ArticleLasers in medical science2023
Diagnosing COVID-19 in nasopharyngeal secretion through Raman spectroscopy: a feasibility study.
Article in Lasers in medical science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
- TB-SERS analyzer: Analysis tool for tuberculosis prediction based on Raman spectroscopy with machine learning and convolutional neural network.PLoS computational biology · 2026Article
- Machine Learning for COVID-19 Determination Using Surface-Enhanced Raman Spectroscopy.Biomedicines · 2024Article
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since the beginning of the COVID-19 pandemic, the scientific community has sought to develop fast and accurate techniques for detecting the SARS-CoV-2 virus. Raman spectroscopy is a promising technique for diagnosing COVID-19 through serum samples. In the present study, the diagnosis of COVID-19 through nasopharyngeal secretion has been proposed. Raman spectra from nasopharyngeal secretion samples (15 Control, negative and 12 COVID-19, positive, assayed by immunofluorescence antigen test) were obtained in triplicate in a dispersive Raman spectrometer (830 nm, 350 mW), accounting for a total of 80 spectra. Using principal component analysis (PCA) the main spectral differences between the Control and COVID-19 samples were attributed to N and S proteins from the virus in the COVID-19 group. Features assigned to mucin (serine, threonine and proline amino acids) were observed in the Control group. A binary model based on partial least squares discriminant analysis (PLS-DA) differentiated COVID-19 versus Control samples with accuracy of 91%, sensitivity of 80% and specificity of 100%. Raman spectroscopy has a great potential for becoming a technique of choice for rapid and label-free evaluation of nasopharyngeal secretion for COVID-19 diagnosis.
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