Quality Control of Olive Oils Using Machine Learning and Electronic Nose

dc.contributor.authorOrdukaya, Emre
dc.contributor.authorKarlik, Bekir
dc.date.accessioned2020-03-26T19:42:25Z
dc.date.available2020-03-26T19:42:25Z
dc.date.issued2017
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThe adulteration of olive oils can be detected with chemical test. This is very expensive and takes very long time. Thus, this study is focused on reducing both time and cost. For this purpose, the raw data has been collected from olive oils by using an e-nose from different regions in Balikesir in Turkey. This study presents two methods to analyze quality control of olive oils. In the first method, 32 inputs are applied to the classifiers directly. In the second, 32-input collected data are reduced to 8 inputs by Principal Component Analysis. These reduced data as 8 inputs are applied to the classifiers. Different machine learning classifiers such as Naive Bayesian,.. - NearestNeighbors (kappa- NN), Linear Discriminate Analysis (LDA), Decision Tree, ArtificialNeuralNetworks (ANN), and Support Vector Machine (SVM) were used. Then performances of these classifiers were compared according to their accuracies.en_US
dc.identifier.doi10.1155/2017/9272404en_US
dc.identifier.issn0146-9428en_US
dc.identifier.issn1745-4557en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://dx.doi.org/10.1155/2017/9272404
dc.identifier.urihttps://hdl.handle.net/20.500.12395/35416
dc.identifier.wosWOS:000414104600001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWILEY-HINDAWIen_US
dc.relation.ispartofJOURNAL OF FOOD QUALITYen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.titleQuality Control of Olive Oils Using Machine Learning and Electronic Noseen_US
dc.typeArticleen_US

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