Classification of the green tea varieties based on Support Vector Machines using Terahertz Spectroscopy

被引：0

作者：

Chen Xi-Ai ^{[1
]}

Zhang Guang-Xin ^{[1
]}

Huang Ping-Jie ^{[1
]}

Hou Di-Bo ^{[1
]}

Kang Xu-Sheng ^{[1
]}

Zhou Ze-Kui ^{[1
]}

机构：

[1] Zhejiang Univ, Dept Control Sci & Engn, State Key Lab Ind Control Technol, Hangzhou 310027, Zhejiang, Peoples R China

来源：

2011 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC) | 2011年

关键词：

Terahertz spectroscopy; Green tea; LS-SVM; Naive Bayes classification; BP Artificial Neural Network; IDENTIFICATION; GRADE; LEVEL;

D O I：

暂无

中图分类号：

TH7 [仪器、仪表];

学科分类号：

0804 ; 080401 ; 081102 ;

摘要：

Terahertz time-domain spectroscopy have been applied in research of four different varieties of Chinese green tea, the absorption and refractive Terahertz Spectrum of these tea were got in the range of 0.2 to 1.5 THz. Least Squares Support Vector Machines, Naive Bayes and Back Propagation Artificial Neural Network were applied to achieve Multi-class classification of these four kinds of tea, and the classification results of three algorithms were analyzed in detail. The results shows that support vector machine have better classification results in the experiment. This study demonstrated the feasibility of time-domain Terahertz Spectroscopy for the classification of difference kinds of tea.

引用

页码：905 / 909

页数：5

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