Application of Machine Learning for the Determination of Damaged Starch Ratio as an Alternative to Medcalf and Gilles Principle

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dc.authoridGunay, M. Erdem/0000-0003-1282-718X
dc.contributor.authorTapan, N. Alper
dc.contributor.authorGunay, M. Erdem
dc.contributor.authorYildirim, Nilufer
dc.date.accessioned2024-07-18T20:42:22Z
dc.date.available2024-07-18T20:42:22Z
dc.date.issued2023
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractAs an alternative to the conventional amperometric method used for the determination of damaged starch ratio in wheat flour, two machine learning techniques were applied to a database constructed of 6264 voltammetric data obtained at two different electrodes, two different potassium iodide concentrations, and three different damaged starch ratios. Lift maps were extracted using association rule mining from the voltammetric database to describe electrode behavior and sensitivity to Chopin Dubois units (UCD) values. K-nearest neighbors (KNN) algorithm applied to the voltammetric experiments was able to predict UCD with higher accuracy when KI concentration was low. In addition, current quartiles, scatter, and shifts in lift maps and distinct regions after KNN classification showed that higher sensitivity towards damaged starch ratio is achieved on GC electrode and at low KI concentration.en_US
dc.description.sponsorshipGazi University Scientific Research Projects [BAP 06/2018-12]en_US
dc.description.sponsorshipThis work was funded by Gazi University Scientific Research Projects, BAP #: 06/2018-12en_US
dc.identifier.doi10.1007/s12161-022-02442-9
dc.identifier.endpage614en_US
dc.identifier.issn1936-9751
dc.identifier.issn1936-976X
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85145180295en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage604en_US
dc.identifier.urihttps://doi.org/10.1007/s12161-022-02442-9
dc.identifier.urihttps://hdl.handle.net/11411/7257
dc.identifier.volume16en_US
dc.identifier.wosWOS:000906184900001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofFood Analytical Methodsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAssociation Rule Miningen_US
dc.subjectK-Nearest Neighborsen_US
dc.subjectDamaged Starch Ratioen_US
dc.subjectVoltammetryen_US
dc.subjectShear Degradationen_US
dc.subjectHigh-Performanceen_US
dc.subjectIodineen_US
dc.subjectOxidationen_US
dc.titleApplication of Machine Learning for the Determination of Damaged Starch Ratio as an Alternative to Medcalf and Gilles Principleen_US
dc.typeArticleen_US

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