Classification of olives using FT-NIR spectroscopy, neural networks and statistical classifiers

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dc.authoridkavdir, ismail/0000-0002-8720-2101
dc.contributor.authorKavdir, Ismail
dc.contributor.authorBuyukcan, M. Burak
dc.contributor.authorKurtulmus, Ferhat
dc.date.accessioned2025-01-27T20:47:22Z
dc.date.available2025-01-27T20:47:22Z
dc.date.issued2018
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractGreen olives (Olea europaea L. cv. Ayvalik') were classified based on their surface features such as existence of bruise and fly-defect using two NIR spectrometer readings of reflectance and transmittance, and classifiers such as artificial neural networks (ANN) and statistical (Ident and Cluster). Spectral readings were performed in the ranges of 780-2500 and 800-1725nm for reflectance and transmittance modes, respectively. Original spectral readings were used as input features to the classifiers. Diameter correction was applied on reflectance spectra used in ANN classifier expecting improved classification results. ANN classifier performed better in general compared to statistical classifiers. Classification performance in detecting bruised olives using diameter corrected reflectance features and ANN classifier was 99% while it was 98% for Ident and Cluster classification approaches using regular reflectance features. Classification between solid and fly-defected olives was performed with success rates of 93% using reflectance features and 58% using transmittance features with ANN classifier while statistical classifiers of Ident and Cluster performed between 52 and 78% success rates using the same spectral readings. ANN classifier resulted 92% classification success for the classification application considering three output classes of solid, bruised and fly-defected olives using reflectance features while it performed 57.3% success rate using transmittance features.
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [104O555]
dc.description.sponsorshipThe authors acknowledge the financial support of the Scientific and Technological Research Council of Turkey (TUBITAK, project 104O555) for this study, and thank Edremit Olive Growing Station for providing olives and Dr. Hanife Genc at the Department of Agricultural Biotechnology for the fly cages.
dc.identifier.doi10.1007/s11694-018-9866-5
dc.identifier.endpage2502
dc.identifier.issn2193-4126
dc.identifier.issn2193-4134
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85048813636
dc.identifier.scopusqualityQ1
dc.identifier.startpage2493
dc.identifier.urihttps://doi.org/10.1007/s11694-018-9866-5
dc.identifier.urihttps://hdl.handle.net/20.500.12428/24861
dc.identifier.volume12
dc.identifier.wosWOS:000452363700026
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofJournal of Food Measurement and Characterization
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20250125
dc.subjectFT-NIR spectroscopy
dc.subjectOlive
dc.subjectBruise
dc.subjectFly-defect
dc.subjectNeural networks
dc.subjectStatistical classifiers
dc.titleClassification of olives using FT-NIR spectroscopy, neural networks and statistical classifiers
dc.typeArticle

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