A Novel Technique of Black Tea Quality Prediction Using Electronic Tongue Signals

Electronic tongue (ET) system is under extensive development for automatic analysis and prediction of quality of different industrial end products. Each sensor in an ET system generates a specific electronic response in presence of different organic or inorganic compounds in the sample. The vital pa...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement Vol. 63; no. 10; pp. 2472 - 2479
Main Authors: Saha, Pradip, Ghorai, Santanu, Tudu, Bipan, Bandyopadhyay, Rajib, Bhattacharyya, Nabarun
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Arrays
Automation
Black tea
Classifiers
Discrete wavelet transforms
Electrodes
Electronic tongue (ET)
Electronic tongues
Electronics
Feature extraction
Instrumentation
Kernel
kernel classifiers
Mathematical analysis
Sensor arrays
support vector machine (SVM)
Support vector machines
vector valued regularized kernel function approximation (VVRKFA)
Vectors
wavelet features
Wavelet transforms
vector valued regularized kernel function approximation (VVRKFA)
Electronic tongue (ET)
feature extraction
support vector machine (SVM)
wavelet features
kernel classifiers
ISSN:0018-9456, 1557-9662
Online Access:Get full text
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Summary:Electronic tongue (ET) system is under extensive development for automatic analysis and prediction of quality of different industrial end products. Each sensor in an ET system generates a specific electronic response in presence of different organic or inorganic compounds in the sample. The vital part of the ET system is the discrimination of the complex pattern generated by the sensor array. In this paper, a novel technique of black tea quality estimation is using the ET signals. A moving window is used to extract discrete wavelet transform coefficients from the transient response of ET. The energy in different frequency bands are used as the features of the ET signal for different positions of the window. The prediction of a new sample is performed by the highest score obtained by a particular class by testing all the patterns generated by windowing ET signal. The performance of the proposed technique is verified to estimate black tea quality using two kernel classifiers, namely support vector machine and recently proposed vector valued regularized kernel function approximation method. High prediction accuracy of both the classifiers confirms the effectiveness of the proposed technique of tea quality estimation using ET signals.
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2014.2310615