Comparison of multivariate preprocessing techniques as applied to electronic tongue based pattern classification for black tea.
In an electronic tongue, preprocessing on raw data precedes pattern analysis and choice of the appropriate preprocessing technique is crucial for the performance of the pattern classifier. While attempting to classify different grades of black tea using a voltammetric electronic tongue, different preprocessing techniques have been explored and a comparison of their performances is presented in this paper. The preprocessing techniques are compared first by a quantitative measurement of separability followed by principle component analysis; and then two different supervised pattern recognition models based on neural networks are used to evaluate the performance of the preprocessing techniques.
Palit
Mousumi
M
Department of Electronics and Telecommunication Engineering, Central Calcutta Polytechnic, Kolkata 700014, India.
Tudu
Bipan
B
Bhattacharyya
Nabarun
N
Dutta
Ankur
A
Dutta
Pallab Kumar
PK
Jana
Arun
A
Bandyopadhyay
Rajib
R
Chatterjee
Anutosh
A