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dc.contributor.authorTorres Lezcano, Estanis
dc.contributor.authorRecasens Guinjuan, Inmaculada
dc.contributor.authorAlegre Castellví, Simó
dc.date.accessioned2021-11-23T12:08:30Z
dc.date.available2021-11-23T12:08:30Z
dc.date.issued2021
dc.identifier.issn2171-9292
dc.identifier.urihttp://hdl.handle.net/10459.1/72397
dc.description.abstractAim of study: A portable VIS/NIR spectrometer and chemometric techniques were combined to identify bitter pit (BP) in Golden apples. Area of study: Worldwide Material and methods: Three different classification algorithms – linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and support-vector machine (SVM) –were used in two experiments. In experiment #1, VIS/NIR measurements were carried out at postharvest on apples previously classified according to 3 classes (class 1: non-BP; class 2: slight symptoms; class 3: severe symptoms). In experiment #2, VIS/NIR measurements were carried out on healthy apples collected before harvest to determinate the capacity of the classification algorithms for detecting BP prior to the appearance of symptoms. Main results: In the experiement #1, VIS/NIR spectroscopy showed great potential in pitted apples detection with visibly symptoms (accuracies of 75–81%). The linear classifier LDA performed better than the multivariate non-linear QDA and SVM classifiers in discriminating between healthy and bitter pitted apples. In the experiment #2, the accuracy to predict bitter pit prior to the appearance of visible symptoms decreased to 44–57%. Research highlights: The identification of apples with bitter pit through VIS/NIR spectroscopy may be due to chlorophyll degradation and/or changes in intercellular water in fruit tissue.ca_ES
dc.language.isoengca_ES
dc.publisherInstituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA)ca_ES
dc.relation.isformatofReproducció del document publicat a https://doi.org/10.5424/sjar/2021191-15656ca_ES
dc.relation.ispartofSpanish Journal of Agricultural Research, 2021, vol. 19, núm. 1, e1001ca_ES
dc.rightscc-by (c) INIA, 2021ca_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectPrediction of disordersca_ES
dc.subjectCalcium disordersca_ES
dc.subjectMulticlass classificationca_ES
dc.subjectBinary-class classificationca_ES
dc.titlePotential of VIS/NIR spectroscopy to detect and predict bitter pit in ‘Golden Smoothee’ applesca_ES
dc.typeinfo:eu-repo/semantics/articleca_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.identifier.doihttps://doi.org/10.5424/sjar/2021191-15656


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Except where otherwise noted, this item's license is described as cc-by (c) INIA, 2021