Modeling excitation–emission fluorescence matrices with pattern recognition algorithms for classification of Argentine white wines according grape variety

Autores
Azcarate, Silvana Mariela; de Araújo Gomes, Adriano; Alcaraz, Mirta Raquel; Ugulino de Araújo, Mário C.; Camiña, José Manuel; Goicoechea, Hector Casimiro
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper reports the modeling of excitation-emission matrices for classification of Argentinean white wines according to the grape variety employing chemometric tools for pattern recognition. The discriminative power of the data was first investigated using Principal Component Analysis (PCA) and Parallel Factor Analysis (PARAFAC). The score plots showed strong overlapping between classes. A forty-one samples set was partitioned into training and test sets by the Kennard-Stone algorithm. The algorithms evaluated were SIMCA, N- and U-PLS-DA and SPA-LDA. The fit of the implemented models was assessed by mean of accuracy, sensitivity and specificity. These models were then used to assign the type of grape of the wines corresponding to the twenty samples test set. The best results were obtained for U-PLS-DA and SPA-LDA with 76% and 80% accuracy.
Fil: Azcarate, Silvana Mariela. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; Argentina
Fil: de Araújo Gomes, Adriano. Universidade Federal da Paraíba; Brasil
Fil: Alcaraz, Mirta Raquel. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Ugulino de Araújo, Mário C.. Universidade Federal da Paraíba; Brasil
Fil: Camiña, José Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; Argentina
Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Materia
White Wine
Excitation-Emission Matrices
Simca
U-Pls-Da
N-Pls-Da
Spa-Lda
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/46313
Acceder
id CONICETDig_3f7e5ab774579e36ef345af40a646635
oai_identifier_str oai:ri.conicet.gov.ar:11336/46313
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Modeling excitation–emission fluorescence matrices with pattern recognition algorithms for classification of Argentine white wines according grape varietyAzcarate, Silvana Marielade Araújo Gomes, AdrianoAlcaraz, Mirta RaquelUgulino de Araújo, Mário C.Camiña, José ManuelGoicoechea, Hector CasimiroWhite WineExcitation-Emission MatricesSimcaU-Pls-DaN-Pls-DaSpa-Ldahttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1This paper reports the modeling of excitation-emission matrices for classification of Argentinean white wines according to the grape variety employing chemometric tools for pattern recognition. The discriminative power of the data was first investigated using Principal Component Analysis (PCA) and Parallel Factor Analysis (PARAFAC). The score plots showed strong overlapping between classes. A forty-one samples set was partitioned into training and test sets by the Kennard-Stone algorithm. The algorithms evaluated were SIMCA, N- and U-PLS-DA and SPA-LDA. The fit of the implemented models was assessed by mean of accuracy, sensitivity and specificity. These models were then used to assign the type of grape of the wines corresponding to the twenty samples test set. The best results were obtained for U-PLS-DA and SPA-LDA with 76% and 80% accuracy.Fil: Azcarate, Silvana Mariela. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; ArgentinaFil: de Araújo Gomes, Adriano. Universidade Federal da Paraíba; BrasilFil: Alcaraz, Mirta Raquel. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Ugulino de Araújo, Mário C.. Universidade Federal da Paraíba; BrasilFil: Camiña, José Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; ArgentinaFil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaElsevier2015-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/46313Azcarate, Silvana Mariela; de Araújo Gomes, Adriano; Alcaraz, Mirta Raquel; Ugulino de Araújo, Mário C.; Camiña, José Manuel; et al.; Modeling excitation–emission fluorescence matrices with pattern recognition algorithms for classification of Argentine white wines according grape variety; Elsevier; Food Chemistry; 184; 10-2015; 214-2190308-8146CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.foodchem.2015.03.081info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0308814615004537info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:51:46Zoai:ri.conicet.gov.ar:11336/46313instacron: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:34982025-09-03 09:51:46.71CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Modeling excitation–emission fluorescence matrices with pattern recognition algorithms for classification of Argentine white wines according grape variety
title Modeling excitation–emission fluorescence matrices with pattern recognition algorithms for classification of Argentine white wines according grape variety
spellingShingle Modeling excitation–emission fluorescence matrices with pattern recognition algorithms for classification of Argentine white wines according grape variety
Azcarate, Silvana Mariela
White Wine
Excitation-Emission Matrices
Simca
U-Pls-Da
N-Pls-Da
Spa-Lda
title_short Modeling excitation–emission fluorescence matrices with pattern recognition algorithms for classification of Argentine white wines according grape variety
title_full Modeling excitation–emission fluorescence matrices with pattern recognition algorithms for classification of Argentine white wines according grape variety
title_fullStr Modeling excitation–emission fluorescence matrices with pattern recognition algorithms for classification of Argentine white wines according grape variety
title_full_unstemmed Modeling excitation–emission fluorescence matrices with pattern recognition algorithms for classification of Argentine white wines according grape variety
title_sort Modeling excitation–emission fluorescence matrices with pattern recognition algorithms for classification of Argentine white wines according grape variety
dc.creator.none.fl_str_mv Azcarate, Silvana Mariela
de Araújo Gomes, Adriano
Alcaraz, Mirta Raquel
Ugulino de Araújo, Mário C.
Camiña, José Manuel
Goicoechea, Hector Casimiro
author Azcarate, Silvana Mariela
author_facet Azcarate, Silvana Mariela
de Araújo Gomes, Adriano
Alcaraz, Mirta Raquel
Ugulino de Araújo, Mário C.
Camiña, José Manuel
Goicoechea, Hector Casimiro
author_role author
author2 de Araújo Gomes, Adriano
Alcaraz, Mirta Raquel
Ugulino de Araújo, Mário C.
Camiña, José Manuel
Goicoechea, Hector Casimiro
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv White Wine
Excitation-Emission Matrices
Simca
U-Pls-Da
N-Pls-Da
Spa-Lda
topic White Wine
Excitation-Emission Matrices
Simca
U-Pls-Da
N-Pls-Da
Spa-Lda
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv This paper reports the modeling of excitation-emission matrices for classification of Argentinean white wines according to the grape variety employing chemometric tools for pattern recognition. The discriminative power of the data was first investigated using Principal Component Analysis (PCA) and Parallel Factor Analysis (PARAFAC). The score plots showed strong overlapping between classes. A forty-one samples set was partitioned into training and test sets by the Kennard-Stone algorithm. The algorithms evaluated were SIMCA, N- and U-PLS-DA and SPA-LDA. The fit of the implemented models was assessed by mean of accuracy, sensitivity and specificity. These models were then used to assign the type of grape of the wines corresponding to the twenty samples test set. The best results were obtained for U-PLS-DA and SPA-LDA with 76% and 80% accuracy.
Fil: Azcarate, Silvana Mariela. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; Argentina
Fil: de Araújo Gomes, Adriano. Universidade Federal da Paraíba; Brasil
Fil: Alcaraz, Mirta Raquel. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Ugulino de Araújo, Mário C.. Universidade Federal da Paraíba; Brasil
Fil: Camiña, José Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; Argentina
Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
description This paper reports the modeling of excitation-emission matrices for classification of Argentinean white wines according to the grape variety employing chemometric tools for pattern recognition. The discriminative power of the data was first investigated using Principal Component Analysis (PCA) and Parallel Factor Analysis (PARAFAC). The score plots showed strong overlapping between classes. A forty-one samples set was partitioned into training and test sets by the Kennard-Stone algorithm. The algorithms evaluated were SIMCA, N- and U-PLS-DA and SPA-LDA. The fit of the implemented models was assessed by mean of accuracy, sensitivity and specificity. These models were then used to assign the type of grape of the wines corresponding to the twenty samples test set. The best results were obtained for U-PLS-DA and SPA-LDA with 76% and 80% accuracy.
publishDate 2015
dc.date.none.fl_str_mv 2015-10
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/11336/46313
Azcarate, Silvana Mariela; de Araújo Gomes, Adriano; Alcaraz, Mirta Raquel; Ugulino de Araújo, Mário C.; Camiña, José Manuel; et al.; Modeling excitation–emission fluorescence matrices with pattern recognition algorithms for classification of Argentine white wines according grape variety; Elsevier; Food Chemistry; 184; 10-2015; 214-219
0308-8146
CONICET Digital
CONICET
url http://hdl.handle.net/11336/46313
identifier_str_mv Azcarate, Silvana Mariela; de Araújo Gomes, Adriano; Alcaraz, Mirta Raquel; Ugulino de Araújo, Mário C.; Camiña, José Manuel; et al.; Modeling excitation–emission fluorescence matrices with pattern recognition algorithms for classification of Argentine white wines according grape variety; Elsevier; Food Chemistry; 184; 10-2015; 214-219
0308-8146
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.foodchem.2015.03.081
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0308814615004537
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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_ 1842269116249931776
score 13.13397

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