performed for each cluster. In the case of cluster 1, non-Sol wines were perceived as higher quality, with the exception of Cha-FR found at the bottom of the range (Table 2). However, Sol-SW (3) was perceived on the top range of the list. Opposite results were found for cluster 2. This group of judges perceived Sol-SW (5) as the wine with the highest quality and Sol-SW (1) was also found among the top range. Nonetheless, Sol-SW (2), (3) and (4) showed the lowest average scores in quality, together with Cha-FR.  
Since typicality is a well-stablished sub dimension of wine quality [44; 45], we expected a significant correlation between these two parameters. However, the regression vector (RV) coefficient between the three clusters typicality matrix and the two clusters quality matrix was low (0.349), indicating that there was no clear relationship between the typicality and quality assessments.