Resolution of heavy metal mixtures from highly overlapped ASV voltammograms employing a wavelet neural network

Juan Manuel Gutiérrez, Laura Moreno-Baŕn, Francisco Céspedes, Roberto Muñoz, Manuel Del Valle

Research output: Contribution to journalArticleResearchpeer-review

13 Citations (Scopus)

Abstract

This work describes the chemometric assisted ASV determination of three heavy metals in water (lead, copper and cadmium) in presence of thallium and indium as interfering species. Stripping was carried out in open atmosphere, employing a graphite-epoxy transducer as working electrode, without any surface regeneration after each analysis. The concentration range studied was from 0.4 to 20 ppm for both analytes and interferents. Due to the overlapping nature of the signals, a wavelet neural network (WNN) was used for deconvolution of the voltammogram. In order to validate the resolution capability, a k-fold cross validation procedure was performed. Mixtures of metals could be resolved with good prediction of their concentrations; obtained vs. expected comparison graphs exhibited, for a set of samples not employed for training, correlation values of 0.996 for lead, 0.989 for cadmium and 0.995 for copper. © 2009 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Original languageEnglish
Pages (from-to)445-451
JournalElectroanalysis
Volume21
Issue number3-5
DOIs
Publication statusPublished - 1 Feb 2009

Keywords

  • Heavy metals
  • Mixture resolution
  • Voltammetry
  • Wavelet neural network

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