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Pollen grains vary in colour and shape and can be detected in honey used as a way of identifying nectar sources. Accurate differentiation between pollen grains record is hampered by the combination of poor taxonomic resolution in pollen identification and the high species diversity of many families. Pollen identification determines the origin and the quality of the honey product, but this indefiniteness is also a big challenge for the beekeepers. This study aimed to develop effective, accurate, rapid and non-destructive analysis methods for pollen classification in honey. Ten different pollen grains of plant species were used for the estimation. GLCM (grey level co-occurrence matrix) texture features and ANN (artificial neural network) were used for the identification of pollen grains in honey by the reference of plant species pollen. GLCM has been calculated in four different angles and offsets for the pollen of the plant and the honey samples. Each angle and offset pair includes five features. At the final step, features were classified using the ANN method; the success of estimation with ANN was 88.00%. These findings suggest that the texture parameters can be useful in identification of the pollen types in honey products.

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The quality of honey from Pervari region was almost known by all over the country in Turkey. This study was undertaken to determine (i) physico-chemical parameters, (ii) antimicrobial analysis and (iii) pollen estimation method with expert computer system obtained from three different sites of Pervari region (Siirt/Turkey). For physico-chemical parameters; moisture, free acidity, diastase activity, hydroxyl methyl furfural (HMF), invert sugar, ash, commercial glucose and proline analysis were examined. For anti-microbial analysis disc dilution method were studied on six different bacteria species. For pollen analysis; different and new expert computer system was used for comparison of pollen of plants and honey samples. The aim of the study was to evaluate the properties of multi floral honey determined from three different locations in the same region and the way to understand to which plants were visited by the bees with comparing of pollen grains of flowers and honey by using the expert computer system. Honey samples of Pervari region were of acceptable quality based on recommended criteria of Turkish Food Codex and International Honey Commission.