Automatic classification of water samples using an optimized SVM model applied to cyclic voltammetry signals.

Background: concern about the quality of the water for human consumption has become widespread among the population. The taste and some problems associated with drinking water have been the cause of increased demand for bottled water. Due to this, day to day, a large number of companies has manifes...

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Autores principales:	Hugo Italo Romero, Ivan RAMÍREZ-MORALES, Cinthia ROMERO FLORES
Formato:	article
Lenguaje:	EN
Publicado:	Universidad de Antioquia 2019
Materias:	Electronic tongue water quality authenticity machine learning voltammetry. Food processing and manufacture TP368-456 Pharmaceutical industry HD9665-9675
Acceso en línea:	https://doaj.org/article/b2a694fdd85f4a2bbde7922b0b2c2d3e
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spelling	oai:doaj.org-article:b2a694fdd85f4a2bbde7922b0b2c2d3e2021-12-02T17:56:04ZAutomatic classification of water samples using an optimized SVM model applied to cyclic voltammetry signals.10.17533/udea.vitae.v26n2a050121-40042145-2660https://doaj.org/article/b2a694fdd85f4a2bbde7922b0b2c2d3e2019-11-01T00:00:00Zhttps://revistas.udea.edu.co/index.php/vitae/article/view/336787https://doaj.org/toc/0121-4004https://doaj.org/toc/2145-2660 Background: concern about the quality of the water for human consumption has become widespread among the population. The taste and some problems associated with drinking water have been the cause of increased demand for bottled water. Due to this, day to day, a large number of companies has manifested their interest in the production of bottled water. Objective: to evaluate a novel automatic classification model that differentiates bottled water from tap water. Methods: the voltammetric technique consisted of three electrode setup. The output current has been considered for data analysis. From the results of grid search, six pairs of values were pre-selected for the parameters of σ and C whose results were similar. High values of accuracy, specificity and sensitivity were achieved in test dataset. The final decision was made after performing an ANOVA test of 100 repetitions of 5-fold cross-validation, 3000 models were evaluated with the parameter combinations described above for the SVM. Results: the oxidation and reduction peaks of the water samples have been observed to be prominent. Absolute values of current (I) increased in the case of public water samples, possibly due to the largest concentration of chloride ions which have higher contributions to the conductivity. 5-fold cross-validation test mean specificity resulted in C parameters values greater than 0 and between 0 and 30; a σ value greater than 10 and between 0 and 15 were found for tap water and bottled water, respectively. The combination (σ = 10, C = 30) presented best results in accuracy 0.988 ± 0.037, specificity 0.973 ± 0.085 and sensitivity 1 ± 0.09. Conclusions: results of this research work have shown that voltammograms for values of current increased for tap water samples, 9.94e-6μA, compared to 7.99e-6μA due to higher chloride ions concentration in the former. The parameters combination (σ = 10, C = 20) was selected as optimal parameters since there were no significant difference between this and the former. Hugo Italo RomeroIvan RAMÍREZ-MORALESCinthia ROMERO FLORESUniversidad de AntioquiaarticleElectronic tonguewater qualityauthenticitymachine learningvoltammetry.Food processing and manufactureTP368-456Pharmaceutical industryHD9665-9675ENVitae, Vol 26, Iss 2 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electronic tongue water quality authenticity machine learning voltammetry. Food processing and manufacture TP368-456 Pharmaceutical industry HD9665-9675
spellingShingle	Electronic tongue water quality authenticity machine learning voltammetry. Food processing and manufacture TP368-456 Pharmaceutical industry HD9665-9675 Hugo Italo Romero Ivan RAMÍREZ-MORALES Cinthia ROMERO FLORES Automatic classification of water samples using an optimized SVM model applied to cyclic voltammetry signals.
description	Background: concern about the quality of the water for human consumption has become widespread among the population. The taste and some problems associated with drinking water have been the cause of increased demand for bottled water. Due to this, day to day, a large number of companies has manifested their interest in the production of bottled water. Objective: to evaluate a novel automatic classification model that differentiates bottled water from tap water. Methods: the voltammetric technique consisted of three electrode setup. The output current has been considered for data analysis. From the results of grid search, six pairs of values were pre-selected for the parameters of σ and C whose results were similar. High values of accuracy, specificity and sensitivity were achieved in test dataset. The final decision was made after performing an ANOVA test of 100 repetitions of 5-fold cross-validation, 3000 models were evaluated with the parameter combinations described above for the SVM. Results: the oxidation and reduction peaks of the water samples have been observed to be prominent. Absolute values of current (I) increased in the case of public water samples, possibly due to the largest concentration of chloride ions which have higher contributions to the conductivity. 5-fold cross-validation test mean specificity resulted in C parameters values greater than 0 and between 0 and 30; a σ value greater than 10 and between 0 and 15 were found for tap water and bottled water, respectively. The combination (σ = 10, C = 30) presented best results in accuracy 0.988 ± 0.037, specificity 0.973 ± 0.085 and sensitivity 1 ± 0.09. Conclusions: results of this research work have shown that voltammograms for values of current increased for tap water samples, 9.94e-6μA, compared to 7.99e-6μA due to higher chloride ions concentration in the former. The parameters combination (σ = 10, C = 20) was selected as optimal parameters since there were no significant difference between this and the former.
format	article
author	Hugo Italo Romero Ivan RAMÍREZ-MORALES Cinthia ROMERO FLORES
author_facet	Hugo Italo Romero Ivan RAMÍREZ-MORALES Cinthia ROMERO FLORES
author_sort	Hugo Italo Romero
title	Automatic classification of water samples using an optimized SVM model applied to cyclic voltammetry signals.
title_short	Automatic classification of water samples using an optimized SVM model applied to cyclic voltammetry signals.
title_full	Automatic classification of water samples using an optimized SVM model applied to cyclic voltammetry signals.
title_fullStr	Automatic classification of water samples using an optimized SVM model applied to cyclic voltammetry signals.
title_full_unstemmed	Automatic classification of water samples using an optimized SVM model applied to cyclic voltammetry signals.
title_sort	automatic classification of water samples using an optimized svm model applied to cyclic voltammetry signals.
publisher	Universidad de Antioquia
publishDate	2019
url	https://doaj.org/article/b2a694fdd85f4a2bbde7922b0b2c2d3e
work_keys_str_mv	AT hugoitaloromero automaticclassificationofwatersamplesusinganoptimizedsvmmodelappliedtocyclicvoltammetrysignals AT ivanramirezmorales automaticclassificationofwatersamplesusinganoptimizedsvmmodelappliedtocyclicvoltammetrysignals AT cinthiaromeroflores automaticclassificationofwatersamplesusinganoptimizedsvmmodelappliedtocyclicvoltammetrysignals
_version_	1718379068277129216

Automatic classification of water samples using an optimized SVM model applied to cyclic voltammetry signals.

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