  The SARS-CoV-2 coronavirus emerged in 2019, causing a COVID-19 pandemic that
resulted in 7 million deaths out of 770 million reported cases over the next
four years. The global health emergency called for unprecedented efforts to
monitor and reduce the rate of infection, pushing the study of new diagnostic
methods. In this paper, we introduce a cheap, fast, and non-invasive detection
system, which exploits only the exhaled breath. Specifically, provided an air
sample, the mass spectra in the 10--351 mass-to-charge range are measured using
an original nano-sampling device coupled with a high-precision spectrometer;
then, the raw spectra are processed by custom software algorithms; the clean
and augmented data are eventually classified using state-of-the-art
machine-learning algorithms. An uncontrolled clinical trial was conducted
between 2021 and 2022 on some 300 subjects who were concerned about being
infected, either due to exhibiting symptoms or having quite recently recovered
from illness. Despite the simplicity of use, our system showed a performance
comparable to the traditional polymerase-chain-reaction and antigen testing in
identifying cases of COVID-19 (that is, 0.95 accuracy, 0.94 recall, 0.96
specificity, and 0.92 F1-score). In light of these outcomes, we think that the
proposed system holds the potential for substantial contributions to routine
screenings and expedited responses during future epidemics, as it yields
results comparable to state-of-the-art methods, providing them in a more rapid
and less invasive manner.
