Author: Marjoniemi, M.

Source: Applied Spectroscopy, Volume 48, Issue 1, Pages 14A-21A and 1-163 (January 1994) , pp. 21-26(6)

Publisher: Society for Applied Spectroscopy

Abstract:

In this article artificial neural networks (ANNs) are applied for multivariate calibration using spectroscopic data and for generation of quantitative estimates of the concentrations of a component (chromium) in solutions. Neural networks are capable of handling nonlinear relationships. Absorbance is nonlinearly dependent on concentration, especially in the case of wide concentration ranges and multicomponent solutions. In addition to the aforementioned reasons, nonlinearities are also caused by aging and by differences in pH and in the temperatures of the chromium-tanning solutions to be modeled. The sigmoid output function was used in the hidden layer to perform nonlinear fitting. The results are compared with the results obtained with principal component regression (PCR) and partial least-squares regression (PLS) methods.

Keywords: Artificial neural networks; Modeling; Principal component regression; Partial least-squares regression; Chromium tanning

Document Type: Research article

DOI: http://dx.doi.org/10.1366/0003702944027552

Affiliations: 1: Tampere University of Technology, Laboratory of Fur and Leather Technology, P.O. Box 692, FIN-33101 Tampere, Finland

Publication date: 1994-01-01

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