The coronavirus disease 2019 (COVID-19) is the latest biological hazard for the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Numerous diagnostic tests for SARS-CoV-2 have been used, which are expensive and require specialized personal. So, new diagnosis strategies are being developed, looking for less expensive methods which could be used as screening for better spread control. Many researchers have described the use of saliva as a potential indicator of COVID-19, and even the same patient could carry out its collection. In this sense, this study aimed to establish specific salivary vibrational modes analyzed by attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy to detect COVID-19 biological fingerprints that allow the discrimination between COVID-19 and healthy patients. Previous written informed consent, clinical dates, laboratories, and saliva samples of COVID-19 patients (n = 255) and healthy persons (n = 1209) were obtained and analyzed through ATR-FTIR spectroscopy. Then, a multivariate linear regression model (MLRM) was developed. The COVID-19 patients showed low SaO2, cough, dyspnea, headache, and fever principally. Obesity was the main comorbidity. Various laboratory blood tests were altered. In the FTIR spectra analysis, changes in amide I and immunoglobulin regions were evidenced, and the MLRM showed clear discrimination between both groups. Specific salivary vibrational modes employing ATR-FTIR spectroscopy were established; moreover, the COVID-19 biological fingerprint in saliva was characterized, allowing the detection for COVID-19 using an MLRM, once it helps to reduce the number of variables, which could be helpful in the future development of diagnostic devices in a faster and cheaper way.
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