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Machine Learning for COVID-19 Determination Using Surface-Enhanced Raman Spectroscopy


[ 1 ] Instytut Badań Materiałowych i Inżynierii Kwantowej, Wydział Inżynierii Materiałowej i Fizyki Technicznej, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[7.6] Chemical sciences

Year of publication


Published in


Journal year: 2024 | Journal volume: vol. 12 | Journal number: no. 1

Article type

scientific article

Publication language



EN The rapid, low cost, and efficient detection of SARS-CoV-2 virus infection, especially in clinical samples, remains a major challenge. A promising solution to this problem is the combination of a spectroscopic technique: surface-enhanced Raman spectroscopy (SERS) with advanced chemometrics based on machine learning (ML) algorithms. In the present study, we conducted SERS investigations of saliva and nasopharyngeal swabs taken from a cohort of patients (saliva: 175; nasopharyngeal swabs: 114). Obtained SERS spectra were analyzed using a range of classifiers in which random forest (RF) achieved the best results, e.g., for saliva, the precision and recall equals 94.0% and 88.9%, respectively. The results demonstrate that even with a relatively small number of clinical samples, the combination of SERS and shallow machine learning can be used to identify SARS-CoV-2 virus in clinical practice.

Pages (from - to)

167-1 - 167-15




License type

CC BY (attribution alone)

Open Access Mode

open journal

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

Ministry points / journal


Impact Factor

4,7 [List 2022]

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