## 3 Results
### Volatolome Profiling of Soil Samples  
Discrepancies in VOCs were identified in various soil samples, including those from S, SM, and SS. These were evaluated via multivariate analysis of the SPME-GC-TOF-MS data set. As can be seen in Figure 6, the PCA score plots based on the SPME-GC-TOF-MS data showed distinct differences from the different soil samples according to PC1 (34.95%) and PC2 (14.12%, Figure 6A). The statistical parameters of PLS-DA models were verified with R\({}^{2}\)X (0.49), R\({}^{2}\)Y (0.983), and Q\({}^{2}\) (0.959), indicating the fitness and prediction accuracy of the model at _p_-values (_p_ < 0.05) obtained through cross-validation (Figure 6B). PLS-DA model analysis confirmed that the SS sample was separated from the remaining treatment group by PLS1 [34.90%], while the S sample was separated from the rest of the treatment group by PLS2 [14.12%]. Therefore, although the PLS-DA (group information included, biased) model was not significant, different metabolites were selected based on the PLS-DA model (VIP > 0.7). As a result of analyzing significantly different VOCs between soil samples, a total of 29 VOCs were identified (9 terpenoid compounds, 2 alcohols, 6 benzenoids, 2 alcohols, 3 ketones, 3 alkane, 2 alkenes, 2 esters, and 6 others). The relative contents of the discriminant metabolites are displayed in a heat map (Figure 6C). According to the heat map analysis, most terpenoids and benzenoids showed relatively high content in the _SS_ samples. Also, geosmin and 2-MIB, selected as target VOCs, were uniquely detected in the _SS_ sample and showed relatively high contents (_p_ < 0.05). Figure 5: (**A**) Comparisons of the Semantic Differential Method (SDM) for each soil mixing activity. (**B**) Subjective stress evaluation. NRS, numeral rating scale. \({}^{*}p\) < 0.05, \({}^{***}p\) < 0.001, \({}^{\text{NS}}p\) > 0.05 by the paired _t_-test, respectively. Values are the mean \(\pm\) SD. Metabolite Analysis and Correlation Analysis of Serum Metabolites after the Effect of the Soil Mixing Activity  
Based on the results of soil variation according to VOCs, metabolite profiling of serum samples was performed to confirm the metabolite levels after the soil mixing activity for both SM and \(SS\) and to clearly understand how they were affected. As a result, the PCA score plot for the serum data set showed an unclear cluster between the OPLS-DA control group (SM) and the experimental group (\(SS\)) (Figure 7A), whereas the OPLS-DA score plot showed a clear difference between SM and \(SS\) (Figure 7B). According to the OPLS-DA model, discriminant metabolites were selected between the control and treatment groups with VIP values of \(>\)1.0. A total of 68 metabolites were identified (5 organic acids, 15 amino acids, 14 fatty acids and lipids, 5 other, and 13 unknowns) (Table S2). For visualization of the various metabolites, all were plotted on a heat map (Figure 7C). Figure 6: (**A**) Principal component analysis (PCA) and (**B**) partial least square discriminant analysis (PLS-DA) score plot derived from SPME–GC–TOF–MS datasets for various soil samples. Symbols: soil treated with distilled water (**S**, **A**); soil treated with culture media (SM, **A**); soil inoculated with \(S\). _rimosus_ (\(SS\), **A**). (**C**) Heat map analysis for the relative abundance of different volatile organic compounds (VOCs) (VIP \(>\) 0.7, \(p\) \(<\) 0.05) derived from the GC–TOF–MS analysis. The colored squares (blue to red) indicate fold changes that are normalized by the average of each metabolite. According to the heat map analysis, most of the fatty acids, lipids, and others, except for the glycerophosphoric acid, oleamide, fatty amide, cholesterol, urea, and uric acid, were relatively higher in \(SS\) samples than in the control group. In particular, serotonin,  
Figure 7: **(A)** Principal component analysis (PCA) and (B) partial least square discriminant analysis (PLS-DA) score plot derived from GC–TOF–MS datasets for serum samples.