Proposal of a metric selection index for correspondence analysis: an application in the sensory evaluation of coffee blends
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Semina. Ciências Agrárias (Online) |
Texto Completo: | https://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/36659 |
Resumo: | Correspondence analysis is a multivariate dimensionality-reduction technique applied to data structured into contingency tables. The main outcome of this approach is the generation of perceptual maps aimed at the study of similarity between categorical levels. In most cases, interpretations of these similarities present subjectivity when different metrics are considered; e.g., Hellinger distance and Chi-square. Thus, in an attempt to minimize this subjectivity, the present study proposes an index that quantifies the shortest distance between those levels. A simulation study was undertaken in which the generated maps were discussed in relation to real data involving the similarity of blends formed by coffees of different species, with sensory evaluations considering the flavor and acidity attributes. In conclusion, the proposed index—named metric selection index (MSI)—made it possible to include a statistic that justifies the most suitable metric for correspondence analysis, thus preventing subjectivity in interpretations of similarities between blend types and grade classes. In the simulation studies, with the metric proposed by Hellinger distance, MSI showed stabler results regarding total inertia distribution on the first two axes. |
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Semina. Ciências Agrárias (Online) |
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Proposal of a metric selection index for correspondence analysis: an application in the sensory evaluation of coffee blendsProposta de um índice de seleção de métrica para análise de correspondência: uma aplicação na avaliação sensorial de blends de cafésC. arabicaC. canephoraHellingerSimulation.C. arábicaC. canéforaHellingerSimulação.Correspondence analysis is a multivariate dimensionality-reduction technique applied to data structured into contingency tables. The main outcome of this approach is the generation of perceptual maps aimed at the study of similarity between categorical levels. In most cases, interpretations of these similarities present subjectivity when different metrics are considered; e.g., Hellinger distance and Chi-square. Thus, in an attempt to minimize this subjectivity, the present study proposes an index that quantifies the shortest distance between those levels. A simulation study was undertaken in which the generated maps were discussed in relation to real data involving the similarity of blends formed by coffees of different species, with sensory evaluations considering the flavor and acidity attributes. In conclusion, the proposed index—named metric selection index (MSI)—made it possible to include a statistic that justifies the most suitable metric for correspondence analysis, thus preventing subjectivity in interpretations of similarities between blend types and grade classes. In the simulation studies, with the metric proposed by Hellinger distance, MSI showed stabler results regarding total inertia distribution on the first two axes.A análise de correspondência é uma técnica multivariada de redução de dimensionalidade aplicada a dados estruturados em tabelas de contingência. Como principal resultado, mapas perceptuais são gerados com o propósito de estudar a similaridade entre os níveis categóricos. Na maioria das vezes, as interpretações dessas similaridades apresentam certa subjetividade, ao considerar diferentes métricas, como por exemplo, a distância de Hellinger e Qui-quadrado. Assim, com o intuito de minimizar essa subjetividade, esse trabalho teve como objetivo propor um índice que quantifique a menor distância entre esses níveis. Foi realizado um estudo de simulação, discutindo-se os mapas gerados em relação a dados reais envolvendo a similaridade de blends formados por cafés de diferentes espécies com avaliações sensoriais considerando os atributos sabor e acidez. Concluiu-se que a proposta do índice, denominado índice de seleção de métrica (ISM), permitiu agregar uma estatística que justifique a métrica mais adequada na análise de correspondência, evitando a subjetividade nas interpretações das similaridades entre os tipos de blends e classe de notas. Em relação aos estudos de simulação a métrica proposta pela distância de Hellinger, o ISM apresentou resultados mais estáveis em relação à distribuição da inércia total nos dois primeiros eixos.UEL2020-03-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/3665910.5433/1679-0359.2020v41n2p479Semina: Ciências Agrárias; Vol. 41 No. 2 (2020); 479-492Semina: Ciências Agrárias; v. 41 n. 2 (2020); 479-4921679-03591676-546Xreponame:Semina. Ciências Agrárias (Online)instname:Universidade Estadual de Londrina (UEL)instacron:UELenghttps://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/36659/26865Copyright (c) 2020 Semina: Ciências Agráriashttp://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccessCosta, Adilson Silva daResende, MarianaNakano, Eduardo YoshioCirillo, Marcelo AngeloBorém, Flávio MeiraRibeiro, Diego Egídio2022-10-10T13:32:06Zoai:ojs.pkp.sfu.ca:article/36659Revistahttp://www.uel.br/revistas/uel/index.php/semagrariasPUBhttps://ojs.uel.br/revistas/uel/index.php/semagrarias/oaisemina.agrarias@uel.br1679-03591676-546Xopendoar:2022-10-10T13:32:06Semina. Ciências Agrárias (Online) - Universidade Estadual de Londrina (UEL)false |
dc.title.none.fl_str_mv |
Proposal of a metric selection index for correspondence analysis: an application in the sensory evaluation of coffee blends Proposta de um índice de seleção de métrica para análise de correspondência: uma aplicação na avaliação sensorial de blends de cafés |
title |
Proposal of a metric selection index for correspondence analysis: an application in the sensory evaluation of coffee blends |
spellingShingle |
Proposal of a metric selection index for correspondence analysis: an application in the sensory evaluation of coffee blends Costa, Adilson Silva da C. arabica C. canephora Hellinger Simulation. C. arábica C. canéfora Hellinger Simulação. |
title_short |
Proposal of a metric selection index for correspondence analysis: an application in the sensory evaluation of coffee blends |
title_full |
Proposal of a metric selection index for correspondence analysis: an application in the sensory evaluation of coffee blends |
title_fullStr |
Proposal of a metric selection index for correspondence analysis: an application in the sensory evaluation of coffee blends |
title_full_unstemmed |
Proposal of a metric selection index for correspondence analysis: an application in the sensory evaluation of coffee blends |
title_sort |
Proposal of a metric selection index for correspondence analysis: an application in the sensory evaluation of coffee blends |
author |
Costa, Adilson Silva da |
author_facet |
Costa, Adilson Silva da Resende, Mariana Nakano, Eduardo Yoshio Cirillo, Marcelo Angelo Borém, Flávio Meira Ribeiro, Diego Egídio |
author_role |
author |
author2 |
Resende, Mariana Nakano, Eduardo Yoshio Cirillo, Marcelo Angelo Borém, Flávio Meira Ribeiro, Diego Egídio |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Costa, Adilson Silva da Resende, Mariana Nakano, Eduardo Yoshio Cirillo, Marcelo Angelo Borém, Flávio Meira Ribeiro, Diego Egídio |
dc.subject.por.fl_str_mv |
C. arabica C. canephora Hellinger Simulation. C. arábica C. canéfora Hellinger Simulação. |
topic |
C. arabica C. canephora Hellinger Simulation. C. arábica C. canéfora Hellinger Simulação. |
description |
Correspondence analysis is a multivariate dimensionality-reduction technique applied to data structured into contingency tables. The main outcome of this approach is the generation of perceptual maps aimed at the study of similarity between categorical levels. In most cases, interpretations of these similarities present subjectivity when different metrics are considered; e.g., Hellinger distance and Chi-square. Thus, in an attempt to minimize this subjectivity, the present study proposes an index that quantifies the shortest distance between those levels. A simulation study was undertaken in which the generated maps were discussed in relation to real data involving the similarity of blends formed by coffees of different species, with sensory evaluations considering the flavor and acidity attributes. In conclusion, the proposed index—named metric selection index (MSI)—made it possible to include a statistic that justifies the most suitable metric for correspondence analysis, thus preventing subjectivity in interpretations of similarities between blend types and grade classes. In the simulation studies, with the metric proposed by Hellinger distance, MSI showed stabler results regarding total inertia distribution on the first two axes. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-03-06 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/36659 10.5433/1679-0359.2020v41n2p479 |
url |
https://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/36659 |
identifier_str_mv |
10.5433/1679-0359.2020v41n2p479 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://ojs.uel.br/revistas/uel/index.php/semagrarias/article/view/36659/26865 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2020 Semina: Ciências Agrárias http://creativecommons.org/licenses/by-nc/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2020 Semina: Ciências Agrárias http://creativecommons.org/licenses/by-nc/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
UEL |
publisher.none.fl_str_mv |
UEL |
dc.source.none.fl_str_mv |
Semina: Ciências Agrárias; Vol. 41 No. 2 (2020); 479-492 Semina: Ciências Agrárias; v. 41 n. 2 (2020); 479-492 1679-0359 1676-546X reponame:Semina. Ciências Agrárias (Online) instname:Universidade Estadual de Londrina (UEL) instacron:UEL |
instname_str |
Universidade Estadual de Londrina (UEL) |
instacron_str |
UEL |
institution |
UEL |
reponame_str |
Semina. Ciências Agrárias (Online) |
collection |
Semina. Ciências Agrárias (Online) |
repository.name.fl_str_mv |
Semina. Ciências Agrárias (Online) - Universidade Estadual de Londrina (UEL) |
repository.mail.fl_str_mv |
semina.agrarias@uel.br |
_version_ |
1799306081600536576 |