University of Belgrade, Vinča Institute of Nuclear Sciences, PO Box 522, 11001 Belgrade, Serbia.
University of Copenhagen, Department of Food Science, Rolighedsvej 30, DK-1958 Frederiksberg C, Denmark.
Food Chem. 2015 May 15;175:284-91. doi: 10.1016/j.foodchem.2014.11.162. Epub 2014 Dec 4.
Fluorescence spectroscopy coupled with parallel factor analysis (PARAFAC) and Partial least squares Discriminant Analysis (PLS DA) were used for characterization and classification of honey. Excitation emission spectra were obtained for 95 honey samples of different botanical origin (acacia, sunflower, linden, meadow, and fake honey) by recording emission from 270 to 640 nm with excitation in the range of 240-500 nm. The number of fluorophores present in honey, excitation and emission spectra of each fluorophore, and their relative concentration are determined using a six-component PARAFAC model. Emissions from phenolic compounds and Maillard reaction products exhibited the largest difference among classes of honey of different botanical origin. The PLS DA classification model, constructed from PARAFAC model scores, detected fake honey samples with 100% sensitivity and specificity. Honey samples were also classified using PLS DA with errors of 0.5% for linden, 10% for acacia, and about 20% for both sunflower and meadow mix.
荧光光谱法结合平行因子分析(PARAFAC)和偏最小二乘判别分析(PLS DA)用于蜂蜜的特征描述和分类。通过记录从 270 到 640nm 的发射,获得了 95 个不同植物来源(刺槐、向日葵、椴树、草地和假蜂蜜)的蜂蜜样本的激发发射光谱,激发范围为 240-500nm。使用六分量 PARAFAC 模型确定蜂蜜中存在的荧光团数量、每个荧光团的激发和发射光谱及其相对浓度。来自酚类化合物和美拉德反应产物的发射在不同植物来源的蜂蜜种类之间表现出最大的差异。基于 PARAFAC 模型得分构建的 PLS DA 分类模型,以 100%的灵敏度和特异性检测到假蜂蜜样本。还使用 PLS DA 对蜂蜜样本进行分类,对于椴树,错误率为 0.5%,对于刺槐,错误率为 10%,对于向日葵和草地混合物,错误率约为 20%。