Fil: Rocca, María Florencia. ANLIS Dr.C.G.Malbrán. Instituto Nacional de Enfermedades Infecciosas; Argentina.Fil: Zintgraff, Jonathan Cristian. ANLIS Dr.C.G.Malbrán. Instituto Nacional de Enfermedades Infecciosas; Argentina.Fil: Dattero, María Elena. ANLIS Dr.C.G.Malbrán. Instituto Nacional de Enfermedades Infecciosas. Departamento de Bacteriología; Argentina.Fil: Silva Santos, Leonardo. Instituto de Química de Recursos Naturales, Universidad de Talca; Chile.Fil: Ledesma, Martín. Red Nacional de Espectrometría de Masas aplicada a la Microbiología Clínica (ReNaEM Argentina), Argentina; Laboratorio de Bacteriología, Departamento de Bioquímica Clínica, Hospital de Clínicas José de San Martín, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires; ArgentinaFil: Vay, Carlos. Red Nacional de Espectrometría de Masas aplicada a la Microbiología Clínica (ReNaEM Argentina), Argentina; Laboratorio de Bacteriología, Departamento de Bioquímica Clínica, Hospital de Clínicas José de San Martín, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires; ArgentinaFil: Prieto, Mónica. ANLIS Dr.C.G.Malbrán. Instituto Nacional de Enfermedades Infecciosas; Argentina.Fil: Benedetti, Estefanía. ANLIS Dr.C.G.Malbrán. Instituto Nacional de Enfermedades Infecciosas. Departamento de Bacteriología; Argentina. ,Fil: Avaro, Martín. ANLIS Dr.C.G.Malbrán. Instituto Nacional de Enfermedades Infecciosas. Departamento de Bacteriología; Argentina.Fil: Russo, Mara. ANLIS Dr.C.G.Malbrán. Instituto Nacional de Enfermedades Infecciosas. Departamento de Bacteriología; Argentina.Fil: Nachtigall, Fabiane Manke. Instituto de Ciencias Químicas Aplicadas, Universidad Autónoma de Chile; Chile.Fil: Baumeister, Elsa. ANLIS Dr.C.G.Malbrán. Instituto Nacional de Enfermedades Infecciosas. Departamento de Bacteriología; Argentina.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.