• Spectroscopy and Spectral Analysis
  • Vol. 34, Issue 4, 942 (2014)
DONG Yan-fei1、2、*, LU Qi-peng1, DING Hai-quan1, and GAO Hong-zhi1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • show less
    DOI: 10.3964/j.issn.1000-0593(2014)04-0942-05 Cite this Article
    DONG Yan-fei, LU Qi-peng, DING Hai-quan, GAO Hong-zhi. Study on the Best Detector-Distance of Noninvasive Biochemical Examination by Monte Carlo Simulation[J]. Spectroscopy and Spectral Analysis, 2014, 34(4): 942 Copy Citation Text show less

    Abstract

    The present paper studies the best detector-distance to improve the near-infrared spectrum signal intensity of the dermis layer and eliminate the interference of the epidermis and subcutaneous layer. First, we analyzed the organizational structure of the skin and calculated the tissue optical parameters of different layers. And we established the Monte Carlo model with the example of glucose absorption peak at 2 270 nm. Then, we used the Monte Carlo method to simulate the light transmission rules in the skin, obtaining the average path length, the average visit depth and the fractions of absorbed energy at each layer with the change in critical angle and detector-distance. The results show that when the photons are incident at an angle less than 45 degrees, you can ignore the effect of the incident angle on photon transmission path, and when the detector-distance is 1 mm, the fraction of absorbed photon energy by the dermis layer is the largest, while it can ensure more energy received by detector. We determined that the best detector-distance is 1mm, which successfully avoids the interference of the epidermis spectral information and obtains large amounts of blood in the dermis layer, which is conducive to the near-infrared non-invasive measurement of biochemical components and the subsequent experiments.
    DONG Yan-fei, LU Qi-peng, DING Hai-quan, GAO Hong-zhi. Study on the Best Detector-Distance of Noninvasive Biochemical Examination by Monte Carlo Simulation[J]. Spectroscopy and Spectral Analysis, 2014, 34(4): 942
    Download Citation