Determination and modeling of multi-element markers for the geographical origin of sweet oranges from Argentina

Autores
Pellerano, Roberto Gerardo; Hidalgo, Melisa Jazmin; Pérez Rodríguez, Michael; Gaiad, José Emilio; Goicoechea, Hector Casimiro; Mendoza, Alberto
Año de publicación
2024
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Sweet oranges stand out as one of the most popular fruits in the market, mainly due to their broad consumer appeal. These citrus fruits are valued for their high nutraceutical value and constitute a rich natural source of various nutrients and bioactive compounds. Ensuring the authenticity of these fruits is essential to maintain consumer confidence, promote transparency in sourcing, and protect the reputation of producers in the marketplace. In this study, we investigated the feasibility of using multi-element profiles combined with pattern recognition algorithms to trace the origin of sweet orange samples. To achieve this goal, an optimized microwave plasma atomic emission spectroscopy (MP-AES) method was used to analyze the elemental composition (Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Sr, and Zn) of 183 orange samples from four production regions in northeastern Argentina. Exploratory analysis using principal component analysis (PCA) revealed pattern trends among the geographic similarities of the samples. Using the region of origin as a classification factor, support vector machine (SVM), random forest (RF), and gradient boosting tree (GBT) models were then built using the collected data to find elemental tracers of origin identity. The GBT model showed the best classification performance, achieving a 96.5% correct prediction rate on test samples, as confirmed by the ROC curve (AUC=0.973). This approach provides compelling evidence for the potential utility of MP-AES in combination with supervised modeling to determine the geographic origin of sweet orange fruit, thus helping to protect consumers from financial fraud.
Fil: Pellerano, Roberto Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina
Fil: Hidalgo, Melisa Jazmin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina
Fil: Pérez Rodríguez, Michael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina
Fil: Gaiad, José Emilio. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Departamento de Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina
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; Argentina
Fil: Mendoza, Alberto. Instituto Tecnologico de Monterrey. Escuela de Ingenieria y Ciencias.; México
XIX Chemometrics in Analytical Chemistry
Santa Fe
Argentina
Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas
Asociación Argentina de Químicos Analíticos
Materia
TRACEABILITY
FOODS
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:11336/291486
Acceder
id CONICETDig_2bd5ccbf55ecafbd8bc6aff342cd43c8
oai_identifier_str oai:ri.conicet.gov.ar:11336/291486
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Determination and modeling of multi-element markers for the geographical origin of sweet oranges from ArgentinaPellerano, Roberto GerardoHidalgo, Melisa JazminPérez Rodríguez, MichaelGaiad, José EmilioGoicoechea, Hector CasimiroMendoza, AlbertoTRACEABILITYFOODShttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1Sweet oranges stand out as one of the most popular fruits in the market, mainly due to their broad consumer appeal. These citrus fruits are valued for their high nutraceutical value and constitute a rich natural source of various nutrients and bioactive compounds. Ensuring the authenticity of these fruits is essential to maintain consumer confidence, promote transparency in sourcing, and protect the reputation of producers in the marketplace. In this study, we investigated the feasibility of using multi-element profiles combined with pattern recognition algorithms to trace the origin of sweet orange samples. To achieve this goal, an optimized microwave plasma atomic emission spectroscopy (MP-AES) method was used to analyze the elemental composition (Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Sr, and Zn) of 183 orange samples from four production regions in northeastern Argentina. Exploratory analysis using principal component analysis (PCA) revealed pattern trends among the geographic similarities of the samples. Using the region of origin as a classification factor, support vector machine (SVM), random forest (RF), and gradient boosting tree (GBT) models were then built using the collected data to find elemental tracers of origin identity. The GBT model showed the best classification performance, achieving a 96.5% correct prediction rate on test samples, as confirmed by the ROC curve (AUC=0.973). This approach provides compelling evidence for the potential utility of MP-AES in combination with supervised modeling to determine the geographic origin of sweet orange fruit, thus helping to protect consumers from financial fraud.Fil: Pellerano, Roberto Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; ArgentinaFil: Hidalgo, Melisa Jazmin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; ArgentinaFil: Pérez Rodríguez, Michael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; ArgentinaFil: Gaiad, José Emilio. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Departamento de Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; ArgentinaFil: 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; ArgentinaFil: Mendoza, Alberto. Instituto Tecnologico de Monterrey. Escuela de Ingenieria y Ciencias.; MéxicoXIX Chemometrics in Analytical ChemistrySanta FeArgentinaUniversidad Nacional del Litoral. Facultad de Bioquímica y Ciencias BiológicasAsociación Argentina de Químicos AnalíticosUniversidad Nacional del Litoral2024info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectCongresoBookhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/291486Determination and modeling of multi-element markers for the geographical origin of sweet oranges from Argentina; XIX Chemometrics in Analytical Chemistry; Santa Fe; Argentina; 2024; 1-1978-987-692-408-5CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.fbcb.unl.edu.ar/cac2024/Nacionalinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-08-25T15:49:31Zoai:ri.conicet.gov.ar:11336/291486instacron: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:34982026-08-25 15:49:32.085CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Determination and modeling of multi-element markers for the geographical origin of sweet oranges from Argentina
title Determination and modeling of multi-element markers for the geographical origin of sweet oranges from Argentina
spellingShingle Determination and modeling of multi-element markers for the geographical origin of sweet oranges from Argentina
Pellerano, Roberto Gerardo
TRACEABILITY
FOODS
title_short Determination and modeling of multi-element markers for the geographical origin of sweet oranges from Argentina
title_full Determination and modeling of multi-element markers for the geographical origin of sweet oranges from Argentina
title_fullStr Determination and modeling of multi-element markers for the geographical origin of sweet oranges from Argentina
title_full_unstemmed Determination and modeling of multi-element markers for the geographical origin of sweet oranges from Argentina
title_sort Determination and modeling of multi-element markers for the geographical origin of sweet oranges from Argentina
dc.creator.none.fl_str_mv Pellerano, Roberto Gerardo
Hidalgo, Melisa Jazmin
Pérez Rodríguez, Michael
Gaiad, José Emilio
Goicoechea, Hector Casimiro
Mendoza, Alberto
author Pellerano, Roberto Gerardo
author_facet Pellerano, Roberto Gerardo
Hidalgo, Melisa Jazmin
Pérez Rodríguez, Michael
Gaiad, José Emilio
Goicoechea, Hector Casimiro
Mendoza, Alberto
author_role author
author2 Hidalgo, Melisa Jazmin
Pérez Rodríguez, Michael
Gaiad, José Emilio
Goicoechea, Hector Casimiro
Mendoza, Alberto
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv TRACEABILITY
FOODS
topic TRACEABILITY
FOODS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Sweet oranges stand out as one of the most popular fruits in the market, mainly due to their broad consumer appeal. These citrus fruits are valued for their high nutraceutical value and constitute a rich natural source of various nutrients and bioactive compounds. Ensuring the authenticity of these fruits is essential to maintain consumer confidence, promote transparency in sourcing, and protect the reputation of producers in the marketplace. In this study, we investigated the feasibility of using multi-element profiles combined with pattern recognition algorithms to trace the origin of sweet orange samples. To achieve this goal, an optimized microwave plasma atomic emission spectroscopy (MP-AES) method was used to analyze the elemental composition (Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Sr, and Zn) of 183 orange samples from four production regions in northeastern Argentina. Exploratory analysis using principal component analysis (PCA) revealed pattern trends among the geographic similarities of the samples. Using the region of origin as a classification factor, support vector machine (SVM), random forest (RF), and gradient boosting tree (GBT) models were then built using the collected data to find elemental tracers of origin identity. The GBT model showed the best classification performance, achieving a 96.5% correct prediction rate on test samples, as confirmed by the ROC curve (AUC=0.973). This approach provides compelling evidence for the potential utility of MP-AES in combination with supervised modeling to determine the geographic origin of sweet orange fruit, thus helping to protect consumers from financial fraud.
Fil: Pellerano, Roberto Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina
Fil: Hidalgo, Melisa Jazmin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina
Fil: Pérez Rodríguez, Michael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina
Fil: Gaiad, José Emilio. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Departamento de Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina
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; Argentina
Fil: Mendoza, Alberto. Instituto Tecnologico de Monterrey. Escuela de Ingenieria y Ciencias.; México
XIX Chemometrics in Analytical Chemistry
Santa Fe
Argentina
Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas
Asociación Argentina de Químicos Analíticos
description Sweet oranges stand out as one of the most popular fruits in the market, mainly due to their broad consumer appeal. These citrus fruits are valued for their high nutraceutical value and constitute a rich natural source of various nutrients and bioactive compounds. Ensuring the authenticity of these fruits is essential to maintain consumer confidence, promote transparency in sourcing, and protect the reputation of producers in the marketplace. In this study, we investigated the feasibility of using multi-element profiles combined with pattern recognition algorithms to trace the origin of sweet orange samples. To achieve this goal, an optimized microwave plasma atomic emission spectroscopy (MP-AES) method was used to analyze the elemental composition (Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Sr, and Zn) of 183 orange samples from four production regions in northeastern Argentina. Exploratory analysis using principal component analysis (PCA) revealed pattern trends among the geographic similarities of the samples. Using the region of origin as a classification factor, support vector machine (SVM), random forest (RF), and gradient boosting tree (GBT) models were then built using the collected data to find elemental tracers of origin identity. The GBT model showed the best classification performance, achieving a 96.5% correct prediction rate on test samples, as confirmed by the ROC curve (AUC=0.973). This approach provides compelling evidence for the potential utility of MP-AES in combination with supervised modeling to determine the geographic origin of sweet orange fruit, thus helping to protect consumers from financial fraud.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
Congreso
Book
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
status_str publishedVersion
format conferenceObject
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/291486
Determination and modeling of multi-element markers for the geographical origin of sweet oranges from Argentina; XIX Chemometrics in Analytical Chemistry; Santa Fe; Argentina; 2024; 1-1
978-987-692-408-5
CONICET Digital
CONICET
url http://hdl.handle.net/11336/291486
identifier_str_mv Determination and modeling of multi-element markers for the geographical origin of sweet oranges from Argentina; XIX Chemometrics in Analytical Chemistry; Santa Fe; Argentina; 2024; 1-1
978-987-692-408-5
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.fbcb.unl.edu.ar/cac2024/
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
application/pdf
dc.coverage.none.fl_str_mv Nacional
dc.publisher.none.fl_str_mv Universidad Nacional del Litoral
publisher.none.fl_str_mv Universidad Nacional del Litoral
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_ 1874775847637876736
score 13.265058

Publicaciones similares