Laser-based classification of olive oils assisted by machine learning.

Journal: Food chemistry
Published Date:

Abstract

Olive oil is an essential diet component in all Mediterranean countries having a considerable impact on the local economies, which are producing almost 90% of the world production. Therefore, the quality assessment of olive oil in terms of its acidity and its authentication in terms of PDO (Protected Designation of Origin) and PGI (Protected Geographical Indications) characterizations are nowadays necessary and of great importance for the market of olive oil and the related economic activities. In the present work, Laser Induced Breakdown Spectroscopy (LIBS) is used assisted by machine learning algorithms for retrieving of the information contained in the LIBS spectra to provide a simple, reliable, and ultrafast methodology for olive oils classification in terms of the degree of acidity and geographical origin. The combination of LIBS technique with machine learning statistical analysis approaches constitute a very powerful tool for the fast, in-situ and remote quality control of olive oil.

Authors

  • Odhisea Gazeli
    Department of Physics, University of Patras, 26504 Rio, Patras, Greece; Institute of Chemical Engineering Sciences (ICE-HT), Foundation for Research and Technology-Hellas (FORTH), Patras 26504, Greece.
  • Elli Bellou
    Department of Physics, University of Patras, 26504 Rio, Patras, Greece; Institute of Chemical Engineering Sciences (ICE-HT), Foundation for Research and Technology-Hellas (FORTH), Patras 26504, Greece.
  • Dimitrios Stefas
    Department of Physics, University of Patras, 26504 Rio, Patras, Greece; Institute of Chemical Engineering Sciences (ICE-HT), Foundation for Research and Technology-Hellas (FORTH), Patras 26504, Greece.
  • Stelios Couris
    Department of Physics, University of Patras, 26504 Rio, Patras, Greece; Institute of Chemical Engineering Sciences (ICE-HT), Foundation for Research and Technology-Hellas (FORTH), Patras 26504, Greece. Electronic address: couris@upatras.gr.