The classification of leek seeds based on fluorescence spectroscopic data using machine learning

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dc.authoridRopelewska, Ewa/0000-0001-8891-236X
dc.authoridSlavova, Vanya/0000-0003-0765-4341
dc.contributor.authorRopelewska, Ewa
dc.contributor.authorSabanci, Kadir
dc.contributor.authorSlavova, Vanya
dc.contributor.authorGenova, Stefka
dc.date.accessioned2024-01-22T12:22:12Z
dc.date.available2024-01-22T12:22:12Z
dc.date.issued2023
dc.departmentKMÜen_US
dc.description.abstractThe objective of this study was to distinguish leek seeds belonging to the Starozagorski kamush variety and two breeding lines based on the selected fluorescence spectroscopic data. The classification models were developed for three classes of Starozagorski kamush vs. breeding line 4 vs. breeding line 39 and pairs of classes of Starozagorski kamush vs. breeding line 4, Starozagorski kamush vs. breeding line 39, and breeding line 4 vs. breeding line 39. The traditional machine learning algorithms, such as PART, Logistic, Naive Bayes, Random Forest, IBk, and Filtered Classifier were applied. All three classes were distinguished with an average accuracy of up to 93.33% for models built using IBk and Filtered Classifier. In the case of each model, Starozagorski kamush variety was completely different (accuracy of 100%, precision, and F-measure, MCC (Matthews correlation coefficient), and ROC (receiver operating characteristic) area of 1.000) from breeding lines, and the mixing of cases was observed between breeding line 4 and breeding line 39. The models built for pairs of leek seed classes distinguished Starozagorski kamush and breeding line 4 with an average accuracy reaching 100% (Logistic, Naive Bayes, Random Forest, IBk). The classification accuracy of Starozagorski kamush and breeding line 39 also reached 100% (Logistic, Naive Bayes, Random Forest, IBk), whereas breeding line 4 and breeding line 39 were classified with an average accuracy of up to 80% (Logistic, Naive Bayes, Random Forest, Filtered Classifier). The proposed approach combining fluorescence spectroscopy and machine learning may be used in practice to distinguish leek seed varieties and breeding lines.en_US
dc.identifier.doi10.1007/s00217-023-04361-3
dc.identifier.endpage3226en_US
dc.identifier.issn1438-2377
dc.identifier.issn1438-2385
dc.identifier.issue12en_US
dc.identifier.scopus2-s2.0-85171160883
dc.identifier.scopusqualityQ1
dc.identifier.startpage3217en_US
dc.identifier.urihttps://doi.org/10.1007/s00217-023-04361-3
dc.identifier.urihttps://hdl.handle.net/11492/7825
dc.identifier.volume249en_US
dc.identifier.wosWOS:001064721100001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Sceince
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringeren_US
dc.relation.ispartofEuropean Food Research and Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzkmusnmz
dc.subjectLeek seed varietyen_US
dc.subjectBreeding linesen_US
dc.subjectFluorescence spectroscopyen_US
dc.subjectClassification modelsen_US
dc.subjectMachine learning algorithmsen_US
dc.titleThe classification of leek seeds based on fluorescence spectroscopic data using machine learningen_US
dc.typeArticle

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