Enhanced Sensitivity of Absorption Spectroscopy Glucose Detection by Machine Learning

Authors

  • Maria Babińska 1Department of Metrology and Optoelectronics, Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Gdańsk, Poland
  • Adam Władziński Department of Metrology and Optoelectronics, Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Gdańsk, Poland

DOI:

https://doi.org/10.4302/plp.v17i1.1319

Abstract

In this study, UV-VIS spectroscopy was used as a tool for detecting low glucose concentrations in urine. Measurements were performed on artificial urine samples and solutions with 0.1% and 0.2% glucose, covering both normal and pathological thresholds. Among the evaluated models, Random Forest reached 0.887 for the 0.1% glucose sample, while Logistic Regression achieved 0.7796 for the 0.2% glucose sample, demonstrating high effectiveness in distinguishing glucose levels. The results confirm that the integration of UV-VIS spectroscopy and machine learning has the potential to serve as a fast and non-invasive screening tool for the early detection of metabolic disorders.

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Published

2025-03-31

How to Cite

[1]
M. Babińska and A. Władziński, “Enhanced Sensitivity of Absorption Spectroscopy Glucose Detection by Machine Learning”, Photonics Lett. Pol., vol. 17, no. 1, pp. 16–19, Mar. 2025.

Issue

Vol. 17 No. 1 (2025)

Section

Articles

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Copyright (c) 2025 Maria Babińska, Adam Władziński

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