Evaluation of SIMCA and PLS algorithms to detect adulterants in canola oil by FT‐IR

Autores
Gagneten, Maite; Buera, Maria del Pilar; Rodríguez, Silvio David
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Adulteration of canola oil with four potential edible oils was analysed using FT‐IR and chemometric methods. The adulterants (corn, peanut, soybean, and sunflower oils) were studied in four different proportions (canola oil + adulterant oils: 90+10, 95+5, 98+2 and 99+1 in volume). Excellent classification results were obtained when multi‐class approaches were performed with a maximum error of 3%, using 1630 or 16 wavenumbers as variables. In the case of one‐class approaches, the selection of variables (16 wavenumbers) was necessary, improving the classification error to 5%. The differences observed using the different methods were related to the nature of each model depending on how the boundaries are set in each of them, responding either to a PCA‐based or PLS‐based algorithm.
Fil: Gagneten, Maite. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Industrias. Instituto de Tecnología de Alimentos y Procesos Quimicos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Tecnología de Alimentos y Procesos Quimicos.; Argentina
Fil: Buera, Maria del Pilar. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Industrias. Instituto de Tecnología de Alimentos y Procesos Quimicos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Tecnología de Alimentos y Procesos Quimicos.; Argentina
Fil: Rodríguez, Silvio David. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Biodiversidad y Biología Experimental y Aplicada. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Biodiversidad y Biología Experimental y Aplicada; Argentina
Materia
canola oil
FT-IR
chemometric analysis
food adulteration
SIMCA
PLS-DA
OC-PLS
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:11336/145797
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Evaluation of SIMCA and PLS algorithms to detect adulterants in canola oil by FT‐IRGagneten, MaiteBuera, Maria del PilarRodríguez, Silvio Davidcanola oilFT-IRchemometric analysisfood adulterationSIMCAPLS-DAOC-PLShttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1Adulteration of canola oil with four potential edible oils was analysed using FT‐IR and chemometric methods. The adulterants (corn, peanut, soybean, and sunflower oils) were studied in four different proportions (canola oil + adulterant oils: 90+10, 95+5, 98+2 and 99+1 in volume). Excellent classification results were obtained when multi‐class approaches were performed with a maximum error of 3%, using 1630 or 16 wavenumbers as variables. In the case of one‐class approaches, the selection of variables (16 wavenumbers) was necessary, improving the classification error to 5%. The differences observed using the different methods were related to the nature of each model depending on how the boundaries are set in each of them, responding either to a PCA‐based or PLS‐based algorithm.Fil: Gagneten, Maite. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Industrias. Instituto de Tecnología de Alimentos y Procesos Quimicos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Tecnología de Alimentos y Procesos Quimicos.; ArgentinaFil: Buera, Maria del Pilar. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Industrias. Instituto de Tecnología de Alimentos y Procesos Quimicos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Tecnología de Alimentos y Procesos Quimicos.; ArgentinaFil: Rodríguez, Silvio David. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Biodiversidad y Biología Experimental y Aplicada. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Biodiversidad y Biología Experimental y Aplicada; ArgentinaWiley Blackwell Publishing, Inc2020-10info: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/11336/145797Gagneten, Maite; Buera, Maria del Pilar; Rodríguez, Silvio David; Evaluation of SIMCA and PLS algorithms to detect adulterants in canola oil by FT‐IR; Wiley Blackwell Publishing, Inc; International Journal of Food Science and Technology; 10-2020; 1-190950-5423CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1111/IJFS.14866info:eu-repo/semantics/altIdentifier/url/https://ifst.onlinelibrary.wiley.com/doi/10.1111/ijfs.14866info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:47:15Zoai:ri.conicet.gov.ar:11336/145797instacron: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 09:47:15.667CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Evaluation of SIMCA and PLS algorithms to detect adulterants in canola oil by FT‐IR
title Evaluation of SIMCA and PLS algorithms to detect adulterants in canola oil by FT‐IR
spellingShingle Evaluation of SIMCA and PLS algorithms to detect adulterants in canola oil by FT‐IR
Gagneten, Maite
canola oil
FT-IR
chemometric analysis
food adulteration
SIMCA
PLS-DA
OC-PLS
title_short Evaluation of SIMCA and PLS algorithms to detect adulterants in canola oil by FT‐IR
title_full Evaluation of SIMCA and PLS algorithms to detect adulterants in canola oil by FT‐IR
title_fullStr Evaluation of SIMCA and PLS algorithms to detect adulterants in canola oil by FT‐IR
title_full_unstemmed Evaluation of SIMCA and PLS algorithms to detect adulterants in canola oil by FT‐IR
title_sort Evaluation of SIMCA and PLS algorithms to detect adulterants in canola oil by FT‐IR
dc.creator.none.fl_str_mv Gagneten, Maite
Buera, Maria del Pilar
Rodríguez, Silvio David
author Gagneten, Maite
author_facet Gagneten, Maite
Buera, Maria del Pilar
Rodríguez, Silvio David
author_role author
author2 Buera, Maria del Pilar
Rodríguez, Silvio David
author2_role author
author
dc.subject.none.fl_str_mv canola oil
FT-IR
chemometric analysis
food adulteration
SIMCA
PLS-DA
OC-PLS
topic canola oil
FT-IR
chemometric analysis
food adulteration
SIMCA
PLS-DA
OC-PLS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Adulteration of canola oil with four potential edible oils was analysed using FT‐IR and chemometric methods. The adulterants (corn, peanut, soybean, and sunflower oils) were studied in four different proportions (canola oil + adulterant oils: 90+10, 95+5, 98+2 and 99+1 in volume). Excellent classification results were obtained when multi‐class approaches were performed with a maximum error of 3%, using 1630 or 16 wavenumbers as variables. In the case of one‐class approaches, the selection of variables (16 wavenumbers) was necessary, improving the classification error to 5%. The differences observed using the different methods were related to the nature of each model depending on how the boundaries are set in each of them, responding either to a PCA‐based or PLS‐based algorithm.
Fil: Gagneten, Maite. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Industrias. Instituto de Tecnología de Alimentos y Procesos Quimicos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Tecnología de Alimentos y Procesos Quimicos.; Argentina
Fil: Buera, Maria del Pilar. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Industrias. Instituto de Tecnología de Alimentos y Procesos Quimicos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Tecnología de Alimentos y Procesos Quimicos.; Argentina
Fil: Rodríguez, Silvio David. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Biodiversidad y Biología Experimental y Aplicada. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Biodiversidad y Biología Experimental y Aplicada; Argentina
description Adulteration of canola oil with four potential edible oils was analysed using FT‐IR and chemometric methods. The adulterants (corn, peanut, soybean, and sunflower oils) were studied in four different proportions (canola oil + adulterant oils: 90+10, 95+5, 98+2 and 99+1 in volume). Excellent classification results were obtained when multi‐class approaches were performed with a maximum error of 3%, using 1630 or 16 wavenumbers as variables. In the case of one‐class approaches, the selection of variables (16 wavenumbers) was necessary, improving the classification error to 5%. The differences observed using the different methods were related to the nature of each model depending on how the boundaries are set in each of them, responding either to a PCA‐based or PLS‐based algorithm.
publishDate 2020
dc.date.none.fl_str_mv 2020-10
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/11336/145797
Gagneten, Maite; Buera, Maria del Pilar; Rodríguez, Silvio David; Evaluation of SIMCA and PLS algorithms to detect adulterants in canola oil by FT‐IR; Wiley Blackwell Publishing, Inc; International Journal of Food Science and Technology; 10-2020; 1-19
0950-5423
CONICET Digital
CONICET
url http://hdl.handle.net/11336/145797
identifier_str_mv Gagneten, Maite; Buera, Maria del Pilar; Rodríguez, Silvio David; Evaluation of SIMCA and PLS algorithms to detect adulterants in canola oil by FT‐IR; Wiley Blackwell Publishing, Inc; International Journal of Food Science and Technology; 10-2020; 1-19
0950-5423
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1111/IJFS.14866
info:eu-repo/semantics/altIdentifier/url/https://ifst.onlinelibrary.wiley.com/doi/10.1111/ijfs.14866
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
dc.publisher.none.fl_str_mv Wiley Blackwell Publishing, Inc
publisher.none.fl_str_mv Wiley Blackwell Publishing, Inc
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_ 1842268845878804480
score 13.13397

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