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Defense & Security

Hand-held device detects chemical and biological warfare agents

Developments in hand-held assay device technology improve the detection and quantification of chemical and biological warfare agents in real time.
26 December 2006, SPIE Newsroom. DOI: 10.1117/2.1200612.0507

Since the 9/11 attacks, chemical and biological warfare (CBW) agents now represent an imminent threat, with drinking-water supplies considered particularly vulnerable. This is because water distribution systems present many opportunities for deliberate contamination: in water reservoirs, at various production andstorage points, and during delivery to consumers. To counter this threat, the US Army Joint Service Agent Water Monitor (JSAWM) program has been tasked with developing hand-held assays (HHAs).1 These devices are based on lateral flow immunoassay technology that can detect CBW agents and incorporate software to analyze receiver operating characteristics (ROC) for the assessment of the CBW threat level.

HHA tickets have been developed (see Figure 1) that can be read either by human eyes or by an optical scanner.1 However, only the latter can provide quantitative measurements. Currently-used HHA scanners were initially developed for medical applications. But they are very expensive (approximately $30,000) and not suitable for analytical purposes. The accurate determination of the sensitivity of HHAs also requires ROC curves that need to be analyzed. To address these issues, a new method using commercial flatbed optical scanners is being investigated for HHA data collection and new 3D ROC curves are also being developed for data analysis.

Figure 1. A hand-held assay ticket can quickly identify selected chemical and biological warefare (CBW) agents. (Image courtesy of ANP Inc.)

An initial demonstration using a $100 flatbed scanner significantly outperformed a $30,000 medical scanner in reading HHAs with more-accurate quantitative CBW information. However, new scanning and image-processing software is required to fully utilize this inexpensive flatbed technology. Development efforts are presently focused on the three key components required for the successful implementation of HHA in CBW detection. The first is ticket development, presently undertaken by ANP Inc. The second requirement is for a dedicated reader with an embedded device that can capture the HHA data with sensitivity. The software is currently being developed by the University of Maryland, Baltimore County (UMBC).2,3 The third requirement is the embedded system designed by UMBC to drive the software designed to analyze the HHA tickets developed by ANP in real time.4

The HHA ticket shown Figure 1 uses a type of biological assay based on immunochromatography that can quickly and accurately identify selected CBW agents. It functions by measuring antigen/antibody interactions1 and exploits the exquisite sensitivity and specificity of antibodies to detect and identify microorganisms.

The hardware design developed by UMBC uses a Stargate SPB400 single-board computer with enhanced communications and sensor signal-processing capabilities which incorporates Intel's latest-generation 400MHz XScale® processor (PXA255), 64MB SDRAM, and 32MB of flash memory.4 A daughter card (SDC400CA) is also included to support a variety of additional communication interfaces (such as RS-232, 10/100 Ethernet, USB host, and JTAG). The software (see Figure 2) is being further developed to both improve the reading of fine details from ticket images and decrease background noise. It is also automated and can be integrated with the reader in a breadboard system.

Figure 2. A window of the software estimates CBW agent concentration.

Also included in the software is a 3D ROC analysis package developed to improve conventional 2D ROC analysis.2,3 In CBW detection, estimation of agent concentration is more critical than agent detection since the lethal level of different agents varies significantly. The 3D ROC package was specifically developed to address this issue by creating a third dimension to specify agent concentration so that a 3D-ROC curve (as shown in Figure 3) could be generated and plotted based on three parameters: detection probability, PD, false alarm probability, PF and threshold t , used for quantification.

Figure 3. A 3D graph illustrates receiver operating characteristics. PF: False alarm probability. PD: Detection probability. t: Threshold.

To increase CBW-agent-detection performance and to make it more cost-effective, the design of hyperspectral sensors to replace tickets seems highly desirable. Several benefits could materialize. First, these sensors could provide a significant increase in detection range to accommodate more agents when compared to tickets. Fine-tuning their spectral range could also allow for the detection of more subtle agents. Another advantage would be their mobility and cost-effectiveness, since carrying tickets would not be required. Hyperspectral sensors would also improve the quality of quantification in the determination of threat levels. Finally, the availability of advanced hyperspectral technology in subpixel/mixed-pixel quantification and analysis would make it compatible with several currently-available techniques.5

Chein-I Chang
Remote Sensing Signal and Image Processing, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County (UMBC)
Baltimore, MD

Chein-I Chang is a professor at the University of Maryland, Baltimore County. He received his PhD in Electrical Engineering from the University of Maryland, College Park. He has authored a book entitled Hyperspectral Imaging, and published 90 journal articles. He is an SPIE Fellow and associate editor of the IEEE Transactions on Geoscience and Remote Sensing. In addition, Chein-I Chang has served on several SPIE program committees including Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery and Imaging Spectrometry, Chemical and Biological Standoff Detection.

5. C.-I Chang,
Hyperspectral Imaging: Techniques for Spectral Detection and Classification,
Kluwer/Plenum Academic Publishers, 2003.