Application of Conformal Predictors to Tea Classification Based on Electronic Nose

  • Ilia Nouretdinov
  • Guang Li
  • Alexander Gammerman
  • Zhiyuan Luo
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 339)


In this paper, we present an investigation into the performance of conformal predictors for discriminating the aroma of different types of tea using an electronic nose system based on gas sensors. We propose a new non-conformity measure for the implementation of conformal predictors based on Support Vector Machine for multi-class classification problems. The experimental results have shown the good performance of the implemented conformal predictors.


Tea Classification Electronic Nose Conformal Predictors Support Vector Machines Pattern Recognition 


  1. 1.
    United Kingdom Tea Council, (last accessed: June 2010)
  2. 2.
    Bhattacharyya, N., Bandyopadhya, R., Bhuyan, M., Tudu, B., Ghosh, D., Jana, A.: Electronic Nose for Black Tea Classification and Correlation of Measurement with “Tea Taster” Marks. IEEE Transactions on Instrumentation and Measurement 57(7), 1313–1321 (2008)CrossRefGoogle Scholar
  3. 3.
    Borah, S., Hines, E.L., Leeson, M.S., Iliescu, D.D., Bhuyan, M., Gardner, J.W.: Neural network based on electronic nose for classification of tea aroma. Sensing and Instrumentation for Food Quality and Safety 2(1), 7–14 (2008)CrossRefGoogle Scholar
  4. 4.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Chichester (2001)zbMATHGoogle Scholar
  5. 5.
    Dutta, R., Kashwan, K.R., Bhuyan, M., Hines, E.L., Gardner, J.W.: Electronic nose based tea quality standardization. Neural Networks 16(5-6), 847–853 (2003)CrossRefGoogle Scholar
  6. 6.
    Gonzalez, E., Li, G., Ruiz, Y., Zhang, J.: A Tea Classification Method Based on an Olfactory System Model. In: Advances in Cognitive Neurodynamics ICCN 2007, pp. 747–751 (2008)Google Scholar
  7. 7.
    Nouretdinov, I., Vovk, V.: Criterion of calibration for Transductive Confidence Machine with limited feedback. Theoretical Computer Science, Algorithmic learning theory 364(1), 3–9 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Tudu, B., Jana, A., Metla, A., Ghosh, D., Bhattacharyya, N., Bandyopadhyay, R.: Electronic nose for black tea quality evaluation by an incremental RBF network. Sensors and Actuators B: Chemical 138, 90–95 (2009)CrossRefGoogle Scholar
  9. 9.
    Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar
  10. 10.
    Vovk, V., Gammerman, A., Shafer, G.: Algorithmic Learning in a Random World. Springer, Heidelberg (2005)Google Scholar
  11. 11.
    Yang, X., Fu, J., Lou, Z., Wang, L., Li, G., Freeman, W.J.: Tea classification based on artificial olfaction using bionic olfactory neural network. In: Proceedings of Third International Symposium on Neural Networks, pp. 343–348 (2006)Google Scholar
  12. 12.
    Yu, H., Wang, J.: Discrimination of LongJing green-tea grade by electronic nose. Sensors and Actuators B 122, 134–140 (2007)CrossRefGoogle Scholar

Copyright information

© IFIP 2010

Authors and Affiliations

  • Ilia Nouretdinov
    • 1
  • Guang Li
    • 2
  • Alexander Gammerman
    • 1
  • Zhiyuan Luo
    • 1
  1. 1.Computer Learning Research Centre, Royal HollowayUniversity of LondonSurreyUK
  2. 2.Department of Control Science and EngineeringZhejiang UniversityZhejiangP.R. China

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