Estimating gas concentration using a microcantilever-based electronic nose

  • Authors:
  • John Leis;Weichang Zhao;Lal A. Pinnaduwage;Anthony C. Gehl;Steve L. Allman;Allan Shepp;Ken K. Mahmud

  • Affiliations:
  • Department of Electrical, Electronic, and Computer Engineering, University of Southern Queensland, Toowoomba, Qld 4350, Australia;Oak Ridge National Laboratory, PO Box 2008, Oak Ridge, TN 37831-6122, United States and Triton Systems, Inc., 200 Turnpike Road, Chelmsford, MA 01824, United States;Oak Ridge National Laboratory, PO Box 2008, Oak Ridge, TN 37831-6122, United States and Department of Physics, University of Tennessee, Knoxville, TN 37996, United States;Oak Ridge National Laboratory, PO Box 2008, Oak Ridge, TN 37831-6122, United States;Oak Ridge National Laboratory, PO Box 2008, Oak Ridge, TN 37831-6122, United States;Triton Systems, Inc., 200 Turnpike Road, Chelmsford, MA 01824, United States;Triton Systems, Inc., 200 Turnpike Road, Chelmsford, MA 01824, United States

  • Venue:
  • Digital Signal Processing
  • Year:
  • 2010

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Abstract

This paper investigates the determination of the concentration of a chemical vapor as a function of several nonspecific microcantilever array sensors. The nerve agent dimethyl methyl phosphonate (DMMP) in parts-per-billion concentrations in binary and ternary mixtures is able to be resolved when present in a mixture containing parts-per-million concentrations of water and ethanol. The goal is to not only detect the presence of DMMP, but additionally to map the nonspecific output of the sensor array onto a concentration scale. We investigate both linear and nonlinear approaches - the linear approach uses a separate least-squares model for each component, and a nonlinear approach which estimates the component concentrations in parallel. Application of both models to experimental data indicate that both models are able to produce bounded estimates of concentration, but that the outlier performance favors the linear model. The linear model is better suited to portable handheld analyzer, where processing and memory resources are constrained.