SYSTEM AND METHOD FOR THE ELECTROSTATIC DETECTION AND IDENTIFICATION OF THREAT AGENTS
A system and method for detecting aerosol threats comprising electrostatic collection and deposition of a threat agent onto a substrate. The threat agent deposited on the substrate is illuminated with a plurality of photons to thereby produce Raman scattered photons. The Raman scattered photons are analyzed in order to identify the threat agent.
This application claims the benefit of U.S. patent application No. 60/651,375 filed Feb. 9, 2005 entitled Development of a Biological Raman Electrostatic Detector Identifier (BioREDI) Sensor.
FIELD OF DISCLOSUREThis application relates generally to systems and methods for detecting and identifying hazardous agents.
BACKGROUNDDeployment of threat agents poses significant threats to both human and economic health. This threat is compounded by a limited ability to detect deployment of the agents. Prior art detection strategies rely on separate instrumentation for detection and identification of the threat agent. Conventional means of detecting airborne matter include UV-LIF and laser inducted breakdown spectroscopy. Convention means to identify a threat agent include wet chemical methods or spectroscopic methods. Identification of biological threat agents includes methods and reagents such as specific antibodies, genetic markers and propagation in culture. These identification methods are slow, labor-intensive and depend on the detection of highly-specific molecular structures. Spectroscopic means, for identification, include UV Raman spectroscopy, mass spectrometry and imaging spectrometry. UV Raman spectroscopy has limited sensitivity and specificity compared to UV Raman. Mass spectrometry is limited by significant preparation steps. Prior art imaging spectroscopy is limited by the need to switch from a broad band light source, for optical imaging, to a substantially monochromatic light source for spectroscopic imaging. This results in a signification time period between detection and identification during which time the sample may degrade.
SUMMARYThe present disclosure provides for a system and method for electrostatic depositing of a threat agent onto a substrate. The threat agent deposited on the substrate is illuminated with a plurality of photons to thereby produce Raman scattered photons. The Raman scattered photons are analyzed in order to identify the threat agent.
The accompanying drawings, which are included to provide further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and, together with the description, serve to explain the principles of the disclosure.
In the drawings:
Reference will now be made in detail to the embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts.
The deposition device deposits a plurality of sample particles 109 onto the substrate 110. In one embodiment, at least 50 sample particles are deposited onto the substrate. In another embodiment, at least about 50-250 sample particles are deposited onto the substrate. In another embodiment, at least about 250-500 sample particles are deposited onto the substrate.
With further reference to
With further reference to
In one embodiment of the present disclosure, the elastic scatter image of the sample is collected on the detector and mode scrambling and frame averaging are used to improve the image contrast by removing the interference pattern of the illumination source producing the final image.
In one embodiment, system 100 utilizes an imaging spectrometer 127 in combination with an elastic scatter imaging detector 118 to identify the sample. The elastic scattered photons are imaged by the detector 118 and the Raman scattered photons, produced by the sample are analyzed by an imaging spectrometer 127. The Raman scattered photons are passed through a filter to produce a plurality of spatially resolved spectra.
With reference to
System 100 also includes a processor 128 that determines the identification of the sample. To identify the sample, the plurality of spatially resolved spectra, produced by the imaging spectrometer, are compared to at least one reference library spectrum to identify the threat agent. In one embodiment, the plurality of spatially resolved Raman spectra are compared to at least one reference Raman library spectrum to identify the threat agent.
In one embodiment, processor 128 utilizes target testing for unmixing signatures and searching the measured mixture spectra relative to the pure component signature library in an automated fashion. Target testing based spectral unmixing compares mixture spectra against pure component library spectra by characterizing the mixture space using principal component analysis (“PCA”); ranking the library spectra by quantifying their goodness of fit into the mixture data space; and determining by target testing the number and identity of the pure spectra present in the mixture sample.
With further reference to
The target testing algorithm includes the following general steps:
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- 1. Use PCA on the mixture spectra to characterize the mixture data space.
- 2. Calculate the angle of projection of each library spectrum with the mixture data space. A dot product of a vector with an n-dimensional space. A dot product of 1.0 represents a perfect fit into the data space.
- 3. Rank all library spectra by the angle of projection into the mixture data space.
- 4. Consider all permutations of the top matches as ranked by angle. Determine the n most likely candidate pure components. Generate all possible m component solutions, where m varies from I to n and is the number of library spectra in a given solution.
- 5. For each candidate solution calculate the correlation coefficient; calculate projected library spectra for each set of m component library spectra (given the known mixture spectra and the known library spectra). Calculate the correlation coefficient of each projected library spectrum with the actual library spectrum.
The correlation coefficient used as the selection criterion is the square root of the sum of squares of the dot products for each member of a given m component solution.
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- 6. The most probable solution is the one with the highest correlation coefficient.
The target testing algorithm requires a Raman signature library that supports differentiation between threat agents, near neighbors, and clutter independent of agent growth or preparation conditions and sample history. Raman spectra of threat agents include certain spectral brands that are highly sensitive to growth conditions and others that are relatively insensitive to growth conditions. The detection and identification algorithms will focus on spectral bands that maximize agent discrimination, but minimize sensitivity to growth conditions can minimize signature library dependence on unwanted biological contributions to variability.
With further reference to
The present disclosure may be embodied in other specific forms without departing from the spirit or essential attributes of the disclosure. Accordingly, reference should be made to the appended claims, rather than the foregoing specification, as indicated the scope of the disclosure. Although the foregoing description is directed to the preferred embodiments of the disclosure, it is noted that other variations and modification will be apparent to those skilled in the art, and may be made without departing from the spirit or scope of the disclosure.
Claims
1. (canceled)
2. A method comprising:
- using electrostatic deposition for collecting and concentrating particles into a collimated, low flow air steam and to deposit a sample of a threat agent onto a substrate;
- illuminating the threat agent deposited on the substrate with a plurality of photons to thereby produce Raman scattered photons; and
- analyzing the Raman scattered photons in order to identify the threat agent;
- analyzing elastic scattered photons, produced by the threat agent on the substrate, using elastic scattering imaging to form an image of the threat agent.
3. The method of claim 2, further comprising comparing Raman spectrum to at least one reference Raman library spectrum to identify the threat agent.
4. The method of claim 2, wherein analyzing the Raman scattered photons further comprises generatig multiple spatially independent image channels anywhere within a Raman shift of about 0 cm−1 to about 3500 cm−1 at a full spectral resolution less than 20 cm−1.
5. The method of claim 2, wherein analyzing die elastic scattered photons produced by the threat agent comprises automatically focusing the image of the threat agent on the substrate using one of the following: a CMOS detector, a CCD detector or a high frame rate digital detector, in combination with a feedback control mechanism.
6. A system, comprising:
- an electrostatic device for collecting and concentrating particles into a collimated, low flow air steam and for depositing a sample of a threat agent into a substrate;
- an illumination source to illuminate the threat agent deposited onto the substrate with a plurality of photons to thereby produce Raman scattered photons; an
- a means for analyzing the Raman scattered photons in order to identify the threat agent; and
- a means for analyzing elastic scattered photons, produced by the threat agent on the substrate, using elastic scattering imaging to form an image of the threat agent.
Type: Application
Filed: Feb 9, 2006
Publication Date: Jan 1, 2009
Inventors: Patrick J. Treado (Pittsburgh, PA), Charles W. Gardner, JR. (Gibsonia, PA), John S. Maier (Pittsburgh, PA), David Keller (Newtown, PA)
Application Number: 11/351,278
International Classification: G01J 3/44 (20060101); G01N 21/65 (20060101);