A Machine Learning Framework for Detecting COVID-19 Infection Using Surface-Enhanced Raman Scattering

Department of Pediatrics, Center for Blood Oxygen Transport and Hemostasis, University of Maryland Baltimore School of Medicine, Baltimore, MD 21201, USA

Department of Nuclear Engineering, The Pennsylvania State University, University Park, PA 16802, USA

Authors to whom correspondence should be addressed.

Biosensors 2022, 12(8), 589; https://doi.org/10.3390/bios12080589

Received: 29 June 2022 / Revised: 27 July 2022 / Accepted: 28 July 2022 / Published: 2 August 2022

(This article belongs to the Special Issue High-Efficiency Surface-Enhanced Raman Scattering Biosensing)

View Full-Text Download PDF

Browse Figures

Citation Export

Abstract

In this study, we explored machine learning approaches for predictive diagnosis using surface-enhanced Raman scattering (SERS), applied to the detection of COVID-19 infection in biological samples. To do this, we utilized SERS data collected from 20 patients at the University of Maryland Baltimore School of Medicine. As a preprocessing step, the positive-negative labels are obtained using Polymerase Chain Reaction (PCR) testing. First, we compared the performance of linear and nonlinear dimensionality techniques for projecting the high-dimensional Raman spectra to a low-dimensional space where a smaller number of variables defines each sample. The appropriate number of reduced features used was obtained by comparing the mean accuracy from a 10-fold cross-validation. Finally, we employed Gaussian process (GP) classification, a probabilistic machine learning approach, to correctly predict the occurrence of a negative or positive sample as a function of the low-dimensional space variables. As opposed to providing rigid class labels, the GP classifier provides a probability (ranging from zero to one) that a given sample is positive or negative. In practice, the proposed framework can be used to provide high-throughput rapid testing, and a follow-up PCR can be used for confirmation in cases where the model’s uncertainty is unacceptably high. View Full-Text

Keywords: surface-enhanced Raman spectroscopy; machine learning; COVID-19; Gaussian processes

▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Supplementary Material

Supplementary File 1:
ZIP-Document (ZIP, 662 KiB)

Share and Cite

MDPI and ACS Style

Ikponmwoba, E.; Ukorigho, O.; Moitra, P.; Pan, D.; Gartia, M.R.; Owoyele, O. A Machine Learning Framework for Detecting COVID-19 Infection Using Surface-Enhanced Raman Scattering. Biosensors 2022, 12, 589. https://doi.org/10.3390/bios12080589

AMA Style

Ikponmwoba E, Ukorigho O, Moitra P, Pan D, Gartia MR, Owoyele O. A Machine Learning Framework for Detecting COVID-19 Infection Using Surface-Enhanced Raman Scattering. Biosensors. 2022; 12(8):589. https://doi.org/10.3390/bios12080589

Chicago/Turabian Style

Ikponmwoba, Eloghosa, Okezzi Ukorigho, Parikshit Moitra, Dipanjan Pan, Manas Ranjan Gartia, and Opeoluwa Owoyele. 2022. "A Machine Learning Framework for Detecting COVID-19 Infection Using Surface-Enhanced Raman Scattering" Biosensors 12, no. 8: 589. https://doi.org/10.3390/bios12080589

Find Other Styles

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Abstract views Pdf views Html views

Article Menu

A Machine Learning Framework for Detecting COVID-19 Infection Using Surface-Enhanced Raman Scattering

Abstract

Supplementary Material

Share and Cite

Article Metrics

Article Access Statistics

Article Access Map by Country/Region

Further Information

Guidelines

MDPI Initiatives

Follow MDPI