A portable electronic nose based on embedded PC technology and GNU/Linux: hardware, software and applications

This paper describes a portable electronic nose based on embedded PC technology. The instrument combines a small footprint with the versatility offered by embedded technology in terms of software development and digital communications services. A summary of the proposed hardware and software solutions is provided with an emphasis on data processing. Data evaluation procedures available in the instrument include automatic feature selection by means of SFFS, feature extraction with linear discriminant analysis (LDA) and principal component analysis (PCA), multi-component analysis with partial least squares (PLS) and classification through k-NN and Gaussian mixture models. In terms of instrumentation, the instrument makes use of temperature modulation to improve the selectivity of commercial metal oxide gas sensors. Field applications of the instrument, including experimental results, are also presented.

[1]  Mário A. T. Figueiredo.  On Gaussian radial basis function approximations: interpretation, extensions, and learning strategies , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

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

[3]  Pavel Paclík,et al.  Adaptive floating search methods in feature selection , 1999, Pattern Recognit. Lett..

[4]  Ricardo Gutierrez-Osuna,et al.  IPNOSE: A PORTABLE ELECTRONIC NOSE BASED ON EMBEDDED TECHNOLOGY FOR INTENSIVE COMPUTATION AND TIME DEPENDENT SIGNAL PROCESSING , 2001 .

[5]  A. F. Smith,et al.  Statistical analysis of finite mixture distributions , 1986 .

[6]  Anil K. Jain,et al.  Feature Selection: Evaluation, Application, and Small Sample Performance , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Nir Friedman,et al.  Building Classifiers Using Bayesian Networks , 1996, AAAI/IAAI, Vol. 2.

[8]  W. Massy.  Principal Components Regression in Exploratory Statistical Research , 1965 .

[9]  Geoffrey J. McLachlan,et al.  Mixture models : inference and applications to clustering , 1989 .

[10]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[11]  Andrei Romanenko,et al.  An RT-Linux based control system of a pilot plant for reaction kinetics and process control studies , 2000 .

[12]  New York Dover,et al.  ON THE CONVERGENCE PROPERTIES OF THE EM ALGORITHM , 1983 .

[13]  Anil K. Jain,et al.  39 Dimensionality and sample size considerations in pattern recognition practice , 1982, Classification, Pattern Recognition and Reduction of Dimensionality.

[14]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[15]  Udo Weimar,et al.  Modular Sensor Systems for Gas Sensing and Odor Monitoring: The MOSES Concept , 1998 .

[16]  A. F. Smith,et al.  Statistical analysis of finite mixture distributions , 1986 .

[17]  Anil K. Jain,et al.  Unsupervised selection and estimation of finite mixture models , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[18]  Y. Hiranaka,et al.  Gas-dependent response in the temperature transient of SnO2 gas sensors☆ , 1992 .

[19]  Shigeki Hirobayashi,et al.  Dynamic model to estimate the dependence of gas sensor characteristics on temperature and humidity in environment , 1999 .

[20]  Antonio Pardo,et al.  Gas identification with tin oxide sensor array and self-organizing maps: adaptive correction of sensor drifts , 1998, IEEE Trans. Instrum. Meas..

[21]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[22]  T. Sundic,et al.  Fuzzy inference system for sensor array calibration: prediction of CO and CH4 levels in variable humidity conditions , 2002 .

[23]  T. Pearce,et al.  Computational parallels between the biological olfactory pathway and its analogue 'the electronic nose': Part II. Sensor-based machine olfaction. , 1997, Bio Systems.

[24]  Anil K. Jain,et al.  Dimensionality reduction using genetic algorithms , 2000, IEEE Trans. Evol. Comput..

[25]  Donald B. Rubin,et al.  Max-imum Likelihood from Incomplete Data , 1972 .

[26]  P. Green,et al.  On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion) , 1997 .

[27]  T. Eklöv,et al.  Selection of variables for interpreting multivariate gas sensor data , 1999 .