Abstract
Metal oxide sensor-based electronic nose (E-Nose) technology provides an easy to use method for breath analysis by detection of volatile organic compound (VOC)-induced changes of electrical conductivity. Resulting signal patterns are then analyzed by machine learning (ML) algorithms. This study aimed to establish breath analysis by E-Nose technology as a diagnostic tool for severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pneumonia within a multi-analyst experiment. Breath samples of 126 subjects with (n = 63) or without SARS-CoV-2 pneumonia (n = 63) were collected using the ReCIVA® Breath Sampler, enriched and stored on Tenax sorption tubes, and analyzed using an E-Nose unit with 10 sensors. ML approaches were applied by three independent data analyst teams and included a wide range of classifiers, hyperparameters, training modes, and subsets of training data. Within the multi-analyst experiment, all teams successfully classified individuals as infected or uninfected with an averaged area under the curve (AUC) larger than 90% and misclassification error lower than 19%, and identified the same sensor as most relevant to classification success. This new method using VOC enrichment and E-Nose analysis combined with ML can yield results similar to polymerase chain reaction (PCR) detection and superior to point-of-care (POC) antigen testing. Reducing the sensor set to the most relevant sensor may prove interesting for developing targeted POC testing.