Surface acoustic wave sensor array system for trace organic vapor detection using pattern recognition analysis

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Abstract
A sensor system using surface acoustic wave (SAW) vapor sensors has been fabricated and tested against hazardous organic vapors, simulants of these vapors, and potential background vapors. The vapor tests included two- and three-component mixtures, and covered a wide relative humidity range. The sensor system was compared of four SAW devices coated with different sorbent materials with different vapor selectivities. Preconcentrators were included to improve sensitivity. The vapor experiments were organized into a large data set analyzed using pattern recognition techniques. Pattern recognition algorithms were developed to identify two different classes of hazards. The algorithms were verified against a second data set not included in the training. Excellent sensitivity was achieved by the sensor coatings, and the pattern recognition analysis, and was also examined by the preconcentrators. The system can detect hazardous vapors of interest in the ppb range even in varying relative humidity and in the presence of background vapors. The system does not false alarm to a variety of other vapors including gasoline, jet fuel, diesel fuel and cigarette smoke.

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