## 3 Results and Discussion
### Analysis of Volatile Compounds in Three Wines with Different Levels of Infestation
#### 3.2.2 PLS-DA of GC-MS Data  
Projections to latent structures discriminant analysis (PLS-DA) and SIMCA software (ver. 14.1; Sartorius) were used to evaluate the GC-MS data.  
The three groups of wine samples showed intra-group aggregation and inter-group dispersion, clearly distinguishing them. The PLS-DA diagram shows the contribution of volatile compounds (Figure 2). Volatile compounds that are further from the central origin and the main compound groups make a greater contribution [20]. Compounds such as isoamyl acetate, diethyl succinate, styrene, hexyl acetate, ethyl 3-hexenoate, 3-methyl-1-butanol, and acetic acid are located close to wines made from normal grapes (Figure 2b). Ethyl heptanoate, ethyl acetate, phenylethanol, 2,4-di(tert-butyl)phenol, and hexanoic acid were major flavor substances in the group A samples. Ethyl undecanoate, 3-methylbutyl decanoate,2,6,8-trimethyl-4-nonanone, Methyl 10-methylundecylate, and 3-methylbutyl caprate were major flavor substances in the group B samples. 2-nonanol and other volatile compounds were major flavor substances in the group C samples. When PLS-DA is used with fewer dimensions, overfitting may occur. Therefore, the PLS-DA model was tested for overfitting using a replacement test with 200 responses (Figure 3).  
In the permutation test (200 permutations) of our PLS-DA model (Figure 3), R\({}^{2}\) represents the proportion of variance in the data explained by the model. A value of R\({}^{2}\) = 0.143 indicates that the model accounts for 14.3% of the total variance in the GC-MS data, reflecting the goodness-of-fit between the model and the original data.  
Figure 2: Two-dimensional score plot (**a**) and two-dimensional load plot (**b**) of PLS for wine volatile compounds.  
Q\({}^{2}\) is a cross-validated measure of predictive ability, calculated using a leave-one-out approach. Our Q\({}^{2}\) value of \(-0.311\), with the Q\({}^{2}\) intercept on the \(y\)-axis being negative, confirms that the model does not suffer from overfitting. A negative Q\({}^{2}\) in permutation tests typically indicates that the original model's predictive power is significantly better than that of randomly permuted models, validating the stability and reliability of our PLS-DA model for distinguishing the three wine groups (A, B, and C) and identifying characteristic flavor compounds.  
This interpretation aligns with the model's performance in clearly separating the groups in the score plot and highlights its suitability for subsequent screening of key volatile compounds via VIP analysis.  
This indicated that there was no overfitting in the model, and that the model was stable, predictive, and could be used to screen for flavor substances. A variable importance in projection value > 1 was used to distinguish the important flavor substances in the three groups of wines, and the following eight substances were identified as the major flavor substances in Petit Manseng wines: isoamyl acetate, ethyl decanoate, phenethyl acetate, ethyl laurate, caproic acid, linalool, decanoic acid, and citronellol.