Abstract
In this paper, electronic nose systems consisting of five low cost gas sensors and three auxiliary sensors are described. The devices are effectively applied to gases mixtures classification in refinery environment and in monitoring of patients’ breath on haemodialysis treatment. The systems exploit a classification algorithm based on support vector machine method and a least square regression model for concentration estimation. In particular, in the present work, the systems implementation and the results obtained during data acquisition and post-processing phases are reported.
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References
Jeong, Y.: Choi—Time horizon selection using feature feedback for the implementation of an E-nose system. IEEE Sens. J. 13(5), 1575–1581 (2013)
Saha, P., et al.: Multi-class support vector machine for quality estimation of black tea using electronic nose. In: Proceedings of the International Conference on Sensing Technology (2012)
Wongchoosuk, C., et al.: WiFi electronic nose for indoor air monitoring. In: 2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON (2012)
Güney, S., et al.: Multiclass classification of n-butanol concentrations with k-nearest neighbor algorithm and support vector machine in an electronic nose. Sens. Actuators B: Chem. 166–167 (2012)
Zhang, L., et al.: Chaos based neural network optimization for concentration estimation of indoor air contaminants by an electronic nose. S&A, A: Phys. 189 (2013)
Zhanga, L., et al.: Classification of multiple indoor air contaminants by an electronic nose and a hybrid support vector machine. Sens. Actuators B 174 (2012)
Neri, G., Bonavita, A., Galvagno, S., Pace, C., Donato, N.: Preparation, characterization and CO sensing of Au/Iron oxide thin films. J. Mater. Sci. Mater. Electron. 13, 561–565 (2002)
Solga, S.F., et al.: Current status of clinical breath analysis. Appl. Phys. B 85 (2006)
Goerl, T., et al.: Volatile breath biomarkers for patient monitoring during haemodialysis. J. Breath Res. 7 (2013)
Neri, G., et al.: Real-time monitoring of breath ammonia during haemodialysis: use of ion mobility spectrometry (IMS) and cavity ring-down spectroscopy (CRDS) techniques. Nephrol Dial Transp. 27, 2945–2952 (2012)
Chang, C.C., Lin, C.J.: Libsvm: A Library for Support Vector Machines. Version 3.17 released on April Fools’ day, 2013. http://www.csie.ntu.edu.tw/~cjlin/libsvm/
Khalaf, W., et al.: Least square regression method for estimating gas concentration in an electronic nose system. Sensors 9, 1678–1691 (2009)
Pace, C., Khalaf, W., Latino, M., Donato, N., Neri, G.: E-nose development for safety monitoring applications in refinery environment. Proc. Eng. Elsevier 47, 1267–1270 (2012). ISSN: 1877-7058
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Pace, C., Fragomeni, L., Khalaf, W. (2016). Developments and Applications of Electronic Nose Systems for Gas Mixtures Classification and Concentration Estimation. In: De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. Lecture Notes in Electrical Engineering, vol 351. Springer, Cham. https://doi.org/10.1007/978-3-319-20227-3_1
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DOI: https://doi.org/10.1007/978-3-319-20227-3_1
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