Multivariate modeling for detecting adulteration of extra virgin olive oil with soybean oil using fluorescence and UV–Vis spectroscopies: A preliminary approach

Autores
Melo Milanez, Karla Danielle Tavares de; Nóbrega, Thiago César Araújo; Silva Do Nascimento, Danielle; Insausti, Matías; Fernández Band, Beatriz Susana; Pontes, Márcio José Coelho
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This work presents a comparative study of chemometric methods used to quantify adulteration of extra virgin olive oil (EVOO) with soybean edible oil using fluorescence and UV–Vis spectroscopies. The adulteration was prepared by adding soybean edible oil in different concentrations (10, 50, 100, 150, 200, 250 and 300 g/kg). Different multivariate regression strategies were evaluated: partial least squares (PLS) using full spectrum; PLS with significant regression coefficients selected by the Jack-Knife algorithm (PLS-JK) and multiple linear regression (MLR) with previous selection of variables by stepwise algorithms (SW-MLR); successive projections algorithm (SPA-MLR); and genetic algorithm (GA-MLR). The predictive ability of the models was assessed, for each spectroscopic technique. For fluorescence spectroscopy, satisfactory prediction results were obtained for all the regression models with Root Mean Square Error of Prediction (RMSEP) values varying from 14.0 to 17.5 g/kg. When the regression methods were evaluated for UV–Vis spectra, higher RMSEP values were found, varying from 13.3 to 30.4 g/kg. The results indicate that the two spectroscopic techniques have similar performances with respect to predictive ability of the regression models.
Fil: Melo Milanez, Karla Danielle Tavares de. Universidade Federal da Paraíba. Departamento de Química; Brasil
Fil: Nóbrega, Thiago César Araújo. Universidade Federal da Paraíba. Departamento de Química; Brasil
Fil: Silva Do Nascimento, Danielle. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; Argentina
Fil: Insausti, Matías. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; Argentina
Fil: Fernández Band, Beatriz Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; Argentina
Fil: Pontes, Márcio José Coelho. Universidade Federal da Paraíba. Departamento de Química; Brasil
Materia
Authenticity
Multiple Linear Regression
Partial Least Squares Regression
Variable Selection
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:11336/56511
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/56511
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Multivariate modeling for detecting adulteration of extra virgin olive oil with soybean oil using fluorescence and UV–Vis spectroscopies: A preliminary approachMelo Milanez, Karla Danielle Tavares deNóbrega, Thiago César AraújoSilva Do Nascimento, DanielleInsausti, MatíasFernández Band, Beatriz SusanaPontes, Márcio José CoelhoAuthenticityMultiple Linear RegressionPartial Least Squares RegressionVariable Selectionhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1This work presents a comparative study of chemometric methods used to quantify adulteration of extra virgin olive oil (EVOO) with soybean edible oil using fluorescence and UV–Vis spectroscopies. The adulteration was prepared by adding soybean edible oil in different concentrations (10, 50, 100, 150, 200, 250 and 300 g/kg). Different multivariate regression strategies were evaluated: partial least squares (PLS) using full spectrum; PLS with significant regression coefficients selected by the Jack-Knife algorithm (PLS-JK) and multiple linear regression (MLR) with previous selection of variables by stepwise algorithms (SW-MLR); successive projections algorithm (SPA-MLR); and genetic algorithm (GA-MLR). The predictive ability of the models was assessed, for each spectroscopic technique. For fluorescence spectroscopy, satisfactory prediction results were obtained for all the regression models with Root Mean Square Error of Prediction (RMSEP) values varying from 14.0 to 17.5 g/kg. When the regression methods were evaluated for UV–Vis spectra, higher RMSEP values were found, varying from 13.3 to 30.4 g/kg. The results indicate that the two spectroscopic techniques have similar performances with respect to predictive ability of the regression models.Fil: Melo Milanez, Karla Danielle Tavares de. Universidade Federal da Paraíba. Departamento de Química; BrasilFil: Nóbrega, Thiago César Araújo. Universidade Federal da Paraíba. Departamento de Química; BrasilFil: Silva Do Nascimento, Danielle. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; ArgentinaFil: Insausti, Matías. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; ArgentinaFil: Fernández Band, Beatriz Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; ArgentinaFil: Pontes, Márcio José Coelho. Universidade Federal da Paraíba. Departamento de Química; BrasilElsevier Science2017-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/56511Melo Milanez, Karla Danielle Tavares de; Nóbrega, Thiago César Araújo; Silva Do Nascimento, Danielle; Insausti, Matías; Fernández Band, Beatriz Susana; et al.; Multivariate modeling for detecting adulteration of extra virgin olive oil with soybean oil using fluorescence and UV–Vis spectroscopies: A preliminary approach; Elsevier Science; LWT - Food Science and Technology; 85; Parte A; 11-2017; 9-150023-6438CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0023643817304644info:eu-repo/semantics/altIdentifier/doi/10.1016/j.lwt.2017.06.060info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:17:50Zoai:ri.conicet.gov.ar:11336/56511instacron: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:34982025-09-10 13:17:50.396CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Multivariate modeling for detecting adulteration of extra virgin olive oil with soybean oil using fluorescence and UV–Vis spectroscopies: A preliminary approach
title Multivariate modeling for detecting adulteration of extra virgin olive oil with soybean oil using fluorescence and UV–Vis spectroscopies: A preliminary approach
spellingShingle Multivariate modeling for detecting adulteration of extra virgin olive oil with soybean oil using fluorescence and UV–Vis spectroscopies: A preliminary approach
Melo Milanez, Karla Danielle Tavares de
Authenticity
Multiple Linear Regression
Partial Least Squares Regression
Variable Selection
title_short Multivariate modeling for detecting adulteration of extra virgin olive oil with soybean oil using fluorescence and UV–Vis spectroscopies: A preliminary approach
title_full Multivariate modeling for detecting adulteration of extra virgin olive oil with soybean oil using fluorescence and UV–Vis spectroscopies: A preliminary approach
title_fullStr Multivariate modeling for detecting adulteration of extra virgin olive oil with soybean oil using fluorescence and UV–Vis spectroscopies: A preliminary approach
title_full_unstemmed Multivariate modeling for detecting adulteration of extra virgin olive oil with soybean oil using fluorescence and UV–Vis spectroscopies: A preliminary approach
title_sort Multivariate modeling for detecting adulteration of extra virgin olive oil with soybean oil using fluorescence and UV–Vis spectroscopies: A preliminary approach
dc.creator.none.fl_str_mv Melo Milanez, Karla Danielle Tavares de
Nóbrega, Thiago César Araújo
Silva Do Nascimento, Danielle
Insausti, Matías
Fernández Band, Beatriz Susana
Pontes, Márcio José Coelho
author Melo Milanez, Karla Danielle Tavares de
author_facet Melo Milanez, Karla Danielle Tavares de
Nóbrega, Thiago César Araújo
Silva Do Nascimento, Danielle
Insausti, Matías
Fernández Band, Beatriz Susana
Pontes, Márcio José Coelho
author_role author
author2 Nóbrega, Thiago César Araújo
Silva Do Nascimento, Danielle
Insausti, Matías
Fernández Band, Beatriz Susana
Pontes, Márcio José Coelho
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Authenticity
Multiple Linear Regression
Partial Least Squares Regression
Variable Selection
topic Authenticity
Multiple Linear Regression
Partial Least Squares Regression
Variable Selection
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv This work presents a comparative study of chemometric methods used to quantify adulteration of extra virgin olive oil (EVOO) with soybean edible oil using fluorescence and UV–Vis spectroscopies. The adulteration was prepared by adding soybean edible oil in different concentrations (10, 50, 100, 150, 200, 250 and 300 g/kg). Different multivariate regression strategies were evaluated: partial least squares (PLS) using full spectrum; PLS with significant regression coefficients selected by the Jack-Knife algorithm (PLS-JK) and multiple linear regression (MLR) with previous selection of variables by stepwise algorithms (SW-MLR); successive projections algorithm (SPA-MLR); and genetic algorithm (GA-MLR). The predictive ability of the models was assessed, for each spectroscopic technique. For fluorescence spectroscopy, satisfactory prediction results were obtained for all the regression models with Root Mean Square Error of Prediction (RMSEP) values varying from 14.0 to 17.5 g/kg. When the regression methods were evaluated for UV–Vis spectra, higher RMSEP values were found, varying from 13.3 to 30.4 g/kg. The results indicate that the two spectroscopic techniques have similar performances with respect to predictive ability of the regression models.
Fil: Melo Milanez, Karla Danielle Tavares de. Universidade Federal da Paraíba. Departamento de Química; Brasil
Fil: Nóbrega, Thiago César Araújo. Universidade Federal da Paraíba. Departamento de Química; Brasil
Fil: Silva Do Nascimento, Danielle. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; Argentina
Fil: Insausti, Matías. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; Argentina
Fil: Fernández Band, Beatriz Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; Argentina
Fil: Pontes, Márcio José Coelho. Universidade Federal da Paraíba. Departamento de Química; Brasil
description This work presents a comparative study of chemometric methods used to quantify adulteration of extra virgin olive oil (EVOO) with soybean edible oil using fluorescence and UV–Vis spectroscopies. The adulteration was prepared by adding soybean edible oil in different concentrations (10, 50, 100, 150, 200, 250 and 300 g/kg). Different multivariate regression strategies were evaluated: partial least squares (PLS) using full spectrum; PLS with significant regression coefficients selected by the Jack-Knife algorithm (PLS-JK) and multiple linear regression (MLR) with previous selection of variables by stepwise algorithms (SW-MLR); successive projections algorithm (SPA-MLR); and genetic algorithm (GA-MLR). The predictive ability of the models was assessed, for each spectroscopic technique. For fluorescence spectroscopy, satisfactory prediction results were obtained for all the regression models with Root Mean Square Error of Prediction (RMSEP) values varying from 14.0 to 17.5 g/kg. When the regression methods were evaluated for UV–Vis spectra, higher RMSEP values were found, varying from 13.3 to 30.4 g/kg. The results indicate that the two spectroscopic techniques have similar performances with respect to predictive ability of the regression models.
publishDate 2017
dc.date.none.fl_str_mv 2017-11
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/11336/56511
Melo Milanez, Karla Danielle Tavares de; Nóbrega, Thiago César Araújo; Silva Do Nascimento, Danielle; Insausti, Matías; Fernández Band, Beatriz Susana; et al.; Multivariate modeling for detecting adulteration of extra virgin olive oil with soybean oil using fluorescence and UV–Vis spectroscopies: A preliminary approach; Elsevier Science; LWT - Food Science and Technology; 85; Parte A; 11-2017; 9-15
0023-6438
CONICET Digital
CONICET
url http://hdl.handle.net/11336/56511
identifier_str_mv Melo Milanez, Karla Danielle Tavares de; Nóbrega, Thiago César Araújo; Silva Do Nascimento, Danielle; Insausti, Matías; Fernández Band, Beatriz Susana; et al.; Multivariate modeling for detecting adulteration of extra virgin olive oil with soybean oil using fluorescence and UV–Vis spectroscopies: A preliminary approach; Elsevier Science; LWT - Food Science and Technology; 85; Parte A; 11-2017; 9-15
0023-6438
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://www.sciencedirect.com/science/article/pii/S0023643817304644
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.lwt.2017.06.060
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
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 12.993085

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