## 3 Results  
The composition of non-selective extracted metabolomics are complex due to their chemo-diversity. This multidimensionality presents holdups in metabolomics studies. However, recent high-resolution (HR) technological advancements, such as the GC-HR-time-of-flight-(TOF)-MS, have enabled researchers to concurrently detect multiple analytes with high sensitivity, thus providing more detailed information about the metabolic profile of the sample. Some of the advantages of the GC-TOF-MS include: (i) the deconvolution power of the software, allowing the detection and resolution of overlapping peaks within seconds and (ii) high-speed data acquisition without distorting the peak height and constant MS spectral scans across a peak, regardless of peak intensity, to assist with annotation. Visual inspection of the generated MS chromatograms showed differential profiles of the analyzed samples with characterized unique peak populations (absent, present and/or differing in peak intensity), thus info-graphically reflecting variances among the sampled VOCs from the four PGPR strains (Figure S2). Data pre-processing [47] and comparative chemometrics [48] were used to extract the underlying features that contribute to the observed differences. Following data pre-processing, the three-dimensional (Rt, _m/z_ ratio and peak area) output that was created was exported to software (SIMCA Data Analytics software) for further multivariate statistical analysis. PCA models were computed in order to compare the volatile profiles of the four strains investigated. PCA, as an unsupervised modeling tool for data exploration, reduced the dimensionality of the data and allowed for inspection of the global distribution patterns (clustering of sample groups, trends and outliers). The computed PCA (Figure 1A) shows sample grouping of the various strains and the characterization of the volatile profiles. It is important to note that the two _Pseudomonas_ strains (_Ps. koreensis_ (N19) and _Ps. fluorescens_ (N04)) are clustered next to each other, which could indicate that these have similar profiles, whereas _Pa. alvei_ (T19) was found to cluster nearer to the uninoculated LB media. _L. sphareicus_ (T22) was found to cluster on its own further away from the rest, indicating that the volatile profile of this strain is significantly different from that of the other strains. The PCA-extracted trends and patterns were further examined by HCA for comparative exploration. The HCA models were computed using the linkage method of [PERSON], considering 'between' and 'within' cluster distances. The trees were sorted based on the size of the clusters [39; 40; 41]. The computed HCA (Figure 1B) shows two major clusters of _L. sphareicus_ (T22) vs. the other samples, where the latter are further divided into two subgroups: LB (medium control) and _Pa. alvei_ (T19), and the two _Pseudomonas_ strains (N04 and N19). Although the models generated from unsupervised PCA and HCA revealed the structures within/between the datasets, the modeling lacks predictive power. Hence, this modeling was followed by an alternative method, namely orthogonal projection to latent discriminant analysis (OPLS-DA). OPLS-DA models were calculated using two predefined conditions (LB vs. each bacterial strain) in order to extract differences in the samples under investigation so as to assist with the identification of features that were responsible for the observed dissimilarities [48; 49]. As an example, the OPLS-DA score plot for _L. sphareicus_ (T22) (Figure 2A) shows distinct separation of the inoculated sample from the uninoculated sample. The corresponding S-plot (Figure 2B) was used to identify features that positively correlated to the various inoculations. Figure 1: Exploratory data analysis of volatile organic compounds produced by the four PGPR strains (N19, N04, T19, T22) with unsupervised chemometric methods. LB = control comprising of uninoculated Luria-Bertani medium. (**A**): A PCA scores scatter plot of all the samples colored according to PGPR strain.