1. Author Correction: Rapid diagnosis of COVID-19 using FT-IR ATR spectroscopy and machine learning
- Author
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Luciene Cristina Gastalho Campos, Raquel Lemos Pimentel, Leonardo dos Santos, Valério Garrone Barauna, Wena Dantas Marcarini, Luis Felipe das Chagas e Silva de Carvalho, Paula Frizera Vassallo, Marcelo Saito Nogueira, Matheus Muller, Leonardo Barbosa Leal, Wilson Barros Luiz, and José Geraldo Mill
- Subjects
Adult ,Male ,2019-20 coronavirus outbreak ,Materials science ,Time Factors ,Coronavirus disease 2019 (COVID-19) ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Science ,Analytical chemistry ,Machine Learning ,COVID-19 Testing ,Spectroscopy, Fourier Transform Infrared ,Humans ,Fourier transform infrared spectroscopy ,Least-Squares Analysis ,Spectroscopy ,Author Correction ,Aged ,Multidisciplinary ,SARS-CoV-2 ,COVID-19 ,Middle Aged ,Early Diagnosis ,Medicine ,Female ,Brazil - Abstract
Early diagnosis of COVID-19 in suspected patients is essential for contagion control and damage reduction strategies. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in oropharyngeal swab suspension fluid to predict COVID-19 positive samples. The study included samples of 243 patients from two Brazilian States. Samples were transported by using different viral transport mediums (liquid 1 or 2). Clinical COVID-19 diagnosis was performed by the RT-PCR. We built a classification model based on partial least squares (PLS) associated with cosine k-nearest neighbours (KNN). Our analysis led to 84% and 87% sensitivity, 66% and 64% specificity, and 76.9% and 78.4% accuracy for samples of liquids 1 and 2, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective solution for high-throughput screening of suspect patients for COVID-19 in health care centres and emergency departments.
- Published
- 2021