Predicting Harbertson-Adams Assay Phenolic Parameters In Red Wines Using Visible Spectra

Autores
Catania, Aníbal; Catania, Carlos; Sari, Santiago; Fanzone, Martín
Año de publicación
2020
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The Harbertson-Adams phenolic parameter assay is a well- known method to measure a panel of phenolic compounds in red wines. However, the multistep analyses required by the method fail at producing results on multiple parameters rapidly. In the present article, we analyze the bene ts of applying a statistical model based on Principal Component Analysis (PCA) and a statistical learning technique denoted as Support Vector Regression Machines (SVR) for correlating sample spectra data to the Harbertson-Adams assay, on each of the phenolics components. The resulting model showed a high correlation between the measured and predicted values for each of the phenolic parameters despite the multicollinearity and high dimensions of the dataset.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Phenolic components
Wine making
Statistical learning
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/115420
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/115420
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Predicting Harbertson-Adams Assay Phenolic Parameters In Red Wines Using Visible SpectraCatania, AníbalCatania, CarlosSari, SantiagoFanzone, MartínCiencias InformáticasPhenolic componentsWine makingStatistical learningThe Harbertson-Adams phenolic parameter assay is a well- known method to measure a panel of phenolic compounds in red wines. However, the multistep analyses required by the method fail at producing results on multiple parameters rapidly. In the present article, we analyze the bene ts of applying a statistical model based on Principal Component Analysis (PCA) and a statistical learning technique denoted as Support Vector Regression Machines (SVR) for correlating sample spectra data to the Harbertson-Adams assay, on each of the phenolics components. The resulting model showed a high correlation between the measured and predicted values for each of the phenolic parameters despite the multicollinearity and high dimensions of the dataset.Sociedad Argentina de Informática e Investigación Operativa2020-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf98-101http://sedici.unlp.edu.ar/handle/10915/115420enginfo:eu-repo/semantics/altIdentifier/url/http://49jaiio.sadio.org.ar/pdfs/cai/CAI_14.pdfinfo:eu-repo/semantics/altIdentifier/issn/2525-0949info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/3.0/Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:26:50Zoai:sedici.unlp.edu.ar:10915/115420Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:26:50.853SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Predicting Harbertson-Adams Assay Phenolic Parameters In Red Wines Using Visible Spectra
title Predicting Harbertson-Adams Assay Phenolic Parameters In Red Wines Using Visible Spectra
spellingShingle Predicting Harbertson-Adams Assay Phenolic Parameters In Red Wines Using Visible Spectra
Catania, Aníbal
Ciencias Informáticas
Phenolic components
Wine making
Statistical learning
title_short Predicting Harbertson-Adams Assay Phenolic Parameters In Red Wines Using Visible Spectra
title_full Predicting Harbertson-Adams Assay Phenolic Parameters In Red Wines Using Visible Spectra
title_fullStr Predicting Harbertson-Adams Assay Phenolic Parameters In Red Wines Using Visible Spectra
title_full_unstemmed Predicting Harbertson-Adams Assay Phenolic Parameters In Red Wines Using Visible Spectra
title_sort Predicting Harbertson-Adams Assay Phenolic Parameters In Red Wines Using Visible Spectra
dc.creator.none.fl_str_mv Catania, Aníbal
Catania, Carlos
Sari, Santiago
Fanzone, Martín
author Catania, Aníbal
author_facet Catania, Aníbal
Catania, Carlos
Sari, Santiago
Fanzone, Martín
author_role author
author2 Catania, Carlos
Sari, Santiago
Fanzone, Martín
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Phenolic components
Wine making
Statistical learning
topic Ciencias Informáticas
Phenolic components
Wine making
Statistical learning
dc.description.none.fl_txt_mv The Harbertson-Adams phenolic parameter assay is a well- known method to measure a panel of phenolic compounds in red wines. However, the multistep analyses required by the method fail at producing results on multiple parameters rapidly. In the present article, we analyze the bene ts of applying a statistical model based on Principal Component Analysis (PCA) and a statistical learning technique denoted as Support Vector Regression Machines (SVR) for correlating sample spectra data to the Harbertson-Adams assay, on each of the phenolics components. The resulting model showed a high correlation between the measured and predicted values for each of the phenolic parameters despite the multicollinearity and high dimensions of the dataset.
Sociedad Argentina de Informática e Investigación Operativa
description The Harbertson-Adams phenolic parameter assay is a well- known method to measure a panel of phenolic compounds in red wines. However, the multistep analyses required by the method fail at producing results on multiple parameters rapidly. In the present article, we analyze the bene ts of applying a statistical model based on Principal Component Analysis (PCA) and a statistical learning technique denoted as Support Vector Regression Machines (SVR) for correlating sample spectra data to the Harbertson-Adams assay, on each of the phenolics components. The resulting model showed a high correlation between the measured and predicted values for each of the phenolic parameters despite the multicollinearity and high dimensions of the dataset.
publishDate 2020
dc.date.none.fl_str_mv 2020-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/115420
url http://sedici.unlp.edu.ar/handle/10915/115420
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://49jaiio.sadio.org.ar/pdfs/cai/CAI_14.pdf
info:eu-repo/semantics/altIdentifier/issn/2525-0949
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/3.0/
Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/3.0/
Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
dc.format.none.fl_str_mv application/pdf
98-101
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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