A voltammetric electronic tongue for the resolution of ternary nitrophenol mixtures

Andreu González-Calabuig, Xavier Cetó, Manel Del Valle

Research output: Contribution to journalArticleResearchpeer-review

7 Citations (Scopus)

Abstract

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This work reports the applicability of a voltammetric sensor array able to quantify the content of 2,4-dinitrophenol, 4-nitrophenol, and picric acid in artificial samples using the electronic tongue (ET) principles. The ET is based on cyclic voltammetry signals, obtained from an array of metal disk electrodes and a graphite epoxy composite electrode, compressed using discrete wavelet transform with chemometric tools such as artificial neural networks (ANNs). ANNs were employed to build the quantitative prediction model. In this manner, a set of standards based on a full factorial design, ranging from 0 to 300 mg·L -1 , was prepared to build the model; afterward, the model was validated with a completely independent set of standards. The model successfully predicted the concentration of the three considered phenols with a normalized root mean square error of 0.030 and 0.076 for the training and test subsets, respectively, and r ≥ 0.948.
Original languageEnglish
Article number216
JournalSensors
Volume18
Issue number1
DOIs
Publication statusPublished - 13 Jan 2018

Keywords

  • Artificial neural networks
  • Electronic tongue
  • Nitrophenols
  • Persistent pollutants

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