Improving craft beer style classification through physicochemical determination and the application of deep learning techniques

Autores
Gómez Pamies, Laura Cecilia; Bianchi, María Agostina; Farco, Andrea Paola; Vázquez, Raimundo; Benítez, Elisa Inés
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The consumption of craft beer at fairs and festivals is a phenomenon that keeps growing in the world. For this reason, it is important to control the quality characteristics of the different styles. This study aimed to analyze the different styles of beer, classify them according to their physicochemical parameters, and propose a predictive pattern-based model known as deep learning that best defines the styles that are presented at festivals. Physicochemical analyses of final gravity, color, alcohol, bitterness, and α-acids were carried out on eight styles of beer. The first four parameters are those that characterize the styles according to the Beer Judge Certification Program style guide. The incorporation of the α-acid determination allowed a more realistic classification that considers the brewers’ new tendencies. This study will lay the foundations to improve local recipes, implement standardization, and provide training to local brewers
Fil: Gómez Pamies, Laura Cecilia. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Centro de Química e Ingeniería Teórica y Experimental; Argentina. Fil: Gómez Pamies, Laura Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina.
Fil: Bianchi, María Agostina. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Centro de Química e Ingeniería Teórica y Experimental; Argentina. Fil: Bianchi, María Agostina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina.
Fil: Farco, Andrea Paola. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina
Fil: Vázquez, Raimundo. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo Universitario de Automatización; Argentina.
Fil: Benítez, Elisa Inés. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Centro de Química e Ingeniería Teórica y Experimental; Argentina. Fil: Benítez, Elisa Inés. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina
Peer Reviewed
Materia
physicochemical attributes
beer
predictive analysis
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2024-04-09T12:18:55Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/10414
Acceder
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network_acronym_str RIAUTN
repository_id_str a
network_name_str Repositorio Institucional Abierto (UTN)
spelling Improving craft beer style classification through physicochemical determination and the application of deep learning techniquesGómez Pamies, Laura CeciliaBianchi, María AgostinaFarco, Andrea PaolaVázquez, RaimundoBenítez, Elisa Inésphysicochemical attributesbeerpredictive analysisThe consumption of craft beer at fairs and festivals is a phenomenon that keeps growing in the world. For this reason, it is important to control the quality characteristics of the different styles. This study aimed to analyze the different styles of beer, classify them according to their physicochemical parameters, and propose a predictive pattern-based model known as deep learning that best defines the styles that are presented at festivals. Physicochemical analyses of final gravity, color, alcohol, bitterness, and α-acids were carried out on eight styles of beer. The first four parameters are those that characterize the styles according to the Beer Judge Certification Program style guide. The incorporation of the α-acid determination allowed a more realistic classification that considers the brewers’ new tendencies. This study will lay the foundations to improve local recipes, implement standardization, and provide training to local brewersFil: Gómez Pamies, Laura Cecilia. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Centro de Química e Ingeniería Teórica y Experimental; Argentina. Fil: Gómez Pamies, Laura Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina.Fil: Bianchi, María Agostina. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Centro de Química e Ingeniería Teórica y Experimental; Argentina. Fil: Bianchi, María Agostina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina.Fil: Farco, Andrea Paola. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Química Básica y Aplicada del Nordeste Argentino; ArgentinaFil: Vázquez, Raimundo. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo Universitario de Automatización; Argentina.Fil: Benítez, Elisa Inés. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Centro de Química e Ingeniería Teórica y Experimental; Argentina. Fil: Benítez, Elisa Inés. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Química Básica y Aplicada del Nordeste Argentino; ArgentinaPeer Reviewed2024-04-09T12:18:55Z2024-04-09T12:18:55Z2024-04-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloplainapplication/pdf0101-20611678-457X (online)http://hdl.handle.net/20.500.12272/10414https://doi.org/10.5327/fst.00071engengPATCARE0008193TCDesarrollo de indicadores de calidad higiénica en plantas elaboradoras de cerveza artesanalinfo:eu-repo/semantics/openAccess2024-04-09T12:18:55Zhttp://creativecommons.org/licenses/by-nc-sa/4.0/Atribución-NoComercial-CompartirIgual 4.0 InternacionalAcceso abiertoreponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2026-10-01T12:00:01Zoai:ria.utn.edu.ar:20.500.12272/10414instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-10-01 12:00:01.675Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Improving craft beer style classification through physicochemical determination and the application of deep learning techniques
title Improving craft beer style classification through physicochemical determination and the application of deep learning techniques
spellingShingle Improving craft beer style classification through physicochemical determination and the application of deep learning techniques
Gómez Pamies, Laura Cecilia
physicochemical attributes
beer
predictive analysis
title_short Improving craft beer style classification through physicochemical determination and the application of deep learning techniques
title_full Improving craft beer style classification through physicochemical determination and the application of deep learning techniques
title_fullStr Improving craft beer style classification through physicochemical determination and the application of deep learning techniques
title_full_unstemmed Improving craft beer style classification through physicochemical determination and the application of deep learning techniques
title_sort Improving craft beer style classification through physicochemical determination and the application of deep learning techniques
dc.creator.none.fl_str_mv Gómez Pamies, Laura Cecilia
Bianchi, María Agostina
Farco, Andrea Paola
Vázquez, Raimundo
Benítez, Elisa Inés
author Gómez Pamies, Laura Cecilia
author_facet Gómez Pamies, Laura Cecilia
Bianchi, María Agostina
Farco, Andrea Paola
Vázquez, Raimundo
Benítez, Elisa Inés
author_role author
author2 Bianchi, María Agostina
Farco, Andrea Paola
Vázquez, Raimundo
Benítez, Elisa Inés
author2_role author
author
author
author
dc.subject.none.fl_str_mv physicochemical attributes
beer
predictive analysis
topic physicochemical attributes
beer
predictive analysis
dc.description.none.fl_txt_mv The consumption of craft beer at fairs and festivals is a phenomenon that keeps growing in the world. For this reason, it is important to control the quality characteristics of the different styles. This study aimed to analyze the different styles of beer, classify them according to their physicochemical parameters, and propose a predictive pattern-based model known as deep learning that best defines the styles that are presented at festivals. Physicochemical analyses of final gravity, color, alcohol, bitterness, and α-acids were carried out on eight styles of beer. The first four parameters are those that characterize the styles according to the Beer Judge Certification Program style guide. The incorporation of the α-acid determination allowed a more realistic classification that considers the brewers’ new tendencies. This study will lay the foundations to improve local recipes, implement standardization, and provide training to local brewers
Fil: Gómez Pamies, Laura Cecilia. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Centro de Química e Ingeniería Teórica y Experimental; Argentina. Fil: Gómez Pamies, Laura Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina.
Fil: Bianchi, María Agostina. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Centro de Química e Ingeniería Teórica y Experimental; Argentina. Fil: Bianchi, María Agostina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina.
Fil: Farco, Andrea Paola. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina
Fil: Vázquez, Raimundo. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Grupo Universitario de Automatización; Argentina.
Fil: Benítez, Elisa Inés. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Centro de Química e Ingeniería Teórica y Experimental; Argentina. Fil: Benítez, Elisa Inés. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina
Peer Reviewed
description The consumption of craft beer at fairs and festivals is a phenomenon that keeps growing in the world. For this reason, it is important to control the quality characteristics of the different styles. This study aimed to analyze the different styles of beer, classify them according to their physicochemical parameters, and propose a predictive pattern-based model known as deep learning that best defines the styles that are presented at festivals. Physicochemical analyses of final gravity, color, alcohol, bitterness, and α-acids were carried out on eight styles of beer. The first four parameters are those that characterize the styles according to the Beer Judge Certification Program style guide. The incorporation of the α-acid determination allowed a more realistic classification that considers the brewers’ new tendencies. This study will lay the foundations to improve local recipes, implement standardization, and provide training to local brewers
publishDate 2024
dc.date.none.fl_str_mv 2024-04-09T12:18:55Z
2024-04-09T12:18:55Z
2024-04-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv 0101-2061
1678-457X (online)
http://hdl.handle.net/20.500.12272/10414
https://doi.org/10.5327/fst.00071
identifier_str_mv 0101-2061
1678-457X (online)
url http://hdl.handle.net/20.500.12272/10414
https://doi.org/10.5327/fst.00071
dc.language.none.fl_str_mv eng
eng
language eng
dc.relation.none.fl_str_mv PATCARE0008193TC
Desarrollo de indicadores de calidad higiénica en plantas elaboradoras de cerveza artesanal
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2024-04-09T12:18:55Z
http://creativecommons.org/licenses/by-nc-sa/4.0/
Atribución-NoComercial-CompartirIgual 4.0 Internacional
Acceso abierto
eu_rights_str_mv openAccess
rights_invalid_str_mv 2024-04-09T12:18:55Z
http://creativecommons.org/licenses/by-nc-sa/4.0/
Atribución-NoComercial-CompartirIgual 4.0 Internacional
Acceso abierto
dc.format.none.fl_str_mv plain
application/pdf
dc.source.none.fl_str_mv reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
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score 13.365483

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