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dc.contributor.authorMunera, Sandra
dc.contributor.authorBlasco, José
dc.contributor.authorCubero, Sergio
dc.contributor.authorBesada, Cristina
dc.contributor.authorSalvador, Alejandra
dc.contributor.authorTalens, Pau
dc.contributor.authorAleixos, Nuria
dc.date.accessioned2021-09-02T10:06:03Z
dc.date.available2021-09-02T10:06:03Z
dc.date.issued2016es
dc.identifier.citationMunera, S., Blasco, J., Cubero, S., Besada, C., Salvador, A., Talens, P., & Aleixos, N. (2016). Maturity assessment of'Rojo Brillante'persimmon by hyperspectral imaging. In: CIGR-AgEng Conference, 1-7.es
dc.identifier.urihttp://hdl.handle.net/20.500.11939/7586
dc.description.abstractPersimmon cv. ‘Rojo Brillante’ is an astringent cultivar highly appreciated by consumers due to its good aspect, high size, sweetness and absence of seeds. However, this cultivar is very astringent and the fruit cannot be consumed until a high degree of overripeness with takes long time and makes the fruit difficult to handle. A method based on exposing fruit to high CO2 concentrations was recently developed to eliminate quickly the astringency preserving the firmness; however, the adequate duration of this treatment depends mostly on the maturity at harvest. Therefore the aim of this work was to investigate a non-destructive and reliable method based on hyperspectral imaging to assess the maturity of persimmon cv ‘Rojo Brillante’ before deastringency treatments. For this purpose, 150 persimmon fruits were harvested at three different stages of commercial maturity and flesh firmness was determined after the image acquisition. Hyperspectral images of each fruit were taken using a hyperspectral system based on two liquid crystal tuneable filters, sensitive in the spectral range 420-1080 nm. Partial Least Square-Discriminant Analysis (PLS-DA) was used on the hyperspectral images to select optimal wavelengths and classify persimmon fruits by maturity. The results achieved 90.1% of correct classification using six selected wavelengths. Additionally, flesh firmness was predicted by using partial least square regression (PLS-R) and the selected wavelengths. A R2 of 0.80 and a square error of prediction (SEP) of 4.34 N were obtained. All of these results were considered as good for a non-invasive maturity assessment technique of ‘Rojo Brillante’ persimmon.es
dc.language.isoeses
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectInternal qualityes
dc.titleMaturity assessment of ‘Rojo Brillante’ persimmon by Hyperspectral Imaginges
dc.typeconferenceObjectes
dc.authorAddressmunera_san@gva.eses
dc.entidadIVIACentro de Agroingenieríaes
dc.identifier.urlhttps://conferences.au.dk/uploads/tx_powermail/2016cigr_ageng_full_paper_smp.pdfes
dc.page.final7es
dc.page.initial1es
dc.relation.conferenceDate2016-06-26
dc.relation.conferenceName4th CIGR International Conference of Agricultural Engineering (CIGR-AgEng2016).es
dc.relation.conferencePlaceAarhus, Denmarkes
dc.relation.projectIDThis work has been partially funded by the Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria de España (INIA) through research projects RTA2012-00062-C04-01, RTA2012-00062-C04-03 and RTA2013-00043-C02 with the support of European FEDER funds and by the Conselleria d' Educació, Investigació, Cultura i Esport, Generalitat Valenciana, through the project AICO/2015/122.es
dc.rights.accessRightsopenAccesses
dc.source.typeelectronicoes
dc.subject.agrisQ01 Food science and technologyes
dc.subject.agrisN01 Agricultural engineeringes
dc.subject.agrisJ10 Handling, transport, storage and protection of agricultural productses
dc.subject.agrovocFruites
dc.subject.agrovocClassificationes
dc.subject.agrovocPredictiones
dc.subject.agrovocComputer visiones


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Atribución-NoComercial-SinDerivadas 3.0 España
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España