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    Fruit Juice-Alcohol Mixture Analysis Using Machine Learning and Electronic Nose
    (WILEY-BLACKWELL, 2016) Ordukaya, Emre; Karlik, Bekir
    The 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.
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    Quality Control of Olive Oils Using Machine Learning and Electronic Nose
    (WILEY-HINDAWI, 2017) Ordukaya, Emre; Karlik, Bekir
    The adulteration of olive oils can be detected with chemical test. This is very expensive and takes very long time. Thus, this study is focused on reducing both time and cost. For this purpose, the raw data has been collected from olive oils by using an e-nose from different regions in Balikesir in Turkey. This study presents two methods to analyze quality control of olive oils. In the first method, 32 inputs are applied to the classifiers directly. In the second, 32-input collected data are reduced to 8 inputs by Principal Component Analysis. These reduced data as 8 inputs are applied to the classifiers. Different machine learning classifiers such as Naive Bayesian,.. - NearestNeighbors (kappa- NN), Linear Discriminate Analysis (LDA), Decision Tree, ArtificialNeuralNetworks (ANN), and Support Vector Machine (SVM) were used. Then performances of these classifiers were compared according to their accuracies.