## 3 Results and Discussion
### Multivariate Data Analysis  
Principal component analysis (PCA) was employed to visually differentiate large yellow croaker samples preserved by different methods over a storage period of 0 to 12 days. As shown in Figure 1A, the short distances between the three parallel samples reflect the reproducibility of the data. The plot in Figure 1A shows a slight overlap between 0 and 6 days in Group C, followed by a significant diagonal separation by day 12, indicating marked changes in volatile components over time for this group. Similarly, Group L showed comparable volatile compound profiles on days 0 and 6, whereas notable differences were observed on day 12, indicating significant changes in volatile composition over the storage period. Although partial clustering trends were observed in the PCA plot, considerable overlap among six of the nine groups suggests that the separation of volatile profiles was not distinct. The PCA plot shows that the first two principal components explain 48.1% of the total variance, with PC1 and PC2 accounting for 30.4% individually in Figure 1A. Although the variance explained by the first component is relatively low, the separation trend among the various groups is evident. [PERSON] et al. (2021) [24] reported that the PCA model is influenced by experimental factors, such as instrumentation, and to mitigate these issues, partial least squares discriminant analysis (PLS-DA) was applied. Figure 1B presents the PLS-DA score plot, where partial group separation can be observed. Although some overlap exists among at least three groups, the relatively compact clustering within individual groups suggests acceptable repeatability of the PLS-DA model. Compared to PCA, the PLS-DA score plot emphasizes classification by maximizing the groups. In the present analysis, PC1 and PC2 of the PLS-DA model accounted for 14% and 26.1% of the variation, respectively, suggesting a trade-off between variance explanation and class discrimination. To further analyze the association between each volatile compound and the principal components, we conducted a loading analysis based on the PCA results. The loading plots for the different treatment groups are displayed in Figure 2, where greater distance from the zero point indicates a stronger contribution to the model. In Figure 2A, triethyl phosphate, pentyl phosphate, and benzoic acid 2-methylpropyl ester show significant contributions to  
Figure 1: Multivariate statistical analysis of the mass spectral data set for large yellow croaker samples. (**A**) PCA score plot showing the spatial distribution of nine sample groups. (**B**) PLS-DA score plot presenting the distribution of 9 sample groups. Each group is color-coded and plotted according to its score on the two components. both PC1 and PC2. In Figure 2B, heptane, benzeneacetic acid, and benzene,1,3-dimethyl are located at relatively greater distances from the origin, suggesting potential relevance to group separation. Figure 2C displays dimethyl phthalate, butylated hydroxytoluene, and benzene,1,3-dimethyl as compounds with comparatively prominent loading values. However, it should be emphasized that PCA is an unsupervised method and does not inherently provide information on the statistical significance or odor activity of individual compounds. Therefore, while these compounds may appear to influence sample distribution in the PCA space, further statistical validation is required to confirm their roles in odor differentiation. When data from all treatment groups across different storage durations were combined into a unified PCA loading plot, aldehydes such as benzaldehyde and nonanal, along with hydrocarbons like toluene, n-dodecane, and limonene, emerged as consistent contributors to the variance in volatile profiles. These compounds may represent key odor-active markers in large yellow croaker during storage. Nonetheless, their definitive impact should be confirmed through complementary analyses such as VIP scoring, odor threshold evaluation, or sensory correlation studies.