Abstract
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.
Chapter PDF
Similar content being viewed by others
Keywords
References
United Kingdom Tea Council, http://www.tea.co.uk/the-home-of-tea-all-you-need-to-know-about-tea (last accessed: June 2010)
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)
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)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Chichester (2001)
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)
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)
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)
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)
Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)
Vovk, V., Gammerman, A., Shafer, G.: Algorithmic Learning in a Random World. Springer, Heidelberg (2005)
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)
Yu, H., Wang, J.: Discrimination of LongJing green-tea grade by electronic nose. Sensors and Actuators B 122, 134–140 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 IFIP
About this paper
Cite this paper
Nouretdinov, I., Li, G., Gammerman, A., Luo, Z. (2010). Application of Conformal Predictors to Tea Classification Based on Electronic Nose. In: Papadopoulos, H., Andreou, A.S., Bramer, M. (eds) Artificial Intelligence Applications and Innovations. AIAI 2010. IFIP Advances in Information and Communication Technology, vol 339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16239-8_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-16239-8_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16238-1
Online ISBN: 978-3-642-16239-8
eBook Packages: Computer ScienceComputer Science (R0)