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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In this work, a boron-doped diamond (BDD) electrode was used for the electroanalytical determination of indole-3-acetic acid (IAA) phytohormone by square-wave voltammetry. IAA yielded a well-defined voltammetric response at + 0.93 V (vs. Ag/AgCl) in Britton-Robinson buffer, pH 2.0. The process could be used to determine IAA in the concentration range of 5.0 to 50.0 mM (n = 8, r = 0.997), with a detection limit of 1.22 mM. The relative standard deviation of ten measurements was 2.09 % for 20.0 mM IAA. As an example, the practical applicability of BDD electrode was tested with the measurement of IAA in some plant seeds.

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Pollen grains are complex three-dimensional structures, and are identified using specific 14 distinctive morphological characteristics. An efficient automatic system for the accurate and pollen grains from ten species of Onopordum (a thistle genus) from Turkey were used. In 21 total, 30 different images were acquired for each of the ten species studied. The images were 22 then used to 11 measure morphological parameters; these were the colpus length, the colpus 23 width, the equatorial axis (E), the polar axis (P), the P/E ratio, the columellae length, the 24 echinae length, and the thicknesses of the exine, intine, nexine and tectum. Pollen recognition 25 was performed using the ELM for the 50-50%, 70-30% and 80-20% training-test partitions 26 of the overall dataset. The classification accuracies of these three training-test partitions of 27 were 84.67%, 91.11% and 95.00% respectively. Therefore, the ELM exhibited a very high 28 success rate for identifying the pollen types considered here. The use of computer-based 29