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dc.contributor.authorMunera, Sandra
dc.contributor.authorAmigo, Jose Manuel
dc.contributor.authorAleixos, Nuria
dc.contributor.authorTalens, Pau
dc.contributor.authorCubero, Sergio
dc.contributor.authorBlasco, Jose
dc.date.accessioned2017-12-12T15:45:38Z
dc.date.available2017-12-12T15:45:38Z
dc.date.issued2018es
dc.identifier.citationMunera S, Amigo J.M., Aleixos N, Talens P, Cubero S, Blasco J (2018). Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine. Food Control, 86, 1-10.es
dc.identifier.issn0956-7135
dc.identifier.urihttp://hdl.handle.net/20.500.11939/5740
dc.description.abstractProduct inspection is essential to ensure good quality and to avoid fraud. New nectarine cultivars with similar external appearance but different physicochemical properties may be mixed in the market, causing confusion and rejection among consumers, and consequently affecting sales and prices. Hyperspectral reflectance imaging in the range of 450–1040 nm was studied as a non-destructive method to differentiate two cultivars of nectarines with a very similar appearance but different taste. Partial least squares discriminant analysis (PLS-DA) was used to develop a prediction model to distinguish intact fruits of the cultivars using pixel-wise and mean spectrum approaches, and then the model was projected onto the complete surface of fruits allowing visual inspection. The results indicated that mean spectrum of the fruit was the most accurate method, a correct discrimination rate of 94% being achieved. Wavelength selection reduced the dimensionality of the hyperspectral images using the regression coefficients of the PLS-DA model. An accuracy of 96% was obtained by using 14 optimal wavelengths, whereas colour imaging and a trained inspection panel achieved a rate of correct classification of only 57% of the fruits.es
dc.language.isoenes
dc.publisherElsevieres
dc.subjectStone fruites
dc.subjectQuality controles
dc.subjectCultivar discriminationes
dc.subjectNon-destructivees
dc.subjectPLS-DAes
dc.subjectColour analysises
dc.subjectHyperspectral imagees
dc.titlePotential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarinees
dc.authorAddressInstituto Valenciano de Investigaciones Agrarias (IVIA). Ctra. Moncada-Náquera Km 4.5, 46113, Moncada, Valencia (Spain)es
dc.entidadIVIACentro de Agroingenieríaes
dc.identifier.doi10.1016/j.foodcont.2017.10.037.es
dc.identifier.urlhttp://www.sciencedirect.com/science/article/pii/S0956713517305224?via%3Dihubes
dc.journal.issueNumber86es
dc.journal.titleFood Controles
dc.page.final10es
dc.page.initial1es
dc.relation.projectIDSistemas no destructivos para la determinación automática de la calidad interna de frutas en línea utilizando métodos ópticos e información espectral INIA (RTA2015-00078-00-00)es
dc.relation.projectIDSistemas de sensado de interés agrario. Incorporación de tecnologías ópticas para evaluar la cantidad y calidad de la cosecha (IVIA 51431)es
dc.source.typeimpresoes
dc.subject.agrisQ01 Food science and technologyes
dc.subject.agrovocStone fruitses
dc.subject.agrovocCultivarses
dc.subject.agrovocQuality controlses
dc.subject.agrovocImage processinges


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