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Ethanol quantification in pineapple waste by an electrochemical impedance spectroscopy-based system and artificial neural networks

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Ethanol quantification in pineapple waste by an electrochemical impedance spectroscopy-based system and artificial neural networks

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dc.contributor.author Conesa Domínguez, Claudia es_ES
dc.contributor.author Gil Sánchez, Luís es_ES
dc.contributor.author Seguí Gil, Lucía es_ES
dc.contributor.author Fito Maupoey, Pedro es_ES
dc.contributor.author Laguarda-Miro, Nicolas es_ES
dc.date.accessioned 2018-03-15T05:09:34Z
dc.date.available 2018-03-15T05:09:34Z
dc.date.issued 2017 es_ES
dc.identifier.issn 0169-7439 es_ES
dc.identifier.uri http://hdl.handle.net/10251/99365
dc.description.abstract [EN] Electrochemical impedance spectroscopy (EIS) technique has been applied to determine the ethanol concentration in pineapple waste samples. To do this, six different concentrations of ethanol were added to the pineapple samples and were analyzed using the system designed by our research group and consisting of the Advanced Voltammetry, Impedance Spectroscopy & Potentiometry Analyzer (AVISPA) device associated to a stainless steel double needle electrode. Results indicated that phase data in frequencies between 6.0 x 10(5) Hz and 8.0 x 10(5) Hz showed the highest sensitivity to ethanol concentrations. A principal component analysis (PCA) confirmed the potential discrimination and partial least squares (PLS) regression showed mathematical models able to quantify ethanol in samples accurately. In order to implement flexible and precise models in programmable equipment, different types of artificial neural networks (ANNs) have been studied: Fuzzy ARTMAP and multi-layer feed-forward (MLFF) algorithms. As a result, a coefficient of determination (R2) = 0.996 and a root mean square error of prediction (RMSEP) = 0.408 have been obtained. Therefore, it allows us to introduce this technique as an alternative method for ethanol quantification along the fermentation of pineapple waste in an easy, low-cost, rapid and portable way. es_ES
dc.description.sponsorship Financial support from the European FEDER and the Spanish government (MAT2012-34829-C04-04), the Generalitat Valenciana (PROMETEOII/2014/047) and the FPI-UPV Program funds are gratefully acknowledged. en_EN
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation MINECO/MAT2012-38429-C04-04 es_ES
dc.relation GENERALITAT VALENCIANA/PROMETEOII/2014/047 es_ES
dc.relation.ispartof Chemometrics and Intelligent Laboratory Systems es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Electrochemical impedance spectroscopy es_ES
dc.subject Ethanol es_ES
dc.subject Pineapple waste es_ES
dc.subject Artificial neural networks es_ES
dc.subject.classification INGENIERIA QUIMICA es_ES
dc.subject.classification TECNOLOGIA DE ALIMENTOS es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Ethanol quantification in pineapple waste by an electrochemical impedance spectroscopy-based system and artificial neural networks es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.chemolab.2016.12.005 es_ES
dc.rights.accessRights Abierto es_ES
dc.date.embargoEndDate 2019-02-15 es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Química y Nuclear - Departament d'Enginyeria Química i Nuclear es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Tecnología de Alimentos - Departament de Tecnologia d'Aliments es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica es_ES
dc.description.bibliographicCitation Conesa Domínguez, C.; Gil Sánchez, L.; Seguí Gil, L.; Fito Maupoey, P.; Laguarda-Miro, N. (2017). Ethanol quantification in pineapple waste by an electrochemical impedance spectroscopy-based system and artificial neural networks. Chemometrics and Intelligent Laboratory Systems. 161:1-7. doi:10.1016/j.chemolab.2016.12.005 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.chemolab.2016.12.005 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 7 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 161 es_ES
dc.relation.pasarela S\322101 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Ministerio de Economía, Industria y Competitividad es_ES


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