## 3 Results and Discussion
### Correlation Analysis of Flavor Profiles  
To further explore the flavor distinctions among the three heat-treated SEYs, a detailed analysis was conducted on the volatile substance data. In contrast, PLS-DA served as a supervised statistical method for discrimination, different from PCA for characterizing sample classification trends. The correlation analysis of changes in VOCs, using Pearson's correlation coefficient, was presented in Figure 3a. It can be observed that pentadecanl, benzeneacetaldehyde, pentanal, 3-ethyl-2.5-dimethylpyrazine, 2,6-diethyl-pyrazine, heptanal, ethyl-pyrazine, 1-hepten-3-one, 2,3-dimethyl-pyrazine, 2-ethyl-6-methylpyrazine, octanal, benzaldehyde, hexadecan, nonanal, 2,5-dimethyl-pyrazine, and trimethyl-pyrazine were positively correlated with the microwaving group. In contrast, hexanal, 3-methyl-hexanal, 3-methyl-tridecane, (_Z_)-2-decenal, tetradecane, dodecane, 2-pentyl-furan, caprolactam, (_E_)-2-ocental, tridecane, and dibutyl phthalate showed positive correlations in the steaming group. Furthermore, the aroma of the baking group and the cooking group exhibited a higher degree of similarity compared to that of the microwave group. The Venn diagram in Figure 3b summarized the SEY volatile compounds produced through rapid preparation under different heat treatments and visually demonstrated the distinct characteristics and variations of \begin{table}
\begin{tabular}{c c c c c} \hline
**Compound** & **RI** & **Steaming** & **Baking** & **Microwaving** \\ \hline Ethyl-pyrazine & 1334 & Nd & Nd & 2.48 \(\pm\) 0.05 \\
2,3-Dimethyl-pyrazine & 1340 & Nd & Nd & 2.53 \(\pm\) 0.08 \\
2-Ethyl-6-methyl-pyrazine & 1379 & Nd & Nd & 2.54 \(\pm\) 0.07 \\ Trimethyl-pyrazine & 1398 & Nd & 10.28 \(\pm\) 0.11 & 10.38 \(\pm\) 1.05 \\
3-Ethyl-2,5-dimethyl- pyrazine & 1437 & Nd & 3.70 \(\pm\) 0.08 & 15.20 \(\pm\) 0.23 \\
2,6-Diethyl-pyrazine & 1433 & Nd & Nd & 3.60 \(\pm\) 0.18 \\
2-Etherly-6-methyl- pyrazine & 1504 & Nd & 2.53 \(\pm\) 0.13 & 3.64 \(\pm\) 0.11 \\ Thiazoles (1) & & & & \\ Caprolactam & 2196 & 4.89 \(\pm\) 0.17 a  & 4.52 \(\pm\) 0.23 a  & 2.42 \(\pm\) 0.18 b  \\ Sulfides (1) & & & & \\
2-Acetylthiazole & 1642 & Nd & 4.83 \(\pm\) 0.16 & 3.70 \(\pm\) 0.12 \\ \hline \end{tabular} Nd = not detected. Different letters in the same row indicated significant differences (_p_ < 0.05).  
\end{table} 
Table 3: _Cont._aromatic substances. As shown in Figure 3c, a supervised PLS-DA was utilized to examine the differences between the labeled SEY samples [48]. It is evident that all three samples have been clearly distinguished, and PLS-DA has effectively responded to different heating methods of SEY. This finding aligned with results obtained from E-nose analysis. The model exhibited high-grade explained variance (R\({}^{2}\)Y = 0.999) and cross-validated predictive capability (Q\({}^{2}\) = 0.993), which demonstrated its feasibility. The scores of PC1 (horizontal coordinates) and PC2 (vertical coordinates) were the new variables that summarized the variables. The scores were orthogonal and completely independent of each other. PC1 and PC2 accounted for 59.3% and 31.7% of the total variance, respectively. The scores of PC1 accounted for the highest variation in X-space, followed by PC2. Therefore, the scatterplot of PC1 and PC2 provided insight into how the X observations were interrelated. In order to compare the performance of the PLS-DA models, statistical validation parameters including accuracy, goodness-of-fit (R\({}^{2}\)), and predictive goodness-of-fit (Q\({}^{2}\)) were utilized. The class specificity was excellent, with sensitivities above 90% for all models (R\({}^{2}\) = 0.91, Q\({}^{2}\) = 0.971), indicating robust class modeling without any false-positive or false-negative sample classifications [49]. The VIP method was used to explore the characteristic aroma substances in the PLS-DA model with a total sample size of 9 (three varieties in triplicate).