Elemental tracer determination and modeling for geographical origin designation of sweet oranges

Autores
Hidalgo, Melisa Jazmin; Pérez Rodríguez, Michael; Gaiad, José Emilio; Goicoechea, Hector Casimiro; Mendoza, Alberto; Pellerano, Roberto Gerardo
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Sweet oranges have long been an integral part of global health and culinary practices, offering a wealth of nutrients and bioactive compounds. Ensuring the authenticity of these citrus fruits is essential for maintaining consumer confidence, promoting transparency in sourcing, and protecting producers´ reputations in the marketplace. In this study, we explored the feasibility of using multi-element profiling combined with pattern recognition algorithms to trace the origin of sweet orange samples. To achieve this, we employed an optimized microwave plasma atomic emission spectroscopy (MP-AES) method 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. Support vector machine (SVM), random forest (RF), and gradient boosting tree (GBT) models were then built using the collected data to identify elemental tracer´s indicative of origin. Based on a comprehensive evaluation of overall accuracy, receiver operating characteristic (ROC) curves, and area under the curve (AUC), the GBT model demonstrated the best classification performance, achieving a 96.5 % correct prediction rate on test samples, as confirmed by the ROC curve (AUC = 0.973). Consequently, this approach provides compelling evidence for the potential utility of MP-AES combined with supervised modeling to determine the geographic origin of sweet oranges produced in Argentina, thereby contributing to consumer protection against fraud.
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. 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: 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. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Mendoza, Alberto. Instituto Tecnológico de Monterrey; México
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
Materia
MP-AES
Classification modeling
Sweet orange fruits
Geographical origin designation
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:11336/244573
Acceder
id CONICETDig_389ed8a1c07a6273030e96f6f721a213
oai_identifier_str oai:ri.conicet.gov.ar:11336/244573
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Elemental tracer determination and modeling for geographical origin designation of sweet orangesHidalgo, Melisa JazminPérez Rodríguez, MichaelGaiad, José EmilioGoicoechea, Hector CasimiroMendoza, AlbertoPellerano, Roberto GerardoMP-AESClassification modelingSweet orange fruitsGeographical origin designationhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1Sweet oranges have long been an integral part of global health and culinary practices, offering a wealth of nutrients and bioactive compounds. Ensuring the authenticity of these citrus fruits is essential for maintaining consumer confidence, promoting transparency in sourcing, and protecting producers´ reputations in the marketplace. In this study, we explored the feasibility of using multi-element profiling combined with pattern recognition algorithms to trace the origin of sweet orange samples. To achieve this, we employed an optimized microwave plasma atomic emission spectroscopy (MP-AES) method 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. Support vector machine (SVM), random forest (RF), and gradient boosting tree (GBT) models were then built using the collected data to identify elemental tracer´s indicative of origin. Based on a comprehensive evaluation of overall accuracy, receiver operating characteristic (ROC) curves, and area under the curve (AUC), the GBT model demonstrated the best classification performance, achieving a 96.5 % correct prediction rate on test samples, as confirmed by the ROC curve (AUC = 0.973). Consequently, this approach provides compelling evidence for the potential utility of MP-AES combined with supervised modeling to determine the geographic origin of sweet oranges produced in Argentina, thereby contributing to consumer protection against fraud.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; 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. 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: 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. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Mendoza, Alberto. Instituto Tecnológico de Monterrey; MéxicoFil: 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; ArgentinaElsevier2024-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/244573Hidalgo, Melisa Jazmin; Pérez Rodríguez, Michael; Gaiad, José Emilio; Goicoechea, Hector Casimiro; Mendoza, Alberto; et al.; Elemental tracer determination and modeling for geographical origin designation of sweet oranges; Elsevier; Journal of Agriculture and Food Research; 18; 9-2024; 1-82666-1543CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S266615432400423Xinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.jafr.2024.101386info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:47:58Zoai:ri.conicet.gov.ar:11336/244573instacron: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 09:47:59.225CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Elemental tracer determination and modeling for geographical origin designation of sweet oranges
title Elemental tracer determination and modeling for geographical origin designation of sweet oranges
spellingShingle Elemental tracer determination and modeling for geographical origin designation of sweet oranges
Hidalgo, Melisa Jazmin
MP-AES
Classification modeling
Sweet orange fruits
Geographical origin designation
title_short Elemental tracer determination and modeling for geographical origin designation of sweet oranges
title_full Elemental tracer determination and modeling for geographical origin designation of sweet oranges
title_fullStr Elemental tracer determination and modeling for geographical origin designation of sweet oranges
title_full_unstemmed Elemental tracer determination and modeling for geographical origin designation of sweet oranges
title_sort Elemental tracer determination and modeling for geographical origin designation of sweet oranges
dc.creator.none.fl_str_mv Hidalgo, Melisa Jazmin
Pérez Rodríguez, Michael
Gaiad, José Emilio
Goicoechea, Hector Casimiro
Mendoza, Alberto
Pellerano, Roberto Gerardo
author Hidalgo, Melisa Jazmin
author_facet Hidalgo, Melisa Jazmin
Pérez Rodríguez, Michael
Gaiad, José Emilio
Goicoechea, Hector Casimiro
Mendoza, Alberto
Pellerano, Roberto Gerardo
author_role author
author2 Pérez Rodríguez, Michael
Gaiad, José Emilio
Goicoechea, Hector Casimiro
Mendoza, Alberto
Pellerano, Roberto Gerardo
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv MP-AES
Classification modeling
Sweet orange fruits
Geographical origin designation
topic MP-AES
Classification modeling
Sweet orange fruits
Geographical origin designation
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 have long been an integral part of global health and culinary practices, offering a wealth of nutrients and bioactive compounds. Ensuring the authenticity of these citrus fruits is essential for maintaining consumer confidence, promoting transparency in sourcing, and protecting producers´ reputations in the marketplace. In this study, we explored the feasibility of using multi-element profiling combined with pattern recognition algorithms to trace the origin of sweet orange samples. To achieve this, we employed an optimized microwave plasma atomic emission spectroscopy (MP-AES) method 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. Support vector machine (SVM), random forest (RF), and gradient boosting tree (GBT) models were then built using the collected data to identify elemental tracer´s indicative of origin. Based on a comprehensive evaluation of overall accuracy, receiver operating characteristic (ROC) curves, and area under the curve (AUC), the GBT model demonstrated the best classification performance, achieving a 96.5 % correct prediction rate on test samples, as confirmed by the ROC curve (AUC = 0.973). Consequently, this approach provides compelling evidence for the potential utility of MP-AES combined with supervised modeling to determine the geographic origin of sweet oranges produced in Argentina, thereby contributing to consumer protection against fraud.
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. 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: 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. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Fil: Mendoza, Alberto. Instituto Tecnológico de Monterrey; México
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
description Sweet oranges have long been an integral part of global health and culinary practices, offering a wealth of nutrients and bioactive compounds. Ensuring the authenticity of these citrus fruits is essential for maintaining consumer confidence, promoting transparency in sourcing, and protecting producers´ reputations in the marketplace. In this study, we explored the feasibility of using multi-element profiling combined with pattern recognition algorithms to trace the origin of sweet orange samples. To achieve this, we employed an optimized microwave plasma atomic emission spectroscopy (MP-AES) method 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. Support vector machine (SVM), random forest (RF), and gradient boosting tree (GBT) models were then built using the collected data to identify elemental tracer´s indicative of origin. Based on a comprehensive evaluation of overall accuracy, receiver operating characteristic (ROC) curves, and area under the curve (AUC), the GBT model demonstrated the best classification performance, achieving a 96.5 % correct prediction rate on test samples, as confirmed by the ROC curve (AUC = 0.973). Consequently, this approach provides compelling evidence for the potential utility of MP-AES combined with supervised modeling to determine the geographic origin of sweet oranges produced in Argentina, thereby contributing to consumer protection against fraud.
publishDate 2024
dc.date.none.fl_str_mv 2024-09
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/11336/244573
Hidalgo, Melisa Jazmin; Pérez Rodríguez, Michael; Gaiad, José Emilio; Goicoechea, Hector Casimiro; Mendoza, Alberto; et al.; Elemental tracer determination and modeling for geographical origin designation of sweet oranges; Elsevier; Journal of Agriculture and Food Research; 18; 9-2024; 1-8
2666-1543
CONICET Digital
CONICET
url http://hdl.handle.net/11336/244573
identifier_str_mv Hidalgo, Melisa Jazmin; Pérez Rodríguez, Michael; Gaiad, José Emilio; Goicoechea, Hector Casimiro; Mendoza, Alberto; et al.; Elemental tracer determination and modeling for geographical origin designation of sweet oranges; Elsevier; Journal of Agriculture and Food Research; 18; 9-2024; 1-8
2666-1543
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/S266615432400423X
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jafr.2024.101386
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
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_ 1842268894872469504
score 13.13397

Publicaciones similares