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Selection of Optimum Level of Data Compression for Voltam metric Electronic Tongue Signal Using DWT

Published: 26 February 2015 Publication History

Abstract

A voltammetric electronic tongue has been developed for black tea assessment. While attempting to standardize the voltammetric electronic tongue a basic taste recognition test has been conducted. This electronic tongue works on cyclic voltammetric principle and has a three electrode configuration (working, reference, and counter electrode). The voltage is applied across the working and reference electrode and the response current is obtained from the counter and reference electrode. Now the huge amount of data, obtained from these response current pulses, was compressed by discrete wavelet transform (DWT). In this paper, in order to determine the optimum level of compression mean square error, separation index and principal component analysis have been employed.

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      cover image ACM Other conferences
      PerMIn '15: Proceedings of the 2nd International Conference on Perception and Machine Intelligence
      February 2015
      269 pages
      ISBN:9781450320023
      DOI:10.1145/2708463
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      • Dept. of Science and Techn., Government of India: Department of Science and Technology, Government of India

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 26 February 2015

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      Author Tags

      1. Electronic tongue
      2. mean square error
      3. principal component analysis
      4. separation index
      5. wavelet transform

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