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Optical method supported by machine learning for dynamics of C‐reactive protein concentrations changes detection in biological matrix samples
Abstract
In this article we present the novel spectroscopy method supported with machine learning for real-time detection of infectious agents in wastewater. In the case of infectious diseases, wastewater monitoring can be used to detect the presence of inflammation biomarkers, such as the proposed C-reactive protein, for monitoring inflammatory conditions and mass screening during epidemics for early detection in communities of concern, such as hospitals, schools, and so on. The proposed spectroscopy method supported with machine learning for real-time detection of infectious agents will eliminate the need for time-consuming processes, which contribute to reducing costs. The spectra in range 220–750 nm were used for the study. We achieve accuracy of our prediction model up to 68% with using only absorption spectrophotometer and machine learning. The use of such a set makes the method universal, due to the possibility of using many different detectors.
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Authors (7)
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Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1002/jbio.202300523
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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Journal of Biophotonics
ISSN: 1864-063X - Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Sokołowski P., Cierpiak K., Szczerska M., Wróbel M., Łuczkiewicz A., Fudala-Książek S., Wityk P.: Optical method supported by machine learning for dynamics of C‐reactive protein concentrations changes detection in biological matrix samples// Journal of Biophotonics -, (2024),
- DOI:
- Digital Object Identifier (open in new tab) 10.1002/jbio.202300523
- Sources of funding:
-
- Statutory activity/subsidy
- Verified by:
- Gdańsk University of Technology
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