## 3 Results
### Marker Detection and Annotation  
To determine possible differences between the volatile metabolite fingerprint of MBW and OBW samples, the volatile composition of two blueberry wines extracted by different methods were compared using OPLS-DA (Figures 3 and 4). These models constructed with a dataset from each extraction method separate MBW from OBW along the first discriminating component (T [1]). The model showed one orthogonal component, with R\({}^{2}\)X = 0.274 (total variation in X explained by the model), R\({}^{2}\)Y = 0.998 (total variation in Y explained by the model) and Q\({}^{2}\) = 0.782 (goodness of  
Figure 2: PCA of the metabolite features detected in the blueberry wines by different sample extraction method (SPE and SPME) followed by GC-QTOF-MS. The explained variances are shown in brackets. (**a**) Score plot of SPE; (**b**) Loading plot of SPE; (**c**) Score plot of SPME; (**d**) Loading plot of SPME. The PCA showing that the volatile metabolites are clearly different between Misty blueberry wine (M1, M2, M3, M4) and O’Neal blueberry wine OBW (O1, O2, O3, O4) despite of the extraction methods.  
prediction) from the SPE dataset, R\({}^{2}\)X = 0.25, R\({}^{2}\)Y = 0.999 and Q\({}^{2}\) = 0.747 from the SPME dataset (Figures 3b and 4b), indicated that both models were validated. Potential markers for separation by different extraction methods were subsequently identified using S-plots, which were represented with covariance (p) against correlation (p(corr)). The S-plots of the OPLS-DA were proposed for the identification of potential markers of group separation by [PERSON] et al. [38]. It shows the most relevant variables on the differentiation of two samples. The 10 identified markers with the highest variable influence on projection (VIP) scores by OPLS-DA are summarized in the boxplots in Figures 3 and 4, with their corresponding compound no. in Table 2. The results showed that some different markers were selected by SPE and SPME. The mass peak intensities of cinnaryl alcohol (11), p-mentha-1(7),8(10)-dien-9-ol (51), benzenepropanol (9), linalool (44), methyl eugenol (58), \(\alpha\)-terpineol (46), phenylethyl alcohol (8), ethyl benzoate (24), 3-hydroxy benzeneethanol (12) and an unknown peak (68) were significantly different between MBW and OBW samples by SPE (Figure 3). While, the mass peak intensities of linalool (11), terpinolene (43), \(\beta\)-citronello1 (49), p-menth-8-en-3-ol (50), (Z)-asarone (73), (E)-asarone (71), phenylethyl alcohol (8), methyl eugenol (58), and two unknown peaks (66, 67) were significantly different between MBW and OBW samples by SPME (Figure 4).  
Figure 3: OPLS-DA of metabolite features detected in the blueberry wines by SPE-GC-QTOF-MS. **(a)** Score plot of all metabolite features; **(b)** Model overview of the OPLS-DA model; **(c-e)** Loadings S-plot showing the variable importance in a model, combining the covariance and the correlation (p(corr)) loading profile. The box-plots at bottom showed the significantly different volatile metabolites between Misty blueberry wine (M1, M2, M3, M4) and O’Neal blueberry wine (O1, O2, O3, O4) in the OPLS-DA model (Line, mean; box, standard error; whisker, standard deviation).