Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/54911
Type: Artigo de periódico
Title: Artificial neural networks applied to potentiometric acid-base flow injection titrations
Author: Zampronio, CG
Rohwedder, JJR
Poppi, RJ
Abstract: Artificial neural network (ANN) was applied for data treatment as a multivariate calibration tool in a potentiometric acid-base flow injection titration. A multilayer feed-forward ANN model, with Levenberg-Marquardt weight error correction was used for data modeling. The neural network parameter architecture was optimized to establish a relationship between the titration profile and the acid concentration. Citric and malic acids in synthetic sample mixtures and in orange juices were analyzed and the performance of ANN was compared with that of partial least squares (PLS) regression. (C) 2002 Elsevier Science B.V. All rights reserved.
Subject: artificial neural networks
flow injection analysis
acid-base titration
Country: Holanda
Editor: Elsevier Science Bv
Rights: fechado
Identifier DOI: 10.1016/S0169-7439(01)00210-6
Date Issue: 2002
Appears in Collections:Unicamp - Artigos e Outros Documentos

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