Reliable discriminant analysis tool for controlling the roast degree of coffee samples through chemical markers approach

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], Pezza, Leonardo [UNESP], Toci, Aline T., Silva, Carlos M.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/s00217-016-2790-1
http://hdl.handle.net/11449/179118
Resumo: Roasting is one of the most influencing stages of coffee processing. Accordingly, a discriminant analysis (DA) was carried out with the objective of identifying key compounds (chemical markers) that enable a differentiation of coffee samples according to their roasting degree. For this, chromatographic data of the volatile fraction of 21 coffee samples submitted to distinct roasting treatments (Light, Medium, Dark, and French Roasts) were employed. Using three discriminant functions that rely on only ten chemical markers, it was possible to explain 100 % of the variance of the data points. If two functions are used, the surprisingly high value of 99.4 % is achieved. The model was cross-validated, and the main function successfully passed a permutation test using two statistical indicators. It was found that half of the markers belong to the pyrazines family, known to grant sensorial notes related to roasted hazelnut and peanuts. In the whole, this essay demonstrates the usefulness of DA as a tool to control the quality of roasting treatment of coffee and can be further extended with advantage to the eight roasting degrees of the AGTRON Roasting Classification as soon as larger databases become available.
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spelling Reliable discriminant analysis tool for controlling the roast degree of coffee samples through chemical markers approachChemical markersCoffee qualityDiscriminant analysisRoastingVolatiles compositionRoasting is one of the most influencing stages of coffee processing. Accordingly, a discriminant analysis (DA) was carried out with the objective of identifying key compounds (chemical markers) that enable a differentiation of coffee samples according to their roasting degree. For this, chromatographic data of the volatile fraction of 21 coffee samples submitted to distinct roasting treatments (Light, Medium, Dark, and French Roasts) were employed. Using three discriminant functions that rely on only ten chemical markers, it was possible to explain 100 % of the variance of the data points. If two functions are used, the surprisingly high value of 99.4 % is achieved. The model was cross-validated, and the main function successfully passed a permutation test using two statistical indicators. It was found that half of the markers belong to the pyrazines family, known to grant sensorial notes related to roasted hazelnut and peanuts. In the whole, this essay demonstrates the usefulness of DA as a tool to control the quality of roasting treatment of coffee and can be further extended with advantage to the eight roasting degrees of the AGTRON Roasting Classification as soon as larger databases become available.Institute 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 – UNESPUniversidade 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]Pezza, Leonardo [UNESP]Toci, Aline T.Silva, Carlos M.2018-12-11T17:33:47Z2018-12-11T17:33:47Z2017-05-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article761-768application/pdfhttp://dx.doi.org/10.1007/s00217-016-2790-1European Food Research and Technology, v. 243, n. 5, p. 761-768, 2017.1438-23851438-2377http://hdl.handle.net/11449/17911810.1007/s00217-016-2790-12-s2.0-850282377652-s2.0-85028237765.pdf5978908591853524Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengEuropean Food Research and Technology0,7370,737info:eu-repo/semantics/openAccess2023-11-16T06:11:25Zoai:repositorio.unesp.br:11449/179118Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:50:58.921007Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Reliable discriminant analysis tool for controlling the roast degree of coffee samples through chemical markers approach
title Reliable discriminant analysis tool for controlling the roast degree of coffee samples through chemical markers approach
spellingShingle Reliable discriminant analysis tool for controlling the roast degree of coffee samples through chemical markers approach
de Toledo, Paulo R. A. B. [UNESP]
Chemical markers
Coffee quality
Discriminant analysis
Roasting
Volatiles composition
title_short Reliable discriminant analysis tool for controlling the roast degree of coffee samples through chemical markers approach
title_full Reliable discriminant analysis tool for controlling the roast degree of coffee samples through chemical markers approach
title_fullStr Reliable discriminant analysis tool for controlling the roast degree of coffee samples through chemical markers approach
title_full_unstemmed Reliable discriminant analysis tool for controlling the roast degree of coffee samples through chemical markers approach
title_sort Reliable discriminant analysis tool for controlling the roast degree of coffee samples through chemical markers approach
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]
Pezza, Leonardo [UNESP]
Toci, Aline T.
Silva, Carlos M.
author_role author
author2 de Melo, Marcelo M. R.
Pezza, Helena R. [UNESP]
Pezza, Leonardo [UNESP]
Toci, Aline T.
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]
Pezza, Leonardo [UNESP]
Toci, Aline T.
Silva, Carlos M.
dc.subject.por.fl_str_mv Chemical markers
Coffee quality
Discriminant analysis
Roasting
Volatiles composition
topic Chemical markers
Coffee quality
Discriminant analysis
Roasting
Volatiles composition
description Roasting is one of the most influencing stages of coffee processing. Accordingly, a discriminant analysis (DA) was carried out with the objective of identifying key compounds (chemical markers) that enable a differentiation of coffee samples according to their roasting degree. For this, chromatographic data of the volatile fraction of 21 coffee samples submitted to distinct roasting treatments (Light, Medium, Dark, and French Roasts) were employed. Using three discriminant functions that rely on only ten chemical markers, it was possible to explain 100 % of the variance of the data points. If two functions are used, the surprisingly high value of 99.4 % is achieved. The model was cross-validated, and the main function successfully passed a permutation test using two statistical indicators. It was found that half of the markers belong to the pyrazines family, known to grant sensorial notes related to roasted hazelnut and peanuts. In the whole, this essay demonstrates the usefulness of DA as a tool to control the quality of roasting treatment of coffee and can be further extended with advantage to the eight roasting degrees of the AGTRON Roasting Classification as soon as larger databases become available.
publishDate 2017
dc.date.none.fl_str_mv 2017-05-01
2018-12-11T17:33:47Z
2018-12-11T17:33:47Z
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/10.1007/s00217-016-2790-1
European Food Research and Technology, v. 243, n. 5, p. 761-768, 2017.
1438-2385
1438-2377
http://hdl.handle.net/11449/179118
10.1007/s00217-016-2790-1
2-s2.0-85028237765
2-s2.0-85028237765.pdf
5978908591853524
url http://dx.doi.org/10.1007/s00217-016-2790-1
http://hdl.handle.net/11449/179118
identifier_str_mv European Food Research and Technology, v. 243, n. 5, p. 761-768, 2017.
1438-2385
1438-2377
10.1007/s00217-016-2790-1
2-s2.0-85028237765
2-s2.0-85028237765.pdf
5978908591853524
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv European Food Research and Technology
0,737
0,737
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 761-768
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_ 1808128866680569856

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