The assessment of the quality of sugar using electronic tongue and machine learning algorithms
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/ICMLA.2012.98 http://hdl.handle.net/11449/73810 |
Resumo: | The correct classification of sugar according to its physico-chemical characteristics directly influences the value of the product and its acceptance by the market. This study shows that using an electronic tongue system along with established techniques of supervised learning leads to the correct classification of sugar samples according to their qualities. In this paper, we offer two new real, public and non-encoded sugar datasets whose attributes were automatically collected using an electronic tongue, with and without pH controlling. Moreover, we compare the performance achieved by several established machine learning methods. Our experiments were diligently designed to ensure statistically sound results and they indicate that k-nearest neighbors method outperforms other evaluated classifiers and, hence, it can be used as a good baseline for further comparison. © 2012 IEEE. |
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The assessment of the quality of sugar using electronic tongue and machine learning algorithmsclassificationelectronic tonguemachine learningsugarK-nearest neighbors methodMachine learning methodsPhysicochemical characteristicsClassification (of information)Electronic tonguesLearning systemsSugarsLearning algorithmsThe correct classification of sugar according to its physico-chemical characteristics directly influences the value of the product and its acceptance by the market. This study shows that using an electronic tongue system along with established techniques of supervised learning leads to the correct classification of sugar samples according to their qualities. In this paper, we offer two new real, public and non-encoded sugar datasets whose attributes were automatically collected using an electronic tongue, with and without pH controlling. Moreover, we compare the performance achieved by several established machine learning methods. Our experiments were diligently designed to ensure statistically sound results and they indicate that k-nearest neighbors method outperforms other evaluated classifiers and, hence, it can be used as a good baseline for further comparison. © 2012 IEEE.Federal University of São Carlos UFSCar, 18052-780, SorocabaDepartment of Physics, Chemistry and Biology São Paulo State University-Unesp, 19060-900, Presidente PrudenteDepartment of Physics, Chemistry and Biology São Paulo State University-Unesp, 19060-900, Presidente PrudenteUniversidade Federal de São Carlos (UFSCar)Universidade Estadual Paulista (Unesp)Sakata, Tiemi C.Faceli, KattiAlmeida, Tiago A.Júnior, Antonio RiulSteluti, Wanessa M. D. M. F. [UNESP]2014-05-27T11:27:17Z2014-05-27T11:27:17Z2012-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject538-541http://dx.doi.org/10.1109/ICMLA.2012.98Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012, v. 1, p. 538-541.http://hdl.handle.net/11449/7381010.1109/ICMLA.2012.982-s2.0-84873595462Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012info:eu-repo/semantics/openAccess2024-06-18T18:18:37Zoai:repositorio.unesp.br:11449/73810Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:14:46.312920Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
The assessment of the quality of sugar using electronic tongue and machine learning algorithms |
title |
The assessment of the quality of sugar using electronic tongue and machine learning algorithms |
spellingShingle |
The assessment of the quality of sugar using electronic tongue and machine learning algorithms Sakata, Tiemi C. classification electronic tongue machine learning sugar K-nearest neighbors method Machine learning methods Physicochemical characteristics Classification (of information) Electronic tongues Learning systems Sugars Learning algorithms |
title_short |
The assessment of the quality of sugar using electronic tongue and machine learning algorithms |
title_full |
The assessment of the quality of sugar using electronic tongue and machine learning algorithms |
title_fullStr |
The assessment of the quality of sugar using electronic tongue and machine learning algorithms |
title_full_unstemmed |
The assessment of the quality of sugar using electronic tongue and machine learning algorithms |
title_sort |
The assessment of the quality of sugar using electronic tongue and machine learning algorithms |
author |
Sakata, Tiemi C. |
author_facet |
Sakata, Tiemi C. Faceli, Katti Almeida, Tiago A. Júnior, Antonio Riul Steluti, Wanessa M. D. M. F. [UNESP] |
author_role |
author |
author2 |
Faceli, Katti Almeida, Tiago A. Júnior, Antonio Riul Steluti, Wanessa M. D. M. F. [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de São Carlos (UFSCar) Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Sakata, Tiemi C. Faceli, Katti Almeida, Tiago A. Júnior, Antonio Riul Steluti, Wanessa M. D. M. F. [UNESP] |
dc.subject.por.fl_str_mv |
classification electronic tongue machine learning sugar K-nearest neighbors method Machine learning methods Physicochemical characteristics Classification (of information) Electronic tongues Learning systems Sugars Learning algorithms |
topic |
classification electronic tongue machine learning sugar K-nearest neighbors method Machine learning methods Physicochemical characteristics Classification (of information) Electronic tongues Learning systems Sugars Learning algorithms |
description |
The correct classification of sugar according to its physico-chemical characteristics directly influences the value of the product and its acceptance by the market. This study shows that using an electronic tongue system along with established techniques of supervised learning leads to the correct classification of sugar samples according to their qualities. In this paper, we offer two new real, public and non-encoded sugar datasets whose attributes were automatically collected using an electronic tongue, with and without pH controlling. Moreover, we compare the performance achieved by several established machine learning methods. Our experiments were diligently designed to ensure statistically sound results and they indicate that k-nearest neighbors method outperforms other evaluated classifiers and, hence, it can be used as a good baseline for further comparison. © 2012 IEEE. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-12-01 2014-05-27T11:27:17Z 2014-05-27T11:27:17Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/ICMLA.2012.98 Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012, v. 1, p. 538-541. http://hdl.handle.net/11449/73810 10.1109/ICMLA.2012.98 2-s2.0-84873595462 |
url |
http://dx.doi.org/10.1109/ICMLA.2012.98 http://hdl.handle.net/11449/73810 |
identifier_str_mv |
Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012, v. 1, p. 538-541. 10.1109/ICMLA.2012.98 2-s2.0-84873595462 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
538-541 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
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1808129040507207680 |