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Electronic Nose for Black Tea Quality Evaluation Using Kernel Based Clustering Approach
Ashis Tripathy, A. K. Mohanty, Mihir Narayan Mohant
Pages - 86 - 93     |    Revised - 15-03-2012     |    Published - 16-04-2012
Volume - 6   Issue - 2    |    Publication Date - April 2012  Table of Contents
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KEYWORDS
Kernel, Feature Space, Nonlinear Mapping, Electronic Nose
ABSTRACT
Black Tea is conventionally tested by human sensory panel called “Tea Tasters”, who assign quality scores to different teas. In this paper electronic nose based evaluation of black tea samples have been described. One of the principal problems encountered in the above studies is collection of tea samples. These tea industries in India are spread over dispersed locations and quality of tea varies considerably on agroclimatic condition, type of plantation, season of flush and method of manufacturing. As a result the nature of data is overlapped, when it is collected from the electronic nose even if, it belongs to different scores of tea. For better separation among the different scores of tea samples, the kernel principal component analysis (KPCA) and kernel discriminate analysis (KLDA) have been employed in the clustering algorithm for black tea aroma discrimination with electronic nose .The performance using KPCA and KLDA is very effective as well as most interesting.
CITED BY (8)  
1 Christian, M. (2015). Electronic nose test unit untuk mengidentifikasi kandungan boraks dalam makanan. semnasteknomedia online, 3(1), 3-8.
2 Jyoti, A., Mohanty, M. N., Kar, S. K., & Biswal, B. N. (2015, January). Optimized Clustering Method for CT Brain Image Segmentation. In Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014 (pp. 317-324). Springer International Publishing.
3 Bakharev, P. V., & McIlroy, D. N. (2015). Signal-to-Noise Enhancement of a Nanospring Redox-Based Sensor by Lock-in Amplification. Sensors, 15(6), 13110-13120.
4 Littel, R. (2014). Neural Network Compound Predictor For Spirits in an Electronic Nose (Doctoral dissertation).
5 Jha, S. K., & Hayashi, K. (2014, April). Optimized KPCA method for chemical vapor class recognition by SAW sensor array response analysis. In Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2014 IEEE Ninth International Conference on (pp. 1-6). IEEE.
6 Deswal, A., Deora, N. S., & Mishra, H. N. Electronic Nose Based On Metal Oxide Semiconductor Sensors as an Alternative Technique for the Spoilage Classification of Oat Milk.
7 Kwon, H. J., Kim, D. G., & Hong, K. S. (2013). Multiple odor recognition and source direction estimation with an electronic nose system. International Journal of Distributed Sensor Networks, 2013.
8 Liu, H., Luo, D., Li, F., & Xie, G. (2013, December). Quality Evaluation for Anxi Tieguanyin Tea Based on Electronic Nose and PCALDA Method. In Information Science and Cloud Computing Companion (ISCC-C), 2013 International Conference on (pp. 543-549). IEEE.
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Belhumeur, N., Hespanha J., and Kriegman, D.,” Eigen faces vs. Fisher faces: Recognition Using Class Specific Linear Projection.” Proc.ECCV,pp.45-58,1996.
Bhattacharyya, N., Bandyopadhyay, R., Bhuyan, M., Tudu, B., Ghosh, D., and Jana, A., “Electronic nose for black tea classification and correlation of measurements with "Tea Taster" marks.” IEEE Trans. Instrum. Meas., 57(7), pp. 1313-1321, 2008.
Bhattacharyya, N., Seth, S., Tudu, B., Tamuly, P., Jana, A., Ghosh, D., Bandyopadhyay, R., Bhuyan, M., and Sabhapandit, S. “Detection of optimum fermentation time for black tea manufacturing using electronic nose.” Sens. Actuators B,122(2), pp. 627-634, 2007.
Boothe, D. D. H., and Arnold, J. W.“Electronic nose analysis of volatile compounds from poultry meat samples, fresh and after refrigerated storage.” J. Sci. Food Agric., 82(3) pp. 315-322, 2002.
Duda D.S.R, Hart P. Pattern Classification. Wiley, New York, 2001.
Dutta, R., Hines, E. L., Gardner, J. W., Kashwan, K. R., and Bhuyan, M., “Tea quality prediction using a tin oxide-based electronic nose: An artificial intelligence approach.” Sens. Actuators B, 94, pp. 228-237, 2003.
Dy, J. G., and Brodley, C. E., “Feature Selection for Unsupervised Learning.” J. Machine Learning Res., 5, pp.845-889, 2004.
Hoffmann, H., “Kernel PCA for novelty detection.” Pattern Recognition, 40,pp. 863 – 874, 2007.
J. Lozano, J. P. Santos, M. Aleixandre, I. Sayago, J. Gutierrez, and M. C. Horrillo. “Identification of typical wine aromas by means of an electronic nose.” IEEE Sensor J., 6(1) pp. 173-178, 2006.
Kermani, B. G., Schiffman, S. S., and Nagle, H. T, “Performance of the Levenberg- Marquardt neural network training method in electronic nose applications.” Sens. Actuators B, 110(1) pp. 13-22, 2005.
Kim, K.I., Park, S.H., and Kim, H.J “Kernel principal component analysis for texture classification.” IEEE Signal Processing Letters, 8(2), pp.39-41., 2001.
Liu, C.J, and Wechsler, H.,”A Shape-and Texture-Based Enhanced Fisher Classifier for Face Recognition.” IEEE Trans.Image Processing. Vol.10, no.4. pp.598-608, 2001 .
O’Connell, M., Valdora, G., Peltzer, G. and Martin Negri, R., “A practical approach for fish freshness determinations using a portable electronic nose.” Sens. Actuators B, 80(2) pp. 149-154, 2001.
Pardo, M., and Sberveglieri, G., “Coffee analysis with an electronic nose.” IEEE Trans. Instrum. Meas., 51(6) pp. 1334-1339, 2002.
R. O. Duda, D. G. Stork, P. E. Hart, Pattern classification, 2nd edition, John Wiley and Sons, (pp. 115), (2001).
Sangita D. Bharkad and Maneshkokare, “Performance evaluation of distance matrices: application to fingerprint recognition.” International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), volume: 25, pp.777-806, 2011.
Scholkopf, B., Smola, A., and Muller, K-R., “Nonlinear component analysis as a kernel eigenvalue problem.” Technical Report No.44, Max-Planck Institute, Germany, 1996.
Scholkopf, B., Smola, A., and Muller, K-R.,”Nonlinear Component Analysis as a Kernel Eigenvalue Problem.” Neural Computation, vol.10,no.5,pp.1299-1319,1998.
Wall, M. E., Rechtsteiner, A., Rocha, L. M., “Singular value decomposition and principal component analysis.” in A Practical Approach to Microarray Data Analysis, Berrar, D. P., Dubitzky, W., and Granzow, M. Eds., Norwell, MA: Kluwer, (Chapter 5), pp.91–109, 2003.
Mr. Ashis Tripathy
- India
Dr. A. K. Mohanty
- India
Mr. Mihir Narayan Mohant
- India
mihir.n.mohanty@gmail.com


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