## 2 Results and Discussion
### Sensory Profiling
This would explain that only negative correlations were found between sensory descriptors and the expert scores--experts have a common understanding of low quality, but differ in their high quality assessment. In our previous work [PERSON] (2010), we found that wine experts use a quality framework that combines both descriptive terms and more subjective, personal preferences. Although there are personal differences among the experts, a common baseline exists for low-quality wines. In two open-ended questions (_Which attributes do you  
Figure 1: PCA of the sensory attributes that differed significantly (\(p<0.05\)) in the ANOVA among the 27 wine samples. (**a**) Score plot showing the sensory space of the wines. Wines are color-coded according to their assigned quality categories based on the wine judgment—red for wines low in quality, (_i.e._, that were awarded no medal), dark yellow for medium quality (_i.e._, either a bronze or silver medal), and green for high quality (_i.e._, a gold or double gold medal). Barplots of sensory attributes for (**b**) the first principal component (PC 1), and (**c**) the second principal component (PC 2). Attributes that are significantly correlated to either one of the first two PCs are denoted with an asterisk (\(p<0.05\)). associate with a high quality wine?_ and _Which attributes do you associate with a low quality wine?_), experts associated low wine quality with the presence of defects and flaws, such as microbial spoilage, presence of atypical aromas (e.g., vegetal-green) or oxidation aromas, or an unbalanced flavor profile [25]. It seems that the descriptors soysauce (\(r(25)=-0.55\), \(p<0.05\)), fresh green (\(r(25)=-0.41\), \(p<0.05\)), and overall aroma (\(r(25)=-0.65\), \(p<0.05\)) fall into these categories, thus, explaining their significant negative correlation to the expert scores. For high quality, the experts named the presence of fruit aroma as an important component of wine quality, therefore, it not surprising that red fruit aroma showed a significant positive correlation to the experts' scores (\(r(25)=0.45\), \(p<0.05\)). For bottle price, six sensory attributes showed significant positive correlations. Bitter taste (\(r(25)=0.53\), \(p<0.05\)), hot mouthfeel (\(r(25)=0.59\), \(p<0.05\)), astringent mouthfeel (\(r(25)=0.40\), \(p<0.05\)), alcoholic aroma (\(r(25)=0.40\), \(p<0.05\)), and Brett aroma (\(r(25)=0.48\), \(p<0.05\)), all showed a positive correlation to bottle price. The price of a bottle of wine reflects to a certain extent the costs of producing this bottle. Wines that are harvested later at higher sugar levels are typically higher in ethanol content, and the higher ethanol leads to higher perceivable alcoholic aroma and hot mouthfeel [28]. Increasing sugar content in grape berries can be accomplished by reducing competition for sugar allocation and improving sunlight exposure, _i.e._, leaving fewer berry clusters on each vine, or reducing the leaf cover to increase sunlight exposure. All these practices increase vineyard management costs. Similarly, this is true for astringency and bitterness, the sensory response to polyphenols, mainly tannins, present in the wine [29]. Tannins in wine come from the grape berries (seed, skin, stem tannins), from enological tannin additions or from oak barrels, which in turn increase again the production costs [30]. The correlation between bottle price and Brett aroma is less intuitive to explain, and might be the result of the wine set used in this study. Typically, the presence of Brett aroma is considered at least an unwanted, if not even faulty, aroma [31]. One possible explanation could be oak barrels infected with Brettanomyces strains. Due to their high costs, oak barrels are typically re-used, difficult to properly sanitize, and provide with a porous surface, small oxygen ingress, and available cellobiose ideal conditions for Brettanomyces colonization [32, 33, 34], which can lead to detectable Brett aromas in the stored wines.