Machine Learning Enhanced Optical Fiber Sensor For Detection Of Glucose Low Concentration In Samples Mimicking Tissue

Authors

  • Maria Babińska Department of Metrology and Optoelectronics, Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Gdańsk, Poland
  • Adam Władziński Gdańsk University of Technology
  • Tomasz Talaśka Department of Decision Systems and Robotics, Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Gdańsk, Poland
  • Małgorzata Szczerska Department of Metrology and Optoelectronics, Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Gdańsk, Poland

DOI:

https://doi.org/10.4302/plp.v17i1.1320

Abstract

This study presents an optical 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 and identify the principal components of the collected data. The Random Forest and XGBoost model 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 an optical fiber sensor enables the detection of low glucose concentrations.

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Published

2025-03-31

How to Cite

[1]
M. Babińska, A. Władziński, T. Talaśka, and M. Szczerska, “Machine Learning Enhanced Optical Fiber Sensor For Detection Of Glucose Low Concentration In Samples Mimicking Tissue”, Photonics Lett. Pol., vol. 17, no. 1, pp. 20–22, Mar. 2025.

Issue

Vol. 17 No. 1 (2025)

Section

Articles

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Copyright (c) 2025 Maria Babińska, Adam Władziński, Tomasz Talaśka, Małgorzata Szczerska

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