Non-targeted approach to detect pistachio authenticity based on digital image processing and hybrid machine learning model

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dc.authoriddogan, nurcan/0000-0001-5414-1819
dc.contributor.authorDogan, Cemhan
dc.contributor.authorSehirli, Eftal
dc.contributor.authorDogan, Nurcan
dc.contributor.authorBuran, Ilkay
dc.date.accessioned2024-09-29T15:54:33Z
dc.date.available2024-09-29T15:54:33Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractIn this study, we propose a new method for detecting green pea adulteration in pistachio based on digital image and machine learning (ML). An algorithm was built using digital image processing techniques to detect region of interest (ROI) on adulterated pistachio images and a hybrid ML to classify the level of adulteration as class 1 (%0), class 2 (%10), class 3 (%20), class 4 (%30), class 5 (%40), and class 6 (%50) in a fully automated way. A dataset with size of 1254 x 15 were created. Training set and test set with the rate of 80% and 20% based on fivefold cross validation were created. Decision tree, random forest (RF), k-nearest neighboring, support vector machines, naive bayes and artificial neural network (ANN) are performed and compared to classify the level of adulteration in two steps as direct and binary classification. ANN has achieved the highest results as 93.65% of accuracy and 0.87 of Matthews correlation coefficient (MCC) based on direct classification to separate class1, class 2, class 5, and class 6 from class 3 and class 4. RF has achieved the highest results as 89.56% of accuracy and 0.79 of MCC based on binary classification to separate class3 from class 4. As a result of this, a hybrid ML model including ANN and RF in the form of a tree structure to classify the level of pistachio adulterated images was built in this study.en_US
dc.identifier.doi10.1007/s11694-022-01671-0
dc.identifier.endpage1702en_US
dc.identifier.issn2193-4126
dc.identifier.issn2193-4134
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85143617368en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1693en_US
dc.identifier.urihttps://doi.org/10.1007/s11694-022-01671-0
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4131
dc.identifier.volume17en_US
dc.identifier.wosWOS:000896512400002en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of Food Measurement and Characterizationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDigital image processingen_US
dc.subjectMachine learningen_US
dc.subjectPistachioen_US
dc.subjectAdulterationen_US
dc.titleNon-targeted approach to detect pistachio authenticity based on digital image processing and hybrid machine learning modelen_US
dc.typeArticleen_US

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