Ordukaya, EmreKarlik, Bekir2020-03-262020-03-2620161931-49731931-4981https://dx.doi.org/10.1002/tee.22250https://hdl.handle.net/20.500.12395/33628The aim of this study is to analyze the raw data collected from a fruit juice-alcohol mixture (a fruit juice-alcohol mixture and a fruit juice-multiple alcohol mixture) and the Halal authentication of a fruit juice-alcohol mixture with electronic nose. Machine learning techniques such as naive Bayesian classifier, K-nearest neighbors (K-NN), linear discriminant analysis (LDA), decision tree, artificial neural network (ANN), and support vector machine (SVM) were used to classify the feature of these collected raw data. There are three types of classification: the first one is a fruit juice and an alcohol mixture type; the second is a fruit juice and multiple alcohol mixture types, and the third is a Halal authentication of a fruit juice and alcohol mixture. We aimed at making cocktails with more successful results on the first two types of classification in the work. Also, we focused on Halal authentication of fruit juice-alcohol mixture in the third classification. (C) 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.en10.1002/tee.22250info:eu-repo/semantics/closedAccesssensor arrayselectronic nosesmachine learningclassification algorithmsArticle11S171S176Q3WOS:000384886700022Q4