Classification of Bee Pollen and Prediction of Sensory and Colorimetric Attributes-A Sensometric Fusion Approach by e-Nose, e-Tongue and NIR.

László Sipos,György Bázár,Rita Végh,Géza Hitka,Zoltan Kovacs,John-Lewis Zinia Zaukuu,Zsanett Bodor

doi:10.3390/s20236768

László Sipos, György Bázár + Show 5 more

Open Access

https://doi.org/10.3390/s20236768

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Abstract

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.

Highlights

The increasing prominence of alternative and complementary medicine in the 21st century has emphasized the growing interest in the protection of insect pollinators
principal component analysis (PCA) results of the electronic nose for using the chosen 20 most selective sensors showed that the method did not have an effect on the variance of the groups with the exception of the sunflower sample, where a scatter could be detected
This pattern was observed in the linear discriminant analysis (LDA) plot, where 82.41% of the variance was expressed in the root1 (Figure 4b)

Summary

Introduction

The increasing prominence of alternative and complementary medicine in the 21st century has emphasized the growing interest in the protection of insect pollinators. A widening range of apicultural products is available in the market. According to Bargańska et al [1], these products can be classified into two groups according to their origin: honey, bee pollen, bee bread, and propolis. Sensors 2020, 20, 6768 are of vegetable origin, while royal jelly, beeswax, and bee venom are secretions of bees. The market growth of beekeeping products entailed a large increase in the number of scientific articles relating to this topic. The number of published papers in the last two years is approximately on par with those published in the 1990s, and the number of scientific studies on bee pollen has grown exponentially over the last 30 years

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