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

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...

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Bibliographic Details
Published in:Biomedicines 2024-01, Vol.12 (1), p.167
Main Authors: Szymborski, Tomasz R, Berus, Sylwia M, Nowicka, Ariadna B, Słowiński, Grzegorz, Kamińska, Agnieszka
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Antibodies
Artificial intelligence
Calibration
Classification
Composite materials
COVID-19
Deep learning
Health aspects
Infection
Influenza
Learning algorithms
Machine learning
Methods
Neural networks
Raman spectroscopy
random forest classifier
Saliva
SARS-CoV-2
SERS
Severe acute respiratory syndrome coronavirus 2
Software
Spectrum analysis
Support vector machines
surface-enhanced Raman spectroscopy
Viruses
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Summary: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.
ISSN:2227-9059
2227-9059
DOI:10.3390/biomedicines12010167