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The paper presents application of differential electronic nose in the dynamic (on-line) volatile measurement. First we compare the classical nose employing only one sensor array and its extension in the differential form containing two sensor arrays working in differential mode. We show that differential nose performs better at changing environmental conditions, especially the temperature, and well performs in the dynamic mode of operation. We show its application in recognition of different brands of tobacco.
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649--662
Opis fizyczny
Bibliogr. 9 poz., rys., tab., wykr., wzory
Twórcy
autor
- Warsaw University of Technology, Faculty of Electrical Engineering, Koszykowa 75, Warsaw, Poland
- Military University of Technology, Faculty of Electronic Engineering, Kaliskiego 1, Warsaw, Poland
autor
- Warsaw University of Technology, Faculty of Electrical Engineering, Koszykowa 75, Warsaw, Poland
autor
- Warsaw University of Technology, Faculty of Electrical Engineering, Koszykowa 75, Warsaw, Poland
autor
- Warsaw University of Technology, Faculty of Chemistry, Noakowskiego 3, Warsaw, Poland
Bibliografia
- [1] Korotcenkov, G., Cho, B. K. (2011). Instability of metal oxide-based gas sensors and approaches to stability improvement (short survey). Sensors and Actuators B, 156, 527-538.
- [2] Gardner, J. W., Bartlett, P.N. (1999). Electronic noses - principles and applications. Oxford: Oxford University Press.
- [3] Brudzewski, K., Osowski, S., Ulaczyk, J. (2010). Differential electronic nose of two chemo sensor arrays for odor discrimination. Sensors and Actuators B, 145, 246-249.
- [4] 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, ID 361378, 1-7.
- [5] Wilson, D., Baietto, M. (2009). Applications and advances in electronic-nose technologies. Sensors, 9, 5099-5148.
- [6] Brudzewski, K., Osowski, S., Golembiecka, A. (2012). Differential electronic nose and Support Vector Machine for fast recognition of tobacco. Expert Systems with Applications, 39, 9886-9891.
- [7] Matlab Signal Processing Toolbox (2013). Natick: Mathworks.
- [8] Schölkopf, B., Smola, A. (2002). Learning with kernels. Cambridge, MA: MIT Press.
- [9] Barman, I., Dingari, N. C., Singh, G. P., Soares, J. S., Dasari, R. R., Smulko, J. M. (2012). Investigation of noise-induced instabilities in quantitative biological spectroscopy and its implications for noninvasive glucose monitoring. Analytical Chemistry, 84(19), 8149-8156.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-35cc037d-fb04-4870-8455-a77539fe82d7