## 2 Results and Discussion
### Volatile Compound Profiling in Grapes Prior to OTA Biosynthesis
#### 2.2.2 OPLS-DA of Volatile Compounds and Correlation of OTA with Volatiles
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{5}{c}{**HS-SPME-GC-MS CK1d vs. 1d**} & \multicolumn{5}{c}{**DLLME-GC-MS CK1d vs. 1d**} \\ \hline \((E)\)-2-hexenal & \(>\) & VIP = 1.25, \(p\) = 0.02 & \((E)\)-2-hexenal & \(>\) & VIP = 1.22, \(p\) = 0.04 \\ styrene & \(>\) & VIP = 1.03, \(p\) = 0.01 & nerol & \(>\) & VIP = 1.28, \(p\) = 0.01 \\ trans-2-(2-pentenyl)furan & \(<\) & VIP = 1.25, \(p\) \(<\) 0.05 & ethyl acetate & \(>\) & VIP = 1.25, \(p\) \(<\) 0.01 \\
1-hexanol & \(<\) & VIP = 1.28, \(p\) \(<\) 0.05 & & & \\
3,7-dimethyl-3,6-octadienal & \(<\) & VIP = 1.21, \(p\) = 0.04 & & & \\ \((E)\)-\(\beta\)-farnesene & \(>\) & VIP = 1.34, \(p\) \(<\) 0.05 & & & \\ nerolic acid & \(>\) & VIP = 1.26, \(p\) \(<\) 0.05 & & & \\ \hline \hline \end{tabular}  
* Volatiles were selected based on the VIP values (\(>\)1) of the OPLS-DA model and the \(p\)-values from \(t\)-tests (\(p<0.05\)). The \(>\) symbol indicates that the volatile compound contents in the control group were significantly higher than those in the experimental group. The \(<\) symbol indicates that the volatile compound contents in the control group were significantly lower than those in the experimental group.  
\end{table} 
Table 1: Significantly differential volatiles in grape samples CK1 and day 1 based on the analysis conducted using HS-SPME-GC-MS or DLLME-GC-MS data. Figure 4: Orthogonal partial least squares discriminant analysis (OPLS-DA) score plots of grape samples on day 1 and CK1 determined using two different methods. (**A**) OPLS-DA score plot of samples on day 1 versus sample CK1 based on HS-SPME-GC-MS data. (**B**) OPLS-DA score plot of samples on day 1 versus sample CK1d based on DLLME-GC-MS data. Notably, (_E_)-2-hexenal, nerol and nerolic acid were detected in the samples using both methods. Other significant differential volatiles were uniquely found based on the HS-SPME-GC-MS or DLLME-GC-MS data. Nerol was also identified using both methods; this was a significant differential volatile that was only in the analysis conducted based on the DLLME-GC-MS data, possibly due to its weak polarity. Nerol was better extracted when the weakly polar dichloromethane was used as the extractant in DLLME-GC-MS analysis. Nerol was adsorbed very lightly by the 50/30 \(\upmu\)m DVB/CAR/PDMS fiber, which was due to the incomplete adsorption of nerol. Thus, no significant difference was observed in the nerol content among the grape samples based on HS-SPME-GC-MS.  
Pearson correlation analysis was employed to investigate the correlation between OTA and significant differential volatile compounds over a seven-day contamination period, as shown in Figure 6. The obtained results showed positive correlations between OTA and ethyl acetate (\(p<0.01\)), styrene (\(p<0.05\)) and 1-hexanol (\(p<0.05\)); OTA was negatively correlated with (_E_)-2-hexenal (\(p<0.05\)) and nerolic acid (\(p<0.05\)). Grapes without _A. carbonarius_ presented higher levels of (_E_)-2-hexenal and nerolic acid. In contrast, higher levels of volatiles, such as ethyl acetate, styrene, and 1-hexanol, were found in grapes inoculated with _A. carbonarius_. In addition, trans-2-(2-pentenyl)furan, 3,7-dimethyl-3,6-ocatdenal, (_E_)-\(\beta\)-farresene and nerol had little correlation with the OTA secretion in _A. carbonarius_-cultured grapes. With the growth of _A. carbonarius_ in the inoculated grapes, the content of ethyl acetate, styrene and 1-hexanol gradually increased; meanwhile, the content of (_E_)-2-hexenal and nerolic acid gradually decreased. The metabolism of OTA in grapes can be affected by several factors, such as the surrounding environment, the geographical location of vineyards, the soil composition, and the grape cultivar [32]. Some scholars have researched the markers of _A. carbonarius_ contamination in grapes. For example, [PERSON] [33] found that the presence of ethyl  
Figure 5: Verification diagram based on orthogonal partial least squares discriminant analysis (OPLS-DA). (**A**) by HS-SPME-GC-MS. (**B**) by DLLME-GC-MS.  
acetate was indicative of _A.