Comparison of different classification algorithms to identify geographic origins of olive oils

Research on investigation and determination of geographic origins of olive oils is increased by consumers' demand to authenticated olive oils. Classification algorithms which are machine learning methods can be employed for the authentication of olive oils. In this study, different classification algorithms were evaluated to reveal the most accurate one for authentication of Turkish olive oils. BayesNet, Naive Bayes, Multilayer Perception, IBK, Kstar, SMO, Random Forest, J48, LWL, Logistic Regression, Simple Logistic, LogitBoost algorithms were implemented on 61 chemical analysis parameters of 49 olive oil samples from 6 different locations at Western Turkey. These 61 parameters were obtained from five different chemical analyses which are stable carbon isotope ratio, trace elements, sterol compositions, FAMEs and TAGs. This study is the most comprehensive study to determine the geographical origin of Turkish olive oils in terms of these mentioned features. Classification performances of the algorithms were compared using accuracy, specificity and sensitivity metrics. Random Forest, BayesNet, and LogitBoost algorithms were found as the best classification algorithms for authentication of Turkish olive oils. Using the classification model in this study, geographic origin of an unknown olive oil can be predicted with high accuracy. Besides, similar models can be developed to obtain useful information for authentication of other food products.