SPECTROSCOPIC DISCRIMINATION OF BEE POLLEN BY COMPOSITION, COLOUR AND BOTANICAL ORIGIN

 

A. Synytsya1, R. Bleha1, T.V. Shevtsova1 and J. Brindza2
1Department of Carbohydrates and Cereals, UCT Prague, Technicka 5, 166 28 Prague 6, Czech Republic
2Institute of Biological Conservation and Biosafety, SUA Nitra, Trieda Andrey Hlinku 2, 949 76 Nitra, Slovak Republic

Bee pollen is a mixture of pollen grains, nectar and bee excreta. This apiculture product is used as food supplement due to high content of many nutrients and bioactive compounds including amino acids, lipids, sugars, carotenoids and phenolics. Bee pollen varies significantly according to the chemical and botanical composition that leads to the necessity of its effective identification and sorting.
This work is devoted to spectroscopic discrimination of bee pollen according to difference in composition and colour connecting with the botanical source. FT-MIR, FT-NIR, FT-Raman and diffuse reflectance VIS spectra of bee pollen samples (homogenates and/or granules) were measured and processed by multivariate statistical methods. The CIE L*a*b* colour space parameters were calculated from the VIS spectra. Vibration spectroscopy showed marked sensitivity to bee pollen composition. In addition, FT-Raman and Vis spectra indicated plant pigments as chemical markers of botanical origin. The combination of spectroscopic and statistical methods is a potent tool for bee pollen discrimination and thus may evaluate quality and authenticity of this bee-keeping product.

Andriy Synytsya

Andriy Synytsya is an associate professor at the Department of Carbohydrates and Cereals, University of Chemistry and Technology in Prague. He received his Ph.D. at the same university in 2000. Andriy Synytsya is a specialist in structural analysis of polysaccharides and other natural compounds. His research also focuses on characterisation of biological materials including natural sources and foodstuff by spectroscopic and other analytical methods. Synytsya’s team works on new approaches based on morphological signs, vibration spectra and multivariate statistics that could be able to discriminate such materials according to composition and authenticity as well as evaluate their quality and safety.