Sensors, Vol. 20, Pages 6768: Classification of Bee Pollen and Prediction of Sensory and Colorimetric Attributes —A Sensometric Fusion Approach by e-Nose, e-Tongue and NIR

Sensors, Vol. 20, Pages 6768: Classification of Bee Pollen and Prediction of Sensory and Colorimetric Attributes—A Sensometric Fusion Approach by e-Nose, e-Tongue and NIR Sensors doi: 10.3390/s20236768 Authors: László Sipos Rita Végh Zsanett Bodor John-Lewis Zinia Zaukuu Géza Hitka György 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 se...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research