## 3 Results
### \(H_{2}so_{4}\) Samples  
The urine samples were treated with sulfuric acid, as detailed in the Materials and Methods section [25] with the aim of increasing both the number of volatile compounds \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time (min)** & _m/z_ & **Compound** & **Confidence (\%)** & **Higher in** \\ \hline
12.6913 & 90 & 2,3-Butanediol & 80 & Control \\
28.8461 & 113 & Caprolactam & 96 & COVID-19 \\
13.581 & 98 & Cyclopentanone, 3-methyl- & 96 & Control \\
28.5382 & 148 & Benzaldehyde, 2,4,5-trimethyl- & 93 & COVID-19 \\
14.9345 & 78 & Dimethyl Sulfoxide (DMSO) & 96 & Control \\
22.8465 & 42 & 2H-Pyram-2-one, tetrahydro- & 90 & Control \\
25.162 & 71 & Oxalic acid, 2-ethylhexyl hexely ester & 64 & COVID-19 \\
5.1405 & 119 & Methane-d, trichloro- & 95 & COVID-19 \\
16.9914 & 170 & Decane & 93 & Control \\
15.7596 & 96 & 2-Cyclopenten-1-one, 2-methyl- & 94 & Control \\
29.1009 & 158 & Formamide, N,N-dibutyl- & 97 & COVID-19 \\
12.851 & 90 & 2,3-Butanediol & 90 & Control \\
26.4242 & 135 & Benzothiazole & 93 & COVID-19 \\
19.8201 & 134 & Benzene, 1,2,3,4-tetramethyl- & 80 & COVID-19 \\
15.1208 & 108 & Pyrazine, 2,6-dimethyl- & 91 & Control \\ \hline \hline \end{tabular}
\end{table} 
Table 3: VOCs obtained from the analysis of lyophilized 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 6.  
Figure 6: Boxplots of the integrals of the GC-MS spectrum regions and the peaks selected by the SPA algorithm as most important for determining the separation between COVID-19 patients and healthy controls. generated and the intensity of the signals while minimizing the impact of the signals generated by the fiber itself, on which the volatile compounds are adsorbed during GC-MS analysis [25]. A good separation was obtained after PLS-LDA modelling (Figure 7). As before, signals from the fiber were eliminated before building the model with PLS-LDA [33]. We obtained a good selection of peaks, as shown in Figure 7B, with a good R2Y value (0.77). To improve the selection and identification of variables associated with VOCs, we applied the SPA algorithm, which allows us to select the most important variables through the COSS value (Figure 8) [32]. Figure 8: COSS values of the variables selected by the SPA algorithm as most important for determining the separation between groups of \(\mathrm{H_{2}SO_{4}}\)-treated urine samples from COVID-19 patients and healthy controls. Figure 7: **(A)** Score plots of the three components of the PLS-LDA model for GC-MS spectra of \(\mathrm{H_{2}SO_{4}}\)-treated urine samples from COVID-19 patients (red diamonds) and healthy controls (blue circles); **(B)** tpLoadings pseudospectrum from the PLS-LDA model for 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 = 0.25, R2Y = 0.77, and AUC =1. (*) Selected peaks appearing in Table 4.  
This allows us to select a series of variables that correspond to the integral values (boxplot) of the peaks in the GC-MS spectrum of the urine samples (Figure 9). Table 4 presents the 15 signals with the highest COSS values for urine samples treated with sulfuric acid, the corresponding identified compounds according to the [PERSON] library [27], the confidence percentage of the identification, and whether the compound is found at a higher concentration in the COVID-19 patient group or the healthy control group.