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

dc.contributor.authorKavdır, İsmail
dc.contributor.authorBüyükcan, Burak M.
dc.contributor.buuauthorKurtulmuş, Ferhat
dc.contributor.departmentUludağ Üniversitesi/Ziraat Fakültesi/Biyosistem Mühendisliği Bölümü.tr_TR
dc.contributor.researcheridR-8053-2016tr_TR
dc.contributor.scopusid15848202900tr_TR
dc.date.accessioned2023-11-01T08:22:24Z
dc.date.available2023-11-01T08:22:24Z
dc.date.issued2018-12
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.en_US
dc.identifier.citationKavdir, İ. vd. (2018). ''Classification of olives using FT-NIR spectroscopy, neural networks and statistical classifiers''. Journal of Food Measurement and Characterization, 12(4), 2493-2502.en_US
dc.identifier.endpage2502tr_TR
dc.identifier.issn2193-4126
dc.identifier.issn2193-4134
dc.identifier.issue4tr_TR
dc.identifier.scopus2-s2.0-85048813636tr_TR
dc.identifier.startpage2493tr_TR
dc.identifier.urihttps://doi.org/10.1007/s11694-018-9866-5
dc.identifier.urihttps://link.springer.com/article/10.1007/s11694-018-9866-5
dc.identifier.urihttp://hdl.handle.net/11452/34730
dc.identifier.volume12tr_TR
dc.identifier.wos000452363700026tr_TR
dc.indexed.scopusScopusen_US
dc.indexed.wosSCIEen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.collaborationYurt içitr_TR
dc.relation.journalJournal of Food Measurement and Characterizationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergitr_TR
dc.relation.tubitak104O555tr_TR
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFood science & technologyen_US
dc.subjectFT-NIR spectroscopyen_US
dc.subjectOliveen_US
dc.subjectBruiseen_US
dc.subjectFly-defecten_US
dc.subjectNeural networksen_US
dc.subjectStatistical classifiersen_US
dc.subjectNear-infrared-spectroscopyen_US
dc.subjectBruise detectionen_US
dc.subjectDamageen_US
dc.subjectInfrared devicesen_US
dc.subjectNeural networksen_US
dc.subjectReflectionen_US
dc.subjectSpectrometersen_US
dc.subjectStatisticsen_US
dc.subjectBruiseen_US
dc.subjectClassification approachen_US
dc.subjectClassification performanceen_US
dc.subjectClassification resultsen_US
dc.subjectFT-NIR spectroscopyen_US
dc.subjectOliveen_US
dc.subjectReflectance spectrumen_US
dc.subjectStatistical classifieren_US
dc.subjectClassification (of information)en_US
dc.subject.scopusHyperspectral Imaging; Total Volatile Basic Nitrogen; Fruiten_US
dc.subject.wosFood science & technologyen_US
dc.titleClassification of olives using FT-NIR spectroscopy, neural networks and statistical classifiersen_US
dc.typeArticle
dc.wos.quartileQ3en_US

Files

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: