# Discrimination of Smoke-Exposed Pinot Noir Wines by Volatile Phenols and Volatile Phenol-Glycosides
## 2 Results and Discussions
### PCA Analysis of VPs and VP-Glycosides  
PCA is an unsupervised method that expresses the variance of a multidimensional dataset with fewer dimensions using principal components (PCs). As a result, the discriminant variables of each group can be distinguished, and the relationship between samples can be established [36]. Here, PCA was used to visualize the dissimilarities between non-smoke-exposed and smoke-exposed wines. As shown in Figure 3a, the two principal components accounted for 90.8% (PC1) and 2.4% (PC2) of the total variance. The cumulative contribution was much higher than the 60% mark, which is generally considered a good separation model [37]. Thus, good separation was observed for the two clusters in the present study. The smoke-exposed wines exhibited greater data dispersion, probably due to different degrees of exposure to wildfire smoke. In agreement with the dendrogram,  
Figure 2: Unsupervised analysis for VPs and VP-glycosides. Hierarchical clustering heatmaps (**a**,**c**) and dendrograms (**b**,**d**) were constructed based on VPs concentration (\(\mu\)g/L) and the relative abundance of VP-glycosides, respectively. Each colored cell on the map corresponds to a wine sample. Samples 1–14 correspond to non-smoke-exposed wines (Control), while 15–35 correspond to smoke-exposed wines.  
samples 27-34 were closely related to non-smoke-exposed wines yet separated well from them. A biplot was performed (Figure 3b) to facilitate visualization of the relationship between loadings (samples) and scores (VPs).  
PCA analysis was also performed to capture the similarities and differences between the 35 tentative annotated VP-glycos (Figure 3c). Two principal components accounted for close to 70% of the total variance of the data (58.49% and 9.56% for PC1 and PC2, respectively). The distinct separation of the two categories suggested that VP-glycos could be potential biomarkers of smoke exposure.  
Univariate analysis identified the top 15 volatile phenol glycosides to differentiate non-smoke-exposed and smoke-exposed wines. The \(p\)-values of the top 15 VP-glycos were very low (\(p<\) 9 \(\times\) 10\({}^{-5}\), Table 1), with a fold change (FC) ranging from 2.11 to 31.88,  
Figure 3: Unsupervised principal component analysis (PCA) for VPs and VP-glycos. (**a**) Score plot-PCA for free VPs in non-smoke-exposed and smoke-exposed wines (Control). Explained variances are shown in the corresponding axes. (**b**) Biplot between the two selected PCs. Each dot corresponds to a wine sample. Sample numbers 1 to 14 correspond to non-smoke-exposed wines (Control), while 15 to 35 correspond to smoke-exposed wines. (**c**) Unsupervised principal component analysis for VP-glycos. Each dot corresponds to a wine sample. PC1 and PCA2 describe 68% of the variation among samples. PCA was computed using Progenesis QI (version 2.3).  
indicating that the contents of the top 15 VP-glycoses were significantly affected by smoke. The higher the FC number, the greater the difference between the smoke-exposed and non-smoke-exposed samples.