期刊
FOODS
卷 10, 期 8, 页码 -出版社
MDPI
DOI: 10.3390/foods10081682
关键词
bee pollen; discrimination; spectroscopic methods; image analysis; color
资金
- Specific University Research of UCT Prague [21-SVV/2019, 21-SVV/2020, 51501111]
- V4EaP Scholarship Program of International Visegrad Fund
Bee pollen samples were discriminated using vibrational spectroscopic methods by connecting with botanical sources, composition, and color. Fourier transform mid- and near-infrared (FT-MIR, FT-NIR), and FT-Raman spectra of bee pollen samples were measured and processed by principal component analysis (PCA), showing marked sensitivity to bee pollen composition. Furthermore, the combination of imaging, spectroscopic, and statistical methods is a potent tool for bee pollen discrimination.
Bee pollen samples were discriminated using vibrational spectroscopic methods by connecting with botanical sources, composition, and color. SEM and light microscope images of bee pollen loads were obtained and used to assess the botanical origin. Fourier transform (FT) mid- and near-infrared (FT-MIR, FT-NIR), and FT-Raman spectra of bee pollen samples (a set of randomly chosen loads can be defined as an independent sample) were measured and processed by principal component analysis (PCA). The CIE L*a*b* color space parameters were calculated from the image analysis. FT-MIR, FT-NIR, and FT-Raman spectra showed marked sensitivity to bee pollen composition. In addition, FT-Raman spectra indicated plant pigments as chemical markers of botanical origin. Furthermore, the fractionation of bee pollen was also performed, and composition of the fractions was characterized as well. The combination of imaging, spectroscopic, and statistical methods is a potent tool for bee pollen discrimination and thus may evaluate the quality and composition of this bee-keeping product.
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