• Spectroscopy and Spectral Analysis
  • Vol. 36, Issue 6, 1674 (2016)
REN Zhong1、2, LIU Guo-dong1, HUANG Zhen1, and XIONG Zhi-hua1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    DOI: 10.3964/j.issn.1000-0593(2016)06-1674-06 Cite this Article
    REN Zhong, LIU Guo-dong, HUANG Zhen, XIONG Zhi-hua. Non-Invasive Detection of Blood Glucose Concentration Based on Photoacoustic Spectroscopy Combined with Principle Component Regression Method[J]. Spectroscopy and Spectral Analysis, 2016, 36(6): 1674 Copy Citation Text show less

    Abstract

    This paper presents a photoacoustic noninvasive setup of detecting blood glucose based on the tunable pulsed laser coupled with the confocal ultrasonic transducer and the forward detection model. To validate the reliability of the setup, in the experiments, the different concentrations of glucose aqueous solution are excitated by the Q-switched 532 nm pumped Nd∶YAG pulsed laser to generate the time-resolved photoacoustic signals. And the glucose aqueous solutions are scanned by the tunable pulsed laser in the infrared waveband from 1 300 to 2 300 nm with the interval of 10nm and the photoacoustic peak-to-peak values are gotten. The difference spectral method is used to get the characteristic wavelengths of glucose, and the principle component regression algorithm is used to determine three optimal wavelengths and establish the correction mathematical model between the photoacoustic peak-to-peak values and the concentrations. The experimental results demonstrate that the mechanism of the photoacoustic signal is agreement with the cylindrical model, and the predicted results of the correction and prediction samples based on the established correction model demonstrate that the root-mean-square error of correction and prediction are all less than 10 mg·dl-1, the correlation coefficient reaches 0.993 6.
    REN Zhong, LIU Guo-dong, HUANG Zhen, XIONG Zhi-hua. Non-Invasive Detection of Blood Glucose Concentration Based on Photoacoustic Spectroscopy Combined with Principle Component Regression Method[J]. Spectroscopy and Spectral Analysis, 2016, 36(6): 1674
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