Gas Detection via Machine Learning

We present an Electronic Nose (ENose), which is aimed at identifying the presence of one out of two gases, possibly detecting the presence of a mixture of the two. Estimation of the concentrations of the components is also performed for a volatile organic compound (VOC) constituted by methanol and acetone, for the ranges 40-400 and 22-220 ppm (parts-per-million), respectively. Our system contains 8 sensors, 5 of them being gas sensors (of the class TGS from FIGARO USA, INC., whose sensing element is a tin dioxide (SnO2) semiconductor), the remaining being a temperature sensor (LM35 from National Semiconductor Corporation), a humidity sensor (HIH–3610 from Honeywell), and a pressure sensor (XFAM from Fujikura Ltd.). Our integrated hardware–software system uses some machine learning principles and least square regression principle to identify at first a new gas sample, or a mixture, and then to estimate the concentrations. In particular we adopt a training model using the Support Vector Machine (SVM) approach with linear kernel to teach the system how discriminate among different gases. Then we apply another training model using the least square regression, to predict the concentrations. The experimental results demonstrate that the proposed multiclassification and regression scheme is effective in the identification of the tested VOCs of methanol and acetone with 96.61% correctness. The concentration prediction is obtained with 0.979 and 0.964 correlation coefficient for the predicted versus real concentrations of methanol and acetone, respectively.

Authors:

• Walaa Khalaf
• Calogero Pace
• Manlio Gaudioso

Keywords:

• Electronic nose
• Least square regression
• Mixture ofgases
• Support Vector Machine.

References:

[1] P. Bartlett and J. Gardner. Odour sensors for an electronic nose, in:
Sensors and sensory systems for an electronic nose. NATO ASI Series E:
Applied Science, 212: 197-216, 1992.
[2] C. Burges. A tutorial on support vector machines for pattern recognition.
Data Mining and knowledge Discovery, 2 (2): 121-167, 1998.
[3] C. C. Chang and C. J. Lin. Libsvm: A library for support machines.
[Online]. Available :http://www.csie.ntu.edu.tw/ cjlin/libsvm, 2001.
[4] N. Cristianini and J. Shawe-Taylor. An introduction to support vector
machines and other kernel-based learning methods. Cambridge
University Press, 2000.
[5] C. Distante, N. Ancona, and P. Siciliano. Support vector machines for
olfactory signals recognition. Sensors and Actuators B, 88 (1): 30-39,
2003.
[6] M. Gaudioso, W. Khalaf, and C. Pace. On the use of the SVM approach
in analyzing an electronic nose. Proceedings of the seventh International
Conference on Hybrid Intelligent Systems, Kaiserslautern-Germany,
pages 42-46, 17-19 September 2007.
[7] R. Gutierrez-Osuna. Pattern analysis for machine olfaction: A review.
IEEE Sensors Journal, 2 (3): 189-202, June 2002.
[8] K. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf. An
introduction to kernel-based learning algorithms. IEEE Trans. on Neural
Networks 12 (2): 181-201, March 2001.
[9] M. Pardo and G. Sberveglieri. Classification of electronic nose data with
support vector machines. Sensors and Actuators B, 107: 730-737, 2005.
[10] T. C. Pearce, S. S. Schiffman, H. T. Nagle, and J. W. Gardner.
Handbook of machine olfaction: Electronic nose technology. WILEY-
VCH, 2003.
[11] M. Penza, G. Cassano, and F. Tortorella. Identification and
quantification of individual volatile organic compounds in a binary
mixture by saw multisensor array and pattern recognition analysis.
Measurement Science and Technology, 13: 846-858, 2002.
[12] V. N. Vapnik. Statistical learning theory. John Wiley and Sons, 1998.
[13] X. Wang, H. Zhang, and C. Zhang. Signals recognition of electronic
nose based on support vector machines. Proceedings of the Fourth
International Conference on Machine Learning and Cybernetics,
Guangzhou, pages 3394-3398, 18-21 August 2005.