Multivariate statistical methods for Port Salut Argentino cheese analysis based on ripening time, storage conditions, and sampling sites

Autores
Verdini, Roxana Andrea; Zorrilla, Susana; Rubiolo, Amelia Catalina; Nakai, S.
Año de publicación
2007
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The objective of the present work was to compare multivariate statistical methods for the classification of Port Salut Argentino cheese samples based on ripening time (1, 6, 13, 27, and 56 days), storage conditions (traditionally ripened and ripened after frozen storage) and sampling sites (internal and external zones) using the contents of caseins, peptides and amino acids measured by chromatographic analysis as well as textural and physical parameters. In particular, two linear methods, principal component analysis (PCA) and principal component similarity (PCS), and a nonlinear method, the Kohonen self-organizing artificial neural network (Kohonen ANN), were compared. The two linear methods showed the same grouping of cheese samples according to ripening time, sampling site and storage condition. These methods are closely related in their mathematical basis and the similar grouping showed by both methods can be explained by the fact that the first three principal components explained 89.3% of the data set variation. The non-linear Kohonen ANN uses a mathematical procedure completely different from PCA; however, only slight differences were observed in the grouping of cheese samples. Those differences may be related to the weight that each model gives to every variable. One interesting feature of Kohonen ANN is that weight maps (contour plots) sometimes are superior to principal component loadings (vectors) for the understanding of relationships between the groups and the original variables.
Fil: Verdini, Roxana Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina. Universidad Nacional de Rosario; Argentina
Fil: Zorrilla, Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Rubiolo, Amelia Catalina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Nakai, S.. University of British Columbia; Canadá
Materia
Multivariate Analysis
Neural Networks
Cheese Ripening
Freezing
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:11336/26143
Acceder
id CONICETDig_4352f1192fc0f40f509ea42c736dcdb9
oai_identifier_str oai:ri.conicet.gov.ar:11336/26143
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Multivariate statistical methods for Port Salut Argentino cheese analysis based on ripening time, storage conditions, and sampling sitesVerdini, Roxana AndreaZorrilla, SusanaRubiolo, Amelia CatalinaNakai, S.Multivariate AnalysisNeural NetworksCheese RipeningFreezinghttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2The objective of the present work was to compare multivariate statistical methods for the classification of Port Salut Argentino cheese samples based on ripening time (1, 6, 13, 27, and 56 days), storage conditions (traditionally ripened and ripened after frozen storage) and sampling sites (internal and external zones) using the contents of caseins, peptides and amino acids measured by chromatographic analysis as well as textural and physical parameters. In particular, two linear methods, principal component analysis (PCA) and principal component similarity (PCS), and a nonlinear method, the Kohonen self-organizing artificial neural network (Kohonen ANN), were compared. The two linear methods showed the same grouping of cheese samples according to ripening time, sampling site and storage condition. These methods are closely related in their mathematical basis and the similar grouping showed by both methods can be explained by the fact that the first three principal components explained 89.3% of the data set variation. The non-linear Kohonen ANN uses a mathematical procedure completely different from PCA; however, only slight differences were observed in the grouping of cheese samples. Those differences may be related to the weight that each model gives to every variable. One interesting feature of Kohonen ANN is that weight maps (contour plots) sometimes are superior to principal component loadings (vectors) for the understanding of relationships between the groups and the original variables.Fil: Verdini, Roxana Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina. Universidad Nacional de Rosario; ArgentinaFil: Zorrilla, Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Rubiolo, Amelia Catalina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Nakai, S.. University of British Columbia; CanadáElsevier Science2007-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/26143Verdini, Roxana Andrea; Zorrilla, Susana; Rubiolo, Amelia Catalina; Nakai, S.; Multivariate statistical methods for Port Salut Argentino cheese analysis based on ripening time, storage conditions, and sampling sites; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 86; 1; 12-2007; 60-670169-7439CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0169743906001663info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2006.08.006info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:51:31Zoai:ri.conicet.gov.ar:11336/26143instacron: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 09:51:32.238CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Multivariate statistical methods for Port Salut Argentino cheese analysis based on ripening time, storage conditions, and sampling sites
title Multivariate statistical methods for Port Salut Argentino cheese analysis based on ripening time, storage conditions, and sampling sites
spellingShingle Multivariate statistical methods for Port Salut Argentino cheese analysis based on ripening time, storage conditions, and sampling sites
Verdini, Roxana Andrea
Multivariate Analysis
Neural Networks
Cheese Ripening
Freezing
title_short Multivariate statistical methods for Port Salut Argentino cheese analysis based on ripening time, storage conditions, and sampling sites
title_full Multivariate statistical methods for Port Salut Argentino cheese analysis based on ripening time, storage conditions, and sampling sites
title_fullStr Multivariate statistical methods for Port Salut Argentino cheese analysis based on ripening time, storage conditions, and sampling sites
title_full_unstemmed Multivariate statistical methods for Port Salut Argentino cheese analysis based on ripening time, storage conditions, and sampling sites
title_sort Multivariate statistical methods for Port Salut Argentino cheese analysis based on ripening time, storage conditions, and sampling sites
dc.creator.none.fl_str_mv Verdini, Roxana Andrea
Zorrilla, Susana
Rubiolo, Amelia Catalina
Nakai, S.
author Verdini, Roxana Andrea
author_facet Verdini, Roxana Andrea
Zorrilla, Susana
Rubiolo, Amelia Catalina
Nakai, S.
author_role author
author2 Zorrilla, Susana
Rubiolo, Amelia Catalina
Nakai, S.
author2_role author
author
author
dc.subject.none.fl_str_mv Multivariate Analysis
Neural Networks
Cheese Ripening
Freezing
topic Multivariate Analysis
Neural Networks
Cheese Ripening
Freezing
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 objective of the present work was to compare multivariate statistical methods for the classification of Port Salut Argentino cheese samples based on ripening time (1, 6, 13, 27, and 56 days), storage conditions (traditionally ripened and ripened after frozen storage) and sampling sites (internal and external zones) using the contents of caseins, peptides and amino acids measured by chromatographic analysis as well as textural and physical parameters. In particular, two linear methods, principal component analysis (PCA) and principal component similarity (PCS), and a nonlinear method, the Kohonen self-organizing artificial neural network (Kohonen ANN), were compared. The two linear methods showed the same grouping of cheese samples according to ripening time, sampling site and storage condition. These methods are closely related in their mathematical basis and the similar grouping showed by both methods can be explained by the fact that the first three principal components explained 89.3% of the data set variation. The non-linear Kohonen ANN uses a mathematical procedure completely different from PCA; however, only slight differences were observed in the grouping of cheese samples. Those differences may be related to the weight that each model gives to every variable. One interesting feature of Kohonen ANN is that weight maps (contour plots) sometimes are superior to principal component loadings (vectors) for the understanding of relationships between the groups and the original variables.
Fil: Verdini, Roxana Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina. Universidad Nacional de Rosario; Argentina
Fil: Zorrilla, Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Rubiolo, Amelia Catalina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Nakai, S.. University of British Columbia; Canadá
description The objective of the present work was to compare multivariate statistical methods for the classification of Port Salut Argentino cheese samples based on ripening time (1, 6, 13, 27, and 56 days), storage conditions (traditionally ripened and ripened after frozen storage) and sampling sites (internal and external zones) using the contents of caseins, peptides and amino acids measured by chromatographic analysis as well as textural and physical parameters. In particular, two linear methods, principal component analysis (PCA) and principal component similarity (PCS), and a nonlinear method, the Kohonen self-organizing artificial neural network (Kohonen ANN), were compared. The two linear methods showed the same grouping of cheese samples according to ripening time, sampling site and storage condition. These methods are closely related in their mathematical basis and the similar grouping showed by both methods can be explained by the fact that the first three principal components explained 89.3% of the data set variation. The non-linear Kohonen ANN uses a mathematical procedure completely different from PCA; however, only slight differences were observed in the grouping of cheese samples. Those differences may be related to the weight that each model gives to every variable. One interesting feature of Kohonen ANN is that weight maps (contour plots) sometimes are superior to principal component loadings (vectors) for the understanding of relationships between the groups and the original variables.
publishDate 2007
dc.date.none.fl_str_mv 2007-12
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/11336/26143
Verdini, Roxana Andrea; Zorrilla, Susana; Rubiolo, Amelia Catalina; Nakai, S.; Multivariate statistical methods for Port Salut Argentino cheese analysis based on ripening time, storage conditions, and sampling sites; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 86; 1; 12-2007; 60-67
0169-7439
CONICET Digital
CONICET
url http://hdl.handle.net/11336/26143
identifier_str_mv Verdini, Roxana Andrea; Zorrilla, Susana; Rubiolo, Amelia Catalina; Nakai, S.; Multivariate statistical methods for Port Salut Argentino cheese analysis based on ripening time, storage conditions, and sampling sites; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 86; 1; 12-2007; 60-67
0169-7439
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0169743906001663
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2006.08.006
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
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
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_ 1842269100454182912
score 13.13397

Publicaciones similares