FT-IR and untargeted chemometric analysis for adulterant detection in chia and sesame oils

Autores
Rodríguez, Silvio David; Gagneten, Maite; Farroni, Abel Eduardo; Percibaldi, Nora Mabel; Buera, Maria del Pilar
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Chia (Salvia hispanica L.) and sesame (Sesamum indicum L.) oils are valorized for their health benefits and both are extensively used as ingredients in different food formulations and/or processes. Their retail prices are higher than those of other edible oils and might promote fraudulent adulterations. Spectroscopic methods associated to untargeted analysis are appropriate and faster than traditional techniques, requiring less time to prepare and run the samples. In the present study Fourier transform infrared spectroscopy was used in combination with one class partial least squares and soft independent modelling by class analogy to detect the presence of four possible adulterants: corn, peanut, soybean and sunflower oils, in four different proportions (pure + adulterant: 90 + 10, 95 + 5, 98 + 2 and 99 + 1, in volume). Untargeted approaches were successful in the detection of adulterated chia and sesame oils with acceptable prediction errors ranging between 1% and 5%.
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
Fil: Gagneten, Maite. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Industrias. Instituto de Tecnología de Alimentos y Procesos Químicos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Tecnología de Alimentos y Procesos Químicos; Argentina
Fil: Farroni, Abel Eduardo. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino; Argentina
Fil: Percibaldi, Nora Mabel. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino; 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 Químicos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Tecnología de Alimentos y Procesos Químicos; Argentina
Materia
CHIA OIL
FOOD ADULTERATION
FT-IR
OC-PLS
SESAME OIL
SIMCA
UNTARGETED ANALYSIS
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:11336/163618
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/163618
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling FT-IR and untargeted chemometric analysis for adulterant detection in chia and sesame oilsRodríguez, Silvio DavidGagneten, MaiteFarroni, Abel EduardoPercibaldi, Nora MabelBuera, Maria del PilarCHIA OILFOOD ADULTERATIONFT-IROC-PLSSESAME OILSIMCAUNTARGETED ANALYSIShttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2Chia (Salvia hispanica L.) and sesame (Sesamum indicum L.) oils are valorized for their health benefits and both are extensively used as ingredients in different food formulations and/or processes. Their retail prices are higher than those of other edible oils and might promote fraudulent adulterations. Spectroscopic methods associated to untargeted analysis are appropriate and faster than traditional techniques, requiring less time to prepare and run the samples. In the present study Fourier transform infrared spectroscopy was used in combination with one class partial least squares and soft independent modelling by class analogy to detect the presence of four possible adulterants: corn, peanut, soybean and sunflower oils, in four different proportions (pure + adulterant: 90 + 10, 95 + 5, 98 + 2 and 99 + 1, in volume). Untargeted approaches were successful in the detection of adulterated chia and sesame oils with acceptable prediction errors ranging between 1% and 5%.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; ArgentinaFil: Gagneten, Maite. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Industrias. Instituto de Tecnología de Alimentos y Procesos Químicos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Tecnología de Alimentos y Procesos Químicos; ArgentinaFil: Farroni, Abel Eduardo. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino; ArgentinaFil: Percibaldi, Nora Mabel. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino; 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 Químicos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Tecnología de Alimentos y Procesos Químicos; ArgentinaElsevier2019-11info: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/11336/163618Rodríguez, Silvio David; Gagneten, Maite; Farroni, Abel Eduardo; Percibaldi, Nora Mabel; Buera, Maria del Pilar; FT-IR and untargeted chemometric analysis for adulterant detection in chia and sesame oils; Elsevier; Food Control; 105; 11-2019; 78-850956-7135CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0956713519302336info:eu-repo/semantics/altIdentifier/doi/10.1016/j.foodcont.2019.05.025info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:46:35Zoai:ri.conicet.gov.ar:11336/163618instacron: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 09:46:35.461CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv FT-IR and untargeted chemometric analysis for adulterant detection in chia and sesame oils
title FT-IR and untargeted chemometric analysis for adulterant detection in chia and sesame oils
spellingShingle FT-IR and untargeted chemometric analysis for adulterant detection in chia and sesame oils
Rodríguez, Silvio David
CHIA OIL
FOOD ADULTERATION
FT-IR
OC-PLS
SESAME OIL
SIMCA
UNTARGETED ANALYSIS
title_short FT-IR and untargeted chemometric analysis for adulterant detection in chia and sesame oils
title_full FT-IR and untargeted chemometric analysis for adulterant detection in chia and sesame oils
title_fullStr FT-IR and untargeted chemometric analysis for adulterant detection in chia and sesame oils
title_full_unstemmed FT-IR and untargeted chemometric analysis for adulterant detection in chia and sesame oils
title_sort FT-IR and untargeted chemometric analysis for adulterant detection in chia and sesame oils
dc.creator.none.fl_str_mv Rodríguez, Silvio David
Gagneten, Maite
Farroni, Abel Eduardo
Percibaldi, Nora Mabel
Buera, Maria del Pilar
author Rodríguez, Silvio David
author_facet Rodríguez, Silvio David
Gagneten, Maite
Farroni, Abel Eduardo
Percibaldi, Nora Mabel
Buera, Maria del Pilar
author_role author
author2 Gagneten, Maite
Farroni, Abel Eduardo
Percibaldi, Nora Mabel
Buera, Maria del Pilar
author2_role author
author
author
author
dc.subject.none.fl_str_mv CHIA OIL
FOOD ADULTERATION
FT-IR
OC-PLS
SESAME OIL
SIMCA
UNTARGETED ANALYSIS
topic CHIA OIL
FOOD ADULTERATION
FT-IR
OC-PLS
SESAME OIL
SIMCA
UNTARGETED ANALYSIS
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Chia (Salvia hispanica L.) and sesame (Sesamum indicum L.) oils are valorized for their health benefits and both are extensively used as ingredients in different food formulations and/or processes. Their retail prices are higher than those of other edible oils and might promote fraudulent adulterations. Spectroscopic methods associated to untargeted analysis are appropriate and faster than traditional techniques, requiring less time to prepare and run the samples. In the present study Fourier transform infrared spectroscopy was used in combination with one class partial least squares and soft independent modelling by class analogy to detect the presence of four possible adulterants: corn, peanut, soybean and sunflower oils, in four different proportions (pure + adulterant: 90 + 10, 95 + 5, 98 + 2 and 99 + 1, in volume). Untargeted approaches were successful in the detection of adulterated chia and sesame oils with acceptable prediction errors ranging between 1% and 5%.
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
Fil: Gagneten, Maite. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Industrias. Instituto de Tecnología de Alimentos y Procesos Químicos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Tecnología de Alimentos y Procesos Químicos; Argentina
Fil: Farroni, Abel Eduardo. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino; Argentina
Fil: Percibaldi, Nora Mabel. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino; 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 Químicos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Tecnología de Alimentos y Procesos Químicos; Argentina
description Chia (Salvia hispanica L.) and sesame (Sesamum indicum L.) oils are valorized for their health benefits and both are extensively used as ingredients in different food formulations and/or processes. Their retail prices are higher than those of other edible oils and might promote fraudulent adulterations. Spectroscopic methods associated to untargeted analysis are appropriate and faster than traditional techniques, requiring less time to prepare and run the samples. In the present study Fourier transform infrared spectroscopy was used in combination with one class partial least squares and soft independent modelling by class analogy to detect the presence of four possible adulterants: corn, peanut, soybean and sunflower oils, in four different proportions (pure + adulterant: 90 + 10, 95 + 5, 98 + 2 and 99 + 1, in volume). Untargeted approaches were successful in the detection of adulterated chia and sesame oils with acceptable prediction errors ranging between 1% and 5%.
publishDate 2019
dc.date.none.fl_str_mv 2019-11
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/11336/163618
Rodríguez, Silvio David; Gagneten, Maite; Farroni, Abel Eduardo; Percibaldi, Nora Mabel; Buera, Maria del Pilar; FT-IR and untargeted chemometric analysis for adulterant detection in chia and sesame oils; Elsevier; Food Control; 105; 11-2019; 78-85
0956-7135
CONICET Digital
CONICET
url http://hdl.handle.net/11336/163618
identifier_str_mv Rodríguez, Silvio David; Gagneten, Maite; Farroni, Abel Eduardo; Percibaldi, Nora Mabel; Buera, Maria del Pilar; FT-IR and untargeted chemometric analysis for adulterant detection in chia and sesame oils; Elsevier; Food Control; 105; 11-2019; 78-85
0956-7135
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/S0956713519302336
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.foodcont.2019.05.025
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
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_ 1842268804685496320
score 13.13397

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