Odour classification system for continuous monitoring applications

In this paper, we investigate the classification performance of an electronic nose system, based on tin dioxide gas sensors. In contrast to previous studies, the electronic nose is mounted on a mobile platform and samples are analyzed using only transient information in the signals. The motivation behind this work is to explore the feasibility of using electronic nose devices for odour classification in a number of future application domains which require fast and possibly real-time odour identification. To perform transient based analysis of the signals, a comparative study of different methods for feature extraction was performed. Additionally, the application of a relevance vector machine classifier is explored to further analyze the classification performance based on quality of the obtained samples. The results presented in this study can be used for the development of electronic nose devices particularly suitable for environmental monitoring applications.

[1]  Andreas Zell,et al.  Gas source declaration with a mobile robot , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[2]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[3]  R. Gutierrez-Osunaa,et al.  Transient response analysis for temperature-modulated chemoresistors , 2003 .

[4]  T. Eklöv,et al.  Enhanced selectivity of MOSFET gas sensors by systematical analysis of transient parameters , 1997 .

[5]  Christopher Bishop,et al.  Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics , 2003 .

[6]  George Eastman House,et al.  Sparse Bayesian Learning and the Relevance Vector Machine , 2001 .

[7]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[8]  Clark C. K. Liu,et al.  Environmental Fluid Mechanics : Theories and Applications , 2002 .

[9]  Ricardo Gutierrez-Osuna,et al.  A method for evaluating data-preprocessing techniques for odour classification with an array of gas sensors , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[10]  Doron Lancet,et al.  A feature extraction method for chemical sensors in electronic noses , 2003 .

[11]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[12]  P. J. Roberts,et al.  TURBULENT DIFFUSION , 2002 .

[13]  Silvia Coradeschi,et al.  Gas distribution mapping of multiple odour sources using a mobile robot , 2009, Robotica.

[14]  Martin Vetterli,et al.  Fast Fourier transforms: a tutorial review and a state of the art , 1990 .

[15]  T. Moriizumi,et al.  Remote sensing of gas/odor source location and concentration distribution using mobile system , 1998 .

[16]  Amine Bermak,et al.  Pattern Recognition Techniques for Odor Discrimination in Gas Sensor Array , 2005 .

[17]  Wolfgang Göpel,et al.  Characterisation of food freshness with sensor arrays , 1994 .

[18]  Dominique Martinez,et al.  A Biomimetic Robot for Tracking Specific Odors in Turbulent Plumes , 2022 .

[19]  Jing Gu,et al.  An artificial moth: Chemical source localization using a robot based neuronal model of moth optomotor anemotactic search , 2006, Auton. Robots.

[20]  J W Gardner and P N Bartlett,et al.  Electronic Noses: Principles and Applications , 1999 .

[21]  Richard T. Vaughan,et al.  The Player/Stage Project: Tools for Multi-Robot and Distributed Sensor Systems , 2003 .

[22]  H. T. Nagle,et al.  Transient response analysis of an electronic nose using multi-exponential models , 1999 .

[23]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[24]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[25]  Michael E. Tipping,et al.  Fast Marginal Likelihood Maximisation for Sparse Bayesian Models , 2003 .

[26]  C. Distante,et al.  On the study of feature extraction methods for an electronic nose , 2002 .

[27]  Amy Loutfi,et al.  Airborne Chemical Sensing with Mobile Robots , 2006, Sensors (Basel, Switzerland).