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dc.contributor.authorMunera, Sandra 
dc.contributor.authorAncillo, Gema 
dc.contributor.authorPrieto, Andrés
dc.contributor.authorPalou, Lluís 
dc.contributor.authorAleixos, Nuria
dc.contributor.authorCubero, Sergio 
dc.contributor.authorBlasco, José 
dc.date.accessioned2023-09-21T07:32:24Z
dc.date.available2023-09-21T07:32:24Z
dc.date.issued2023es
dc.identifier.citationMunera, S., Ancillo, G., Prieto, A., Palou, L., Aleixos, N., Cubero, S. et al. (2023). Quantifying the ultraviolet-induced fluorescence intensity in green mould lesions of diverse citrus varieties: Towards automated detection of citrus decay in postharvest. Postharvest Biology and Technology, 204, 112468.es
dc.identifier.issn0925-5214 (Print ISSN)
dc.identifier.issn1873-2356 (Online ISSN)
dc.identifier.urihttps://hdl.handle.net/20.500.11939/8714
dc.description.abstractCitrus fungal infections developing during fruit storage and transportation can cause significant economic losses after harvest. The most important is caused by the fungus Penicillium digitatum, which infects the fruit through rind wounds and causes a rot lesion. The symptoms of decay are difficult to notice by the human eye in the initial stages of decay development because the colour of the lesion is very similar to that of the healthy rind. One method to detect this disease early is to illuminate the fruit with ultraviolet (UV) light since the disease causes visible fluorescence. Manual inspection exposes the workers to UV light, which is dangerous for their skin and eyes. An alternative is to use artificial vision systems. But not all varieties show the same level of fluorescence, and even some do not produce this phenomenon, making it challenging to create effective automatic detection systems based on image analysis. This work has studied and determined the fluorescence level of 104 varieties of oranges and mandarins using hyperspectral and colour imaging. The samples were inoculated with spores of the P. digitatum in controlled conditions. Images were captured exposing the fruit under UV light (380 nm) using a colour camera and a hyperspectral imaging system. The fluorescence level of each variety was measured using three colour coordinates and the hyperspectral images. Best correlations between the spectral and the colour-based systems were achieved using the green (G) colour coordinate of the RGB colour space (R2 =0.85). Navel and common oranges emit the most fluorescence, while 16 varieties (mostly blood oranges and other mandarins) have very low or undetectable fluorescence.es
dc.language.isoenes
dc.publisherElsevieres
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectFruit storagees
dc.subjectHyperspectral imaginges
dc.subjectColour imaginges
dc.subjectDetectiones
dc.titleQuantifying the ultraviolet-induced fluorescence intensity in green mould lesions of diverse citrus varieties: Towards automated detection of citrus decay in postharvestes
dc.typearticlees
dc.entidadIVIACentro de Agroingenieríaes
dc.entidadIVIACentro de Citricultura y Producción Vegetales
dc.entidadIVIACentro de Tecnología Post-recolecciónes
dc.identifier.doi10.1016/j.postharvbio.2023.112468es
dc.identifier.urlhttps://www.sciencedirect.com/science/article/abs/pii/S0925521423002296es
dc.journal.issueNumber204es
dc.journal.titlePostharvest Biology and Technologyes
dc.page.final112468es
dc.page.initial112468es
dc.relation.projectIDThis work was partially funded by projects AEI PID2019-107347RR-C31 and C32 with ERDF funds of the GVA 2021–2027, and GVA-PROMETEO CIPROM/2021/014. Andres Prieto thanks INIA for the FPI-INIA grant BES-2017-082419, partially supported by ESF. Sandra Munera thanks the Juan de la Cierva-Formación (FJC2021-047786-I), co-funded by MCIN/AEI/10.13039/501100011033 and NextGenerationEU /PRTR.es
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i/PID2019-107347RR-C31/ES/INSPECCION NO DESTRUCTIVA Y PREDICCION DE LA CALIDAD INTERNA Y PROPIEDADES DE LAS FRUTAS MEDIANTE ESPECTROSCOPIA VIS/NIR Y MODELOS BASADOS EN APRENDIZAJE PROFUNDOes
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i/PID2019-107347RR-C32/ES/INSPECCION Y PREDICCION NO DESTRUCTIVA DE CALIDAD INTERNA Y PROPIEDADES DE FRUTAS UTILIZANDO IMAGEN HIPERESPECTRAL VIS/NIR UTILIZANDO MODELOS BASADOS EN APRENDIZAJE PROFUNDOes
dc.rights.accessRightsopenAccesses
dc.source.typeelectronicoes
dc.subject.agrisH20 Plant diseaseses
dc.subject.agrisJ10 Handling, transport, storage and protection of agricultural productses
dc.subject.agrisQ02 Food processing and preservationes
dc.subject.agrisN20 Agricultural machinery and equipmentes
dc.subject.agrovocCitrus es
dc.subject.agrovocFungal diseases es
dc.subject.agrovocPostharvest diseases es
dc.subject.agrovocPenicillium digitatum es
dc.subject.agrovocFluorescence es
dc.type.hasVersionpublishedVersiones


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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