Discriminant analysis for unveiling the origin of roasted coffee samples: A tool for quality control of coffee related products
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , |
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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Repositório Institucional da UNESP |
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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 |
|
_version_ |
1808129006081409024 |