Classification of beer by thermogravimetric and chemometric techniques
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , |
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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Repositório Institucional da UNESP |
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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 |
|
_version_ |
1808128881523163136 |