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Application of neural networks with novel independent component analysis methodologies for the simultaneous determination of cadmium, copper, and lead using an ISE array
Author(s) -
Wang Liang,
Yang Die,
Chen Zuliang,
Lesniewski Peter J.,
Naidu Ravi
Publication year - 2014
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2599
Subject(s) - robustness (evolution) , independent component analysis , artificial neural network , biological system , copper , electrode , cadmium , computer science , chemometrics , analytical chemistry (journal) , chemistry , materials science , artificial intelligence , chromatography , machine learning , biochemistry , organic chemistry , biology , gene
The paper introduces a novel chemometric strategy based on independent component analysis (ICA) coupled with a back‐propagation neural network. In this approach, one of the most popular ICA methods, the fast fixed‐point algorithm for ICA ( fast ICA), was implemented by the genetic algorithm ( genetic ICA) to avoid the local maxima problem commonly observed with fast ICA. As a case study, an ion‐selective electrode (ISE) array, consisting of three working electrodes and one reference electrode, was used for the simultaneous determination of three heavy metals (cadmium, copper, and lead) in aqueous solutions, which are normally prone to severe interferences. The robustness and appropriateness of the approach were assessed using the average mean of relative error (MRE) of triplicated external validation. After configuration and optimization, the average MRE for Cu was <5%. For the determination of Cd and Pb, whose ISEs normally cannot tolerate Cu ions even at the microgram per liter levels, the MREs were 8%. This article demonstrated that this approach can be applied to the detection of heavy metal contamination in industrial wastewater with prediction accuracies comparable with other popular quantitative chemometric neural network methods. Copyright © 2014 John Wiley & Sons, Ltd.