Identification of tea based on mineral content and support vector machines

被引：0
|
作者：
Li Q. [1 ]
Li X. [2 ]
Zhong F. [1 ]
机构：
[1] School of Food Science and Technology, Jiangnan University, Wuxi
[2] School of Business, Jiangnan University, Wuxi
来源：
Jiangsu Daxue Xuebao (Ziran Kexue Ban)/Journal of Jiangsu University (Natural Science Edition) | 2011年 / 32卷 / 06期
关键词：
Identification; Mineral element; Origin; Support vector machines; Tea; Variety;
D O I：
10.3969/j.issn.1671-7775.2011.06.004
中图分类号：
学科分类号：
摘要：
In order to identify variety and origin of teas, a method was proposed based on mineral content and support vector machines (SVM). The contents of Mg, Al, P, Ca, Mn, Fe, Cu, Zn and Ba were analyzed by ICP-OES and were normalized. The data were collected randomly as learning samples for designing and training multielement classifier to identify tea variety and origin by SVM. The results show that classification method which is based on "one versus one" multi-class support vector machine has better classification ability and stronger anti-jamming capability than that of cluster analysis. For small samples, the tea variety and origin identification accuracy can reach 91.67%, which illuminates that the method is effective for indentifying tea variety and origin.
引用
收藏
页码：636 / 641
页数：5
相关论文
共 12 条
  • [1] Chen Q.S., Zhao J.W., Fang C.H., Et al., Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM), Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 66, 3, pp. 568-574, (2007)
  • [2] Mckenzie J.S., Jurado J.M., Pablos F.D., Characterisation of tea leaves according to their total mineral content by means of probabilistic neural networks, Food Chemistry, 123, 3, pp. 859-864, (2010)
  • [3] Zhao J., Guo Z., Chen Q., Et al., Identification of green teas by HPLC and pattern recognition, Journal of Jiangsu University: Natural Science Edition, 31, 3, pp. 249-253, (2010)
  • [4] Wu D., Yang H.Q., Chen X.J., Et al., Application of image texture for the sorting of tea categories using multi-spectral imaging technique and support vector machine, Journal of Food Engineering, 88, 4, pp. 474-483, (2008)
  • [5] Chen Q.S., Guo Z.M., Zhao J.W., Identification of green tea's (Camellia sinensis (L.)) quality level according to measurement of main catechins and caffeine contents by HPLC and support vector classification pattern recognition, Journal of Pharmaceutical and Biomedical Analysis, 48, 5, pp. 1321-1325, (2008)
  • [6] Chen B., Ye J., Yan H., Et al., Influence of calibration sets number on NIR model of tea, Journal of Jiangsu University: Natural Science Edition, 30, 4, pp. 330-333, (2009)
  • [7] Zhao J.W., Chen Q.S., Huang X.Y., Et al., Qualitative identification of tea categories by near infrared spectroscopy and support vector machine, Journal of Pharmaceutical and Biomedical Analysis, 41, 4, pp. 1198-1204, (2006)
  • [8] Pilgrim T.S., John W.R., Kliti G., Application of trace element and stable isotope signatures to determine the provenance of tea (Camellia sinensis) samples, Food Chemistry, 118, 4, pp. 921-926, (2010)
  • [9] Han L., Li R., Determination of minerals and trace elements in various tea by ICP-AES, Spectroscopy and Spectral Analysis, 22, 2, pp. 304-306, (2002)
  • [10] Kang H., Yang M., Chen B., Et al., Trace element determination in teas and discrimination analysis for teas, Rock and Mineral Analysis, 25, 1, pp. 22-26, (2006)
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