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

  • Z. Pinar Gumus
  • Hasan Ertas
  • Erkan Yasar
  • Ozgur Gumus
Original Paper


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.


Olive oil Sterols Fatty acids Triacylglycerols Principal component analysis Artificial neural networks 



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.

Compliance with ethical standards

Conflict of interest

The authors have no competing interest to disclose.

Ethical approval

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

Supplementary material

11694_2018_9746_MOESM1_ESM.doc (454 kb)
Supplementary material 1 (DOC 454 KB)
11694_2018_9746_MOESM2_ESM.docx (15 kb)
Supplementary material 2 (DOCX 14 KB)


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Z. Pinar Gumus
    • 1
    • 2
  • Hasan Ertas
    • 2
    • 3
  • Erkan Yasar
    • 4
  • Ozgur Gumus
    • 4
  1. 1.Institute on Drug Abuse, Toxicology and Pharmaceutical Science, BATI INSTITUEEge UniversityIzmirTurkey
  2. 2.Drug Research and Pharmacokinetic Development and Applied Centre, ARGEFAREge UniversityIzmirTurkey
  3. 3.Department of Chemistry, Faculty of ScienceEge UniversityIzmirTurkey
  4. 4.Department of Computer Engineering, Faculty of EngineeringEge UniversityIzmirTurkey

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