## 3 Results and Discussion
### Discrimination of the White and Red Musts and the Corresponding Wines  
Unsupervised multivariate statistical analysis (PCA) was performed in order to discriminate between different white and red musts and the corresponding wines and to identify specific phenolic biomarkers for each category. In the case of white varieties, PCA based on the main bioactive characteristics (TPFs, TCs, TTs, TAs, and AA) explained 96.23% of the total variation using two main components, with a higher contribution brought by PC1 (84.10%) compared to PC2 (12.13%) (Figure 3a). From the plot map (Figure 3a), white wines are grouped separately from their musts on either side of the PC1 axis, and positive correlations have been achieved between all the bioactive characteristics of white musts, with TPFs characterizing SB must and TCs, TTs, and AA characterizing RI and MO musts. In the case of red varieties, the PCA explained 87.13% of the overall variation using two main components, with PC1 (60.65%) contributing more than PC2 (26.48%) (Figure 3b). Similar to the white varieties, the red musts and wines were clearly distinguished, with M must defined by TTs and TAs characteristics, while CS and BM musts are characterized by TPFs, TCs, and AA (Figure 3b). Considering individual phenolic compounds, similar discrimination pathways were observed for white and red varieties; PCA analysis explained 59.36% of the total variation in the case of white varieties (Figure 4) and 74.2% of the total variation in the case of red varieties (Figure 4). From the score plot map, it is noted that white musts were grouped on the right side of the PC1 axis, and the corresponding wines were grouped on the left side (Figure 4a). Along the PC1 axis, the white musts are correlated with caffe (CafA), chlorogenic (ChIA), and t-cinnamic (CinA) acids, flavonoids (isorhamtein--iRh, kaempfer--Kae, chrysim--Chry, galangin--Ga, quercetin--Qu), (+)-catechin (Cat) and (--)-epictechin (ECat), and t-resveratrol--Res, while white wines are correlated with pheno  
Figure 3: Discrimination of musts (m) and the corresponding wines (w) based on the main bioactive characteristics (TPFs, TCs, TTs, TAs and AA): (**a**) white varieties (SBm/v—Sauvignon Blanc must/wine, RIm/w—Riesling Italian must/wine, MOm/w—Muscat Ottonel must/wine, FRm/w—Fetesaca Regala must/wine) and (**b**) red varieties (Mm/w—Merlot must/wine, CSm/w—Cabernet Sauvignon must/wine, BMm/w—Burgund Mare must/wine). In the case of red varieties, the PCA explained 74.20% of the overall variation using the main components, with PC1 (48.35%) contributing more than PC2 (25.85%) (Figure 5a). The red musts were grouped on the right side of the PC1 axis, whereas the corresponding wines were grouped on the left, indicating a clear difference in the phenolic composition of musts versus wines. It is observed that the Burgund Mare variety (B) can be distinguished from other red varieties in both musts and wines (CS and M). PCA analysis also revealed that red musts are well correlated with flavonoids (Qu, Kae, iRh, Apig, Chry, Ga), Cat, and ECat, Ca/A, and Res, while red wines are well correlated with phenolic acids (SyA, GaA, CinA, p-CoumA, 3,4-DHBA, 4-HBA, FA, Ch1A) (Figure 5b). [PERSON]'s correlation analysis applied on quantitative data reveals strong and moderate correlations between the main phenolic compounds in musts and wines. The correlation coefficient with values greater than 0.5 was used to interpret the correlation coefficient with values greater than 0.5. In the case of white varieties, strong positive correlations were observed between gallic acid and syringe and 4-hydroxibenzoic acids and pinochemprin, between caffe acid and quercetin, syringic acid and pinochemprin, between (+)-catechin and (-)-epicatechin, between p-coumaric acid and ferulic acid, and also between kaempfer and isorhamethein, apigenin and chrysin, and between galangin and apigenin (Table S5).