## 3 Results
### Supervised Analysis  
The supervised analysis used a partial least squares discriminant analysis (PLS-DA) with two latent variables (LVs) to optimize discrimination and evaluate the differential volatile organic compound profiles of each land use type. The obtained PLS-DA models are presented in Figure S2, and the method's performance after cross-validation is summarized in Table 2. The table includes the area under the curve (AUC) values, which quantify the classification performance of the models, and the sensitivity and specificity values, which further validate the models' performance in distinguishing between the land use types.  
Figure S2 shows the variance captured by the first two latent variables (LV1 and LV2) of the PLS-DA model, with LV 1 being the primary contributor to the discrimination between the land use types. Complete discrimination between forest (F), grassland (G), and agricultural (A) land use types was achieved by LV1 and LV2, which accounted for 77.7% (Figure S2a), 81.5% (Figure S2b), and 79.2% (Figure S2c) of the explained variance, respectively, without overlapping 95% confidence intervals. In all cases, LV1 was the primary contributor to the discrimination, with its highest contribution (73.2%, Figure S2c) observed for the discrimination between the volatilome profiles of the forest and agricultural soils.  
The receiver operating characteristic (ROC) curves (Figure 4) complement the PLS-DA analysis by quantitatively evaluating the classification performance of the model. The AUC values for F vs. G (0.810), G vs. A (0.810), and A vs. F (0.868) confirm the model's strong discriminatory ability. The analysis revealed that the classification model for A vs. F exhibited a superior performance, as indicated by an area under the curve (AUC) of 0.868. In contrast, the models for F vs. G and G vs. A both presented an AUC of 0.810. Although these values reflect a robust performance that significantly exceeds chance, they are marginally inferior to those of the A vs. F classifier. Furthermore, the ROC plot includes a reference diagonal line, representing a random classifier with an AUC of 0.5. The ROC curves' deviation from the diagonal line of random classification underscores the model's robustness in distinguishing between the land use types. The sensitivity and specificity values derived from these curves further validated the model's performance (Table 2).  
The fact that all the models significantly surpass this threshold confirms their effectiveness in discriminating between the different classes. The highest AUC values align with the clear separation observed in the PLS-DA plot. Notably, the A vs. F curve consistently maintains higher true positive rates over a range of false positive rates, emphasizing its optimal sensitivity-specificity balance under various threshold conditions.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Figures of Merit** & **R** & **Cutt Point** & **Q** & **Accuracy** & **AUC** & **Sensitivity** & **Specificity** \\ \hline F vs. G & 0.74 & 0.714 & 0.822 & 0.74 & 0.81 & 0.72 & 0.76 \\ G vs. A & 0.747 & 0.697 & 0.839 & 0.75 & 0.81 & 0.82 & 0.68 \\ A vs. F & 0.795 & 0.751 & 0.838 & 0.795 & 0.868 & 0.81 & 0.78 \\ \hline \end{tabular}
\end{table}
Table 2: Figures of merit obtained for the PLS-DA model used to discriminate F vs. G, G vs. A, and A vs. F.