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Machine Learning Enhanced Optical Fiber Sensor For Detection Of Glucose Low Concentration In Samples Mimicking Tissue
Abstract
This study presents anoptical fiber sensor for detecting low glucose concentrations in a sample mimicking urine. Our research focused on designing a sensor capable of detecting 0.5% glucose concentrations in artificial urine. Algorithms were applied to analyze and accurately classify the data andidentify the principal components of the collected data. The Random Forest and XGBoostmodel achieved the highest accuracy, confirming that frequency domain analysis combined with machine learning can significantly enhance glucose detection accuracy. These findings demonstrate that integrating machine learning with anoptical fibersensor enables the detection of low glucose concentrations.
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
Photonics Letters of Poland
no. 17,
pages 20 - 22,
ISSN: 2080-2242 - Language:
- English
- Publication year:
- 2025
- Bibliographic description:
- Babińska M., Władziński A., Talaśka T., Szczerska M.: Machine LearningEnhancedOptical Fiber Sensor For Detection Of Glucose LowConcentration In Samples Mimicking Tissue// Photonics Letters of Poland -Vol. 17,iss. 1 (2025), s.20-22
- DOI:
- Digital Object Identifier (open in new tab) 10.4302/plp.v17i1.1320
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
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