Volume 12 Issue 6
Dec.  2019
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Chinese Optics  > 2019  >  12(6): 1235-1248.
GUO Shuai, SU Hang, HUANG Xing-can, LIU Jian. Research progress in optical methods for noninvasive blood glucose detection[J]. Chinese Optics, 2019, 12(6): 1235-1248. doi: 10.3788/CO.20191206.1235
Citation: GUO Shuai, SU Hang, HUANG Xing-can, LIU Jian. Research progress in optical methods for noninvasive blood glucose detection[J]. Chinese Optics, 2019, 12(6): 1235-1248. doi: 10.3788/CO.20191206.1235
shu

Research progress in optical methods for noninvasive blood glucose detection

  • Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan 250061, China

Funds:

Natural Science Foundation of China 61801256

Key Research and Development Projects of Shandong Province 2018GGX109016

Shandong Natural Science Foundation ZR2019MEE051

Natural Science Foundation of Beijing 4182075

More Information
  • Corresponding author: LIU Jian, E-mail:lj@sdu.edu.cn
  • Received Date: 29 Nov 2018
  • Rev Recd Date: 09 Jan 2019
  • Publish Date: 01 Dec 2019
    Abstract
  • Continuous monitoring of blood glucose levels is a prerequisite for controlling diabetes and its complications. Noninvasive methods have attracted great attention for their lack of injury and widespread acceptance. With the improvement of measurement accuracy in recent decades, optics-based methods of noninvasive blood glucose detection have shown great potential in clinical applications. In this paper, the main optics-based methods of noninvasive blood glucose detection, such as polarimetry, optical coherence tomography and infrared spectroscopy, are reviewed with regards to their principles, advantages, accuracy, problems and the possible solutions to those problems. By comparison, it concludes that the method of infrared spectroscopy has obvious advantages in detection accuracy. In the future, major challenges will be in increasing the signal-to-noise ratio of instruments, eliminating background interference and establishing universal calibration models.

     

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