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.

翻译：SARS-CoV-2冠状病毒于2019年出现，引发的新冠肺炎大流行在随后四年中导致7.7亿确诊病例中的700万人死亡。这场全球卫生紧急事件要求以前所未有的努力监测和降低感染率，推动了对新型诊断方法的研究。本文介绍了一种廉价、快速且无创的检测系统，该系统仅利用呼出气体。具体而言，通过原始纳米采样装置耦合高精度质谱仪，在10-351质荷比范围内测量空气样本的质谱；随后，原始光谱经定制软件算法处理；最终，采用最先进的机器学习算法对清洁和增强后的数据进行分类。2021年至2022年间，我们对约300名因出现症状或刚从疾病中康复而担心被感染的受试者开展了一项非对照临床试验。尽管操作简便，我们的系统在识别新冠肺炎病例方面表现出与传统聚合酶链式反应和抗原检测相当的性能（即准确率0.95、召回率0.94、特异度0.96、F1分数0.92）。基于这些结果，我们认为该所提系统具有在常规筛查和未来疫情快速响应中做出重大贡献的潜力，因为它不仅能提供与最先进方法相当的结果，而且检测速度更快、侵入性更低。