## 3 Results and Discussion
### Comparative Analysis of HS-GC-IMS and HS-SPME-GC-MS in Identifying VOCs
#### 3.4.3 Identification of Aroma Markers Based on HS-GC-IMS and HS-SPME-GC-MS Data  
In the PCA model, sample data visualization is enhanced by projecting the observations into a reduced dimensional space that captures the variance in the first few principal components, thereby highlighting classification trends across the samples. In contrast, the supervised PLS-DA model seeks to maximize the discrimination between different groups by focusing on the most influential differential metabolites across samples [47]. Specifically, VOCs with VIP > 1 were selected as the key discriminatory features in the PLS-DA model [48], as these VOCs substantially contribute to the overall aroma profile.  
Figure 6 illustrates the effectiveness of PLS-DA modeling in distinguishing the VOC content differences detected via HS-GC-IMS and HS-SPME-GC-MS, alongside the critical aroma volatiles that contribute prominently to sample discrimination. The PLS-DA score  
plots derived from HS-GC-IMS (Figure 6(Aa)) and HS-SPME-GC-MS (Figure 6(Ba)) datasets explain 68.5% and 58.9% of the total variance, respectively. Both score plots exhibit clear separation of the sample groups, demonstrating the capability of PLS-DA to effectively distinguish between the different sample groups. To validate the accuracy and robustness of the PLS-DA models, cross-validation (CV) was performed, and the statistical parameters--accuracy, R\({}^{2}\), and Q\({}^{2}\)--all exceeded 0.95 (Figure 6(A),B(c)), confirming the reliability of the PLS-DA models employed.  
Furthermore, in the VIP score plot (Figure 6(B)), 21 VOCs with VIP > 1 were identified as potential characteristic compounds in the PLS-DA model based on HS-GC-IMS data. Given that aroma presentation is ultimately related to the OAV of VOCs, nine volatiles--including methyl salicylate, Styrene-D, 2-pentanone, (E)-2-hexen-1-ol, \(\beta\)-ocimene, 3-Methyl-3-buten-1-ol, but-(E)-2-enal, (E)-hept-2-enal-M and (E)-hept-2-enal-D--had OAVs all >1, indicating their potential as key aroma markers that drive the discrimination in the HS-GC-IMS PLS-DA model. In comparison, for the PLS-DA model based on HS-SPME-GC-MS data, a total of 52 VOCs had VIP > 1, with 22 VOCs exhibiting VIP > 1.5 (Table S5). Thus, a VIP score plot was generated for these 22 VOCs (Figure 6(Bb)), which could serve as potential characteristic VOCs for the PLS-DA model based on HS-SPME-GC-MS data. Combining the criteria of VIP > 1 and OAV > 1, 13 VOCs--methyl salicylate, \(\beta\)-damascone, linalool oxide I, oct-(2E)-enal, nona-(2E,4E)-dienal, (Z)-hex-3-en-1-ol, (E,E)-2-4-heptdiagnetal, linalool oxide II, cedrol, nonanal, (Z)-jasmome, hexanal, and 1-octen-3-one--were highlighted as critical aroma markers for the HS-SPME-GC-MS-based PLS-DA model.  
Figure 6: PLS-DA analysis on data obtained from HS-GC-IMS and HS-SPME-GC-MS. (**A**) PLS-DA score plot (**a**), VIP score plot (**b**), and model prediction accuracy for HS-GC-IMS data (**c**); (**B**) PLS-DA score plot (**a**), VIP score plot (**b**), and model prediction accuracy (**c**) for HS-SPME-GC-MS data. Filled circles in panel (a) represent the 95% confidence region and the red star in panel (c) represents the best classifier.