## 3 Results
### Lyophilized Samples  
Urine samples were freeze-dried, as detailed in the Materials and Methods section. This method of sample pretreatment increases both the number of volatile compounds generated and the intensity of the signals, minimizing the impact of signals produced by the fiber on which the VOCs are adsorbed during the GC-MS analysis [26]. A good separation was obtained after PLS-LDA (Figure 4).  
Although found to be less intense than in the analysis of untreated urine samples, signals corresponding to the fiber were also eliminated at this stage. We obtained a good selection of peaks, as illustrated in Figure 4B. Peak integrals were further obtained to improve the identification of urine samples from COVID-19 patients compared to those from healthy individuals. The data analysis included the SPA algorithm, which, with the COSS value, allows the selection of the most important variables (Figure 5) [32].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Time (min)** & _m/z_ & **Compound** & **Confidence (\%)** & **Higher in** \\ \hline
26.0744 & 164 & 2\({}^{\prime}\)-Hydroxy-4\({}^{\prime}\),5\({}^{\prime}\)-dimethylacetophenone & 87 & COVID-19 \\ \hline
34.2336 & 206 & Phenol, 2,4-bis(1,1-dimethylethyl) & 97 & COVID-19 \\ \hline
29.751 & 208 & Decahydro-4,4,8,9,10-pentamethylnaphthalene & 49 & Control \\ \hline
13.2882 & 106 & p-Xylene & 97 & Control \\ \hline
29.948 & 152 & 3-(But-3-enyl)-cyclohexanone & 53 & COVID-19 \\ \hline
26.5193 & 135 & Benzothiazole & 94 & COVID-19 \\ \hline
9.5204 & 92 & Toluene & 95 & Control \\ \hline
25.8843 & 72 & 1-(1-Propen-1-yl)-2-(2-thiophen-3-yl) disulfide & 72 & COVID-19 \\ \hline
41.024 & 236 & 2,4-Diphenyl-4-methyl-2(E)-pentene & 95 & Control \\ \hline
24.4244 & 154 & l-Menthone & 98 & Control \\ \hline
30.4316 & 252 & 5-Octadecene, (E)- & 49 & COVID-19 \\ \hline
32.0588 & 170 & Methacrylic acid, tetradecyl ester & 74 & COVID-19 \\ \hline
17.4058 & 126 & Dimethyl trisulfide & 95 & COVID-19 \\ \hline
31.4391 & 200 & 1-Dodecanol, 2-methyl-, (S)- & 80 & COVID-19 \\ \hline
30.2871 & 138 & 2-(2,2-Dimethylvinyl)thiophene & 83 & COVID-19 \\ \hline \end{tabular}
\end{table}
Table 2: VOCs obtained from the analysis of untreated urine samples from COVID-19 patients and healthy controls. They are classified based on the SPA algorithm and their importance in classifying urine samples. The first four compounds in the table correspond to those shown in Figure 3.  
The representation of the selected regions and the corresponding peaks allows for a better visualization of the different selected volatile compounds of interest with a boxplot peak area (Figure 6).  
Table 3 presents the 15 signals with the highest COSS values for the lyophilized urine samples, the corresponding identified compounds according to the Wiley library [27], the confidence percentage of the identification, and whether the compound is found at a higher concentration in the COVID-19 group or the healthy control group.  
Figure 4: (**A**) Score plots of the three components of the PLS-LDA model for the GC-MS spectra of lyophilized urine samples from COVID-19 patients (red diamonds) and healthy controls (blue circles); (**B**) tpLoadings pseudospectrum from the PLS-LDA model for the GC-MS spectra of urine samples from COVID-19 patients and healthy controls. The intensity of the peaks (positive or negative) in the pseudospectrum is determined by the most significant spectral shift regions in the PLS-LDA model. The model performances were the following: R2X = 0.22, R2Y = 0.87, and AUC =1. (*) Selected peaks appearing in Table 3.  
Figure 5: COSS values of the variables selected by the SPA algorithm as most important for determining the separation between groups of lyophilized urine samples from COVID-19 patients and healthy controls.