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; Vazquez, Raimundo Damian; Benitez, Elisa Ines
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. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Departamento de Ingeniería Química. Laboratorio de Química Teórica y Experimental; Argentina
Fil: Bianchi, María Agostina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina
Fil: Farco, Andrea Paola. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina
Fil: Vazquez, Raimundo Damian. Universidad Tecnológica Nacional. Facultad Reg. Resistencia; Argentina
Fil: Benitez, Elisa Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina
Materia
PHYSICOCHEMICAL ATTRIBUTES
BEER
PREDICTIVE ANALYSIS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/239655
Acceder
id CONICETDig_e4ae98b8cecceffa9893357cc95a9443
oai_identifier_str oai:ri.conicet.gov.ar:11336/239655
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Improving craft beer style classification through physicochemical determination and the application of deep learning techniquesGómez Pamies, Laura CeciliaBianchi, María AgostinaFarco, Andrea PaolaVazquez, Raimundo DamianBenitez, Elisa InesPHYSICOCHEMICAL ATTRIBUTESBEERPREDICTIVE ANALYSIShttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2The 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. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Departamento de Ingeniería Química. Laboratorio de Química Teórica y Experimental; ArgentinaFil: Bianchi, María Agostina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; ArgentinaFil: Farco, Andrea Paola. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; ArgentinaFil: Vazquez, Raimundo Damian. Universidad Tecnológica Nacional. Facultad Reg. Resistencia; ArgentinaFil: Benitez, Elisa Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; ArgentinaSociedade Brasileira de Ciência e Tecnologia de Alimentos2024-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/239655Gómez Pamies, Laura Cecilia; Bianchi, María Agostina; Farco, Andrea Paola; Vazquez, Raimundo Damian; Benitez, Elisa Ines; Improving craft beer style classification through physicochemical determination and the application of deep learning techniques; Sociedade Brasileira de Ciência e Tecnologia de Alimentos; Ciência e Tecnologia de Alimentos; 44; 4-2024; 1-70101-20611678-457XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://fstjournal.com.br/revista/article/view/71info:eu-repo/semantics/altIdentifier/doi/10.5327/fst.00071info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:56:34Zoai:ri.conicet.gov.ar:11336/239655instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:56:34.883CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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
Vazquez, Raimundo Damian
Benitez, Elisa Ines
author Gómez Pamies, Laura Cecilia
author_facet Gómez Pamies, Laura Cecilia
Bianchi, María Agostina
Farco, Andrea Paola
Vazquez, Raimundo Damian
Benitez, Elisa Ines
author_role author
author2 Bianchi, María Agostina
Farco, Andrea Paola
Vazquez, Raimundo Damian
Benitez, Elisa Ines
author2_role author
author
author
author
dc.subject.none.fl_str_mv PHYSICOCHEMICAL ATTRIBUTES
BEER
PREDICTIVE ANALYSIS
topic PHYSICOCHEMICAL ATTRIBUTES
BEER
PREDICTIVE ANALYSIS
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
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. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional. Facultad Regional Resistencia. Departamento de Ingeniería Química. Laboratorio de Química Teórica y Experimental; Argentina
Fil: Bianchi, María Agostina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina
Fil: Farco, Andrea Paola. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina
Fil: Vazquez, Raimundo Damian. Universidad Tecnológica Nacional. Facultad Reg. Resistencia; Argentina
Fil: Benitez, Elisa Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina
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
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 http://hdl.handle.net/11336/239655
Gómez Pamies, Laura Cecilia; Bianchi, María Agostina; Farco, Andrea Paola; Vazquez, Raimundo Damian; Benitez, Elisa Ines; Improving craft beer style classification through physicochemical determination and the application of deep learning techniques; Sociedade Brasileira de Ciência e Tecnologia de Alimentos; Ciência e Tecnologia de Alimentos; 44; 4-2024; 1-7
0101-2061
1678-457X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/239655
identifier_str_mv Gómez Pamies, Laura Cecilia; Bianchi, María Agostina; Farco, Andrea Paola; Vazquez, Raimundo Damian; Benitez, Elisa Ines; Improving craft beer style classification through physicochemical determination and the application of deep learning techniques; Sociedade Brasileira de Ciência e Tecnologia de Alimentos; Ciência e Tecnologia de Alimentos; 44; 4-2024; 1-7
0101-2061
1678-457X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://fstjournal.com.br/revista/article/view/71
info:eu-repo/semantics/altIdentifier/doi/10.5327/fst.00071
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Sociedade Brasileira de Ciência e Tecnologia de Alimentos
publisher.none.fl_str_mv Sociedade Brasileira de Ciência e Tecnologia de Alimentos
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846782273122205696
score 12.982451

Publicaciones similares