Paper Title:
Detection of XinyangMaojian Green Tea Quality and Age by Electronic Nose
  Abstract

In this work, the capacity of an electronic nose (E-nose, PEN2) to classify tea quality grades is investigated. Three tea groups with different quality grades were harvested at different times. Principal component analysis (PCA) and artificial neural network (ANN) were applied to identify the different tea samples. PCA provided perfect classification of tea quality grades. In the analysis of age, six groups of XinyangMaojian green tea were distinguished completely by PCA. The results of ANN analysis gave a high percentage of correct discrimination of green tea samples. The correct identification rates of the training and testing data were 98.6% and 83%, respectively, for three grades of green tea samples harvested in 2009. The correct identification rates of the training and testing data were 100% and 87.8%, respectively, for three grades of green tea samples harvested in 2010. In the analysis of age, the correct discrimination percentages for six groups of XinyangMaojian green tea were 99.4% and 88.9% for training and testing data, respectively. These results indicate that the electronic nose could be successfully used for the detection of teas of different quality grades and ages.

  Info
Periodical
Advanced Materials Research (Volumes 239-242)
Edited by
Zhong Cao, Xueqiang Cao, Lixian Sun, Yinghe He
Pages
2096-2100
DOI
10.4028/www.scientific.net/AMR.239-242.2096
Citation
H. M. Zhang, M. X. Chang, Y. C. Yu, H. Tian, Y. Q. He, X. C. Chen, "Detection of XinyangMaojian Green Tea Quality and Age by Electronic Nose", Advanced Materials Research, Vols. 239-242, pp. 2096-2100, 2011
Online since
May 2011
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Wei Liang, Li Na Zhang, Xiao Wei Li, Yan Di Zuo
Chapter 17: Signal and Intelligent Information Processing
Abstract:In order to improve the recognition rate of the electronic nose system for small samples, an electronic nose pattern recognition algorithm...
2244
Authors: De Han Luo, Ya Wen Shao
Chapter 10: Intelligence Algorithm, Optimization Algorithm and their Applications
Abstract:Linear discriminant analysis (LDA) is a popular method among pattern recognition algorithms of machine olfaction. However, “Small Sample...
1532
Authors: Wen Na Zhang, Guo Jun Qin, Niao Qing Hu
Chapter 3: Measurement and Instrumentation, Monitoring and Detection Technologies, Fault Diagnosis
Abstract:Data from sensor array are often arranged in three-dimension as sample × time × sensor. Traditional methods are mainly used for two-dimension...
955