Estimating gas concentration using a microcantilever-based electronic nose

Article

Leis, John, Zhao, Weichang, Pinnaduwage, Lal A., Gehl, Anthony C., Allman, Steve L., Shepp, Allan and Mahmud, Ken K.. 2010. "Estimating gas concentration using a microcantilever-based electronic nose." Digital Signal Processing. 20 (4), pp. 1229-1237. https://doi.org/10.1016/j.dsp.2009.10.026
Article Title

Estimating gas concentration using a microcantilever-based electronic nose

ERA Journal ID4407
Article CategoryArticle
AuthorsLeis, John (Author), Zhao, Weichang (Author), Pinnaduwage, Lal A. (Author), Gehl, Anthony C. (Author), Allman, Steve L. (Author), Shepp, Allan (Author) and Mahmud, Ken K. (Author)
Journal TitleDigital Signal Processing
Journal Citation20 (4), pp. 1229-1237
Number of Pages9
Year2010
Place of PublicationMaryland Heights, USA
ISSN1051-2004
1095-4333
Digital Object Identifier (DOI)https://doi.org/10.1016/j.dsp.2009.10.026
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.

Keywordssignal processing; electronic nose; information fusion; pattern recognition; chemical vapor; component concentration; dimethyl methylphosphonate; gas concentration; linear model; memory resources; micro-cantilevers; microcantilever arrays; nerve agents; ternary mixtures
ANZSRC Field of Research 2020469999. Other information and computing sciences not elsewhere classified
400907. Industrial electronics
340108. Sensor technology (incl. chemical aspects)
Public Notes

File reproduced in accordance with the copyright policy of the publisher/author.

Byline AffiliationsDepartment of Electrical, Electronic and Computer Engineering
Oak Ridge National Laboratory, United States
Triton Systems, United States
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