# The Use of Biological Sensors and Instrumental Analysis to Discriminate COVID-19 Odor Signatures
## 3 Results
### HS-SPME-GC-MS Results  
Initial analysis of the formed dataset revealed a separation of data points. The observed separation is believed to be driven by one or more unidentified class characteristics. As seen in Figure 3, there is subgrouping occurring within the dataset that has not been attributed to a sole influence or characteristic, such as COVID-19 status. The separation may be due to a confounding effect or a combination of class characteristics. The characteristics which have been investigated and ruled out as sole contributors to the dataset grouping are gender, age, vaccination status, and pet ownership. An insight into subgrouping composition was achieved when viewing the dataset in terms of self-identified race. The consideration of donor race indicated that all donors in one subgroup (right) share the racial identification of \"White or Caucasian.\" Due to this observation, the following co-interactions were considered for further explanation of data subgrouping: (a) race and COVID-19 status, (b) race and symptomatic presentation, (c) race and gender, (d) race and age, and (e) race and vaccination status. Although these co-interactions were investigated, they were not seen to provide any further explanation for the observed phenomenon. Figure 3: Initial COVID T-shirt PCA labelled by donor’s COVID-19 status. The peaks of interest contributing to the subgrouping (i.e., the split observed) seen in Figure 3 were identified (by retention time) and removed from the dataset. This action constituted a removal of 15 peaks of interest. This caused the dataset to decrease from 126 features of interest to 111 features of interest. The removal of this subsetting factor mitigates the risk of building an instrumental model off erroneous information, which is not related to the COVID-19 positive/negative status of a donor. The refined dataset no longer demonstrated the previously observed, unexplained subgrouping indicating that the developed instrumental model would not be informed by the unidentified effect (Figure 4). Model Development  
Following the pre-processing procedure described above, the dataset containing retention-time-matched peaks of interest was square-root transformed. Note that the dataset was refined to remove peaks found to be present in all of the analyzed background samples (unworn T-shirts) and peaks that contributed to the subgrouping viewed in Figure 3. This dataset was used to build a linear discriminant analysis (LDA) model using JMP(r), Version 16.1.0. (SAS Institute Inc., Cary, NC, USA 1989-2021). LDA is a supervised learning technique that uses a linear combination of features of interest (variables) to discriminate between classes. LDA allows for linear modeling of the COVID-19 positive and COVID-19 negative samples using the TIC peaks of interest (identified in the GC-MS data) to separate and differentiate between COVID-19 status in the sample set. The LDA model displayed in Figure 5 demonstrates the separation of 54 COVID-19 positive and 94 COVID-19 negative donor profiles. The displayed model was seen to perform moderately when all data points were used to train it; true positive rate (TPR) = 96.3%, true negative rate (TNR) = 96.8%, false positive rate (FPR) = 3.2%, and false negative rate (FNR) = 3.7%. This model resulted in five misclassified samples with three False Positives and two False Negatives. When leave-one-out cross-validation (LOOCV) was applied to the model, the performance was very poor, with TPR = 57.4%, TNR = 61.7, FPR = 38.3%, and FNR = 42.6%. LOOCV was performed using R-3.6.1, an open-source software. Figure 4: COVID T-shirt PCA: subgrouping removed, labelled by donor’s COVID-19 status. Further review of the misclassified donor samples, seen in the unvalidated model, revealed that the five misclassified individuals retained the class characteristic of \"sharing their bed with at least one pet cat or pet dog\". The researchers believe that the transfer of pet fur and dander onto the collection substrate influenced the collected volatile organic compound profiles.