## 4 Discussion  
### SARS-CoV-2 Infection Diagnostic Model Performance  
SARS-CoV-2-infected human odor expression was investigated through the collection and analysis of hand odor samples. Hand odor samples were collected from 102 individuals; of these participants, 56 were SARS-CoV-2-positive and 46 were negative for SARS-CoV-2 at the time of donation. PCR tests were used as the primary confirmatory method in diagnosing SARS-CoV-2 infection status. The samples were collected using sterile gauze and analyzed using HS-SPME-GC-MS. The resulting human hand odor profile was analyzed using the previously described data pre-processing methods, resulting in a key list of 40 features of interest. Class separation (SARS-CoV-2-positive vs. -negative) was \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Retention Time** & \multirow{2}{*}{**CAS\#**} & \multirow{2}{*}{**Compound of Interest**} & \multicolumn{2}{c}{**Reported Presence in Human Skin**} \\
**(15 M HP5-MS)** & & & **Emanations** \\ \hline
3.109 & 00123-42-2 & Diacetone alcohol & - \\
4.033 & 00100-42-5 & Styrene & [16; 30] \\
6.577 & 0377-69-3 & 2-Pentyfuran & [16; 31] \\
7.897 & 00122-78-1 & Phenylacetaldehyde & [32] \\
9.552 & 01120-21-4 & Undecane & [16; 24; 25; 33; 34] \\
10.366 & 00111-11-5 & Methyl acrylate & [24; 25; 31; 34; 35] \\
11.235 & 18829-56-6 & trans-2-Nonneal & [16; 24; 25; 31; 35] \\
11.679 & 00143-08-7 & 1-Nonanol & [24; 31; 34; 35] \\
12.561 & 00112-31-2 & Decanal & [16; 24; 25; 30; 31; 33; 35] \\
12.836 & 00122-99-6 & 2-Phenoxyethanol & - \\
17.798 & 00112-54-9 & Dodecanal & [24; 26; 30; 31; 35] \\
18.841 & 00689-67-8 & 6,10-Dimethyl-5,9- & [16; 24; 25; 30; 31; 33; 34; 35] \\
19.366 & 00112-53-8 & 1-Dodecanol & [36; 37] \\
21.668 & 00143-07-7 & Dodecanoic acid & [24; 30; 31; 35; 38; 39] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Compounds contributing to the discrimination of SARS-CoV-2 infection. Figure 4: Mid-2022 sPLS-DA Model; (+) Mid-2022 Hand Odor Samples vs. (–) SARS-CoV-2 Samples. (**A**) Individual sample plot; ellipses = 95% CI. (**B**) Individual sample plot; background delineates class assignment. modeled using the log10-transformed total ion chromatogram peak areas of the features of interest. The resulting sPLS-DA modeling of all of the collected SARS-CoV-2-positive and -negative samples (Figure 1) demonstrated class grouping within the negative and positive samples. Although a defined separation of classes was not observed, the model reflected a moderate performance ability with 75.8% accuracy when compared to other screening and diagnostic techniques such as antigen tests and detection amines (as summarized in a previous publication [12]). This rate reflected a sensitivity (TPR) of 81.8% and a specificity (TNR) of 69.7% for predicting the SARS-CoV-2 status. The performance of each model based on a repeated five-fold cross-validation (\(n=200\)) can be seen in Table 3 below. The SARS-CoV-2-positive samples were further separated into Late-2021, Early-2022, and Mid-2022 samples and are suspected to correspond to Delta, Omicron BA.2, and Omicron BA.5 variants, respectively. The variant type was not confirmed; it was assigned based upon the variant that was dominant in the collection area (Philadelphia, Pennsylvania) at the time of sample donation. This assignment was conducted using the Centers for Disease Control and Prevention's variant proportional COVID data reporting platform [40]. The Late-2021 SARS-CoV-2-positive samples were modeled against the SARS-CoV-2-negative samples, revealing an increased separability between the classes (Figure 2) compared to that demonstrated in Figure 1. This observed separation translated into an improved performance in correctly identifying SARS-CoV-2-infected sample status (86.7% accuracy). Similarly, the modeling of Mid-2022 SARS-CoV-2 (Figure 4)-positive samples reflected an increase in the rate of accuracy to 84.4%.