scholarly journals 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 ◽  
Vol 9 (10) ◽  
pp. 8073-8082 ◽  
Author(s):  
Huichun Yu ◽  
Yongwei Wang ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Discrimination Analysis ◽  
Linear Discrimination ◽  
Sensor Signals ◽  
Components Analysis ◽  
Storage Times

The Compare of Microbial Electronic Tongue Data Based on Direct and Two-Stage Processing

2010 ◽  
Vol 20-23 ◽  
pp. 331-336
Author(s):  
Hong Men ◽  
Jian Ping Sun ◽  
Jing Zhang ◽  
Yu Ming Guo ◽  
Zhi Ming Xu
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Back Propagation ◽  
Electronic Tongue ◽  
Back Propagation Neural Network ◽  
Broth Culture ◽  
Two Stage ◽  
Raw Data ◽  
Sulfate Reducing ◽  
Components Analysis

In this paper, we used three working electrodes of platinum, gold, glassy carbon which constituted the microbial electronic tongue to identify microorganisms. The main purpose of this article is to classify sulfate-reducing bacteria and iron bacteria with the reference of broth culture. The accuracy of classification was compared based on directly processing (raw data processed by Back- Propagation Neural Network (BPNN), raw data processed by Partial LeastSquares (PLS) and raw data processed by Principal Components Analysis (PCA)) and two-stage processing (Principal Components Analysis (PCA) outputs processed by BPNN,PLS(score) outputs processed by BPNN and PLS(ypred) outputs processed by BPNN). The result showed that the performance of both PLS (ypred) and BPNN were optimum, which proved that the combine of linear and nonlinear model will get the best result for classification of bacterial.

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