Chemometric Determination of the Botanical Origin for Chinese Honeys on the Basis of Mineral Elements Determined by ICP-MS

Citations Over TimeTop 10% of 2014 papers

Abstract

In this work, the potential of mineral elements and chemometric methods as a tool to classify Chinese honeys according to their botanical origin was examined. Twelve mineral elements (Na(23), Mg(24), P(31), K(39), Ca(43), Mn(55), Fe(56), Cu(63), Zn(66), Rb(85), Sr(88), and Ba(137)) of 163 Chinese honey samples, including linden, vitex, rape, and acacia, collected from Heilongjiang, Beijing, Hebei, and Shaanxi, China, in 2013 were determined by the ICP-MS method. Principal component analysis (PCA) reduced 10 variables to four principal components and could explain 93.06% of the total variance. Partial least-squares discriminant analysis (PLS-DA) and back-propagation artificial neural network (BP-ANN) were explored to construct a classification model. By PLS-DA, the total correct classification rates for model training and cross-validation were 90.9 and 88.4%, respectively. By BP-ANN, the total correct classification rates for model training and cross-validation were 100 and 92.6%, respectively. The performance of BP-ANN was better than that of PLS-DA. The validation of the developed BP-ANN model was tested by the independent test set of 42 honey samples. Linden, vitex, and rape honey samples were predicted with an accuracy of 100%, whereas one acacia honey was predicted as rape honey with an accuracy of 92.3%. It is concluded that the profiles of mineral elements by ICP-MS with chemometric methods could be a potential and powerful tool for the classification of Chinese honey samples from different botanical origins.

Related Papers

• → Principal component analysis: Most favourite tool in chemometrics(2017)58 cited
• → Non-destructive discrimination of paddy seeds of different storage age based on Vis/NIR spectroscopy(2008)41 cited
• → Principal Component Analysis in Linear Discriminant Analysis Space for Face Recognition(2014)5 cited
• Comparative Analysis Of The Performance Of Principal Component Analysis (PCA) And Linear Discriminant Analysis (LDA) As Face Recognition Techniques(2017)