## 3 Results and Discussion
### Identification of Key Parameters at Different Dehydration Degree
The discrimination model of free fractions was built after the observation classes were set according to the sampling point and the variables were scaled using the 'Par' scaling type. Four predictive components were achieved in the model with high cross-validation parameters, as follows: R2X(cum) = 0.996, R2Y(cum) = 0.992, and Q2(cum) = 0.965. High R2 and Q2 values indicate a good descriptive ability and predictability of the model [44]. A permutation test involving 200 runs was also applied, in order to observe the overfitting phenomenon of the model. With all intercepts of the Q2 regression line below zero, the permutation plots (shown in Supplementary Figure S4) indicated that the overfitting phenomenon did not exist; thus, the model was reliable. Consequently, the statistical model established for the free forms of volatiles expressed as ng/berry was employed to identify the key parameters caused by differing dehydration degree. For this purpose, the variable importance in projection (VIP) value was used to estimate the contribution of a given compound to the OPLS-DA model. Referring to SIMCA guidelines and other studies [45; 46; 47; 48], the importance of a variable to the discriminant model was determined according to its VIP value, where the larger the VIP value, the more critical the compound is in the discriminant analysis. In general, compounds with VIP values above 1.0 can be considered as metabolic markers with strong contributions to the discriminant model. This screening criterion has also been set to 2.0 by some authors [48]. As shown in the VIP plot in Figure 7c, a total of twenty-seven free-form compounds with VIP values greater than 1.0 were screened as metabolic markers to discriminate the grapes at five degrees of on-vine dehydration. These differential metabolic markers consisted predominantly of ADDs and FADs, with thirteen ADDs and eleven FADs, while the only IPD was geraniol (with a VIP value of 1.03). To visually elucidate the relationship between these metabolic markers and the five degrees of dehydration, hierarchical clustering heatmap analysis was conducted in Metabo-Analyst 5.0, with the samples and compounds clustered separately. After hierarchical clustering, the twenty-seven metabolic markers were divided into three clusters that each showed a different evolutionary pattern during dehydration. As shown in Figure 8,Cluster I included a total of twenty compounds that gradually decreased during dehydration; Cluster II contained three compounds, whose content fluctuated during dehydration; and Cluster III contained five compounds, whose content increased significantly in the later stages of dehydration. Remarkably, the samples were also clustered in order of dehydration degree, indicating that the screened metabolic markers were able to discriminate the degree of dehydration very well. Four free-form volatiles with VIP values above 2.0 were the most important metabolic markers for discriminating the dehydration degree, namely, 2_A_-di-_tert_-butylphenol (ADD09, in Cluster I, VIP = 4.34), 2-phenethyl acetate (ADD23, in Cluster III, VIP = 2.65), 2-methyl-1-propanol (ADD24, in Cluster II, VIP = 2.10), and hexanol (FAD01, in Cluster II, VIP = 2.08). Figure 7: OPLS–DA score scatter plots of free (**a**) and bound (**b**) forms of volatiles expressed as ng/berry, and the key parameters with VIP scores above 1.0 (**c**) during on-vine grape dehydration. TI, T2, T3, T4, and T5 represent the sampling points at different dehydration degrees. The corresponding compounds for the abbreviations are listed in Tables S1 and S2. Among the free-form volatiles in the early stage of dehydration (T1), 2,\(\mathcal{A}\)-di-_tert_-butylphenol was the main component of volatile phenols and AADs, with its content accounting for 99.07% and 32.43% of the total amount of these two groups in a single berry, respectively (Table S2).