Estimating gas concentration using a microcantilever-based electronic nose
Article
Article Title | Estimating gas concentration using a microcantilever-based electronic nose |
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ERA Journal ID | 4407 |
Article Category | Article |
Authors | Leis, 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 Title | Digital Signal Processing |
Journal Citation | 20 (4), pp. 1229-1237 |
Number of Pages | 9 |
Year | 2010 |
Place of Publication | Maryland Heights, USA |
ISSN | 1051-2004 |
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. |
Keywords | signal 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 2020 | 469999. 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 Affiliations | Department of Electrical, Electronic and Computer Engineering |
Oak Ridge National Laboratory, United States | |
Triton Systems, United States |
https://research.usq.edu.au/item/9z496/estimating-gas-concentration-using-a-microcantilever-based-electronic-nose
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