The science behind the nose: correlating volatile organic compound characterisation with canine biodetection of COVID-19

Abstract

Background: The SARS-CoV-2 pandemic stimulated the advancement and research in the field of canine scent detection of COVID-19 and volatile organic compound (VOC) breath sampling. It remains unclear which VOCs are associated with positive canine alerts. This study aimed to confirm that the training aids used for COVID-19 canine scent detection were indeed releasing discriminant COVID-19 VOCs detectable and identifiable by gas chromatography (GC-MS).

Methods: Inexperienced dogs (two Labradors and one English Springer Spaniel) were trained over 19 weeks to discriminate between COVID-19 infected and uninfected individuals and then independently validated. Getxent tubes, impregnated with the odours from clinical gargle samples, used during the canines' maintenance training process were also analysed using GC-MS.

Results: Three dogs were successfully trained to detect COVID-19. A principal components analysis model was created and confirmed the ability to discriminate between VOCs from positive and negative COVID-19 Getxent tubes with a sensitivity of 78% and a specificity of 77%. Two VOCs were found to be very predictive of positive COVID-19 cases. When comparing the dogs with GC-MS, F1 and Matthew's correlation coefficient, correlation scores of 0.69 and 0.37 were observed, respectively, demonstrating good concordance between the two methods.

Interpretation: This study provides analytical confirmation that canine training aids can be safely and reliably produced with good discrimination between positive samples and negative controls. It is also a further step towards better understanding of canine odour discrimination of COVID-19 as the scent of interest and defining what VOC elements the canines interpret as "essential".

FIGURE 1

Comparison of performance of Getxent tubes per number of sessions (y-axis) per gargle with and without COVID-19 (x-axis) per dog.

FIGURE 2

Two-component principal components analysis (PCA-X) model. PCA-X model of 35 Getxent tubes for COVID-19 positive (pos) versus negative (neg) gargle samples, based on nine features selected by orthogonal partial least squares-discriminant analysis. Green cluster: samples assigned as COVID-19 negative; blue cluster: samples assigned as COVID-19 positive; orange cluster: intermediate mixed cluster.

FIGURE 3

Dendrogram from hierarchal analysis of 35 COVID-19 positive (pos; +) and negative (neg; −) gargle samples, showing three distinctive classes: positive, negative and intermediate mixed cluster with two branches with positive and negative separation.

FIGURE 4

Chemometric score and predictive model. a) Box and whisker plot of chemometric score based on peak areas of nine features selected from multivariance analysis, according to Equation 1. b) Receiver operating curve (ROC) based on principal components analysis dendrography model classification; with accuracy of separation between positive (Pos) and negative (Neg) gargle samples collected on Getxent tubes of 77.1% (sensitivity of 78% and specificity of 77%).