EIS multianalyte sensing with an automated SIA system-An electronic tongue employing the impedimetric signal

Montserrat Cortina-Puig, Xavier Muñoz-Berbel, M. Asunción Alonso-Lomillo, Francisco J. Muñoz-Pascual, Manuel del Valle

Research output: Contribution to journalArticleResearchpeer-review

41 Citations (Scopus)


In this work, the simultaneous quantification of three alkaline ions (potassium, sodium and ammonium) from a single impedance spectrum is presented. For this purpose, a generic ionophore - dibenzo-18-crown-6 - was used as a recognition element, entrapped into a polymeric matrix of polypyrrole generated by electropolymerization. Electrochemical impedance spectroscopy (EIS) and artificial neural networks (ANNs) were employed to obtain and process the data, respectively. In fact, EIS detected the ions exchanged between the medium and the sensing layer whereas ANNs, after an appropriated training process, could turn the impedance spectrum into concentrations values. A sequential injection analysis (SIA) system was employed for operation and to automatically generate the information required for the training of the ANN. Best results were obtained by using a backpropagation neural network made up by two hidden layers: the first one contained three neurons with the radbas transfer function and the second one ten neurons with the tansig transfer function. Three commercial fertilizers were tested employing the proposed methodology on account of the high complexity of their matrix. The experimental results were compared with reference methods. © 2006 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)774-779
Issue number2
Publication statusPublished - 30 Apr 2007


  • Artificial neural network
  • Electronic tongue
  • Impedance measurements
  • Ionophore-polypyrrole modified chip
  • Sequential injection analysis


Dive into the research topics of 'EIS multianalyte sensing with an automated SIA system-An electronic tongue employing the impedimetric signal'. Together they form a unique fingerprint.

Cite this