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, María 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%.
EEA Pergamino
Fil: Rodríguez, Silvio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Instituto de Biodiversidad y Biología Experimental y Aplicada (IBBEA); Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina
Fil: Gagneten, Maite. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina, Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Instituto de Alimentos y Procesos Químicos (ITAPROQ); Argentina
Fil: Farroni, Abel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Laboratorio de Calidad de Alimentos, Suelos y Aguas; Argentina
Fil: Percibaldi, Nora Mabel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Laboratorio de Calidad de Alimentos, Suelos y Aguas; Argenina
Fil: Buera, María del Pilar. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina, Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Instituto de Alimentos y Procesos Químicos (ITAPROQ); Argentina
Fuente
Food Control 105 : 78-85 (November 2019)
Materia
Calidad de los Alimentos
Salvia (género)
Aceite de Sésamo
Adulteración de Alimentos
Análisis
Food Quality
Salvia
Sesame Oil
Food Adulteration
Analysis
Análisis no dirigido
Aceite de chía
Nivel de accesibilidad
acceso restringido
Condiciones de uso
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/5224
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network_name_str INTA Digital (INTA)
spelling FT-IR and untargeted chemometric analysis for adulterant detection in chia and sesame oilsRodríguez, Silvio DavidGagneten, MaiteFarroni, Abel EduardoPercibaldi, Nora MabelBuera, María del PilarCalidad de los AlimentosSalvia (género)Aceite de SésamoAdulteración de AlimentosAnálisisFood QualitySalviaSesame OilFood AdulterationAnalysisAnálisis no dirigidoAceite de chíaChia (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%.EEA PergaminoFil: Rodríguez, Silvio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Instituto de Biodiversidad y Biología Experimental y Aplicada (IBBEA); Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Gagneten, Maite. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina, Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Instituto de Alimentos y Procesos Químicos (ITAPROQ); ArgentinaFil: Farroni, Abel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Laboratorio de Calidad de Alimentos, Suelos y Aguas; ArgentinaFil: Percibaldi, Nora Mabel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Laboratorio de Calidad de Alimentos, Suelos y Aguas; ArgeninaFil: Buera, María del Pilar. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina, Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Instituto de Alimentos y Procesos Químicos (ITAPROQ); ArgentinaElsevier2019-05-30T14:52:46Z2019-05-30T14:52:46Z2019-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttps://www.sciencedirect.com/science/article/pii/S0956713519302336http://hdl.handle.net/20.500.12123/52240956-7135 (digital)https://doi.org/10.1016/j.foodcont.2019.05.025Food Control 105 : 78-85 (November 2019)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccess2025-09-04T09:47:58Zoai:localhost:20.500.12123/5224instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-04 09:47:59.465INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
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
Calidad de los Alimentos
Salvia (género)
Aceite de Sésamo
Adulteración de Alimentos
Análisis
Food Quality
Salvia
Sesame Oil
Food Adulteration
Analysis
Análisis no dirigido
Aceite de chía
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, María del Pilar
author Rodríguez, Silvio David
author_facet Rodríguez, Silvio David
Gagneten, Maite
Farroni, Abel Eduardo
Percibaldi, Nora Mabel
Buera, María del Pilar
author_role author
author2 Gagneten, Maite
Farroni, Abel Eduardo
Percibaldi, Nora Mabel
Buera, María del Pilar
author2_role author
author
author
author
dc.subject.none.fl_str_mv Calidad de los Alimentos
Salvia (género)
Aceite de Sésamo
Adulteración de Alimentos
Análisis
Food Quality
Salvia
Sesame Oil
Food Adulteration
Analysis
Análisis no dirigido
Aceite de chía
topic Calidad de los Alimentos
Salvia (género)
Aceite de Sésamo
Adulteración de Alimentos
Análisis
Food Quality
Salvia
Sesame Oil
Food Adulteration
Analysis
Análisis no dirigido
Aceite de chía
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%.
EEA Pergamino
Fil: Rodríguez, Silvio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Instituto de Biodiversidad y Biología Experimental y Aplicada (IBBEA); Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina
Fil: Gagneten, Maite. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina, Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Instituto de Alimentos y Procesos Químicos (ITAPROQ); Argentina
Fil: Farroni, Abel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Laboratorio de Calidad de Alimentos, Suelos y Aguas; Argentina
Fil: Percibaldi, Nora Mabel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Laboratorio de Calidad de Alimentos, Suelos y Aguas; Argenina
Fil: Buera, María del Pilar. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina, Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Instituto de Alimentos y Procesos Químicos (ITAPROQ); 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 2019-05-30T14:52:46Z
2019-05-30T14:52:46Z
2019-05
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 https://www.sciencedirect.com/science/article/pii/S0956713519302336
http://hdl.handle.net/20.500.12123/5224
0956-7135 (digital)
https://doi.org/10.1016/j.foodcont.2019.05.025
url https://www.sciencedirect.com/science/article/pii/S0956713519302336
http://hdl.handle.net/20.500.12123/5224
https://doi.org/10.1016/j.foodcont.2019.05.025
identifier_str_mv 0956-7135 (digital)
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
eu_rights_str_mv restrictedAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Food Control 105 : 78-85 (November 2019)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
_version_ 1842341366736093184
score 12.623145

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