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

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Abstract

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.

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Acknowledgements

This study was supported Ege University, Council of Scientific Research Projects (Project No. 14-MUH-063 BAP project). Chemical analyses of this work was supported by the EGE University Drug Research and Pharmacokinetic Development and Applied Center (ARGEFAR).

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Ege University, 35100, Bornova, Izmir, Turkey

    Ozgur Gumus & Erkan Yasar

  2. Central Research Testing and Analysis Laboratory Research and Application Center (EGE-MATAL), Ege University, 35100, Bornova, Izmir, Turkey

    Z. Pinar Gumus

  3. Department of Chemistry, Faculty of Science, Ege University, 35100, Bornova, Izmir, Turkey

    Hasan Ertas

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  1. Ozgur Gumus
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  2. Erkan Yasar
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  3. Z. Pinar Gumus
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Correspondence to Z. Pinar Gumus.

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Gumus, O., Yasar, E., Gumus, Z.P. et al. Comparison of different classification algorithms to identify geographic origins of olive oils. J Food Sci Technol 57, 1535–1543 (2020). https://doi.org/10.1007/s13197-019-04189-4

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  • DOI: https://doi.org/10.1007/s13197-019-04189-4

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