Abstract
Electronic nose (e-nose) is a machine olfaction system that has shown significant possibilities as an improved alternative of human taster as olfactory perceptions vary from person to person. In contrast, electronic noses also detect smells with their sensors, but in addition describe those using electronic signals. An efficient e-nose system should analyze and recognize these electronic signals accurately. For this it requires a robust pattern classifier that can perform well on unseen data. This research work shows the efficient prediction of black tea quality by means of modern kernel classifiers using the e-nose signatures. As kernel classifiers, this work investigates the potential of state of the art support vector machine (SVM) classifier and very recently developed nonparallel plane proximal classifier (NPPC) and vector-valued regularized kernel function approximation (VVRKFA) technique of multiclass data classification to build taster-specific computational models. Experimental results show that VVRKFA and one-versus-rest (OVR) SVM models offer high accuracies to predict the considerable variation in tea quality.
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References
R. Dutta, E.L. Hines, J.W. Gardner, K.R. Kashwan, M. Bhuyan, Tea quality prediction using a tin oxide-based electronic nose: An artificial intelligence approach. Sens. Actuators B: Chem. 94(2), 228–237 (2003)
M. Pardo, G. Sberveglieri, Coffee analysis with an electronic nose. IEEE Trans. Instrum. Meas. 51(6), 1334–1339 (2002)
D.D.H. Boothe, J.W. Arnold, Electronic nose analysis of volatile compounds from poultry meat samples, fresh and after refrigerated storage. J. Sc. Food Agric. 82(3), 315–322 (2002)
J. Lozano, J.P. Santos, M. Aleixandre, I. Sayago, J. Gutierrez, M.C. Horrillo, Identification of typical wine aromas by means of an electronic nose. IEEE Sensor J. 6(1), 173–178 (2006)
M. O’Connel, G. Valdora, G. Peltzer, R. Martin Negri, A practical approach for fish freshness determinations using a portable electronic nose. Sens. Actuators, B: Chem. 80(2), 149–154 (2001).
N. Bhattacharyya, R. Bandyopadhyay, M. Bhuyan, B. Tudu, D. Ghosh, A. Jana, Electronic nose for black tea classification and correlation of measurements with “Tea Taster” marks. IEEE Trans. Instrum. Meas. 57(7), 1313–1321 (2008)
B. Tudu, A. Jana, A. Metla, D. Ghosh, N. Bhattacharyya, R. Bandopadhyay, Electronic nose for black tea quality evaluation by an incremental RBF network. Sens. Actuat. B: Chem. 138, 90–95 (2009)
T. Hastie, R. Tibshirani and J. Friedman, The Elemetns of Statistical Learning: Data mining, Inference, Prediction, 2nd edn. (Springer, New York, 2009)
R. O. Duda, P. E. Hart and D. J. Stork, Pattern Classification, 2nd edn. (John Wiley & Sons, Inc., Singapore, 2006)
J. R. Quinlan, C4.5: Programs for Machine Learning (Morgan Kaufmann Inc., 1993)
L. Breiman, J. Friedman, C.J. Stone, R.A. Olshen, Classification and Regression Trees (Chapman and Hall, New York, 1993)
E.L. Allwein, R.E. Schapire, Y. Singer, Reducing multiclass to binary: a unifying approach for margin classifiers. J. Mach. Learn. Res. 1, 113–141 (2000)
L. Bottou, C. Cortes, J. Denker, H. Drucker, I. Guyon, L. Jackel, Y. LeCun, U. Muller, E. Sackinger, P. Simard, V. Vapnik, Comparison of classifier methods: A case study in hand writing digit recognition. in Proceedings of the International Conference Pattern Recognition, 1994, pp. 77–87
U. Kreßel, Pairwise Classification and Support Vector Machines, in Advances in Kernel Methods-Support Vector Learning, ed. by B. Schölkopf, C. J. C. Burges, A. J. Smola (MIT Press, Cambridge, 1999), pp. 255–268
J.C. Platt, N. Cristianini, J. Shawe-Taylor, Large margin DAG’s for multiclass classification. Adv. Neu. Inform. Process. Syst. 12, 547–553 (2000)
K. Crammer and Y. Singer, On the learn ability and design of output codes for multiclass problems. Mach. Learning 47(2–3), 201–233 (2002)
V.N. Vapnik, The nature of statistical learning theory (John Wiley & Sons, Inc., New York, 1998)
N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines, (Cambridge University Press, Cambridge, 2000)
C.-W. Hsu, C.-J. Lin, A comparison of methods for multiclass support vector machines. IEEE Trans. N. Nets. 13(2), 415–425 (2002)
S. Ghorai, A. Mukherjee, S. Sengupta, P. K. Dutta, Multicategory Cancer Classification from Gene Expression Data by Multiclass NPPC Ensemble. in IEEE International Conference on Systems in Medicine and Biology, (IIT-Kharagpur, 2010)
S. Ghorai, A. Mukherjee, P.K. Dutta, Nonparallel plane proximal classifier. Sign. Process. 89(4), 510–522 (2009)
S. Ghorai, S.J. Hossain, A. Mukherjee, P.K. Dutta, Newton method for nonparallel plane proximal classifier with unity norm hyperplanes. Sign. Proces. 90(1), 93–104 (2010)
S. Ghorai, A. Mukherjee, P.K. Dutta, Discriminant analysis for fast multiclass data classification through regularized kernel function approximation. IEEE Trans. Neu. Netw 21(6), 1020–1029 (2010)
S. Ghorai, A. Mukherjee, P.K. Dutta, Advances in Proximal Kernel Classifiers (LAP LAMBERT Academic Publishing, Germany, 2012)
M. Pardo, G. Sberveglieri, Classification of electronic nose data with Support vector machines. Sens. Actuat. B: Chem. 107, 730–737 (2005)
M. Gaudioso, W. Khalaf, On the Use of the SVM Approach in Analyzing an Electronic Nose, in Proceedings of the 7th international Conference on Hybrid Intelligent Systems (Kaiserslautern, 2007), pp. 42–46
L. Xie, X. Wang, Gas quantitative analysis with support vector machine. in Proceedings of Chinese Control and Decision Conference, (Jinhua, 2009), pp. 2362–2366
W. Khalaf, C.P.M. Gaudioso, Least square regression method for estimating gas concentration in an electronic nose system. Sensors 9(3), 1678–1691 (2009)
X. Wang, J. Chang, K. Wang, M. Ye, Least square support vector machines in combination with principal component analysis for electronic nose data classification, in Proceedings of 2nd International Symposium on Information Science and Engineering (Shanghai, China, 2009), pp. 348–352
E. Phaisangittisagul, Improving sensor subset selection of machine olfaction using multiclass SVM, in Proceedings of 3rd International Conference on Knowledge Discovery and Data Mining (Bangkok, 2010), pp. 28–31
E. Phaisangittisagul, Approximating sensors’ responses of odor mixture on machine olfaction, in Proceedings of International Conference on Artificial Intelligence and Computational Intelligence, vol. 2 (2009), pp. 60–64
C. Cortes, V.N. Vapnik, Support Vector Networks. Mach. Learn. 20(3), 273–297 (1995)
J. Friedman, Another approach to polychotomous classification, Department of Statistics, Stanford University Stanford, Technical Report. Available at: http://www-stat.stanford.edu/reports/friedman/poly.ps.Z, 1996
C. C. Chang and C. J. Lin, LIBSVM: A library for support vector machines. Technical Reort, 2001. Available at: http://www.csie.ntu.edu.tw/~cjlin/libsvm
T. M. Mitchell, Machine Learning, (The McGraw-Hill Companies, Inc., Singapore, 1997), p 148 (Chapter 5)
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Saha, P., Ghorai, S., Tudu, B., Bandyopadhyay, R., Bhattacharyya, N. (2014). Multiclass Kernel Classifiers for Quality Estimation of Black Tea Using Electronic Nose. In: Mason, A., Mukhopadhyay, S., Jayasundera, K., Bhattacharyya, N. (eds) Sensing Technology: Current Status and Future Trends II. Smart Sensors, Measurement and Instrumentation, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-02315-1_7
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