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An Artificial Olfactory System for Quality and Geographical Discrimination of Olive Oils

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2774))

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Abstract

In this paper we present an artificial olfactory system for classification and recognition of both quality and geographical origin of olive oil. The olfactory system employs a set of metal oxide sensors. Two different pattern recognition systems are considered: in the first, the sensor signals are modeled by a fuzzy logic-based method, whereas in the second, sensor signals are expressed in terms of a few FFT coefficients and sensor responses are appropriately aggregated.

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References

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Author information

Authors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, Elettronica, Informatica, Telecomunicazioni, University of Pisa, Italy

    Marco Cococcioni, Beatrice Lazzerini & Francesco Marcelloni

Authors
  1. Marco Cococcioni
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  2. Beatrice Lazzerini
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  3. Francesco Marcelloni
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Editor information

Editors and Affiliations

  1. Computing Laboratory, Oxford University, Parks Road, OXI 3QD, Oxford, United Kingdom

    Vasile Palade

  2. Centre for SMART Systems, School of Environment and Technology, University of Brighton, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. Knowledge-Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, SA 5095, Adelaide, Australia

    Lakhmi Jain

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© 2003 Springer-Verlag Berlin Heidelberg

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Cococcioni, M., Lazzerini, B., Marcelloni, F. (2003). An Artificial Olfactory System for Quality and Geographical Discrimination of Olive Oils. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2774. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45226-3_89

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  • DOI: https://doi.org/10.1007/978-3-540-45226-3_89

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40804-8

  • Online ISBN: 978-3-540-45226-3

  • eBook Packages: Springer Book Archive

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