Real-time estimation of olive oil quality parameters: a combined approach based on ANNs and machine vision

  • Authors:

  • Monica Carfagni;Marco Daou;Rocco Furferi

  • Affiliations:

  • Dipartimento di Meccanica e Tecnologie Industriali, University of Florence, Firenze, Italy;Departmento di Ingegneria Agraria e Forestale, University of Florence, Firenze, Italy;Dipartimento di Meccanica e Tecnologie Industriali, University of Florence, Firenze, Italy

  • Venue:
  • ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
  • Year:
  • 2008

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Abstract

The present work describes a combined approach based on Artificial Neural Networks and Machine Vision for the real-time estimation of some qualitative olive oil parameters. The proposed methodology proves to be a useful tool for the real-time estimation of acidity level and of peroxides number of olive oil extracted with a continuous extraction process. The two qualitative parameters are estimated on the basis of a number of technological and agronomical parameters. Some of the parameters correlated to the sanitary condition of olives and to ripeness are evaluated by means of image processing algorithms. The estimation may be performed during the extraction thus allowing a quality control of the oil quality without the requirement of a time-expensive chemical analysis.