# Metabolomics of _Pseudomonas aeruginosa_ Treated by Atmospheric-Pressure Cold Plasma
## 3 Results
### Detection and Analysis of Bacterial Metabolites  
#### 3.2.1 Data Analysis  
* Data Pre-Analysis  
The completeness and accuracy of the data are of great importance to biology. A single peak was first filtered to remove noise. Deviation values were filtered based on the interquartile range, followed by normalization of the data using the internal standard. (IS) [38]. The internal standard used in this experiment was 5 \(\mu\)L 2-chloro-l-phenylalanine (1 mg/mL). After pretreatment of the metabolite data, 272 peaks were obtained (the processed data table is shown under the Mean in the Supplementary File S2). Figure 4: (**a**) Spectrogram of portable surface discharge; (**b**) Detection of liquid phase active particles in portable plasma activated water. Figure 5: Activity test of _Pseudomonas aeruginosa_ treated by plasma activated water. * OPLS-DA was used for the unsupervised evaluation of metabolite characteristics  
Through OPLS-DA analysis, we screened out orthogonal variables unrelated to classification variables in bacterial metabolites, and analyzed the differences between non-orthogonal variables and orthogonal variables, so as to obtain more reliable bacterial metabolites. Firstly, the first principal component was analyzed by the OPLS-DA model. The validity of the model was verified by R\({}^{2}\)Y (the interpretability of the model to the classification variable Y) and Q\({}^{2}\) (the predictability of the model) obtained by sevenfold cross-validation. Parameters of the OPLS-DA model are shown in Table 2.  
Figure 6 shows that the two groups of samples are remarkably differentiated, with the samples essentially within the 95% confidence interval (Hoteling's T-squared ellipse). The abscissa represents the differences between the two groups of samples, and the ordinate represents the differences within the groups of samples. The displacement test randomly changes the displacement order and times of the classification variable Y (n = 200) to avoid overfitting the test model, so as to establish the corresponding OPLS-DA model to obtain the R\({}^{2}\)Y and Q\({}^{2}\) values of the random model, which plays an important role in the evaluation of the importance of the model. The closer the R\({}^{2}\)Y and Q\({}^{2}\) values are to one, the better the model will be. In general, R\({}^{2}\)Y and Q\({}^{2}\) values higher than 0.5 are better, while a higher R\({}^{2}\)Y and Q\({}^{2}\) is acceptable. This shows that the original model has good robustness and there is no over-fitting phenomenon. Therefore, plasma treatment of _Pseudomonas aeruginosa_ has a great impact on metabolomics. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **A** & **N** & **R\({}^{2}\)X (cum)** & **R\({}^{2}\)Y (cum)** & **Q\({}^{2}\) (cum)** & **Title** \\ \hline OPLS-DA & \(1+1+0\) & 10 & 0.584 & 0.968 & 0.667 & 1T VS C \\ \hline \hline \end{tabular}
\end{table} 
Table 2: OPLS-DA model parameter table. The model parameters are the comparison between the treatment group and the control group. A is the number of principal components of the model, N is the number of observations of the model (here, it is the number of samples), R\({}^{2}\)X represents the model’s interpretation of X variable, R\({}^{2}\)Y represents the model’s interpretation of Y variable, and Q\({}^{2}\) represents the predictability of the model. Figure 6: Dispersion points diagram of the OPLS\(-\)DA model of group 1T versus Group C.  
* UVA According to the obtained metabolite data (mean form in Supplementary File), we selected the card value of differential metabolites when the \(p\)-value of the Student's \(t\)-test was less than 0.05, and the projection importance of the first principal component variable of the OPLS-DA model was greater than 1, as shown in Table 3. Fold change is the multiple relationship between the two experimental data groups (1T group data/C group data).