Show simple item record

A Data Science Study For Determining Food Quality: An Application To Wine

dc.contributor.authorOzalp, A. E.
dc.contributor.authorAskerzade, I. N.
dc.date.accessioned2021-06-09T06:05:39Z
dc.date.available2021-06-09T06:05:39Z
dc.date.issued2019
dc.identifier.issn1303-5991
dc.identifier.urihttp://dx.doi.org/10.31801/cfsuasmas.469131
dc.identifier.urihttp://hdl.handle.net/11655/24825
dc.description.abstractIn this paper, wine quality is investigated based on physicochemical ingredients which include fixed acidity, volatile acidity, citric acid, residual sugar, chloride, free sulfur dioxide, total sulfur dioxide, density, pH, sulphate and alcohol, by ANFIS (Adaptive Neuro Fuzzy Inference System) method and by random forest algorithm which is a powerful classification algorithm. Although this study specifically investigate the relation between physicochemical ingredients and the quality of wine, the results can be adaped to determination of the quality of any food product in terms of the ingredients.
dc.language.isoen
dc.relation.isversionof10.31801/cfsuasmas.469131
dc.rightsAttribution 4.0 United States
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectAdaptive Neuro Fuzzy Inference System
dc.subjectdata science
dc.subjectfuzzy logic
dc.subjectrandom forest algorithm
dc.titleA Data Science Study For Determining Food Quality: An Application To Wine
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.relation.journalCommunications Faculty Of Sciences University Of Ankara-Series A1 Mathematics And Statistics
dc.contributor.departmentİstatistik
dc.identifier.volume68
dc.identifier.issue1
dc.description.indexWoS


Files in this item

Name:
21.pdf
Size:
505.9Kb
Format:
PDF
View/Open

This item appears in the following Collection(s)

Show simple item record

Attribution 4.0 United States
Except where otherwise noted, this item's license is described as Attribution 4.0 United States