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Proceedings Paper

Explosives and landmine detection using an artificial olfactory system
Author(s): Joel E. White; L. Paul Waggoner; John S. Kauer
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Paper Abstract

We are developing a portable, artificial olfactory system based on multiple attributes of the sense of smell to identify air-borne odors, including those associated with buried landmines. Brief (1-2 sec) air samples are drawn over an array of optically-interrogated, cross-reactive chemical sensors. These consist of polymers with high sensitivity and relatively narrow specificity for nitroaromatics (Timothy Swager, MIT), as well as those with broader responses, thus permitting discrimination among substances that may be confused for nitroaromatics. Biologically-based pattern matching algorithms automatically identify odors as one of several to which the device has been trained. In discrimination tests, after training to one concentration of 6 odors, the device gave 95% correct identification when tested at the original plus three different concentrations. Thus, as required in real world applications, the device can identify odors at multiple concentrations without explicitly training on each. In sensitivity tests, the device showed 100% detection and no false alarms for the landmine-related compound DNT at concentrations as low as 500 pp-trillion (quantified by GC/MS) - 10 times lower than average canine behavioral thresholds. To investigate landmine detection capabilities, field studies were conducted at Ft. Leonard Wood, MO. In calibration tests, signals from buried PMA1A anti-personnel landmines were clearly discriminated from background. In a limited 9 site "blind" test, PMA1A detection was 100% with false alarms of 40%. Although requiring further development, these data indicate that a device with appropriate sensors and exploiting olfactory principles can detect and discriminate low concentration vapor signatures, including those of buried landmines.

Paper Details

Date Published: 21 September 2004
PDF: 12 pages
Proc. SPIE 5415, Detection and Remediation Technologies for Mines and Minelike Targets IX, (21 September 2004); doi: 10.1117/12.547451
Show Author Affiliations
Joel E. White, CogniScent, Inc. (United States)
Tufts Univ. (United States)
L. Paul Waggoner, Auburn Univ. (United States)
John S. Kauer, CogniScent, Inc. (United States)
Tufts Univ. (United States)


Published in SPIE Proceedings Vol. 5415:
Detection and Remediation Technologies for Mines and Minelike Targets IX
Russell S. Harmon; J. Thomas Broach; John H. Holloway, Editor(s)

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