Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/47520
DC FieldValueLanguage
dc.contributor.authorMoscetti, Robertoit
dc.contributor.authorSaeys, Wouterit
dc.contributor.authorKeresztes, Janos C.it
dc.contributor.authorGoodarzi, Mohammadit
dc.contributor.authorCecchini, Massimoit
dc.contributor.authorMonarca, Daniloit
dc.contributor.authorMassantini, Riccardoit
dc.date.accessioned2022-04-12T11:05:52Z-
dc.date.available2022-04-12T11:05:52Z-
dc.date.issued2015it
dc.identifier.issn1935-5130it
dc.identifier.urihttp://hdl.handle.net/2067/47520-
dc.description.abstractA rapid, robust, unbiased, and inexpensive discriminant method capable of classifying hazelnut (Corylus avellana, L.) in compliance with the statements set out by the Commission Regulation (EC) No. 1284/2002 is economically important to the fresh and processed industries. Thus, in this study, the feasibility of high dynamic range (HDR) hyperspectral imaging for hazelnut kernel sorting (cv. Tonda Gentile Romana) of four quality classes (Class Extra, Class I, Class II, and Waste) has been investigated. Two different exposure times (5 and 8 ms) were selected for experiments, and the respective spectra were combined to obtain a HDR over the full spectral range. The illumination setup was optimized to improve the intensity and uniformity of the light along the field of view of the camera. PLS-DA was used to classify the kernels based on their spectra and the spectral pretreatment was optimized through an iterative routine. The performance of each PLS-DA model was defined based on its accuracy, sensitivity, and selectivity rates. All of the selected models provided a very good (>90 %) or good (>80 %) sensitivity and selectivity for the predefined classes. Misclassified kernels were primarily assigned to the low-quality classes (i.e., Class II and Waste). Moreover, the spatial domain was used to evaluate the feasibility of distinguishing hazelnut classes on the basis of their size and shape. It was found that hazelnut dimensions can be used to improve the accuracy of the classification of the kernels. Thanks to this combination of both spectral and spatial information spectral imaging could be used for quality sorting of hazelnuts.it
dc.language.isoengit
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleHazelnut Quality Sorting Using High Dynamic Range Short-Wave Infrared Hyperspectral Imagingit
dc.typearticle*
dc.identifier.doi10.1007/s11947-015-1503-2it
dc.identifier.scopus2-s2.0-84930276801it
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/84930276801it
dc.relation.journalFOOD AND BIOPROCESS TECHNOLOGYit
dc.relation.firstpage1593it
dc.relation.lastpage1604it
dc.relation.volume8it
dc.relation.issue7it
dc.type.refereeREF_1it
dc.type.miur262*
item.grantfulltextrestricted-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.fulltextWith Fulltext-
item.openairetypearticle-
item.cerifentitytypePublications-
crisitem.journal.journalissn1935-5130-
crisitem.journal.anceE194404-
Appears in Collections:A1. Articolo in rivista
Files in This Item:
File Description SizeFormat
hazelnut swir.pdf4.01 MBAdobe PDFView/Open
Show simple item record

SCOPUSTM   
Citations 5

36
Last Week
0
Last month
checked on Aug 11, 2022

Page view(s)

49
Last Week
0
Last month
19
checked on Aug 12, 2022

Download(s)

6
checked on Aug 12, 2022

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons