• DocumentCode
    1501550
  • Title

    Classification of Black Tea Taste and Correlation With Tea Taster´s Mark Using Voltammetric Electronic Tongue

  • Author

    Palit, Mousumi ; Tudu, Bipan ; Dutta, Pallab Kumar ; Dutta, Ankur ; Jana, Arun ; Roy, Jayanta Kumar ; Bhattacharyya, Nabarun ; Bandyopadhyay, Rajib ; Chatterjee, Anutosh

  • Author_Institution
    Dept. of Electron. & Telecommun. Eng., Central Calcutta Polytech., Kolkata, India
  • Volume
    59
  • Issue
    8
  • fYear
    2010
  • Firstpage
    2230
  • Lastpage
    2239
  • Abstract
    Tea quality assessment is a difficult task because of the presence of innumerable compounds and their diverse contribution to tea quality. As a result, instrumental evaluation of tea quality is not practiced in the industry, and tea samples are assessed by experienced tea tasters. There had been a very few reports where an electronic tongue has been used for the discrimination of taste of tea samples. In this paper, a voltammetric electronic tongue instrument is described, which can declare tea-taster-like scores for black tea. The electronic tongue is based on the principle of pulse voltammetry and consists of an array of five working electrodes along with a counter and a reference electrode. The five working electrodes are of gold, iridium, palladium, platinum, and rhodium. The voltage equivalent of the output current from between the working electrode and the counterelectrode generated out of the tea liquor when excited with pulse voltage between the working electrode and the reference electrode has been considered for data analysis. First, the sampled data have been compressed using discrete wavelet transform (DWT) and are then processed using principal component analysis (PCA) and linear discriminant analysis (LDA) for visualization of underlying clusters. Finally, different pattern recognition models based on neural networks are investigated to carry out a correlation study with the tea tasters´ score of five different grades of black tea samples obtained from a tea garden in India. The efficacy of the classifier has been established using tenfold cross-validation methods.
  • Keywords
    beverages; discrete wavelet transforms; electronic tongues; neural nets; pattern recognition; principal component analysis; production engineering computing; quality control; DWT; India; LDA; PCA; black tea taster; discrete wavelet transform; linear discriminant analysis; neural network; pattern recognition; principal component analysis; quality assessment; tea liquor; voltammetric electronic tongue; Black tea grade; data clustering; discrete wavelet transform (DWT); electronic tongue; neural networks; voltammetry;
  • fLanguage
    English
  • Journal_Title
    Instrumentation and Measurement, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9456
  • Type

    jour

  • DOI
    10.1109/TIM.2009.2032883
  • Filename
    5288610