Automated electronic tongue based on potentiometric sensors for the determination of a trinary anionic surfactant mixture

Montserrat Cortina, Christina Ecker, Daniel Calvo, Manuel del Valle

Research output: Contribution to journalArticleResearchpeer-review

17 Citations (Scopus)

Abstract

An automated electronic tongue consisting of an array of potentiometric sensors and an artificial neural network (ANN) has been developed to resolve mixtures of anionic surfactants. The sensor array was formed by five different flow-through sensors for anionic surfactants, based on poly(vinyl chloride) membranes having cross-sensitivity features. Feedforward multilayer neural networks were used to predict surfactant concentrations. As a great amount of information is required for the correct modelling of the sensors response, a sequential injection analysis (SIA) system was used to automatically provide it. Dodecylsulfate (DS-), dodecylbenzenesulfonate (DBS-) and α-alkene sulfonate (ALF-) formed the three-analyte study case resolved in this work. Their concentrations varied from 0.2 to 4 mM for ALF- and DBS- and from 0.2 to 5 mM for DS-. Good prediction ability was obtained with correlation coefficients better than 0.933 when the obtained values were compared with those expected for a set of 16 external test samples not used for training. © 2007 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)213-218
JournalJournal of Pharmaceutical and Biomedical Analysis
Volume46
Issue number2
DOIs
Publication statusPublished - 22 Jan 2008

Keywords

  • Anionic surfactants
  • Artificial neural networks
  • Electronic tongue
  • Ion-selective electrodes
  • Sequential injection analysis

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