Predicting odor mixture's responses on machine olfaction sensors

Sensors and Actuators, B: Chemical

Volumn 155, Issue 2, 2011, Pages 473-482

  Phaisangittisagul, Ekachai ^a   Nagle, H Troy ^b,c  

^a KASETSART UNIVERSITY   (Thailand)

^b North Carolina State University   (United States)

^c USA and North Carolina State University   (United States)

Author keywords

Electronic noses (e noses); Odor mixtures; Real valued genetic algorithm; Sensor response; Support vector regression; Wavelet decomposition reconstruction

Indexed keywords

ELECTRONIC NOSE; REAL-VALUED GENETIC ALGORITHM; SENSOR RESPONSE; SUPPORT VECTOR REGRESSION; WAVELET DECOMPOSITION/RECONSTRUCTION;

ARTIFICIAL ORGANS; FORECASTING; HARDWARE; MIXTURES; WAVELET ANALYSIS;

SENSORS;

EID: 79957869675     PISSN: 09254005     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.snb.2010.12.049     Document Type: Article

References (37)

1. J.W. Gardner, H.W. Shin, and E.L. Hines An electronic nose system to diagnose illness Sens. Actuators B: Chem. 70 2000 19 24
2. M.E. Monge, D. Bulone, D. Giacomazza, D.L. Bernik, and R.M. Negri Detection of flavour release from pectin gels using electronic noses Sens. Actuators B: Chem. 101 2004 28 38
3. C. Arnold, M. Harms, and J. Goschnick Air quality mornitoring and fire detection with the karlsruhe electronic micronose KAMINA IEEE Sensor 2 2002 179 188
4. R. Dutta, E.L. Hines, J.W. Gardner, K.R. Kashwan, and M. Bhuyan Tea quality prediction using a tin oxide-based electronic nose: an artificial intelligence approach Sens. Actuators B: Chem. 94 2003 228 237
5. M. Penza, and G. Cassano Chemometric characterization of Italian wines by thin-film multisensors array and artificial neural networks Food Chem. 86 2004 283 296 (Pubitemid 38283584)
6. H.T. Nagle, R. Gutierrez-Osuna, and S.S. Schiffman The how and why of electronic noses IEEE Spectr. 35 9 1998 22 31
7. K. Tominaga, S. Honda, T. Ohsawa, H. Shigeno, K. Okada, and Y. Matsushita Friend park: expression of the wind and the scent on virtual space Proceedings of the 7th International Conference on Virtual Systems and Multimedia Berkeley, CA, USA, October 25-27 2001
8. D. Harel, L. Carmel, and D. Lancet Towards an Odor communication system Comput. Biol. Chem. 27 2003 121 133
9. L. Carmel, and D. Harel Mix-to-mimic odor synthesis for electronic noses Sens. Actuators B vol.125 2007 635 643 (Pubitemid 47095248)
10. T. Nakamoto, Y. Nakahira, H. Hiramatsu, and T. Moriizumi Odor recorder using active odor sensing system Sens. Actuators B vol.76 2001 465 469 (Pubitemid 32513920)
11. L. Carmel, N. Sever, and D. Harel On predicting responses to mixtures in quartz microbalance sensors Sens. Actuators B 106 2005 128 135
12. B. Wyszynski, T. Yamanaka, and T. Nakamoto Recording and reproducing citrus flavors using odor recorder Sens. Actuators B 106 2005 388 393 (Pubitemid 40480273)
13. T. Yamanaka, R. Matsumoto, and T. Nakamoto Fundamental study of odor recorder for multicomponent odor using recipe exploration method based on singular value decomposition IEEE Sensors 3 4 2003 468 474
14. P. Somboon, B. Wyszynski, and T. Nakamoto Novel odor recorder for extending range of recordable odor Sens. Actuators B 121 2007 583 589 (Pubitemid 46186909)
15. K. Arshak, E. Moore, G.M. Lyons, F. Harris, and S. Clifford A review of gas sensors employed in electronic nose application Sens. Rev. 24 2 2004 181 198
16. C.S. Burrus, R.A. Gopinath, and H. Guo Introduction to Wavelets and Wavelet Transforms: A primer 1998 Prentice Hall NJ
17. M. Vetterli, and J. Kovacevic Wavelets and Subband Coding 1995 Prentice Hall NJ
18. S. Mallat A Wavelet Tour of Signal Processing 2nd ed. 1999 Academic Press San Diego, CA
19. ®, 2004.
20. B.E. Boser, I.M. Guyon, and V.N. Vapnik A training algorithm for optimal margin classifiers D. Haussler, Proceedings of the Annual Conference on Computational Learning Theory Pittsburgh, PA 1992 ACM Press 144 152
21. I. Guyon, B. Boser, and V. Vapnik Automatic capacity tuning of very large VC-dimension classifiers S.J. Hanson, J.D. Cowan, C.L. Giles, Advances in Neural Information Processing Systems 5 1993 Morgan Kaufmann Publishers 147 155
22. C. Cortes, and V. Vapnik Support vector networks Mach. Learn. 20 1995 273 297
23. V. Vapnik, S. Golowich, and A. Smola Support vector method for function approximation, regression estimation, and signal processing M.C. Mozer, M.I. Jordan, T. Petsche, Advances in Neural Information Processing Systems 9 1997 MIT Press Cambridge, MA 281 287
24. V. Vapnik The Nature of Statistical Learning Theory 1995 Springer New York
25. K.P. Bennett, and O.L. Mangasarian Robust linear programming discrimination of two linearly inseparable sets Optim. Methods Softw. 1 1992 23 34 (Pubitemid 23615796)
26. F. Girosi An equivalence between sparse approximation and support vector machines Neural Comput. 10 6 1998 1455 1480 (Pubitemid 128464285)
27. R. Fletcher Practical Methods of Optimization 1989 John Wiley and Sons New York
28. O.L. Mangasarian Nonlinear Programming 1969 McGraw-Hill New York
29. M.A. Aizerman, É.M. Braverman, and L.I. Rozonoér Theoretical foundations of the potential function method in pattern recognition learning Automat. Remote Control 25 1964 821 837
30. N.J. Nilsson Learning Machines: Foundations of Trainable Pattern Classifying Systems 1965 McGraw-Hill
31. W. Karush, Minima of functions of several variables with inequalities as side constraints, Master's Thesis, Department of Mathematics, University of Chicago, 1939.
32. H.W. Kuhn, and A.W. Tucker Nonlinear programming Proceedings of 2nd Berkeley Symposium on Mathematical Statistics and Probabilistics Berkeley 1951 University of California Press 481 492
33. N. Cristianini, and J. Shawe-Taylor An Introduction to Support Vector Machines 2000 Cambridge University Press Cambridge, UK
34. B. Schölkopf, and A.J. Smola Learning with Kernels 2002 MIT Press
35. R. Haupt, and S. Haupt Practical Genetic Algorithms 2004 John Wiley & Sons, Inc. New Jersey
36. R. Gutierrez-Osuna, H.T. Nagle, B. Kermani, and S. Schiffman Signal conditioning and preprocessing T.C. Pearce, S.S. Schiffman, H.T. Nagle, J.W. Gardner, Handbook of Machine Olfaction: Electronic Nose Technology 2002 Wiley-VCH Weinheim, Germany
37. S. Canu, Y. Grandvalet, V. Guigue, and A. Rakotomamonjy SVM and Kernel Methods Matlab Toolbox, Perception Systems et Information 2005 INSA de Rouen Rouen, France