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Classification of olive oils using chromatography, principal component analysis and artificial neural network modelling

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Abstract

Classification and addressing, and geographical origin of different olive oils is of great importance due to their differentiation in quality, and for commercial concerns. In this study, quantification of sterols, fatty acids, and triacylglycerol composition of forty-nine olive oils collected from six different locations of western part of Turkey (İzmir, Manisa, Aydın, Muğla, Bursa, and Edremit Bay) were performed by using chromatographic methods. Data for those olive oil samples were compiled, and classified with the artificial neural network (ANN) modelling and principal components analysis (PCA). The analytical results included resourceful information about determining geographical origin and traceability of olive oil in Turkey by using ANN and PCA. The ANN model for sterol composition showed the highest accuracy with 85.71%. The FAME and TAG profiles followed this with 83.67 and 81.63% accuracy respectively. However, İzmir and Manisa regions have poor sensitivity values with all ANN models since they are geographically very close to each other. Furthermore, the PCA results of the sterol composition have provided separation and clustering between locations. β-sitosterol, campesterol, stigmasterol and 24-metilen cholesterol have an important role in determining the separation of the locations of origin. While separation of the Bursa location has been under the pressure of FAME composition, the TAGs have been effective on the clustering of the Aydın and Edremit Bay. In conclusion, the geographical authentication of Turkish olive oils can be done with high accuracy by using ANN and PCA.

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Acknowledgements

This work was supported by EGE University Drug Research and Pharmacokinetic Development and Applied Center, ARGEFAR. Artificial Neural Networks part of this study was supported by Ege University Research Fund through the 14-MUH-063 BAP project.

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Authors and Affiliations

  1. Institute on Drug Abuse, Toxicology and Pharmaceutical Science, BATI INSTITUE, Ege University, Bornova, 35100, Izmir, Turkey

    Z. Pinar Gumus

  2. Drug Research and Pharmacokinetic Development and Applied Centre, ARGEFAR, Ege University, Bornova, 35100, Izmir, Turkey

    Z. Pinar Gumus & Hasan Ertas

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

    Hasan Ertas

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

    Erkan Yasar & Ozgur Gumus

Authors
  1. Z. Pinar Gumus
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  2. Hasan Ertas
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  3. Erkan Yasar
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  4. Ozgur Gumus
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Correspondence to Hasan Ertas.

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The manuscript does not contain experiments using animals and human studies.

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Gumus, Z.P., Ertas, H., Yasar, E. et al. Classification of olive oils using chromatography, principal component analysis and artificial neural network modelling. Food Measure 12, 1325–1333 (2018). https://doi.org/10.1007/s11694-018-9746-z

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  • DOI: https://doi.org/10.1007/s11694-018-9746-z

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