Discriminant analysis for unveiling the origin of roasted coffee samples: A tool for quality control of coffee related products

Detalhes bibliográficos
Autor(a) principal: de Toledo, Paulo R.A.B. [UNESP]
Data de Publicação: 2017
Outros Autores: de Melo, Marcelo M.R., Pezza, Helena R. [UNESP], Toci, Aline T., Pezza, Leonardo [UNESP], Silva, Carlos M.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.foodcont.2016.08.001
http://hdl.handle.net/11449/178411
Resumo: Coffee quality is highly dependent on geographical factors. Based on the chemical characterization of 25 coffee samples from worldwide provenances and same roasting degree, Discriminant Analysis (DA) was employed to develop models that are able to identify the continental or country (Brazil) provenance of blind coffee samples. These models are based on coffee composition, particularly on several key compounds either with or without significant impact on aroma, such as 2,3-butanedione, 2,3-pentanedione, 2-methylbutanal and 2-ethyl-6-methylpyrazine. All models were validated with new and independent data from literature, and also through cross validation and permutation tests. Furthermore, the robustness of the proposed models in case of incomplete characterization data was also tested, being concluded that missing data is supportable by the models. In the whole, this article provides compelling arguments for the development of DA-based tools with the purpose of controlling the quality of coffee in terms of their continental and/or national origins.
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spelling Discriminant analysis for unveiling the origin of roasted coffee samples: A tool for quality control of coffee related productsChemical markersCoffee qualityDiscriminant analysisGeographic originVolatiles compositionCoffee quality is highly dependent on geographical factors. Based on the chemical characterization of 25 coffee samples from worldwide provenances and same roasting degree, Discriminant Analysis (DA) was employed to develop models that are able to identify the continental or country (Brazil) provenance of blind coffee samples. These models are based on coffee composition, particularly on several key compounds either with or without significant impact on aroma, such as 2,3-butanedione, 2,3-pentanedione, 2-methylbutanal and 2-ethyl-6-methylpyrazine. All models were validated with new and independent data from literature, and also through cross validation and permutation tests. Furthermore, the robustness of the proposed models in case of incomplete characterization data was also tested, being concluded that missing data is supportable by the models. In the whole, this article provides compelling arguments for the development of DA-based tools with the purpose of controlling the quality of coffee in terms of their continental and/or national origins.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Centro de Investigação em Materiais Cerâmicos e CompósitosConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Federación Española de Enfermedades RarasInstitute of Chemistry State University of São Paulo – UNESPCICECO – Aveiro Institute of Materials Department of Chemistry University of AveiroLatin American Institute of Science of Life and Nature Federal University of Latin American Integration – UNILAInstitute of Chemistry State University of São Paulo – UNESPCentro de Investigação em Materiais Cerâmicos e Compósitos: POCI-01-0145-FEDER-007679Universidade Estadual Paulista (Unesp)University of AveiroFederal University of Latin American Integration – UNILAde Toledo, Paulo R.A.B. [UNESP]de Melo, Marcelo M.R.Pezza, Helena R. [UNESP]Toci, Aline T.Pezza, Leonardo [UNESP]Silva, Carlos M.2018-12-11T17:30:10Z2018-12-11T17:30:10Z2017-03-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article164-174application/pdfhttp://dx.doi.org/10.1016/j.foodcont.2016.08.001Food Control, v. 73, p. 164-174.0956-7135http://hdl.handle.net/11449/17841110.1016/j.foodcont.2016.08.0012-s2.0-849955287452-s2.0-84995528745.pdf5978908591853524Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengFood Control1,502info:eu-repo/semantics/openAccess2023-11-28T06:17:51Zoai:repositorio.unesp.br:11449/178411Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:58:39.563489Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Discriminant analysis for unveiling the origin of roasted coffee samples: A tool for quality control of coffee related products
title Discriminant analysis for unveiling the origin of roasted coffee samples: A tool for quality control of coffee related products
spellingShingle Discriminant analysis for unveiling the origin of roasted coffee samples: A tool for quality control of coffee related products
de Toledo, Paulo R.A.B. [UNESP]
Chemical markers
Coffee quality
Discriminant analysis
Geographic origin
Volatiles composition
title_short Discriminant analysis for unveiling the origin of roasted coffee samples: A tool for quality control of coffee related products
title_full Discriminant analysis for unveiling the origin of roasted coffee samples: A tool for quality control of coffee related products
title_fullStr Discriminant analysis for unveiling the origin of roasted coffee samples: A tool for quality control of coffee related products
title_full_unstemmed Discriminant analysis for unveiling the origin of roasted coffee samples: A tool for quality control of coffee related products
title_sort Discriminant analysis for unveiling the origin of roasted coffee samples: A tool for quality control of coffee related products
author de Toledo, Paulo R.A.B. [UNESP]
author_facet de Toledo, Paulo R.A.B. [UNESP]
de Melo, Marcelo M.R.
Pezza, Helena R. [UNESP]
Toci, Aline T.
Pezza, Leonardo [UNESP]
Silva, Carlos M.
author_role author
author2 de Melo, Marcelo M.R.
Pezza, Helena R. [UNESP]
Toci, Aline T.
Pezza, Leonardo [UNESP]
Silva, Carlos M.
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
University of Aveiro
Federal University of Latin American Integration – UNILA
dc.contributor.author.fl_str_mv de Toledo, Paulo R.A.B. [UNESP]
de Melo, Marcelo M.R.
Pezza, Helena R. [UNESP]
Toci, Aline T.
Pezza, Leonardo [UNESP]
Silva, Carlos M.
dc.subject.por.fl_str_mv Chemical markers
Coffee quality
Discriminant analysis
Geographic origin
Volatiles composition
topic Chemical markers
Coffee quality
Discriminant analysis
Geographic origin
Volatiles composition
description Coffee quality is highly dependent on geographical factors. Based on the chemical characterization of 25 coffee samples from worldwide provenances and same roasting degree, Discriminant Analysis (DA) was employed to develop models that are able to identify the continental or country (Brazil) provenance of blind coffee samples. These models are based on coffee composition, particularly on several key compounds either with or without significant impact on aroma, such as 2,3-butanedione, 2,3-pentanedione, 2-methylbutanal and 2-ethyl-6-methylpyrazine. All models were validated with new and independent data from literature, and also through cross validation and permutation tests. Furthermore, the robustness of the proposed models in case of incomplete characterization data was also tested, being concluded that missing data is supportable by the models. In the whole, this article provides compelling arguments for the development of DA-based tools with the purpose of controlling the quality of coffee in terms of their continental and/or national origins.
publishDate 2017
dc.date.none.fl_str_mv 2017-03-01
2018-12-11T17:30:10Z
2018-12-11T17:30:10Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1016/j.foodcont.2016.08.001
Food Control, v. 73, p. 164-174.
0956-7135
http://hdl.handle.net/11449/178411
10.1016/j.foodcont.2016.08.001
2-s2.0-84995528745
2-s2.0-84995528745.pdf
5978908591853524
url http://dx.doi.org/10.1016/j.foodcont.2016.08.001
http://hdl.handle.net/11449/178411
identifier_str_mv Food Control, v. 73, p. 164-174.
0956-7135
10.1016/j.foodcont.2016.08.001
2-s2.0-84995528745
2-s2.0-84995528745.pdf
5978908591853524
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Food Control
1,502
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 164-174
application/pdf
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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