Coronavirus disease 2019, known as COVID-19, is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The early, sensitive and specific detection of SARS-CoV-2 virus is widely recognized as the critical point in responding to the ongoing outbreak. Currently, the diagnosis is based on molecular real time RT-PCR techniques, although their implementation is being threatened due to the extraordinary demand for supplies worldwide. That is why the development of alternative and / or complementary tests becomes so relevant. Here, we exploit the potential of mass spectrometry technology combined with machine learning algorithms, for the detection of COVID-19 positive and negative protein profiles directly from nasopharyngeal swabs samples. According to the preliminary results obtained, accuracy = 67.66%, sensitivity = 61.76%, specificity = 71.72%, and although these parameters still need to be improved to be used as a screening technique, mass spectrometry-based methods coupled with multivariate analysis showed that it is an interesting tool that deserves to be explored as a complementary diagnostic approach due to the low cost and fast performance. However, further steps, such as the analysis of a large number of samples, should be taken in consideration to determine the applicability of the method developed.

2019 年冠状病毒病，称为 COVID-19，是由严重急性呼吸系统综合症冠状病毒 2 (SARS-CoV-2) 引起的。SARS-CoV-2 病毒的早期、灵敏和特异性检测被广泛认为是应对持续爆发的关键点。目前，诊断基于分子实时 RT-PCR 技术，尽管由于全球对供应的巨大需求，它们的实施受到威胁。这就是替代和/或补充测试的开发变得如此重要的原因。在这里，我们利用质谱技术与机器学习算法相结合的潜力，直接从鼻咽拭子样本中检测 COVID-19 阳性和阴性蛋白质谱。根据得到的初步结果，准确率=67.66%，灵敏度=61.76%，特异性 = 71.72%，虽然这些参数仍需要改进才能用作筛选技术，但基于质谱的方法与多变量分析相结合表明它是一种有趣的工具，值得作为一种补充诊断方法进行探索，因为低成本和快速性能。但是，应考虑进一步的步骤，例如分析大量样品，以确定所开发方法的适用性。