## 3 Results
The ellipse represents the [PERSON]’s T\({}^{2}\) distribution with 95% confidence. The PCA model presented here was a 5-component model, with R\({}^{2}\)X of 93.6% and Q\({}^{2}\)(cum) of 91.3%. (**B**): The HCA dendrogram corresponding to the PC analysis. Unsupervised statistical analysis is used to generate the subgrouping of samples based on similar observations in (**A**), whereas the HCA dendrogram shows the hierarchical relationship between samples (**B**). To prevent bias in selecting significant features, variable importance in projection (VIP) plots were constructed to rank the importance of individual variables to the models [48,49]. As such, VIP plots (Figure 2C) were used to validate the variable selection based on the S-plots, and only variables with a VIP score of \(>\)1 were selected for annotation--these are summarized in Table S1. Furthermore, to validate the predictive capability of the computed OPLS-DA models, a response permutation test (with n = 50) (Figure 2D) was used. In this statistical test, the R\({}^{2}\) and Q\({}^{2}\) values of the true model are compared with that of the permutated model. The test was carried out by random assignment of the dataset rows to two different groups, after which the OPLS-DA models were fitted to each permutated class variable. The R\({}^{2}\) and Q\({}^{2}\) values were then computed for the permutated models and compared to the values of the true models. The model validity was supported by having all Q\({}^{2}\) values of the permuted dataset (to the left) lower than the Q\({}^{2}\) value on the actual data set (to the right). Furthermore, the regression line (the line joining the point of observed Q\({}^{2}\) to the centroid of a cluster of permuted Q\({}^{2}\) values) has a negative intercept value on the \(Y\)-axis [50,51]. The \(y\)-axis displays R\({}^{2}\) and Q\({}^{2}\), whereas the \(x\)-axis shows the correlation coefficient of the permuted and observed data. The two points on the right represent the observed R\({}^{2}\) and Q\({}^{2}\). The green and blue dots represent R\({}^{2}\) and Q\({}^{2}\) values, respectively. The dashed lines indicate the corresponding fitted regression lines for the observed and permutated R\({}^{2}\) and Q\({}^{2}\). PGPR produce a large spectrum of VOCs, contributing to their ability to interact with and influence neighboring organisms or plants. The 'volatilome' of a specific strain  
Figure 2: OPLS-DA modeling and variable/feature selection of _Lysinibacillus sphareicus_ (T22) data acquired on GC–TOF–MS. **(A)**: A typical OPLS-DA score separating uninoculated media (LB) vs. inoculated media (T22) (1 + 1 + 0 components, R\({}^{2}\)X = 0.895, Q\({}^{2}\) = 0.999, CV-ANOVA \(p\)-value = 1.3 \(\times\) 10\({}^{-13}\)). **(B)**: An OPLS-DA loadings S-plot for the same model in **(A)**; only variables with a correlation [\(p\)(corr)] of \(\geq\) 1 0.51 and a covariance of (\(p\)1) \(\geq\) 1 0.051 were chosen as discriminating variables and were identified using the \(m\)/\(z\) to generate elemental composition. **(C)**: A variable importance for the projection (VIP) plot for the same model, pointing mathematically to the importance of each variable in contributing to group separation in the OPLS-DA model. **(D)**: The response permutation test plot (n = 50) for the same OPLS-DA model: R\({}^{2}\) (0.0, 0.207) and Q\({}^{2}\) (0.0\(-\)0.739) values of the permuted models are represented on the left-hand side of the plot, corresponding to \(y\)-axis intercepts. Similar figures for VOCs secreted by N04, N19 and T19 are presented in Figures S3–S5. consists of wide and diverse VOCs with regards to their chemical and physical properties. The composition and relative abundance of the VOC blend is highly dependent on the specific strain, but also on the growth phase and growth conditions [52; 53]. Moreover, the emission of specific VOCs can be induced or influenced by interacting organisms present in the bacterial environment.