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Voltammetric Electronic Tongue and Support Vector Machines for Identification of Selected Features in Mexican Coffee

This paper describes a new method based on a voltammetric electronic tongue (ET) for the recognition of distinctive features in coffee samples. An ET was directly applied to different samples from the main Mexican coffee regions without any pretreatment before the analysis. The resulting electrochem...

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Autores principales: Domínguez, Rocio Berenice, Moreno-Barón, Laura, Muñoz, Roberto, Gutiérrez, Juan Manuel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208248/
https://www.ncbi.nlm.nih.gov/pubmed/25254303
http://dx.doi.org/10.3390/s140917770
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author Domínguez, Rocio Berenice
Moreno-Barón, Laura
Muñoz, Roberto
Gutiérrez, Juan Manuel
author_facet Domínguez, Rocio Berenice
Moreno-Barón, Laura
Muñoz, Roberto
Gutiérrez, Juan Manuel
author_sort Domínguez, Rocio Berenice
collection PubMed
description This paper describes a new method based on a voltammetric electronic tongue (ET) for the recognition of distinctive features in coffee samples. An ET was directly applied to different samples from the main Mexican coffee regions without any pretreatment before the analysis. The resulting electrochemical information was modeled with two different mathematical tools, namely Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). Growing conditions (i.e., organic or non-organic practices and altitude of crops) were considered for a first classification. LDA results showed an average discrimination rate of 88% ± 6.53% while SVM successfully accomplished an overall accuracy of 96.4% ± 3.50% for the same task. A second classification based on geographical origin of samples was carried out. Results showed an overall accuracy of 87.5% ± 7.79% for LDA and a superior performance of 97.5% ± 3.22% for SVM. Given the complexity of coffee samples, the high accuracy percentages achieved by ET coupled with SVM in both classification problems suggested a potential applicability of ET in the assessment of selected coffee features with a simpler and faster methodology along with a null sample pretreatment. In addition, the proposed method can be applied to authentication assessment while improving cost, time and accuracy of the general procedure.
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spelling pubmed-42082482014-10-24 Voltammetric Electronic Tongue and Support Vector Machines for Identification of Selected Features in Mexican Coffee Domínguez, Rocio Berenice Moreno-Barón, Laura Muñoz, Roberto Gutiérrez, Juan Manuel Sensors (Basel) Article This paper describes a new method based on a voltammetric electronic tongue (ET) for the recognition of distinctive features in coffee samples. An ET was directly applied to different samples from the main Mexican coffee regions without any pretreatment before the analysis. The resulting electrochemical information was modeled with two different mathematical tools, namely Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). Growing conditions (i.e., organic or non-organic practices and altitude of crops) were considered for a first classification. LDA results showed an average discrimination rate of 88% ± 6.53% while SVM successfully accomplished an overall accuracy of 96.4% ± 3.50% for the same task. A second classification based on geographical origin of samples was carried out. Results showed an overall accuracy of 87.5% ± 7.79% for LDA and a superior performance of 97.5% ± 3.22% for SVM. Given the complexity of coffee samples, the high accuracy percentages achieved by ET coupled with SVM in both classification problems suggested a potential applicability of ET in the assessment of selected coffee features with a simpler and faster methodology along with a null sample pretreatment. In addition, the proposed method can be applied to authentication assessment while improving cost, time and accuracy of the general procedure. MDPI 2014-09-24 /pmc/articles/PMC4208248/ /pubmed/25254303 http://dx.doi.org/10.3390/s140917770 Text en © 2014 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 license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Domínguez, Rocio Berenice
Moreno-Barón, Laura
Muñoz, Roberto
Gutiérrez, Juan Manuel
Voltammetric Electronic Tongue and Support Vector Machines for Identification of Selected Features in Mexican Coffee
title Voltammetric Electronic Tongue and Support Vector Machines for Identification of Selected Features in Mexican Coffee
title_full Voltammetric Electronic Tongue and Support Vector Machines for Identification of Selected Features in Mexican Coffee
title_fullStr Voltammetric Electronic Tongue and Support Vector Machines for Identification of Selected Features in Mexican Coffee
title_full_unstemmed Voltammetric Electronic Tongue and Support Vector Machines for Identification of Selected Features in Mexican Coffee
title_short Voltammetric Electronic Tongue and Support Vector Machines for Identification of Selected Features in Mexican Coffee
title_sort voltammetric electronic tongue and support vector machines for identification of selected features in mexican coffee
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208248/
https://www.ncbi.nlm.nih.gov/pubmed/25254303
http://dx.doi.org/10.3390/s140917770
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