Infection with SARS-CoV-2 causes Corona Virus Disease (COVID-19). The most standard diagnostic method is reverse transcription-polymerase chain reaction (RT-PCR) on a nasopharyngeal and/or an oropharyngeal swab. The high occurrence of false-negative results due to the non-presence of SARS-CoV-2 in the oropharyngeal environment renders this sampling method not ideal. Therefore, a new sampling device is desirable. This proof-of-principle study investigates the possibility to train machine-learning classifiers with an electronic nose (Aeonose) to differentiate between COVID-19 positive- and negative persons based on volatile organic compounds (VOCs) analysis.

Methods: between April and June 2020, participants were invited for breath analysis when a swab for RT-PCR was collected. If the RT-PCR resulted negative, presence of SARS-CoV-2 specific antibodies was checked to confirm the negative result. All participants breathed through the Aeonose for five minutes. This device contains metal-oxide sensors that change in conductivity upon reaction with VOCs in exhaled breath. These conductivity changes are input data for machine-learning and used for pattern recognition. The result is a value between -1 and +1, indicating the infection probability.

Inclusion Criteria:

• Participants of whom an oropharyngeal or nasopharyngeal swab was collected to perform RT-PCR on.

Exclusion Criteria:

• Participants who were experiencing dyspnea or needed supplemental oxygen.