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The on-line measurement of chemical composition under different operating conditions is an important problem in many industries. An approach based on hybrid signal preprocessing and artificial neural network paradigms for estimating composition from chemometric data has been developed.
The performance of this methodology was tested with the use of near-infrared (NIR) and Raman spectra from both laboratory and industrial samples. The sensitivity-of-composition estimation as a function of spectral errors, spectral preprocessing, and choice of parameter vector was studied.
The optimal architecture of multilayer neural networks and the guidelines for achieving them were also studied. The results of applications to FT-Raman data and NIR data demonstrate that this methodology is highly effective in establishing a generalized mapping between spectral information
and sample composition, and that the parameters can be estimated with high accuracy.
Department of Nuclear Engineering, University of Tennessee, Knoxville, Tennessee 37996-2300
Publication date: January 1, 1993
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The Society publishes the internationally recognized, peer reviewed journal, Applied Spectroscopy, which is available both in print and online. Subscriptions are included with membership or can be purchased by institutional or corporate organizations. Abstracts may be viewed free of charge. Previously published as Bulletin (Society for Applied Spectroscopy)