Classification of Bee Pollen and Prediction of Sensory and Colorimetric Attributes—A Sensometric Fusion Approach by e-Nose, e-Tongue and NIR
| Autor: | Zsanett Bodor, Rita Végh, John-Lewis Zinia Zaukuu, Zoltan Kovacs, Géza Hitka, György Bázár, László Sipos |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
electronic nose
spectra Electronic tongue Statistical pattern Sensory system electronic tongue medicine.disease_cause lcsh:Chemical technology 01 natural sciences Biochemistry CIE L*a*b* colour coordinates Article Analytical Chemistry 0404 agricultural biotechnology Tongue Pollen Partial least squares regression medicine linear discriminant analysis (LDA) Animals lcsh:TP1-1185 principal component analysis (PCA) Electrical and Electronic Engineering Instrumentation Mathematics Electronic nose business.industry 010401 analytical chemistry Discriminant Analysis Pattern recognition 04 agricultural and veterinary sciences Bees 040401 food science Atomic and Molecular Physics and Optics 0104 chemical sciences sensory panel performance multivariate analysis partial least square regression (PLSR) Bee pollen Plant species palynological analysis Colorimetry Artificial intelligence business |
| Zdroj: | Sensors, Vol 20, Iss 6768, p 6768 (2020) Sensors (Basel, Switzerland) Sensors Volume 20 Issue 23 |
| ISSN: | 1424-8220 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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