A complex study by scientists of MATE presents a classification approach of bee pollen by means of different methodologies. Tests for volatile components were performed in the Correltech® laboratory of ADEXGO Kft. Based on the results, another area of application emerges where the electronic nose as a fast and objective analytical methodology has significant potential.
Classification of bee pollen and prediction of sensory and colorimetric attributes — a sensometric fusion approach by e-nose, e-tongue and NIR
László Sipos, Rita Végh, Zsanett Bodor, John-Lewis Z. Zaukuu, Géza Hitka, George Bázár, Zoltan Kovacs
The chemical composition of bee pollens differs greatly and depends primarily on the botanical origin of the product. Therefore, it is a crucially important task to discriminate pollens of different plant species. In our work, we aim to determine the applicability of microscopic pollen analysis, spectral colour measurement, sensory, NIR spectroscopy, e-nose and e-tongue methods for the classification of bee pollen of five different botanical origins. Chemometric methods (PCA, LDA) were used to classify bee pollen loads by analysing the statistical pattern of the samples and to determine the independent and combined effects of the above-mentioned methods. The results of the microscopic analysis identified 100% of sunflower, red clover, rapeseed and two polyfloral pollens mainly containing lakeshore bulrush and spiny plumeless thistle. The colour profiles of the samples were different for the five different samples. E-nose and NIR provided 100% classification accuracy, while e-tongue > 94% classification accuracy for the botanical origin identification using LDA. Partial least square regression (PLS) results built to regress on the sensory and spectral colour attributes using the fused data of NIR spectroscopy, e-nose and e-tongue showed higher than 0.8 R2 during the validation except for one attribute, which was much higher compared to the independent models built for instruments.
Access the full paper free of charge on the website of the journal:
» Bee pollen classification by means of microscopic pollen analysis, spectral colour measurement, sensory, NIR spectroscopy, e-nose and e-tongue methods – sensometric fusion approach