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Use of neural networks in solving interferences caused by formation of intermetallic compounds in anodic stripping voltammetry
Author(s) -
Lastres Ernesto,
de Armas Graciela,
Catasüs Miguel,
Alpízar Jesüs,
García Luciano,
Cerdà Víctor
Publication year - 1997
Publication title -
electroanalysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.574
H-Index - 128
eISSN - 1521-4109
pISSN - 1040-0397
DOI - 10.1002/elan.1140090313
Subject(s) - intermetallic , anodic stripping voltammetry , hanging mercury drop electrode , voltammetry , anode , artificial neural network , electrode , materials science , stripping (fiber) , mercury (programming language) , analyte , computer science , chemistry , electrochemistry , alloy , metallurgy , chromatography , artificial intelligence , composite material , programming language
Neural networks (NNs) were used to overcome interferences by formation of intermetallic compounds in anodic stripping voltammetry (ASV). The software developed for this purpose allows one to construct NNs of virtually any type of architecture and train it automatically using the back‐propagation method. The ability of NNs for addressing interferences arising from interactions with the hanging mercury drop electrode is demonstrated for the Cu–Zn binary system.