Chen Hui, Fan Chun-lin, Chang Qiao-ying, Pang Guo-fang, Cao Ya-fei, Jin Ling-he, Hu Xue-yan
Guang Pu Xue Yu Guang Pu Fen Xi. 2015 Jan;35(1):212-6.
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, T1, 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.
在本研究中,采用电感耦合等离子体质谱法(ICP-MS)测定了来自北京顺义、河北阜平和平山的65份荆条(Vitex negundo var. heterophylla Rehd.)蜂蜜样品中38种元素的含量。其中,硼(B)、钠(Na)、镁(Mg)、磷(P)、钾(K)、钙(Ca)、铁(Fe)和锌(Zn)是含量最为丰富的元素,平均含量超过1 mg kg-1。可以发现,元素含量与荆条蜂蜜样品的地理来源之间存在关联。以38种矿物元素中的29种(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)的含量为变量,运用主成分分析(PCA)和反向传播人工神经网络(BP-ANN)等化学计量学方法,根据地理来源对荆条蜂蜜样品进行分类。PCA将所有变量简化为四个主成分,可解释总方差的81.6%。结果表明PCA主要可将荆条蜂蜜样品分为三组。探索了BP-ANN以构建基于地理来源的荆条蜂蜜分类模型。对于整个数据集,所提出的BP-ANN模型的总体正确分类率和交叉验证(留一法)率分别为100%和95.4%。为进一步测试所开发模型预测的稳定性,每个地理来源随机选取75%的蜂蜜样品作为模型训练集,其余样品使用构建的模型进行分类。所提出的BP-ANN模型的总体正确分类率和预测率均为100%。结论是,ICP-MS结合化学计量学方法的多元素分析概况可能是对不同地理来源的荆条蜂蜜样品进行分类的一种潜在且强大的工具。