## 4 Discussion  
It is generally accepted that volatile compounds influence the overall aroma profile when their concentration is above their odor thresholds. However, even when the concentration is below the odor threshold, some volatile compounds can interact with other volatiles to enhance or suppress the aroma perception [39]. Furthermore, some of them are only present at very low concentrations but often contribute greatly to the overall aroma [40]. Thus, it is important to have analytical tools suitable to detect these odor compounds, to learn the complexity behind wine aroma and to be used for selection  
Figure 4: OPLS-DA of metabolite features detected in the blueberry wines by SPME-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). and quality control [41]. Performances of different volatile extraction methods has been compared in many studies [41; 42]. [PERSON] et al. [42] reported that SPE using a LiChroult-EN cartridge showed very good linearity, covering a wide range of concentrations of wine volatile compounds, compared to the HS-SPME procedure. The advantage of SPME is that it can detect the highly volatile compounds which are often covered by the solvent peaks, and can eliminate problems associated with chemically and thermally unstable samples, thus avoiding generation of artifacts [43]. It has to be mentioned that the performance of the analytical methods are not only dependent on the compound extraction, but also depend on many other factors, such as column selection and instrument conditions, which are less feasible to change in real practice. In order to recover a wider range of volatile compounds in the blueberry wines, both SPME and SPE methods were used in our study. For SPE, LiChroult-EN resins were selected because previous studies showed that they had an excellent ability for the extraction of aroma compounds from wine [42; 44]. For SPME, triphase SPME fiber (DVB/CAR/PDMS) was chosen because it had been found to extract the representative blueberry volatiles [15]. The results showed a good coverage of compounds with different polarity, as well as a good coverage from the highly volatile compounds to semi-volatile compounds. The PCA plots showed that the wines made from OBW and MBW were clearly separated by the PC1, indicating the metabolomic analysis based on different extraction methods is useful to reveal the compositional differences of volatile compounds. However, we also observed different projections of the four replicates between SPE and SPME within the same variety. Misty blueberry wines in Figure 2a are projected in opposite quadrants along the PC1, showing that differences between replicates exist. The differences between replicates might come from multiple sources, since several variables were not easy to control, such as the inner temperature of fermenter, pre-fermentation extraction, as well as the yeast growth [45]. It was also interesting to note that the replicates of MBW were much closer in Figure 2c compared to Figure 2a, indicating the two extraction methods extracted different compounds thus could affect the sample being distinguished. Significant differences were observed between MBW and OBW samples in terms of composition and amount of the volatile compounds. However, due to the different selectivity of SPE and SPME, OPLS-DA found different markers from the two datasets, which could be complementary to each other. Linalool, methyl eugenol, and phenylethyl alcohol were selected as significantly different volatile metabolites between MBW and OBW in both of the OPLS-DA models.