Fast tool based on electronic nose to predict olive fruit quality after harvest

uality analyses of oil from olive fruit are performed according to regulated procedures and in accredited la- boratories that are usually separated from the oil mill. These analytics include organoleptic features involving smelling by human experts. Therefore, oil features depend on the physicochemi...

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Autores: Martínez-Gila, Diego Manuel, Gámez-García, Javier, Bellincontro, Andrea, Mencarelli, Fabio, Gómez-Ortega, Juan
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2019
País:España
Institución:Universidad de Jaén
Repositorio:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
OAI Identifier:oai:ruja.ujaen.es:10953/4372
Acceso en línea:https://doi.org/10.1016/j.postharvbio.2019.111058
https://hdl.handle.net/10953/4372
Access Level:acceso abierto
Palabra clave:Electronic nose
Harvested olive fruit
Pattern recognition
Olive oil quality
Organoleptic assessment
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repository_id_str
spelling Fast tool based on electronic nose to predict olive fruit quality after harvestMartínez-Gila, Diego ManuelGámez-García, JavierBellincontro, AndreaMencarelli, FabioGómez-Ortega, JuanElectronic noseHarvested olive fruitPattern recognitionOlive oil qualityOrganoleptic assessmentuality analyses of oil from olive fruit are performed according to regulated procedures and in accredited la- boratories that are usually separated from the oil mill. These analytics include organoleptic features involving smelling by human experts. Therefore, oil features depend on the physicochemical conditions of the harvested fruit. An automatic and non-invasive system for monitoring and controlling the process in postharvest stages could optimize the quality of the processed oil. To validate this hypothesis we proposed a methodology based on an electronic nose sensor and pattern recognition algorithms to predict the quality of the oil to be processed from measurements on freshly harvested olive fruit. The pattern recognition algorithms applied were the Naïve Bayes (NB) classifier, the partial least squares discriminant analysis (PLSDA) and a multilayer perceptron (MLP) ar- tificial neural network. Using the measurements performed on 82 samples of olives, the best result was obtained with the MLP network, with 90.2 % success obtained in the classification of the virgin and extra virgin olive oil quality by applying 10-fold cross-validation. Integration of this methodology to virgin olive oil production allows prediction of the quality of the final oil from the olive fruit received from the farmer.This work has been partially supported by the project of the Ministry of Spain with reference DPI2016-78290-R. The authors thank the oil mill Picualia (www.piculia.com) for the olive samples provided to carry out this study. Also thank CM Europa (www.cmeuropa.com) for the organoleptic analysis.ELSEVIER202520252019info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://doi.org/10.1016/j.postharvbio.2019.111058https://hdl.handle.net/10953/4372reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaéninstname:Universidad de JaénInglésPostharvest Biology and TechnologyAtribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:ruja.ujaen.es:10953/43722026-06-24T12:41:07Z
dc.title.none.fl_str_mv Fast tool based on electronic nose to predict olive fruit quality after harvest
title Fast tool based on electronic nose to predict olive fruit quality after harvest
spellingShingle Fast tool based on electronic nose to predict olive fruit quality after harvest
Martínez-Gila, Diego Manuel
Electronic nose
Harvested olive fruit
Pattern recognition
Olive oil quality
Organoleptic assessment
title_short Fast tool based on electronic nose to predict olive fruit quality after harvest
title_full Fast tool based on electronic nose to predict olive fruit quality after harvest
title_fullStr Fast tool based on electronic nose to predict olive fruit quality after harvest
title_full_unstemmed Fast tool based on electronic nose to predict olive fruit quality after harvest
title_sort Fast tool based on electronic nose to predict olive fruit quality after harvest
dc.creator.none.fl_str_mv Martínez-Gila, Diego Manuel
Gámez-García, Javier
Bellincontro, Andrea
Mencarelli, Fabio
Gómez-Ortega, Juan
author Martínez-Gila, Diego Manuel
author_facet Martínez-Gila, Diego Manuel
Gámez-García, Javier
Bellincontro, Andrea
Mencarelli, Fabio
Gómez-Ortega, Juan
author_role author
author2 Gámez-García, Javier
Bellincontro, Andrea
Mencarelli, Fabio
Gómez-Ortega, Juan
author2_role author
author
author
author
dc.subject.none.fl_str_mv Electronic nose
Harvested olive fruit
Pattern recognition
Olive oil quality
Organoleptic assessment
topic Electronic nose
Harvested olive fruit
Pattern recognition
Olive oil quality
Organoleptic assessment
description uality analyses of oil from olive fruit are performed according to regulated procedures and in accredited la- boratories that are usually separated from the oil mill. These analytics include organoleptic features involving smelling by human experts. Therefore, oil features depend on the physicochemical conditions of the harvested fruit. An automatic and non-invasive system for monitoring and controlling the process in postharvest stages could optimize the quality of the processed oil. To validate this hypothesis we proposed a methodology based on an electronic nose sensor and pattern recognition algorithms to predict the quality of the oil to be processed from measurements on freshly harvested olive fruit. The pattern recognition algorithms applied were the Naïve Bayes (NB) classifier, the partial least squares discriminant analysis (PLSDA) and a multilayer perceptron (MLP) ar- tificial neural network. Using the measurements performed on 82 samples of olives, the best result was obtained with the MLP network, with 90.2 % success obtained in the classification of the virgin and extra virgin olive oil quality by applying 10-fold cross-validation. Integration of this methodology to virgin olive oil production allows prediction of the quality of the final oil from the olive fruit received from the farmer.
publishDate 2019
dc.date.none.fl_str_mv 2019
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1016/j.postharvbio.2019.111058
https://hdl.handle.net/10953/4372
url https://doi.org/10.1016/j.postharvbio.2019.111058
https://hdl.handle.net/10953/4372
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Postharvest Biology and Technology
dc.rights.none.fl_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
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 reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
instname:Universidad de Jaén
instname_str Universidad de Jaén
reponame_str RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
collection RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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