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dc.contributor.authorLorente, Delia
dc.contributor.authorAleixos, Nuria
dc.contributor.authorGómez-Sanchís, Juan
dc.contributor.authorCubero, Sergio 
dc.contributor.authorBlasco, José 
dc.date.accessioned2017-06-01T10:12:32Z
dc.date.available2017-06-01T10:12:32Z
dc.date.issued2013
dc.identifier.citationLorente, Delia, Aleixos, N., Gomez-Sanchis, J., Cubero, Sergio, Blasco, J. (2013). Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks. Food and Bioprocess Technology, 6(2), 530-541.
dc.identifier.issn1935-5130
dc.identifier.urihttp://hdl.handle.net/20.500.11939/5544
dc.description.abstractEarly automatic detection of fungal infections in post-harvest citrus fruits is especially important for the citrus industry because only a few infected fruits can spread the infection to a whole batch during operations such as storage or exportation, thus causing great economic losses. Nowadays, this detection is carried out manually by trained workers illuminating the fruit with dangerous ultraviolet lighting. The use of hyperspectral imaging systems makes it possible to advance in the development of systems capable of carrying out this detection process automatically. However, these systems present the disadvantage of generating a huge amount of data, which must be selected in order to achieve a result that is useful to the sector. This work proposes a methodology to select features in multi-class classification problems using the receiver operating characteristic curve, in order to detect rottenness in citrus fruits by means of hyperspectral images. The classifier used is a multilayer perceptron, trained with a new learning algorithm called extreme learning machine. The results are obtained using images of mandarins with the pixels labelled in five different classes: two kinds of sound skin, two kinds of decay and scars. This method yields a reduced set of features and a classification success rate of around 89%.
dc.language.isoen
dc.titleSelection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks
dc.typearticle
dc.authorAddressInstituto Valenciano de Investigaciones Agrarias (IVIA), Carretera CV-315, Km. 10’7, 46113 Moncada (Valencia), Españaes
dc.date.issuedFreeFormFEB 2013
dc.entidadIVIACentro de Agroingeniería
dc.identifier.doi10.1007/s11947-011-0737-x
dc.journal.issueNumber2
dc.journal.titleFood and Bioprocess Technology
dc.journal.volumeNumber6
dc.page.final541
dc.page.initial530
dc.rights.accessRightsopenAccess
dc.source.typeImpreso
dc.type.hasVersionacceptedVersion


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