Application of Support Vector Machines to Melissopalynological Data for Honey Classification

Application of Support Vector Machines to Melissopalynological Data for Honey Classification

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)
DOI: 10.4018/jaeis.2010070105
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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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1. Introduction

In recent years, an increasing attention has been devoted in Europe to the determination of the botanical and geographical origin of honey. This is partially due to allow European product to compete more effectively with cheap honey appeared on the EU market. To cover the high production costs, honey should be sold at much higher prices than those accepted by international markets. High quality standard, traceability and link to the geographical origin by means of strict regulation, such as Protected Designation of Origin (PDO), can give extra value to European honey. These laws protect a regional food, vegetable or fruit defining specific and objective procedures to determine the quality standard of the product and to verify the link to its geographical origin. At present, the procedure to verify geographical origin of honey is not well established; some attempts have been done to overcome this situation, and a promising field of research relies on the application of statistical methods on data about pollen content of honeys (Aronne, 2010).

Floral honey always contains numerous pollen grains mainly from the plant species foraged by honey bees. Pollen analysis of honey, namely melissopalynology, is of great utility to determine and control both botanical and geographical origin. The determination of geographical origin is based on the entire pollen spectrum being consistent with the flora of a particular region and with any reference spectra or descriptions in the literature (Louveaux, 1978; Herrero, 2001; Persano Oddo, 2004; Persano Oddo, 2007; Aronne, 2010).

Analysis of the geographical origin of honey is based on the assumption that the selection of honey species made by bees is influenced by the peculiarity of local vegetation. Therefore, the palynological component of a honey, if correctly analysed, should provide information on the foraging area. This research topic assumes considerable importance when its aim is to safeguard consumer interests and to protect honest producers of honeys labelled with the indication of geographical origin (Aronne, 2008).

Although some computer-aided methods for the classification of honeys have been developed (Battesti, 1992; Scala, 2004a; Scala, 2004b; Aronne, 2008a; Aronne, 2008b; Aronne, 2008c; Aronne, 2010) at the moment the identification of geographical origin depends mainly on the experience and the knowledge of the palynologists who are asked to compare results from specific samples with a hypothetical pollen spectrum from honey producible in the same geographical area. It is therefore evident that elaboration of melissopalynological data requires precise, sensitive analytical tools which go beyond the subjective evaluation, providing the means to correlate data and information which are otherwise elusive.

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