## 3 Results and Discussion
### Classification of Wines by Geographical Origin and Vintage Based on Phenolic Compounds  
Phenolic compounds are important indicators of the taste characteristics of wine, such as astringency, bitterness, and mouth-feel, and they are present both as monomers and oligomers [26; 33]. In total, 19 phenolic compounds were quantified by standard curve. Including flavanols (epicatechin gallate, epigallocatechin, catechin, and epicatechin), procyanidin oligomers (dimer1, dimer2, dimer3, dimer4, dimer5, dimer6, trimer1, and trimer2), flavonoids (myricetin, isorhamentin, and quercetin), and phenolic acids (syringic, caffeic, gallic, and protocatechuic acids). After ANOVA, compounds with significant differences were used as variables in PCA. PCA was used to display similarities and differences in phenolic composition with significance between samples. The vectors in the variables plot (Figure 5a) show the relative loadings of the most variable phenolic compounds. The individual plot (Figure 5b) shows the similarities and differences between the wine samples. There were fewer phenolic compounds with significant differences between the six Ningxia sub-regions (seven phenolic compounds) than between Ningxia, California, and Bordeaux (sixteen phenolic compounds). Figure 4: HCA (hierarchical cluster analysis) of wines based on volatile compounds from the six sub-regions of Ningxia, Bordeaux, and California. In the PCA plot of phenolic compounds from the 98 wines from the 6 Ningxia subregions (Figure 5a), the first (PC1, 39.2%) and second (PC2, 22.8%) components explained 62.0% of the total variance in the data set. The wine samples from the six Ningxia subregions formed a loose group (Figure 5b) and were evenly distributed over the four quadrants. The seven characteristic phenolic compounds of the six sub-regions formed similar profiles in wines from all the regions. In the PCA variables plot of phenolic compounds from the 114 wines from Ningxia, Bordeaux, and California (Figure 5c), the first (PC1, 56.4%) and second (PC2, 9.4%) components explained 65.8% of the total variance in the data set. The wines were divided into three groups, with the Ningxia wines mainly in quadrants one, three, and four, and those from California and Bordeaux in quadrant two (Figure 5d). The contribution of the 16 characteristic phenolic compounds (Figure 5c) to Ningxia wines was significantly higher than those from Bordeaux and California. In the Bordeaux and California wines, these compounds had opposite trend vectors in PC1, i.e., they were negatively correlated with the Bordeaux and California wines. The regional variation in phenolic compound profiles was similar to that of the volatile compound profiles; those of the six Ningxia subregions were similar, whereas those of the Ningxia, California, and Bordeaux regions were markedly different. Additionally, it can be seen from Figure 5d that in this study, a result similar to volatile compounds, distribution of region is the primary indicator affecting  
Figure 5: PCA (principal components analysis) scores plot for wines based on phenolic compounds of six sub-regions of Ningxia (**a**) and three wine-producing regions (**c**). PCA loadings plot of volatile compounds of six sub-regions of Ningxia (**b**) and three wine-producing regions (**d**). phemolic compounds. The distribution of samples was more concentrated in the region rather than vintage. The phenolic compounds from the 114 wines of the 6 consecutive Ningxia vintages (2013-2018) were analyzed using hierarchical cluster analysis (HCA) to compare differences between the Ningxia sub-regions, Bordeaux, and California (Figure 6a). The 19 phenolic compounds were clustered into 3 groups in the vertical clustering tree, and the wines from Ningxia, Bordeaux, and California formed 3 groups in the longitudinal clustering tree (Figure 6a), with the exception of Hongsipu, which had a phenolic profile similar to that of Bordeaux wines and distinct from the other Ningxia wines.