A comparison of neural networks and partial least squares for deconvoluting fluorescence spectra.
This article compares backpropagation neural networks (BNN) with partial least squares (PLS) techniques in terms of their ability to deconvolute fluorescence spectra. Both actual experimental and simulated spectral data are studied for 2 binary systems. These systems consist of mixtures of tryptophan and tyrosine, and NADH and tryptophan over a total concentration range of 10(-7) to 10(-4) M. It is shown that BNN is superior to PLS for both systems.
McAvoy
T J
TJ
Department of Chemical Engineering, University of Maryland, College Park, MD 20742, USA.
Su
H T
HT
Wang
N S
NS
He
M
M
Horvath
J
J
Semerjian
H
H