## 3 Results
### Changes in VOCs  
The odor is an important factor that affects the life activities of insects, and volatile substances can induce their feeding and migration behaviors [12; 35; 36]. Therefore, HS-SPME-GC-MS can be used to analyze VOCs and their content [37; 38]. The total ion chromatograms (TIC) of the ZSS and ZSS-Pi groups are shown in Figure 2A. It showed that there was no significant difference among the three ZSS samples (ZSS-1-ZSS-3), nor the three ZSS-Pi samples (ZSS-Pi-1-ZSS-Pi-3). But there was significant difference between the ZSS and ZSS-Pi groups, which indicated that the content of VOCs was changed after ZSS was infested by _P. interpunctella_. In total, 41 VOCs were identified based on mass spectra and retention index (RI). The calibration table and its discussion are shown in Table S1. In order to explore the difference in VOCs between the ZSS and ZSS-Pi groups, multivariate statistical analysis of PCA and OPLS-DA were carried out [39]. The PCA score plot is shown in Figure 3A. The contribution rates of PC1 and PC2 were 78.3% and 14.5%, respectively. The total contribution rate of PC1 and PC2 was 92.8%. \(R^{2}\) and \(Q^{2}\) were usually used to evaluate the quality and reliability of the PCA model. Generally, their values close to 1.0 indicated excellent fitness and predictive capability of the model [40]. In this study, the \(R^{2}\) and \(Q^{2}\) values were 0.928 and 0.806, respectively, demonstrating the stability and reliability of the PCA model. The ZSS samples were located on the right side of the \(Y\)-axis, while the ZSS-Pi samples were on the left side. This distribution indicated a significant difference in VOCs between the ZSS and ZSS-Pi groups. Given that PCA might not fully eliminate within-group variations and irrelevant random errors, OPLS-DA was employed to further investigate the differences [41]. The score plot displayed a distinct separation, as shown in Figure 3B. The \(R^{2}\) value of 0.949 indicated a well-fitted model, while the \(Q^{2}\) value of 0.988 suggested strong predictive power. The permutation tests were performed 200 times, and the result of intercept \(Q^{2}\) was lower than 0.05 (Figure 3C), which indicated that the model was statistically valid and did not over-fit [42]. \begin{table}
\begin{tabular}{c c c c} \hline
**Compound** & **Linear Equation 1** & **Linear Range (\(\mu\)g/mL)** & \(R^{2}\) \\ \hline Spinosin & \(Y\) = 2,077,367 \(X\) + 346,638 & 27.40–2192 & 0.9998 \\ Jujuboside A & \(Y\) = 2,029,085,549 \(X\) + 7380 & 12.60–1008 & 0.9998 \\ Jujuboside B & \(Y\) = 248,640,993 \(X\) + 2455 & 12.50–1003 & 0.9996 \\ Betulinic acid & \(Y\) = 6,967,454 \(X\) + 31,766 & 13.26–1058 & 0.9999 \\ \hline \end{tabular} 1  
\end{table} 
Table 2: HPLC calibration curves for spinosin, jujuboside A, jujuboside B, and betulinic acid. Figure 2: The total ion chromatograms (TIC) of the ZSS and ZSS-Pi groups (**A**). The classification of 41 volatile organic compounds (VOCs) (**B**). In the OPLS-DA S-plot, covariance (\(p\)) reflects the contribution of each variable to the variance among observations, while correlation \(p\) (corr) indicates the correlation between samples and the reliability of the results [43]. In this study, the criteria for screening differential compounds included \(\mid\!p\!\mid\geq\!0.8\), \(\mid\!p\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\  
The pathways of differential metabolites were comprehensively analyzed. Seven and twenty metabolic pathways were identified in positive and negative modes, respectively (Figure 8). For the positive mode, a pathway of purine metabolism was the most significant and obvious, involving differential metabolites of hypoxanthine and adenosine.