Application of Support Vector Machines to Melissopalynological Data for Honey Classification

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Author(s): Giovanna Aronne (University of Naples Federico II, Italy), Veronica De Micco (University of Naples Federico II, Italy)and Mario R. Guarracino (Italian National Research Council, Italy)
Copyright: 2012
Pages: 10
Source title: New Technologies for Constructing Complex Agricultural and Environmental Systems
Source Author(s)/Editor(s): Petraq Papajorgji (Universiteti Europian i Tiranes, Albania)and François Pinet (Irstea/Cemagref - Clermont Ferrand, France)
DOI: 10.4018/978-1-4666-0333-2.ch008

Keywords: Environment & Agriculture / Environmental Science and Technologies / Environmental Technologies / Information Science Reference

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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.

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Application of Support Vector Machines to Melissopalynological Data for Honey Classification

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