Applying multivariate curve resolution modelling combined with discriminant tools on near-infrared spectra for distinguishing between cheese varieties and stages of ripening

Autores
Martín Tornero, Elísabet; Durán Merás, Isabel; Alcaraz, Mirta Raquel; Muñoz de la Peña, Arsenio; Galeano Díaz, Teresa; Goicoechea, Hector Casimiro
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this study, near-infrared (NIR) spectra were employed to monitor the ripening process of two kinds of soft cheese produced in the Extremadura region of Spain, manufactured by two different producers, “Torta del Casar” and “Queso de la Serena”. Spectra were collected from the interior of the cheeses and the rind and analysed using appropriate chemometric techniques to distinguish between the two varieties and among different weeks of the maturation process. Different chemometric tools, including multivariate curve resolution with alternating least squares (MCRALS), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and feed-forward artificial neural networks (FF-ANN), were utilised, resulting in outstanding discrimination outcomes with sensitivity, precision, specificity, and accuracy reaching values c.a. 1.00 in optimal scenarios. More comprehensive information was acquired from the rind spectra analysis, indicating that the sampling process can be performed without disturbing the cheese in a non-destructive way. Remarkably, the capability to distinguish between various weeks of ripening for both cheeses could enable manufacturers to produce market-ready products earlier than the typically established timeline.
Fil: Martín Tornero, Elísabet. Universidad de Extremadura; España
Fil: Durán Merás, Isabel. Universidad de Extremadura; España
Fil: Alcaraz, Mirta Raquel. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Laboratorio de Desarrollo Analítico y Quimiometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Muñoz de la Peña, Arsenio. Universidad de Extremadura; España
Fil: Galeano Díaz, Teresa. Universidad de Extremadura; España
Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Laboratorio de Desarrollo Analítico y Quimiometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Materia
Multivariate curve resolution
Torta del Casar
Queso de la Serena
Linear discriminant analysis
Quadratic discriminant analysis
Artificial neural networks
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/239785
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/239785
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Applying multivariate curve resolution modelling combined with discriminant tools on near-infrared spectra for distinguishing between cheese varieties and stages of ripeningMartín Tornero, ElísabetDurán Merás, IsabelAlcaraz, Mirta RaquelMuñoz de la Peña, ArsenioGaleano Díaz, TeresaGoicoechea, Hector CasimiroMultivariate curve resolutionTorta del CasarQueso de la SerenaLinear discriminant analysisQuadratic discriminant analysisArtificial neural networkshttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1In this study, near-infrared (NIR) spectra were employed to monitor the ripening process of two kinds of soft cheese produced in the Extremadura region of Spain, manufactured by two different producers, “Torta del Casar” and “Queso de la Serena”. Spectra were collected from the interior of the cheeses and the rind and analysed using appropriate chemometric techniques to distinguish between the two varieties and among different weeks of the maturation process. Different chemometric tools, including multivariate curve resolution with alternating least squares (MCRALS), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and feed-forward artificial neural networks (FF-ANN), were utilised, resulting in outstanding discrimination outcomes with sensitivity, precision, specificity, and accuracy reaching values c.a. 1.00 in optimal scenarios. More comprehensive information was acquired from the rind spectra analysis, indicating that the sampling process can be performed without disturbing the cheese in a non-destructive way. Remarkably, the capability to distinguish between various weeks of ripening for both cheeses could enable manufacturers to produce market-ready products earlier than the typically established timeline.Fil: Martín Tornero, Elísabet. Universidad de Extremadura; EspañaFil: Durán Merás, Isabel. Universidad de Extremadura; EspañaFil: Alcaraz, Mirta Raquel. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Laboratorio de Desarrollo Analítico y Quimiometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Muñoz de la Peña, Arsenio. Universidad de Extremadura; EspañaFil: Galeano Díaz, Teresa. Universidad de Extremadura; EspañaFil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Laboratorio de Desarrollo Analítico y Quimiometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaElsevier Science2024-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/239785Martín Tornero, Elísabet; Durán Merás, Isabel; Alcaraz, Mirta Raquel; Muñoz de la Peña, Arsenio; Galeano Díaz, Teresa; et al.; Applying multivariate curve resolution modelling combined with discriminant tools on near-infrared spectra for distinguishing between cheese varieties and stages of ripening; Elsevier Science; Microchemical Journal; 204; 9-2024; 1-90026-265XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0026265X24011512info:eu-repo/semantics/altIdentifier/doi/10.1016/j.microc.2024.111039info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-02-26T10:33:31Zoai:ri.conicet.gov.ar:11336/239785instacron: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:34982026-02-26 10:33:31.81CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Applying multivariate curve resolution modelling combined with discriminant tools on near-infrared spectra for distinguishing between cheese varieties and stages of ripening
title Applying multivariate curve resolution modelling combined with discriminant tools on near-infrared spectra for distinguishing between cheese varieties and stages of ripening
spellingShingle Applying multivariate curve resolution modelling combined with discriminant tools on near-infrared spectra for distinguishing between cheese varieties and stages of ripening
Martín Tornero, Elísabet
Multivariate curve resolution
Torta del Casar
Queso de la Serena
Linear discriminant analysis
Quadratic discriminant analysis
Artificial neural networks
title_short Applying multivariate curve resolution modelling combined with discriminant tools on near-infrared spectra for distinguishing between cheese varieties and stages of ripening
title_full Applying multivariate curve resolution modelling combined with discriminant tools on near-infrared spectra for distinguishing between cheese varieties and stages of ripening
title_fullStr Applying multivariate curve resolution modelling combined with discriminant tools on near-infrared spectra for distinguishing between cheese varieties and stages of ripening
title_full_unstemmed Applying multivariate curve resolution modelling combined with discriminant tools on near-infrared spectra for distinguishing between cheese varieties and stages of ripening
title_sort Applying multivariate curve resolution modelling combined with discriminant tools on near-infrared spectra for distinguishing between cheese varieties and stages of ripening
dc.creator.none.fl_str_mv Martín Tornero, Elísabet
Durán Merás, Isabel
Alcaraz, Mirta Raquel
Muñoz de la Peña, Arsenio
Galeano Díaz, Teresa
Goicoechea, Hector Casimiro
author Martín Tornero, Elísabet
author_facet Martín Tornero, Elísabet
Durán Merás, Isabel
Alcaraz, Mirta Raquel
Muñoz de la Peña, Arsenio
Galeano Díaz, Teresa
Goicoechea, Hector Casimiro
author_role author
author2 Durán Merás, Isabel
Alcaraz, Mirta Raquel
Muñoz de la Peña, Arsenio
Galeano Díaz, Teresa
Goicoechea, Hector Casimiro
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Multivariate curve resolution
Torta del Casar
Queso de la Serena
Linear discriminant analysis
Quadratic discriminant analysis
Artificial neural networks
topic Multivariate curve resolution
Torta del Casar
Queso de la Serena
Linear discriminant analysis
Quadratic discriminant analysis
Artificial neural networks
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this study, near-infrared (NIR) spectra were employed to monitor the ripening process of two kinds of soft cheese produced in the Extremadura region of Spain, manufactured by two different producers, “Torta del Casar” and “Queso de la Serena”. Spectra were collected from the interior of the cheeses and the rind and analysed using appropriate chemometric techniques to distinguish between the two varieties and among different weeks of the maturation process. Different chemometric tools, including multivariate curve resolution with alternating least squares (MCRALS), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and feed-forward artificial neural networks (FF-ANN), were utilised, resulting in outstanding discrimination outcomes with sensitivity, precision, specificity, and accuracy reaching values c.a. 1.00 in optimal scenarios. More comprehensive information was acquired from the rind spectra analysis, indicating that the sampling process can be performed without disturbing the cheese in a non-destructive way. Remarkably, the capability to distinguish between various weeks of ripening for both cheeses could enable manufacturers to produce market-ready products earlier than the typically established timeline.
Fil: Martín Tornero, Elísabet. Universidad de Extremadura; España
Fil: Durán Merás, Isabel. Universidad de Extremadura; España
Fil: Alcaraz, Mirta Raquel. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Laboratorio de Desarrollo Analítico y Quimiometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Muñoz de la Peña, Arsenio. Universidad de Extremadura; España
Fil: Galeano Díaz, Teresa. Universidad de Extremadura; España
Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas. Laboratorio de Desarrollo Analítico y Quimiometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
description In this study, near-infrared (NIR) spectra were employed to monitor the ripening process of two kinds of soft cheese produced in the Extremadura region of Spain, manufactured by two different producers, “Torta del Casar” and “Queso de la Serena”. Spectra were collected from the interior of the cheeses and the rind and analysed using appropriate chemometric techniques to distinguish between the two varieties and among different weeks of the maturation process. Different chemometric tools, including multivariate curve resolution with alternating least squares (MCRALS), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and feed-forward artificial neural networks (FF-ANN), were utilised, resulting in outstanding discrimination outcomes with sensitivity, precision, specificity, and accuracy reaching values c.a. 1.00 in optimal scenarios. More comprehensive information was acquired from the rind spectra analysis, indicating that the sampling process can be performed without disturbing the cheese in a non-destructive way. Remarkably, the capability to distinguish between various weeks of ripening for both cheeses could enable manufacturers to produce market-ready products earlier than the typically established timeline.
publishDate 2024
dc.date.none.fl_str_mv 2024-09
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 http://hdl.handle.net/11336/239785
Martín Tornero, Elísabet; Durán Merás, Isabel; Alcaraz, Mirta Raquel; Muñoz de la Peña, Arsenio; Galeano Díaz, Teresa; et al.; Applying multivariate curve resolution modelling combined with discriminant tools on near-infrared spectra for distinguishing between cheese varieties and stages of ripening; Elsevier Science; Microchemical Journal; 204; 9-2024; 1-9
0026-265X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/239785
identifier_str_mv Martín Tornero, Elísabet; Durán Merás, Isabel; Alcaraz, Mirta Raquel; Muñoz de la Peña, Arsenio; Galeano Díaz, Teresa; et al.; Applying multivariate curve resolution modelling combined with discriminant tools on near-infrared spectra for distinguishing between cheese varieties and stages of ripening; Elsevier Science; Microchemical Journal; 204; 9-2024; 1-9
0026-265X
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://linkinghub.elsevier.com/retrieve/pii/S0026265X24011512
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.microc.2024.111039
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv 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
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score 12.665996

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