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A Feasibility Study on Noninvasive Blood Glucose Estimation Using Machine Learning Analysis of Near-Infrared

Tae Wuk Bae1, Byoung Ik Kim2, Kee Koo Kwon1

  • 1Daegu-Gyeongbuk Research Center, Electronics and Telecommunications Research Institute, Daegu 42994, Republic of Korea.

Biosensors
|November 26, 2025
PubMed
Summary

This study investigated noninvasive blood glucose (BG) estimation using near-infrared (NIR) spectroscopy in dog blood. Promising results at specific wavelengths suggest potential for developing noninvasive glucose monitoring devices.

Keywords:
blood glucosemultilayer perceptronnear-infraredreflectanceregression

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Area of Science:

  • Biomedical Engineering
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Accurate blood glucose monitoring is crucial for managing diabetes.
  • Current invasive methods pose challenges for frequent monitoring.
  • Noninvasive techniques offer a promising alternative for continuous glucose assessment.

Purpose of the Study:

  • To evaluate the feasibility of noninvasive blood glucose estimation using near-infrared (NIR) spectroscopy.
  • To identify optimal wavelengths for glucose detection in biological samples.
  • To develop a foundational dataset for future noninvasive glucose monitoring systems.

Main Methods:

  • Utilized a sensor module with NIR wavelengths (770 nm, 850 nm, 970 nm).
  • Tested on artificial blood vessel and thin pig skin models with varying glucose concentrations.
  • Employed diverse regression models (linear, PLS, MLP) for spectral data analysis.
  • Validated against commercial invasive glucometers.

Main Results:

  • Identified negative correlations between NIR spectral responses and glucose levels at 850 nm and 970 nm.
  • Demonstrated the potential of these wavelengths for noninvasive glucose sensing.
  • Established an NIR-glucose database from dog blood samples.

Conclusions:

  • Near-infrared spectroscopy shows feasibility for noninvasive blood glucose estimation.
  • Specific NIR wavelengths (850 nm, 970 nm) are promising for future sensor development.
  • The created dog blood NIR-glucose database can aid advancements in noninvasive glucose monitoring technology.