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)

Abstract

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.

Keywords

Entropy Radionuclide Oligosaccharide Tral Spectrophotometry 

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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

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