Quantitative Multicomponent Spectral Analysis Using Neural Networks

  • Chii-Wann Lin
  • Joseph C. LaManna
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 345)


Recent developments in dye chemistry and modern optics have made simultaneous measurement of multiple intracellular ion species possible1. Analysis of optical spectra from intact tissues is complicated because of the presence of multiple components. Quan­titative descriptions of these components are required to apply these techniques to analyti­cal chemistry and cellular physiology. Ratio methods2 have been applied to many areas of quantitative optical signal measurement because the quantitative data is independent of dye concentration, path length and light source intensity. However, it is known that the ratio method can give erroneous results3 without special attention to the choice of the op­timal wavelengths for these methods. Full spectra carry all the information needed for qualitative and quantitative analysis. Methods like principal component regression (PCR) and partial least-squares (PLS), which have been used for full spectra calibration in most chemometrics literature4,5, or multicomponent stripping for image applications 6,7,8 need heavy computation times to perform their optimization processes.


Artificial Neural Network Artificial Neural Network Model Weight Matrix Output Node Principal Component Regression 
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.
    G.T. Rijkers, L.B. Justement, A.W. Griffioen, and J.C. Cambier, Improved method for measuring intra­cellular Ca with Fluo-3, Cytometry 11: 923–927 (1990)PubMedCrossRefGoogle Scholar
  2. 2.
    V.W. Macdonald, J.H. Keizer, F.F. Jöbsis, Spectrophotometric measurements of metabolically induced pH changed in frog skeletal muscle, Arch. Biocheny. 184: 423–430 (1977)CrossRefGoogle Scholar
  3. 3.
    U. Heinrich, J. Hoffmann, and D.W. Lübbers, Quantitative evaluation of optical reflection spectra of blood-free perfused guinea pig brain using a nonlinear multicomponent analysis, P/hügers Arch. 409: 152–157 (1987)CrossRefGoogle Scholar
  4. 4.
    E.V. Thomas, And D.M. Haaland, Comparison of multivariate calibration methods for quantitative spec­tra analysis, Anal. Chem. 62:1091–1099 (1990)CrossRefGoogle Scholar
  5. 5.
    P.J. Gemperline, J.R. Long, and V.G. Gregoriou, Nonlinear multivariate calibration using principal com­ponents regression and artificial neural networks, Anal. Chem. 63:2313–2323 (1991)CrossRefGoogle Scholar
  6. 6.
    S. Kawata, K. Sasaki, and S. Minami, Component analysis of spatial and spectral patterns in multispec­tral images. I. basis,“ Opt. Soc.Am. A 4: 2101–2106 (1987)CrossRefGoogle Scholar
  7. 7.
    S. Kawata, K. Sasaki, and S. Minami, Component analysis of spatial and spectral patterns in multispec­tral images. II. Entropy minimization, J. Opt. Soc. Arrt. A 6: 73–79 (1989)CrossRefGoogle Scholar
  8. 8.
    M. Nakamura, Y. Suzuki, and S. Kobayashi, A method for recovering physiological components from dy­namic radionuclide images using the maximum entropy principle: a numerical investigation, IEEE trans. Biomed. Eng. 36: 906–917 (1989)PubMedCrossRefGoogle Scholar
  9. 9.
    E. Oja, A simplified neuron model as a principal component analyzer, J.Afath. Biol. 15: 267–273 (1982)CrossRefGoogle Scholar
  10. 10.
    T. Kohonen, Self-organized formation of topological correct feature maps. Biol. Cybern. 43: 59–69 (1982)CrossRefGoogle Scholar
  11. 11.
    C.W. Lin, J.C. LaManna, and Y. Takefuji, Quantitative measurement of two-component pH-sensitive colorimetric spectra using multilayer neural networks, Biol. Cvbern. (in press)Google Scholar
  12. 12.
    T. Kohonen, An adapative associative memoryprinciple, IEEE Compnt. 23: 444–445 (1974)CrossRefGoogle Scholar
  13. 13.
    P. Baldi, and K. Hornik, Neural networks and principal component analysis: learning from examples without local minima, Neural Networks 2: 53–58 (1989)CrossRefGoogle Scholar
  14. 14.
    T.D. Sanger, Optimal unsupervised learning in a single-layer linear feedforward neural network, Neural Networks, 2: 459–473 (1989)CrossRefGoogle Scholar
  15. 15.
    D.E. Rumelhart, and J.L McClelland, “The PDP Research Group: Parallel Distributed Processing”, MIT Press, Cambridge (1988)Google Scholar
  16. 16.
    J. Rubner, and K. Schulten, Development of feature detector by self-organization, Biol. C vbern. 62: 193–199 (1990)CrossRefGoogle Scholar
  17. 17.
    B.J. Wythoff, S.P. Levine, and S.A. Tomellini, Spectral peak verification and recognition using a multi-layered neural network, Anal. Chem. 62: 2702–2709 (1990)PubMedCrossRefGoogle Scholar
  18. 18.
    B. Meyer, T. Hansen, D. Nute, P. Albersheim, A. Darvili, W. York, and J. Sellers, Identification of the 1H-NMR spectra of complex oligosaccharides with artificial neural networks, Science 251: 542–544PubMedCrossRefGoogle Scholar
  19. 19.
    J.E. Whitaker, R.P. Haugland, and F.G. Prendergast, Spectral and Photophysical studies of Ben­zo[c]xanthene dyes: dual emission pH sensors, Anal. 13iochem. 194: 330–344 (1991)CrossRefGoogle Scholar
  20. 20.
    O. Seksek, N. Henry-Toulmé, F. Sureau, and J. Bolard, SNARF-1 as an intracellular pH indicator in laser microspectrofluorometry: a critical assessment, Anal. Biochern. 193: 49–54 (1991)CrossRefGoogle Scholar
  21. 21.
    K.J. Buckler, and R.D. Vaughan-Jones, Application of a new pH-sensitive fluoroprobe (carboxy-SNARF-1) for intracellular pH measurement in small, isolated cells, Pflfigers . Arch. 417: 234–239 (1990)CrossRefGoogle Scholar
  22. 22.
    S. Bassnett, L. Reinisch, and D.C. Beebe, Intracellular pH measurement using single excitation-dual emission fluorescence ratios, Ant. J. Physiol. 258: C171–C178 (1990)Google Scholar
  23. 23.
    J.C. LaManna, and K.A. McCracken, The use of neutral red as an intracellular pH indicator in rat brain cortex in vivo. Anal t. Biochern. 142: 117–125 (1984)CrossRefGoogle Scholar
  24. 24.
    T.J. Sick, T.S. Whittingham, J.C. LaManna, Determination of intracellular pH in the in vitro hippocam­pal slice preparation by transillumination spectrophotometry of neutral red. J. Neurosci. M. 27: 25–34CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1994

Authors and Affiliations

  • Chii-Wann Lin
    • 1
  • Joseph C. LaManna
    • 2
  1. 1.Department of Biomedical EngineeringCase Western Reserve University, School of MedicineClevelandUSA
  2. 2.Department of Neurology, Physiology/Biophysics, and NeurosciencesCase Western Reserve University, School of MedicineClevelandUSA

Personalised recommendations