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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 49, Iss. 3 — Jan. 20, 2010
  • pp: 382–393

Infrared differential-absorption Mueller matrix spectroscopy and neural network-based data fusion for biological aerosol standoff detection

Arthur H. Carrieri, Jack Copper, David J. Owens, Erik S. Roese, Jerold R. Bottiger, Robert D. Everly, II, and Kevin C. Hung  »View Author Affiliations


Applied Optics, Vol. 49, Issue 3, pp. 382-393 (2010)
http://dx.doi.org/10.1364/AO.49.000382


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Abstract

An active spectrophotopolarimeter sensor and support system were developed for a military/civilian defense feasibility study concerning the identification and standoff detection of biological aerosols. Plumes of warfare agent surrogates γ-irradiated Bacillus subtilis and chicken egg white albumen (analytes), Arizona road dust (terrestrial interferent), water mist (atmospheric interferent), and talcum powders (experiment controls) were dispersed inside windowless chambers and interrogated by multiple CO 2 laser beams spanning 9.1 12.0 μm wavelengths (λ). Molecular vibration and vibration-rotation activities by the subject analyte are fundamentally strong within this “fingerprint” middle infrared spectral region. Distinct polarization-modulations of incident irradiance and backscatter radiance of tuned beams generate the Mueller matrix (M) of subject aerosol. Strings of all 15 normalized elements { M i j ( λ ) / M 11 ( λ ) } , which completely describe physical and geometric attributes of the aerosol particles, are input fields for training hybrid Kohonen self-organizing map feed-forward artificial neural networks (ANNs). The properly trained and validated ANN model performs pattern recognition and type-classification tasks via internal mappings. A typical ANN that mathematically clusters analyte, interferent, and control aerosols with nil overlap of species is illustrated, including sensitivity analysis of performance.

© 2010 Optical Society of America

OCIS Codes
(070.5010) Fourier optics and signal processing : Pattern recognition
(120.5050) Instrumentation, measurement, and metrology : Phase measurement
(120.6710) Instrumentation, measurement, and metrology : Susceptibility
(290.1350) Scattering : Backscattering
(280.1415) Remote sensing and sensors : Biological sensing and sensors

ToC Category:
Instrumentation, Measurement, and Metrology

History
Original Manuscript: June 29, 2009
Manuscript Accepted: October 28, 2009
Published: January 14, 2010

Virtual Issues
Vol. 5, Iss. 3 Virtual Journal for Biomedical Optics

Citation
Arthur H. Carrieri, Jack Copper, David J. Owens, Erik S. Roese, Jerold R. Bottiger, Robert D. Everly, II, and Kevin C. Hung, "Infrared differential-absorption Mueller matrix spectroscopy and neural network-based data fusion for biological aerosol standoff detection," Appl. Opt. 49, 382-393 (2010)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-49-3-382


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References

  1. A. G. Sabelnikov, “Airborne exposure limits for chemical and biological warfare agents: is everything set and clear?,” Int. J. Env. Health Res. 16, 241-253 (2006). [CrossRef]
  2. N. B. Munro, K. R. Ambrose, and A. P. Watson, “Toxicity of the organophosphate chemical warfare agents GA, GB, and VX: implications for public protection,” Env. Health Perspect. 102, 18-38 (1994). [CrossRef]
  3. R. M. Black, “An overview of biological markers of exposure to chemical warfare agents: a review,” J. Anal. Toxicol. 32, 2-9(2008). [PubMed]
  4. J. A. Romano Jr., B. J. Lukey, and H. Salem, “Chemical Warfare Agents: Chemistry, Pharmacology, Toxicology and Therapeutics,” 2nd ed. (CRC , 2007). [CrossRef]
  5. P. A. Demirev, A. B. Feldman, and J. S. Lin, “Chemical and biological weapons: current concepts for future defenses,” Johns Hopkins APL Tech. Dig. 26, 321-333 (2005).
  6. M. W. P. Petryk, “Promising spectroscopic techniques for the portable detection of condensed-phase contaminants on surfaces,” Appl. Sp. Rev. 42, 287-343 (2007). [CrossRef]
  7. J. D. Jackson, “Plane electromagnetic waves and wave propagation,” in Classical Electrodynamics (Wiley, 1975), pp. 273-278.
  8. L. Mandel and E. Wolf, “Second-order coherence theory of vector electromagnetic fields,” in Optical Coherence and Quantum Optics (Cambridge U. Press, 1995), pp. 340-373.
  9. W. Shurcliff, Polarized Light: Production and Use (Harvard U. Press, 1962).
  10. D. Clarke and J. F. Grainger, Polarized Light and Optical Measurement (Pergamon, 1971).
  11. D. H. Goldstein and E. Collett, Polarized Light, 2nd ed. (CRC, 2003). [CrossRef]
  12. A. H. Carrieri, D. J. Owens, E. S. Roese, K. C. Hung, P. I. Lim, J. C. Schultz, and M. V. Talbard, “Photopolarimetric lidar dual-beam switching device and Mueller matrix standoff detection method,” J. Appl. Remote Sens. 1, 013502 (2007). [CrossRef]
  13. A. H. Carrieri, J. R. Bottiger, D. J. Owens, and E. S. Roese, “Differential absorption Mueller matrix spectroscopy and the infrared detection of crystalline organics,” Appl. Opt. 37, 6550-6557 (1998). [CrossRef]
  14. A. H. Carrieri, D. J. Owens, and J. C. Schultz, “Infrared Mueller matrix acquisition and preprocessing system,” Appl. Opt. 47, 5019-5027 (2008). [CrossRef] [PubMed]
  15. A. H. Carrieri, “Neural network pattern recognition by means of differential absorption Mueller matrix spectroscopy,” Appl. Opt. 38, 3759-3766 (1999). [CrossRef]
  16. T. Kohonen, Self-Organizing Maps, 3rd ed. (Springer-Verlag, 2001). [CrossRef]
  17. L. Fausett, Fundamentals of Neural Networks (Prentice Hall, 1994).
  18. C. Bishop, Neural Networks for Pattern Recognition (Oxford U. Press, 1995).
  19. S. E. Fahlman and C. Lebiere, “The cascade correlation architecture,” in Advances in Neural Information Processing Systems Vol. 2 (Morgan Kaufmann, 1990).

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