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Proceedings Paper

Integration of multi-array sensors and support vector machines for the detection and classification of organophosphate nerve agents
Author(s): Walker H. Land; Omowunmi A. Sadik; Mark J Embrechts; Dale Leibensperger; Lut Wong; Adam Wanekaya; Michiko Uematsu
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Paper Abstract

Due to the increased threats of chemical and biological weapons of mass destruction (WMD) by international terrorist organizations, a significant effort is underway to develop tools that can be used to detect and effectively combat biochemical warfare. Furthermore, recent events have highlighted awareness that chemical and biological agents (CBAs) may become the preferred, cheap alternative WMD, because these agents can effectively attack large populations while leaving infrastructures intact. Despite the availability of numerous sensing devices, intelligent hybrid sensors that can detect and degrade CBAs are virtually nonexistent. This paper reports the integration of multi-array sensors with Support Vector Machines (SVMs) for the detection of organophosphates nerve agents using parathion and dichlorvos as model stimulants compounds. SVMs were used for the design and evaluation of new and more accurate data extraction, preprocessing and classification. Experimental results for the paradigms developed using Structural Risk Minimization, show a significant increase in classification accuracy when compared to the existing AromaScan baseline system. Specifically, the results of this research has demonstrated that, for the Parathion versus Dichlorvos pair, when compared to the AromaScan baseline system: (1) a 23% improvement in the overall ROC Az index using the S2000 kernel, with similar improvements with the Gaussian and polynomial (of degree 2) kernels, (2) a significant 173% improvement in specificity with the S2000 kernel. This means that the number of false negative errors were reduced by 173%, while making no false positive errors, when compared to the AromaScan base line performance. (3) The Gaussian and polynomial kernels demonstrated similar specificity at 100% sensitivity. All SVM classifiers provided essentially perfect classification performance for the Dichlorvos versus Trichlorfon pair. For the most difficult classification task, the Parathion versus Paraoxon pair, the following results were achieved (using the three SVM kernels: (1) ROC Az indices from approximately 93% to greater than 99%, (2) partial Az values from ≈79% to 93%, (3) specificities from 76% to ≈84% at 100 and 98% sensitivity, and (4) PPVs from 73% to ≈84% at 100% and 98% sensitivities. These are excellent results, considering only one atom differentiates these nerve agents.

Paper Details

Date Published: 4 August 2003
PDF: 11 pages
Proc. SPIE 5103, Intelligent Computing: Theory and Applications, (4 August 2003); doi: 10.1117/12.484819
Show Author Affiliations
Walker H. Land, SUNY, Binghamton (United States)
Omowunmi A. Sadik, SUNY, Binghamton (United States)
Mark J Embrechts, Rensselaer Polytechnic Institute (United States)
Dale Leibensperger, SUNY, Binghamton (United States)
Lut Wong, SUNY, Binghamton (United States)
Adam Wanekaya, SUNY, Binghamton (United States)
Michiko Uematsu, SUNY, Binghamton (United States)


Published in SPIE Proceedings Vol. 5103:
Intelligent Computing: Theory and Applications
Kevin L. Priddy; Peter J. Angeline, Editor(s)

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