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A Voltammetric Electronic Tongue for the Resolution of Ternary Nitrophenol Mixtures

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...

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Detalles Bibliográficos
Autores principales: González-Calabuig, Andreu, Cetó, Xavier, del Valle, Manel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5795887/
https://www.ncbi.nlm.nih.gov/pubmed/29342848
http://dx.doi.org/10.3390/s18010216
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author González-Calabuig, Andreu
Cetó, Xavier
del Valle, Manel
author_facet González-Calabuig, Andreu
Cetó, Xavier
del Valle, Manel
author_sort González-Calabuig, Andreu
collection PubMed
description 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.
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spelling pubmed-57958872018-02-13 A Voltammetric Electronic Tongue for the Resolution of Ternary Nitrophenol Mixtures González-Calabuig, Andreu Cetó, Xavier del Valle, Manel Sensors (Basel) Article 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. MDPI 2018-01-13 /pmc/articles/PMC5795887/ /pubmed/29342848 http://dx.doi.org/10.3390/s18010216 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
González-Calabuig, Andreu
Cetó, Xavier
del Valle, Manel
A Voltammetric Electronic Tongue for the Resolution of Ternary Nitrophenol Mixtures
title A Voltammetric Electronic Tongue for the Resolution of Ternary Nitrophenol Mixtures
title_full A Voltammetric Electronic Tongue for the Resolution of Ternary Nitrophenol Mixtures
title_fullStr A Voltammetric Electronic Tongue for the Resolution of Ternary Nitrophenol Mixtures
title_full_unstemmed A Voltammetric Electronic Tongue for the Resolution of Ternary Nitrophenol Mixtures
title_short A Voltammetric Electronic Tongue for the Resolution of Ternary Nitrophenol Mixtures
title_sort voltammetric electronic tongue for the resolution of ternary nitrophenol mixtures
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5795887/
https://www.ncbi.nlm.nih.gov/pubmed/29342848
http://dx.doi.org/10.3390/s18010216
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