Prediction of the isotherms of human IgG adsorption on Ni(II)-IDA-PEVA membrane using artificial neural networks

Prediction of the isotherms of human IgG adsorption on Ni(II)-IDA-PEVA membrane using artificial neural networks

Author Schmitz, Jones Erni Autor UNIFESP Google Scholar
Lazzarotto Bresolin, Igor Tadeu Autor UNIFESP Google Scholar
Institution Universidade Federal de São Paulo (UNIFESP)
Abstract The use of artificial neural networks (ANNs) to predict the adsorption isotherms of human immunoglobulin G on immobilized Ni(II) affinity hollow fiber membranes was studied. Neural networks were trained using the Levenberg-Marquardt algorithm combined with Bayesian regularization technique and experimental data from different temperatures. the resulting neural network demonstrated to be able to interpolate the behavior of the maximum adsorption capacity and equilibrium concentration in the temperature range (4, 37 degrees C) with correlation coefficients higher than 0.96. Results demonstrated to be very similar to those achieved with traditionally Langmuir model adjustment. the advantage of interpolation ability of ANNs was also showed.
Keywords Artificial neural networks
Adsorption isotherms
Immunoglobulin G
Affinity hollow fiber membranes
Language English
Date 2014-04-10
Published in Adsorption-Journal of the International Adsorption Society. Dordrecht: Springer, v. 20, n. 8, p. 959-965, 2014.
ISSN 0929-5607 (Sherpa/Romeo, impact factor)
Publisher Springer
Extent 959-965
Origin http://dx.doi.org/10.1007/s10450-014-9641-9
Access rights Closed access
Type Article
Web of Science ID WOS:000346359800005
URI http://repositorio.unifesp.br/handle/11600/37662

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