## 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  
OPLS-DA is a supervised method used for classifying samples, constructing discriminant models, and eliminating the influence of irrelevant data [27; 28]. In this study, OPLS-DA was performed on the volatile compounds of samples from day 1 and CK1 to  
Figure 1: Changes in ochratoxin A (OTA) contents during a seven-day contamination period in QuECheRS-HPLC. identify the significant differential volatiles in grapes contaminated with _A. carbonarius_ before OTA biosynthesis. Based on the results of HS-SPME-GC-MS (Figure 4A) and DLLME-GC-MS (Figure 4B), there was no overlap between samples CK1 and day 1, indicating excellent discrimination among samples. The volatile compounds in samples CK1 and day 1 were clustered separately on the x-axis of the OPLS-DA model. Notably, the best classification result was achieved by the OPLS-DA model, indicating significant differences in the volatile compounds between samples CK1 and day 1. R2 and Q2 indicate the explanation ability and the prediction ability of the OPLS-DA model, respectively [29]; R2 > 0.5 and Q2 > 0.5 are indicative of an OPLS-DA model with suitable interpretation and prediction abilities [30]. In the present study, these coefficients were R2X = 0.784, R2Y = 0.996, and Q2 = 0.959 (Figure 4A), and R2X = 0.691, R2Y = 0.994, and Q2 = 0.977 (Figure 4B), collectively indicating two stable and reliable models. Moreover, a significant difference in volatile compounds was observed between the samples CK1 and day 1, allowing them to be classified into two groups (at 95.0% confidence interval). Furthermore, a permutation test with 200 iterations was conducted to validate the fitting degree of the OPLS-DA model (Figure 5). The intercept values of R2 and Q2 in the  
Figure 2: Principal component analysis (PCA) with volatile compounds identified in grape samples from day 0 and CK1 using two identification methods. (**A**) HS-SPME-GC-MS. (**B**) DLLME-GC-MS. Numbers refer to the volatile compounds, as listed in Tables S1 and S2 (Please refer to Figure S1 for the overlapping content). permutation models were lower than the original values, and the slope of the regression line was steep. Moreover, the intercept of Q\({}^{2}\) was negative, indicating that the two models did not overfit. The significant differential volatiles in the grape samples from day 1 and CK1 were analyzed using variable importance in projection (VIP) analysis. The VIP values indicate the variable contribution rate to the classification, with VIP values \(>\) 1 indicating a key variable in the discriminant model [31]. The significant differential volatiles were selected based on the \(p\)-values from \(t\)-tests (\(p<0.05\)). Table 1 summarizes the significant differential volatiles in samples CK1 and day 1 based on the HS-SPME-GC-MS and DLLME-GC-MS data. A total of seven and three volatile compounds were selected as significant differential volatiles in samples CK1 and day 1. In the experimental group, a significant increase was observed in trans-2-(2-pentenyl)furan, 1-hexanol, and 3,7-dimethyl-3,6-ocatdienal compared to CK1, while other significant differential volatiles showed a downward trend in the samples from day 1. Figure 3: Principal component analysis (PCA) with volatile compounds identified in grape samples from day 1 and CK1 using two different identification methods. (**A**) HS-SPME-GC-MS. (**B**) DLLME-GC-MS. Numbers refer to the volatile compounds, as listed in Tables S1 and S2 (Please refer to Figure S2 for the overlapping content).