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Ripeness monitoring of two cultivars of nectarine using VIS-NIR hyperspectral reflectance imaging

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Ripeness monitoring of two cultivars of nectarine using VIS-NIR hyperspectral reflectance imaging

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dc.contributor.author Munera, S. es_ES
dc.contributor.author Amigo, Jose Manuel es_ES
dc.contributor.author BLASCO IVARS, JOSE es_ES
dc.contributor.author Cubero, Sergio es_ES
dc.contributor.author Talens Oliag, Pau es_ES
dc.contributor.author Aleixos Borrás, María Nuria es_ES
dc.date.accessioned 2020-12-01T04:32:59Z
dc.date.available 2020-12-01T04:32:59Z
dc.date.issued 2017-12 es_ES
dc.identifier.issn 0260-8774 es_ES
dc.identifier.uri http://hdl.handle.net/10251/156115
dc.description.abstract [EN] Visible near-infrared (450-1040 nm) hyperspectral reflectance imaging was studied in order to assess the internal physicochemical properties and sensory perception of 'Big Top' and 'Magique' nectarines (Prunus persica L Batsch var. nucipersica) (yellow and white-flesh cultivar, respectively) during ripening using the Ripening Index (RPI) and the Internal Quality Index (IQI). Hyperspectral images of the intact fruits were acquired during the ripeness under controlled conditions, and their physicochemical properties (flesh firmness, total soluble solids, titratable acidity and flesh colour) were analysed. IQI and RPI were used to relate the spectral information obtained from nectarines with the physicochemical properties and the sensory perception of their maturity using Partial Least Square (PLS) regression with proper variable selection. Optimal results were obtained with R-2 values higher than 0.87 for the two indices and the two cultivars. The ripeness of each fruit could be visualised by projecting the PLS models of the IQI on the pixels of the fruits in the images, showing great potential for further monitoring of the evolution of intact nectarine ripeness in industrial setups. (C) 2017 Elsevier Ltd. All rights reserved. es_ES
dc.description.sponsorship This work has been partially funded by the Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria de España (INIA) through research project RTA2015-00078-00-00 with the support of European FEDER funds. Sandra Munera thanks INIA for the grant FPI-INIA num. 43 (CPR2014-0082), partially supported by European Union FSE funds. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Journal of Food Engineering es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Stone fruit es_ES
dc.subject Internal quality es_ES
dc.subject Ripeness es_ES
dc.subject Monitoring es_ES
dc.subject Hyperspectral image es_ES
dc.subject Computer vision es_ES
dc.subject.classification TECNOLOGIA DE ALIMENTOS es_ES
dc.subject.classification EXPRESION GRAFICA EN LA INGENIERIA es_ES
dc.title Ripeness monitoring of two cultivars of nectarine using VIS-NIR hyperspectral reflectance imaging es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.jfoodeng.2017.06.031 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//RTA2015-00078-00-00/ES/Sistemas no destructivos para la determinación automática de la calidad interna de frutas en línea utilizando métodos ópticos e información espectral/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/INIA//CPR2014-0082/ es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Gráfica - Departament d'Enginyeria Gràfica es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Tecnología de Alimentos - Departament de Tecnologia d'Aliments es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Mecanización y Tecnología Agraria - Departament de Mecanització i Tecnologia Agrària es_ES
dc.description.bibliographicCitation Munera, S.; Amigo, JM.; Blasco Ivars, J.; Cubero, S.; Talens Oliag, P.; Aleixos Borrás, MN. (2017). Ripeness monitoring of two cultivars of nectarine using VIS-NIR hyperspectral reflectance imaging. Journal of Food Engineering. 214:29-39. https://doi.org/10.1016/j.jfoodeng.2017.06.031 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.jfoodeng.2017.06.031 es_ES
dc.description.upvformatpinicio 29 es_ES
dc.description.upvformatpfin 39 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 214 es_ES
dc.relation.pasarela S\345854 es_ES
dc.contributor.funder Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria es_ES
dc.contributor.funder Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES


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