应用电感耦合等离子体质谱(ICP-MS)测定了北京顺义、河北阜平和河北平山三个地区65个荆条蜜样品中38种元素含量, 其中B, Na, Mg, P, K, Ca, Fe和Zn等8种元素的含量处于较高水平, 其浓度均高于1 mg·kg-1。比较发现, 不同地区荆条蜜中元素含量存在一定差异。以具有显著性差异(p<0.05)的29种元素(Na, Mg, Al, K, Ti, V, Mn, Fe, Ni, Cu, Zn, Ga, As, Sr, Y, Mo, Cd, Ba, La, Ce, Pr, Nd, Sm, Gd, Dy, Ho, Tl, Pb和U)为变量, 应用主成分分析(PCA)和反向传输人工神经网络(BP-ANN)两种化学计量学方法按照不同产地对荆条蜜进行了分析。PCA可将所有变量降为4个主成分, 并能解释81.6%的变量, 结果表明, PCA基本可以将荆条蜜按照不同产地分开。应用BP-ANN建立不同产地荆条蜜溯源模型, 用全部样本建立模型时, 三个产地荆条蜜分类的正确率均为100%, 采用“留一法”进行交叉检验, 整体准确率为95.4%;以75%的样本建立模型, 25%的样本用于预测, 对BP-ANN模型的可靠性进行验证, 三个产地整体分类和预测的准确率均为100%。ICP-MS测定蜂蜜中多元素含量结合多变量模型可以实现不同产地荆条蜜溯源。

In the present work, the contents of 38 elements of 65 vitex (Vitex negundo var. heterophylla Rehd.) honey samples from Shunyi of Beijing, Fuping and Pingshan of Hebei province were determined by inductively coupled plasma mass spectrometry (ICP-MS). Among them, B, Na, Mg, P, K, Ca, Fe and Zn were the most abundant elements with mean contents more than 1 mg kg-1. It can be found that there were relationships between the contents of elements and the geographical origin of vitex honey samples. Taking the contents of 29 out of 38 mineral elements (Na, Mg, Al, K, Ti, V, Mn, Fe, Ni, Cu, Zn, Ga, As, Sr, Y, Mo, Cd, Ba, La, Ce, Pr, Nd, Sm, Gd, Dy, Ho, Tl, Pb and U) as variables, the chemometric methods, such as principal component analysis (PCA) and back-propagation artificial neural network (BP-ANN), were applied to classify vitex honey samples according to their geographical origins. PCA reduced all of the variables to four principal components and could explain 81.6% of the total variances. The results indicated that PCA could mainly classify the vitex honey samples into three groups. BP-ANN was explored to construct classification model of vitex honeys according to their geographical origin. For the whole data set, the overall correct classification rate and cross-validation (leave one out method) rate of proposed BP-ANN model was 100% and 95.4%, respectively. To further test the stability of the model developed for prediction, 75% of honey samples of each geographical origin were randomly selected for the model training set, and the remaining samples were classified with the use of the constructed model. Both the overall correct classification rate and prediction rate of proposed BP-ANN model were 100%. It is concluded that the profiles of multi-element by ICP-MS with chemometric methods could be a potential and powerful tool for the classification of vitex honey samples from different geographical origins.