• DocumentCode
    1093672
  • Title

    Electronic Nose for Black Tea Classification and Correlation of Measurements With “Tea Taster” Marks

  • Author

    Bhattacharyya, Nabarun ; Bandyopadhyay, Rajib ; Bhuyan, Manabendra ; Tudu, Bipan ; Ghosh, Devdulal ; Jana, Arun

  • Author_Institution
    Centre for the Dev. of Adv. Comput., Kolkata
  • Volume
    57
  • Issue
    7
  • fYear
    2008
  • fDate
    7/1/2008 12:00:00 AM
  • Firstpage
    1313
  • Lastpage
    1321
  • Abstract
    Tea is an extensively consumed beverage worldwide with an expanding market. The major quality attributes of tea are flavor, aroma, color, and strength. Out of these, flavor and aroma are the most important attributes. Human experts called ldquotea tastersrdquo conventionally evaluate tea quality, and they usually assign scores to samples of tea that are under evaluation on a scale of 1 to 10, depending on the flavor, the aroma, and the taste of the sample. This paper presents a study where, first, the selection of appropriate sensors was carried out based on sensitivity with the major aroma-producing chemicals of black tea. Then, this sensor array was exposed to black tea samples that were collected from the tea gardens in India, and the computational model has been developed based on artificial neural network methods to correlate the measurements with the tea taster´s scores. With unknown tea samples, encouraging results have been obtained with a more than 90% classification rate.
  • Keywords
    beverages; electronic noses; neural nets; sensor arrays; India; PCA; aroma-producing chemicals; artificial neural network methods; beverage; black tea classification; computational model; electronic nose; gas sensors; principal component analysis; sensor array; tea aroma; tea quality; tea taster; Artificial neural networks; electronic nose; gas sensors; principal component analysis (PCA); tea aroma; tea taster;
  • fLanguage
    English
  • Journal_Title
    Instrumentation and Measurement, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9456
  • Type

    jour

  • DOI
    10.1109/TIM.2008.917189
  • Filename
    4464111