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Sensors 2009, 9(10), 8073-8082; doi:10.3390/s91008073

Identification of Tea Storage Times by Linear Discrimination Analysis and Back-Propagation Neural Network Techniques Based on the Eigenvalues of Principal Components Analysis of E-Nose Sensor Signals

1
Department of Biosystems Engineering, Zhejiang University, 268 Kaixuan Road, Hangzhou 310029, China
2
Food and Bioengineering Department, Henan University of Science and Technology, 48 Xiyuan Road, Luoyang 471001, China
*
Author to whom correspondence should be addressed.
Received: 2 July 2009 / Revised: 27 July 2009 / Accepted: 29 July 2009 / Published: 14 October 2009
(This article belongs to the Section Chemical Sensors)
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Abstract

An electronic nose (E-nose) was employed to detect the aroma of green tea after different storage times. Longjing green tea dry leaves, beverages and residues were detected with an E-nose, respectively. In order to decrease the data dimensionality and optimize the feature vector, the E-nose sensor response data were analyzed by principal components analysis (PCA) and the five main principal components values were extracted as the input for the discrimination analysis. The storage time (0, 60, 120, 180 and 240 days) was better discriminated by linear discrimination analysis (LDA) and was predicted by the back-propagation neural network (BPNN) method. The results showed that the discrimination and testing results based on the tea leaves were better than those based on tea beverages and tea residues. The mean errors of the tea leaf data were 9, 2.73, 3.93, 6.33 and 6.8 days, respectively.
Keywords: tea; electronic nose; principle components analysis; linear discrimination analysis; BP-neural network; storage time tea; electronic nose; principle components analysis; linear discrimination analysis; BP-neural network; storage time
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Yu, H.; Wang, Y.; Wang, J. Identification of Tea Storage Times by Linear Discrimination Analysis and Back-Propagation Neural Network Techniques Based on the Eigenvalues of Principal Components Analysis of E-Nose Sensor Signals. Sensors 2009, 9, 8073-8082.

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