Classification of beer by thermogravimetric and chemometric techniques

Detalhes bibliográficos
Autor(a) principal: Fernandes, Richard Perosa [UNESP]
Data de Publicação: 2021
Outros Autores: Ekawa, Bruno [UNESP], Ferreira, Laura Teófilo [UNESP], Carvalho, Ana Carina Sobral [UNESP], Freire, Rafael Teixeira, Caires, Flávio Junior [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/s10973-021-10729-y
http://hdl.handle.net/11449/206119
Resumo: Thermogravimetric (TG) technique combined with partial least squares discriminant analysis (PLS-DA) was applied to distinguish five pilsner beers from different brands (Antarctica, Bohemia, Brahma, Budweiser and Imperio). Herein, we develop a TG methodology that use a small volume (40 µL), and an analysis time of 25 min. The major gases evolved during the thermal decomposition were water, ethanol and carbon dioxide. Energy-dispersive X-ray spectrometer of the ashes detected the major constituents: oxygen, sodium, magnesium, silicon, phosphorus, sulfur, chlorine, potassium and calcium. TG and PLS-DA technique together were able to classify the five brands with a classification rate for the model of 97% with a confidence interval of 92–99%, achieving high sensitivity and specificity between the calibration, cross-validation and predicted results.
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spelling Classification of beer by thermogravimetric and chemometric techniquesBeerChemometricsThermal analysisThermogravimetric (TG) technique combined with partial least squares discriminant analysis (PLS-DA) was applied to distinguish five pilsner beers from different brands (Antarctica, Bohemia, Brahma, Budweiser and Imperio). Herein, we develop a TG methodology that use a small volume (40 µL), and an analysis time of 25 min. The major gases evolved during the thermal decomposition were water, ethanol and carbon dioxide. Energy-dispersive X-ray spectrometer of the ashes detected the major constituents: oxygen, sodium, magnesium, silicon, phosphorus, sulfur, chlorine, potassium and calcium. TG and PLS-DA technique together were able to classify the five brands with a classification rate for the model of 97% with a confidence interval of 92–99%, achieving high sensitivity and specificity between the calibration, cross-validation and predicted results.São Paulo State University (UNESP) Institute of ChemistryInstitute for Bioengineering of Catalonia Signal and Information Processing for Sensing SystemsSão Paulo State University (UNESP) School of ScienceSão Paulo State University (UNESP) Institute of ChemistrySão Paulo State University (UNESP) School of ScienceUniversidade Estadual Paulista (Unesp)Signal and Information Processing for Sensing SystemsFernandes, Richard Perosa [UNESP]Ekawa, Bruno [UNESP]Ferreira, Laura Teófilo [UNESP]Carvalho, Ana Carina Sobral [UNESP]Freire, Rafael TeixeiraCaires, Flávio Junior [UNESP]2021-06-25T10:26:53Z2021-06-25T10:26:53Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1007/s10973-021-10729-yJournal of Thermal Analysis and Calorimetry.1588-29261388-6150http://hdl.handle.net/11449/20611910.1007/s10973-021-10729-y2-s2.0-85103356385Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of Thermal Analysis and Calorimetryinfo:eu-repo/semantics/openAccess2021-10-22T21:03:08Zoai:repositorio.unesp.br:11449/206119Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:59:07.069608Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Classification of beer by thermogravimetric and chemometric techniques
title Classification of beer by thermogravimetric and chemometric techniques
spellingShingle Classification of beer by thermogravimetric and chemometric techniques
Fernandes, Richard Perosa [UNESP]
Beer
Chemometrics
Thermal analysis
title_short Classification of beer by thermogravimetric and chemometric techniques
title_full Classification of beer by thermogravimetric and chemometric techniques
title_fullStr Classification of beer by thermogravimetric and chemometric techniques
title_full_unstemmed Classification of beer by thermogravimetric and chemometric techniques
title_sort Classification of beer by thermogravimetric and chemometric techniques
author Fernandes, Richard Perosa [UNESP]
author_facet Fernandes, Richard Perosa [UNESP]
Ekawa, Bruno [UNESP]
Ferreira, Laura Teófilo [UNESP]
Carvalho, Ana Carina Sobral [UNESP]
Freire, Rafael Teixeira
Caires, Flávio Junior [UNESP]
author_role author
author2 Ekawa, Bruno [UNESP]
Ferreira, Laura Teófilo [UNESP]
Carvalho, Ana Carina Sobral [UNESP]
Freire, Rafael Teixeira
Caires, Flávio Junior [UNESP]
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Signal and Information Processing for Sensing Systems
dc.contributor.author.fl_str_mv Fernandes, Richard Perosa [UNESP]
Ekawa, Bruno [UNESP]
Ferreira, Laura Teófilo [UNESP]
Carvalho, Ana Carina Sobral [UNESP]
Freire, Rafael Teixeira
Caires, Flávio Junior [UNESP]
dc.subject.por.fl_str_mv Beer
Chemometrics
Thermal analysis
topic Beer
Chemometrics
Thermal analysis
description Thermogravimetric (TG) technique combined with partial least squares discriminant analysis (PLS-DA) was applied to distinguish five pilsner beers from different brands (Antarctica, Bohemia, Brahma, Budweiser and Imperio). Herein, we develop a TG methodology that use a small volume (40 µL), and an analysis time of 25 min. The major gases evolved during the thermal decomposition were water, ethanol and carbon dioxide. Energy-dispersive X-ray spectrometer of the ashes detected the major constituents: oxygen, sodium, magnesium, silicon, phosphorus, sulfur, chlorine, potassium and calcium. TG and PLS-DA technique together were able to classify the five brands with a classification rate for the model of 97% with a confidence interval of 92–99%, achieving high sensitivity and specificity between the calibration, cross-validation and predicted results.
publishDate 2021
dc.date.none.fl_str_mv 2021-06-25T10:26:53Z
2021-06-25T10:26:53Z
2021-01-01
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/s10973-021-10729-y
Journal of Thermal Analysis and Calorimetry.
1588-2926
1388-6150
http://hdl.handle.net/11449/206119
10.1007/s10973-021-10729-y
2-s2.0-85103356385
url http://dx.doi.org/10.1007/s10973-021-10729-y
http://hdl.handle.net/11449/206119
identifier_str_mv Journal of Thermal Analysis and Calorimetry.
1588-2926
1388-6150
10.1007/s10973-021-10729-y
2-s2.0-85103356385
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Journal of Thermal Analysis and Calorimetry
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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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