Laboratory of Chemistry, Department of Food Science and Human Nutrition, Agricultural University of Athens, Athens, Greece.
Laboratory of Quality and Safety of Agricultural Products, Landscape and Environment, Department of Agriculture, Hellenic Mediterranean University, Crete, Greece.
J Sci Food Agric. 2021 Jun;101(8):3319-3327. doi: 10.1002/jsfa.10961. Epub 2020 Dec 7.
The authenticity of honey is of high importance since it affects its commercial value. The discrimination of the origin of honey is of prime importance to reinforce consumer trust. In this study, four chemometric models were developed based on the physicochemical parameters according to European and Greek legislation and one using Raman spectroscopy to discriminate Greek honey samples from three commercial monofloral botanical sources.
The results of physicochemical (glucose, fructose, electrical activity) parameters chemometric models showed that the percentage of correct recognition fluctuated from 92.2% to 93.8% with cross-validation 90.6-92.2%, and the placement of test set was 79.0-84.3% successful. The addition of maltose content in the previous discrimination models did not significantly improve the discrimination. The corresponding percentages of the Raman chemometric model were 95.3%, 90.6%, and 84.3%.
The five chemometric models developed presented similar and very satisfactory results. Given that the recording of Raman spectra is simple, fast, a minimal amount of sample is needed for the analysis, no solvent (environmentally friendly) is used, and no specialized personnel are required, we conclude that the chemometric model based on Raman spectroscopy is an efficient tool to discriminate the botanical origin of fir, pine, and thyme honey varieties. © 2020 Society of Chemical Industry.
蜂蜜的真实性非常重要,因为它会影响其商业价值。蜂蜜产地的鉴别对于增强消费者信任至关重要。在这项研究中,根据欧洲和希腊的法规,开发了四种基于理化参数的化学计量学模型,以及一种使用拉曼光谱鉴别希腊蜂蜜样品与三种商业单花蜜源的模型。
理化参数(葡萄糖、果糖、电活性)化学计量学模型的结果表明,正确识别率在 92.2%到 93.8%之间波动,交叉验证率为 90.6%到 92.2%,测试集的定位成功率为 79.0%到 84.3%。在先前的判别模型中添加麦芽糖含量并不能显著提高判别能力。拉曼化学计量学模型的相应百分比为 95.3%、90.6%和 84.3%。
开发的五种化学计量学模型得出了相似且非常令人满意的结果。鉴于拉曼光谱的记录简单、快速,分析所需的样品量很少,不使用溶剂(环保),也不需要专门的人员,因此我们得出结论,基于拉曼光谱的化学计量学模型是鉴别冷杉、松树和百里香蜂蜜品种植物来源的有效工具。 © 2020 化学工业协会。