Approaches to the Quantification of Tissue Pigments by NIR in Vivo Using a Double Beam Method to Multicomponent Curve Fitting Analysis

  • Shoko Nioka
  • Kouich Oka


Near infrared spectroscopy (NIR) can be used as a non-invasive method for measuring tissue oxygen transport and metabolism by indicating the levels of hemoglobin saturation and the redox state of cytochrome oxidase. Jobsis and others1,11 have applied NIR spectroscopy to isolated mitochondria, and tissues, in vitro and in vivo. One of the advantages of using near infrared wavelengths rather than visible light is that the penetration of the photons is much greater because of the physical properties of light and the low extinction coefficients of the tissue pigments at these wavelengths. It has been found that the mean light path length varies with wavelength when measured by time resolved spectroscopy (TRS)2 and other techniques3. Physical studies have indicated that in a scattering medium, such as brain tissue and cells, the photons migrate as a diffusing or randomly walking particle making application of the Beer-Lambert law difficult4,5,6. The light pathlength varies depending upon the wavelengths, materials, and concentrations of absorbing pigments. Theoretical equations have been developed to fit the data7,8. Quantification of the scattering factor is necessary in order to quantitate tissue concentration of absorbers. The contribution of the absorber to the total absorption in the scattering medium depends upon the wavelength as well as the extinction coefficient9,5. Time resolved spectroscopy shows similar properties of light diffusion (distribution of the light path) at 700 to 800 nm (Nioka, unpublished data). Near infrared light may be best suited to minimizing the scattering problem. Some researchers have ignored the effect of scattering and calculated the absorptions of hemoglobin and cytochrome oxidase separately10–15,1.


Difference Spectrum Light Path Light Guide Near Infrared Spectroscopy Hemoglobin Saturation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    F. F. Jobsis, Non-invasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters, Science, 198: 1264 1267 (1977).Google Scholar
  2. 2.
    B. Chance, J. S. Leigh, H. Miyake, D. S. Smith, S. Nioka, R. Greenfield, M. Finnander, K. Kaufman, W. Levy, M. Yong, P. Cohen, H. Yoshioka and R. Boretsky, Comparison of time-resolved and -unresolved measurements of deoxyhemoglobin in brain,Proc. Natl. Acad. Sci, 85: 4971–4975 (1988).CrossRefGoogle Scholar
  3. 3.
    D.M. Bensen and J.A. Knopp, Effect of tissue absorption and microscope optical parameters on the depth of penetration for fluorescence and reflectance measurement of tissue samples, Photochem. Photobiol., 39: 495–502 (1984).CrossRefGoogle Scholar
  4. 4.
    B. Blumberg, Light propagation in human tissues: The physical origin of the inhomogeneous scattering mechanisms, Biophys. J., 51: 288a (1987).Google Scholar
  5. 5.
    P. van der Zee and D.T. Delpy, Simulation of the point spread function for light in tissue by a Monte Carlo method, Adv. Exp. Med. Biol, 215: 179–191 (1987).PubMedCrossRefGoogle Scholar
  6. 6.
    B.C. Wilson and C.T. Adam, A Monte Carlo model for the dependence of cellular energy metabolism, Arch. Biochem. Biophys, 195: 485–493 (1983).CrossRefGoogle Scholar
  7. 7.
    R. Wodick and D.W. Lubbers, Ein neues Vergahren zur Bestimmung des oxygenierungogrades von Hämoglobins-pektren bei inhomogenen Licht wegener Läutert under analyse von spektrem der menschlichen Haut, Pflunger arch, 342: 41–60 (1973).CrossRefGoogle Scholar
  8. 8.
    K. Kubelka and F. Munk, Ein beitrag zur Optik der frabanstrich, Z. Tech. Phys, 11 a: 593–603 (1931).Google Scholar
  9. 9.
    D.W. Lubbers, and J. Hoffmann, Absolute reflection photometry at organ surfaces, Adv. Physiol. Sci., 8: 353–361 (1981).Google Scholar
  10. 10.
    C.B. Cairns, D. Fillipo and H.J. Proctor, A non-invasive method for monitoring the effects of increased intracranial pressure with near-infrared spectrophotometry, Surg. Gynecol. Obstetric., 161: 145–148 (1985).Google Scholar
  11. 11.
    B. Chance, Spectrophotometric observations of absorbance changes in the infrared region in suspension of mitochondria) and in submitochondrial particles, in: “Biochemistry of Copper,” J. Peisach, P. Aisen and W.E. Blumberg, eds., Academic Press, New York (1966).Google Scholar
  12. 12.
    M. Cope, D.T. Delpy, E.O.R. Reynolds, S. Wray, J. Wyatt and P. van der Zee, Methods of quantitating cerebral near-infrared spectroscopy data, Adv. Exp. Med. Biol., 222: 183–189 (1987).Google Scholar
  13. 13.
    I. Gianni, M. Ferrari, A. Carpi and P. Fasella, Rat brain monitoring by near-infrared spectroscopy: An assessment of possible clinical significance,Physiol. Chem. Phys, 14: 295–305 (1982).Google Scholar
  14. 14.
    O. Hazeki and M. Tamura, Quantitative analysis of hemoglobin oxygenation state of rat brain in situ by near-infrared spectrophotometry, J. Appl. Physiol., 64: 796–802 (1988).PubMedGoogle Scholar
  15. 15.
    K. Kariman and D.S. Burkhart, Heme-copper relationship of cytochrome oxidase in rat brain in situ, Biochem. Biophys. Res. Commun., 126: 1022–1028 (1985).PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1989

Authors and Affiliations

  • Shoko Nioka
    • 1
  • Kouich Oka
    • 2
  1. 1.Dept. of Biochemistry/BiophysicsUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Otsuka Electronics, Ltd.OsakaJapan

Personalised recommendations