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Electronic Nose for Black Tea Quality Evaluation Using Kernel Based Clustering Approach
Ashis Tripathy, A. K. Mohanty, Mihir Narayan Mohant
Pages - 86 - 93     |    Revised - 15-03-2012     |    Published - 16-04-2012
Volume - 6   Issue - 2    |    Publication Date - April 2012  Table of Contents
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KEYWORDS
Kernel, Feature Space, Nonlinear Mapping, Electronic Nose
ABSTRACT
Black Tea is conventionally tested by human sensory panel called “Tea Tasters”, who assign quality scores to different teas. In this paper electronic nose based evaluation of black tea samples have been described. One of the principal problems encountered in the above studies is collection of tea samples. These tea industries in India are spread over dispersed locations and quality of tea varies considerably on agroclimatic condition, type of plantation, season of flush and method of manufacturing. As a result the nature of data is overlapped, when it is collected from the electronic nose even if, it belongs to different scores of tea. For better separation among the different scores of tea samples, the kernel principal component analysis (KPCA) and kernel discriminate analysis (KLDA) have been employed in the clustering algorithm for black tea aroma discrimination with electronic nose .The performance using KPCA and KLDA is very effective as well as most interesting.
CITED BY (8)  
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Mr. Ashis Tripathy
- India
Dr. A. K. Mohanty
- India
Mr. Mihir Narayan Mohant
- India
mihir.n.mohanty@gmail.com