| 光谱学与光谱分析 |
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| Application of ICP-MS Method in the Determination of Mineral Elements in Vitex Honey for the Classification of Their Geographical Origins with Chemometric Approach |
| CHEN Hui1, FAN Chun-lin1*, CHANG Qiao-ying1,2, PANG Guo-fang1,2*, CAO Ya-fei3, JIN Ling-he3, HU Xue-yan1 |
1. Chinese Academy of Inspection and Quarantine, Beijing 100123, China
2. College of Environmental and Chemical Engineering, Yanshan University, Qinhuangdao 066004, China
3. College of Food Science and Engineering, Shandong Agricultural University, Tai’an 271018, China |
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Abstract 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.
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Received: 2014-01-08
Accepted: 2014-04-15
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Corresponding Authors:
FAN Chun-lin, PANG Guo-fang
E-mail: honeytrace@163.com
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[1] European Commission. Council Regulation (EC) No 510/2006 of 20 March 2006 on the Protection of Geographical Indication and Designations of Origin for Agricultural Products and Foodstuffs. Off. J. Eur. Union, 2006, L93: 12.
[2] Luykx D M A M, Van Ruth S M. Food Chem., 2008, 107(2): 897.
[3] Stankovska E, Stafilov T, ajn R. Environ. Monit. Assess. 2008, 142(1-3): 117.
[4] Ankalm E. Food Chem., 1998, 63(4): 549.
[5] LIU Bo-jing, SUN Jian-min, WANG Ji-kun(刘博静，孙建民，王继坤). Studies of Trace Elements and Health(微量元素与健康研究), 2009, 26(5): 33.
[6] Chen H, Fan C L, Wang Z B, et al. Anal. Methods, 2013, 5(13): 3291.
[7] Madejczyk M, Baralkiewicz D. Anal. Chim. Acta, 2008, 617(1-2): 11.
[8] Kolayli S, Kongur N, Gundogdu A, et al. Asian J. Chem., 2008, 20(3): 2421.
[9] Silici S, Uluozlu O D, Tuzen M, et al. J. Hazard Mater., 2008, 156(1-3): 612.
[10] Terrab A, Hernanz D, Heredia F J. J. Agric. Food Chem.，2004, 52(11): 3441.
[11] Baroni M V, Arrua C, Nores M L, et al. Food Chem., 2009, 114(2): 727.
[12] Chudzinska M, Baralkiewicz D. Food Chem. Toxicol., 2011, 49(11): 2741. |
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