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Application of Support Vector Machines to Melissopalynological Data for Honey Classification

Author

Listed:
  • Giovanna Aronne

    (University of Naples Federico II, Italy)

  • Veronica De Micco

    (University of Naples Federico II, Italy)

  • Mario R. Guarracino

    (Italian National Research Council, Italy)

Abstract

In this paper, the authors address the problem of the discrimination of geographical origin and the selection of marker species of honeys using Support Vector Machines and z-scores. The methodology is based on the elaboration of palynological data with statistical learning methodologies. This innovative solution provides a simple yet powerful tool to detect the origin of honey samples. In case of honeys from Sorrento Peninsula, the discrimination from other Italian honeys is obtained with high accuracy.

Suggested Citation

  • Giovanna Aronne & Veronica De Micco & Mario R. Guarracino, 2010. "Application of Support Vector Machines to Melissopalynological Data for Honey Classification," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 1(2), pages 85-94, July.
    • Handle: RePEc:igg:jaeis0:v:1:y:2010:i:2:p:85-94
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