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Sensors 2006, 6(1), 1-18; doi:10.3390/s6010001

Nonlinear Least-Squares Based Method for Identifying and Quantifying Single and Mixed Contaminants in Air with an Electronic Nose

Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, U.S.A.
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Received: 9 September 2005 / Accepted: 9 December 2005 / Published: 12 December 2005
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Abstract

The Jet Propulsion Laboratory has recently developed and built an electronic nose(ENose) using a polymer-carbon composite sensing array. This ENose is designed to be usedfor air quality monitoring in an enclosed space, and is designed to detect, identify andquantify common contaminants at concentrations in the parts-per-million range. Itscapabilities were demonstrated in an experiment aboard the National Aeronautics and SpaceAdministration’s Space Shuttle Flight STS-95. This paper describes a modified nonlinearleast-squares based algorithm developed to analyze data taken by the ENose, and itsperformance for the identification and quantification of single gases and binary mixtures oftwelve target analytes in clean air. Results from laboratory-controlled events demonstrate theeffectiveness of the algorithm to identify and quantify a gas event if concentration exceedsthe ENose detection threshold. Results from the flight test demonstrate that the algorithmcorrectly identifies and quantifies all registered events (planned or unplanned, as singles ormixtures) with no false positives and no inconsistencies with the logged events and theindependent analysis of air samples.
Keywords: electronic nose; sensor array data analysis; nonlinear least squares. electronic nose; sensor array data analysis; nonlinear least squares.
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Zhou, H.; Homer, M.L.; Shevade, A.V.; Ryan, M.A. Nonlinear Least-Squares Based Method for Identifying and Quantifying Single and Mixed Contaminants in Air with an Electronic Nose. Sensors 2006, 6, 1-18.

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