Integration of multiarray sensors and support vector machines for the detection and classification of organophosphate nerve agents

Walker H. Land, Omowunmi Sadik, Mark J. Embrechts, Dale Leibensperger, Lut Wong, Adam Wanekaya, Michiko Uematsu

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)37-47
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2003
Externally publishedYes
EventPROCEEDINGS OF SPIE SPIE - The International Society for Optical Engineering: Intelligent Computing: Theory and Applications - Orlando, FL, United States
Duration: Apr 21 2003Apr 22 2003

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


  • Bio-terrorism
  • Electronic Nose
  • Feature selection
  • Sensitivity analysis
  • Support Vector Machines


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