# The Use of Biological Sensors and Instrumental Analysis to Discriminate COVID-19 Odor Signatures
## 4 Discussion
Importantly, the dogs did not display a significantly different performance in detecting COVID-19 within a specific demographic group, nor was their performance affected by the storage time of the samples used. The dogs were trained on a large number of samples from a variety of demographic groups. Additionally, dogs saw samples that ranged from 298 days (oldest negative sample seen by last tested dog) and 283 days (oldest positive sample seen by last tested dog) to 21 days (newest negative sample seen by first tested dog) and 63 days (newest positive sample seen by first tested dog), in terms of when the odor/scent was collected on the shirt to the time they were presented to the dogs (See Table A3). The consistent performance of the dogs suggests that the sample collection and storage procedures (collect onto cotton T-shirts and store in a sealed container at room temperature) produce a sample with a stable odor profile and non-depleted headspace through long periods of static storage. The canines' performance in testing reinforces the idea that training detection dogs using a diverse set of samples supports their ability to generalize odors (see [51] for a similar concept in machine learning). HS-SPME-GC-MS was used as a reagent-free technique for the capture and detection of VOCs present in human body odor samples. The human scent profiles obtained through this procedure were used to develop an LDA model for the predictive classification of COVID-19 status, informed by sweat expression. In this experiment, the developed LDA model depicted in Figure 5 was seen to perform very poorly in comparison to the model shown in Figure 6. The difference in model performance was linked to the inclusion of samples collected from donors who reported sharing a bed with their pet(s). The removal of these individuals from the LDA model allowed for the creation of a predictive model that had no misclassifications when cross-validated using a leave-one-out approach. The necessity of the removal of these individuals is contrasted by the results seen in the dog scent work, where the dogs were able to differentiate COVID-19 positive and negative samples regardless of the presence of pet interferents. The new predictive model for the differentiation of COVID-19 positive and COVID-19 negative T-shirt samples was informed by a sample set of 91 participants who did not share their beds with a pet. The model contained 80 features of interest, indicating a high degree of complexity in the characteristic VOC profile representative of COVID-19 expression in sweat. At its best performance, the developed LDA model (Figure 6) had a TPR = 100%, TNR = 100%, FPR = 0.0%, and FNR = 0.0% (validated using LOOCV). The contributing sample set, however, did not retain the same degree of diversity as the original sample population (reduced from 148 to 91 samples). The model requires further training, including the submission of more variation in donor demographics, if it is to be implemented in a non-academic setting. Contrasts between Instrumental and Biological Methods Used for COVID-19 Positive/Negative Odor Discrimination  
The duality of the methodology, using dogs and conventional analytical instrumentation, creates a parallel view of the strengths and challenges posed by each approach. Both techniques were demonstrated to be capable of discriminating between COVID-19 status when informed by human odor signatures. The use of scent work dogs demonstrated a method that allowed for the training of a detection system (dog) that was able to discriminate between samples sourced from a range of genders, races, and age groups, among other factors, without a demonstrated bias in their decision-making. However, while the training method utilized in this study proved to be effective for this set of participants, it also revealed variations in odor recognition between dogs, such as behavioral responses, that could not be controlled for.