New advancement in noninvasive measurement of blood glucose by near-infrared spectroscopy

  • Qingbo Li
  • , Yan Wang
  • , Kexin Xu*
  • *Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

Noninvasive blood glucose measurement with near-infrared spectroscopy suitable for home use would allow a more accuracy and frequent determination of glucose level, along with reducing the long-term health care costs of diabetic patients due to no reagents. The fundamental research results of noninvasive blood glucose sensing of our research group are introduced in this paper. They are involving the aspects as followed, the prediction results of the fundamental experiments by PLS calibration method and artificial neural networks respectively; the wavelength selection by genetic algorithm; the quantitative relation among prediction accuracy, instrumental precision and regression algorithms; and the determination of measuring mode according to the measuring wavelength range, and so on. The experiments that done with the samples from simple ones to complex ones have proved the feasibility of noninvasive blood glucose measurement methodology by near-infrared spectroscopy. In addition, the further research subjects of the fundamental problems involving human body measurement are proposed.

Original languageEnglish
Pages (from-to)457-464
Number of pages8
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume4916
DOIs
StatePublished - 2002
Externally publishedYes
Eventoptics in Health Care and Biomedical Optics: Diagnostics and Treatment - Shanghai, China
Duration: 15 Oct 200218 Oct 2002

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Artificial neural networks
  • Blood glucose
  • Genetic algorithm
  • Instrumental precision
  • Measuring mode
  • Near-infrared spectroscopy
  • Noninvasive measurement
  • Prediction accuracy
  • Signal-to-noise ratio

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