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 CO2 laser beams spanning 9.112.0μm 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 {Mij(λ)/M11(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

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  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).
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    [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).

2008 (2)

R. M. Black, “An overview of biological markers of exposure to chemical warfare agents: a review,” J. Anal. Toxicol. 32, 2-9(2008).
[PubMed]

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]

2007 (2)

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]

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]

2006 (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]

2005 (1)

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).

1999 (1)

1998 (1)

1994 (1)

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]

Ambrose, K. R.

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]

Bishop, C.

C. Bishop, Neural Networks for Pattern Recognition (Oxford U. Press, 1995).

Black, R. M.

R. M. Black, “An overview of biological markers of exposure to chemical warfare agents: a review,” J. Anal. Toxicol. 32, 2-9(2008).
[PubMed]

Bottiger, J. R.

Carrieri, A. H.

Clarke, D.

D. Clarke and J. F. Grainger, Polarized Light and Optical Measurement (Pergamon, 1971).

Collett, E.

D. H. Goldstein and E. Collett, Polarized Light, 2nd ed. (CRC, 2003).
[CrossRef]

Demirev, P. A.

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).

Fahlman, S. E.

S. E. Fahlman and C. Lebiere, “The cascade correlation architecture,” in Advances in Neural Information Processing Systems Vol. 2 (Morgan Kaufmann, 1990).

Fausett, L.

L. Fausett, Fundamentals of Neural Networks (Prentice Hall, 1994).

Feldman, A. B.

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).

Goldstein, D. H.

D. H. Goldstein and E. Collett, Polarized Light, 2nd ed. (CRC, 2003).
[CrossRef]

Grainger, J. F.

D. Clarke and J. F. Grainger, Polarized Light and Optical Measurement (Pergamon, 1971).

Hung, K. C.

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]

Jackson, J. D.

J. D. Jackson, “Plane electromagnetic waves and wave propagation,” in Classical Electrodynamics (Wiley, 1975), pp. 273-278.

Kohonen, T.

T. Kohonen, Self-Organizing Maps, 3rd ed. (Springer-Verlag, 2001).
[CrossRef]

Lebiere, C.

S. E. Fahlman and C. Lebiere, “The cascade correlation architecture,” in Advances in Neural Information Processing Systems Vol. 2 (Morgan Kaufmann, 1990).

Lim, P. I.

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]

Lin, J. S.

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).

Lukey, B. J.

J. A. Romano Jr., B. J. Lukey, and H. Salem, “Chemical Warfare Agents: Chemistry, Pharmacology, Toxicology and Therapeutics,” 2nd ed. (CRC , 2007).
[CrossRef]

Mandel, L.

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.

Munro, N. B.

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]

Owens, D. J.

Petryk, M. W. P.

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]

Roese, E. S.

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]

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]

Romano, J. A.

J. A. Romano Jr., B. J. Lukey, and H. Salem, “Chemical Warfare Agents: Chemistry, Pharmacology, Toxicology and Therapeutics,” 2nd ed. (CRC , 2007).
[CrossRef]

Sabelnikov, A. G.

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]

Salem, H.

J. A. Romano Jr., B. J. Lukey, and H. Salem, “Chemical Warfare Agents: Chemistry, Pharmacology, Toxicology and Therapeutics,” 2nd ed. (CRC , 2007).
[CrossRef]

Schultz, J. C.

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]

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]

Shurcliff, W.

W. Shurcliff, Polarized Light: Production and Use (Harvard U. Press, 1962).

Talbard, M. V.

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]

Watson, A. P.

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]

Wolf, E.

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.

Appl. Opt. (3)

Appl. Sp. Rev. (1)

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]

Env. Health Perspect. (1)

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]

Int. J. Env. Health Res. (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]

J. Anal. Toxicol. (1)

R. M. Black, “An overview of biological markers of exposure to chemical warfare agents: a review,” J. Anal. Toxicol. 32, 2-9(2008).
[PubMed]

J. Appl. Remote Sens. (1)

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]

Johns Hopkins APL Tech. Dig. (1)

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).

Other (10)

J. A. Romano Jr., B. J. Lukey, and H. Salem, “Chemical Warfare Agents: Chemistry, Pharmacology, Toxicology and Therapeutics,” 2nd ed. (CRC , 2007).
[CrossRef]

J. D. Jackson, “Plane electromagnetic waves and wave propagation,” in Classical Electrodynamics (Wiley, 1975), pp. 273-278.

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.

W. Shurcliff, Polarized Light: Production and Use (Harvard U. Press, 1962).

D. Clarke and J. F. Grainger, Polarized Light and Optical Measurement (Pergamon, 1971).

D. H. Goldstein and E. Collett, Polarized Light, 2nd ed. (CRC, 2003).
[CrossRef]

T. Kohonen, Self-Organizing Maps, 3rd ed. (Springer-Verlag, 2001).
[CrossRef]

L. Fausett, Fundamentals of Neural Networks (Prentice Hall, 1994).

C. Bishop, Neural Networks for Pattern Recognition (Oxford U. Press, 1995).

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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Figures (7)

Fig. 1
Fig. 1

Stokes vector and Mueller matrix via polarized scattering from an aerosol mixture. Infrared laser beam is incident to aerosol aggregate comprising biological aerosol analyte (blue) and interferent aerosols (red, yellow, and green). The Mueller matrix (M) is the transformation of Stokes vectors s i into s s , and its elements are generally functions of beam wavelength (λ), polar angle (θ), azimuth angle (φ), and particle shape and size (d). In this research, we are concerned with backscattering ( θ i = θ s , φ i = φ s ) and photoelastic modulations of incident irradiance and backscattering radiance of beams for actual M-elements generation. Those M-elements exhibiting susceptible behaviors by the analyte, as it is driven into and out of molecular vibration and vibration-rotation resonance states, provide a basis for its identification and standoff detection.

Fig. 2
Fig. 2

Computer design of differential-absorption Mueller matrix spectroscopy sensor. (a) L, L * , coherent Select 50 grating-tunable, continuous-wave, linearly polarized, laser systems, where the asterisk signifies an isotopic admixture of CO 2 gas in the gain medium; OMS, optomechanical switch that produces an alternate square-wave train of incident beam pulses [ L : L * ] transmit as output, measures power of L and L * when a small fraction of beams are split and directed into an internal detector, and determines wavelength of beams when directed to optical spectrum analyzers S and S * ; POL-PEM, coupled transmitter linear polarizer and photoelastic modulator optic pair with 45 ° coalignment of axes, mated to precision rotary stage (R) with computer-driven stepper motor control, operating on irradiance from incident beams [ L : L * ] transmit ; SP, spider and mirror mount; C, collimator and telescopic receiver; PEM-POL, coupled receiver photoelastic modulator and linear polarizer optic pair with 45 ° alignment of axes, mated to precision rotary stage (R) with computer-driven stepper motor control, operating on backscattering beams [ L L * ] receive ; V, variable neutral density filter disk with belt-driven servo feedback control for regulation of backscattered radiances in [ L : L * ] receive ; and P, parabolic mirror focusing collimated radiance of beams exiting V onto a 1 mm × 1 mm HgCdTe photoconductive chip detector (D) cooled at liquid nitrogen temperature 77 K . (b) Photograph of differential-absorption Mueller matrix spectroscopy sensor used in this chemical-biological aerosol identification and standoff detection feasibility study.

Fig. 3
Fig. 3

Aerosol chamber and supporting system for dissemination, confinement, and size/concentration measurements of γ-irradiated Bacillus subtilis and CEWA analytes, Arizona road dust interferent, and talcum powder experiment controls. (a) A1 and SH1, extended entrance aperture and shutter, respectively; IP: aerosol intake port to aerodynamic particle sizer (APS); N, high-pressure ejection nozzle; H, hopper for powder sample stock (aerosol reservoir); D1, low vacuum conduit before HEPA filtration for containment of aerosol; D2, high vacuum conduit before HEPA filtration for evacuation of aerosol; D3, high vacuum conduit after HEPA filtration for ventilation of chamber; SW, vacuum switch to exhaust aerosol via D1, D2 and D3; VS, chamber vacuum cleaner; and RG, LG, right and left gloves for chamber wash-down. (b) Alignment of spectrophotopolarimeter sensor to aerosol chamber, inspection of uniformity of aerosol sample dispersed inside chamber, and measurement of particle size distribution and concentration of sample plume. VB1, visible He–Ne laser beam on-axis of sensor receiver [Figs. 2a, 2b]; A2, aerosol chamber exit apertures; SH2, shutter; M1, M2, and M3, flat mirrors aligned outside opposing walls of chamber reflecting visible He–Ne laser beam VB2 in a Z pattern for direct inspections of particle scattering and vertical aerosol density distribution; APS-SC, console for display of APS aerosol statistics. (c) Chamber-within-chamber for dissemination of water mist (cloud interferent). MIS, mist concentration generator in line to inner tube of chamber with ventilation apertures on both ends drawing sample mist MC to outer chamber.

Fig. 4
Fig. 4

Illustrative differential-absorption Mueller matrix spectroscopy datum of biological aerosol protein CEWA. Mantissa, Mueller element measurement at laser wavelength L = 9.155 μm ; abscissa, Mueller element measurement at laser wavelength L * = 10.458 μm . The aerosol’s susceptibility to beam interrogations, and thus its identification potential, relates to those solid rectangles lying furthest from the diagonal line.

Fig. 5
Fig. 5

Overview of differential-absorption Mueller matrix spectroscopy sensor data acquisition, core Mueller matrix database production and management, ANN modeling, ANN performance optimization and sensitivity analyses, and ANN-to-sensor integration for this biological aerosol identification and standoff detection feasibility study.

Fig. 6
Fig. 6

Classification of biological aerosol warfare agent surrogates γ-irradiated Bacillus subtilis and CEWA (analytes), Arizona road dust (terrestrial interferent), water mist (atmospheric interferent), and talcum powders (experiment controls) by a successful hybrid Kohonen SOM feed-forward ANN model trained from the core spectrophotopolarimeter M-elements database. (a) Imaged distributions of SOM training records; (b) Imaged distributions of SOM validation records. There is little overlap of classes and, for the most part, validation records were assigned to the appropriate cluster (very few assignments to incorrect nodes or clusters). SOM training terminated after 20,000 iterations. The SOM input training dataset comprises 1560 M-element records from all aerosols tested.

Fig. 7
Fig. 7

Architecture, data transformation, and sensitivity analysis of the hybrid Kohonen SOM model of Figs. 6a, 6b. The neural network training engine architecture is 94 (input layer):2 (hidden layer):6 (output layer). (a) Some of the transformations that were actively applied to the M-elements input data, and (b) M-elements inputs most influential in this SOM classification model. The standard deviation of M-element [2, 1] highlighted, for instance, is an influential node of the SOM network for yielding accurate pattern recognition and classification of aerosols.

Tables (2)

Tables Icon

Table 1 Calibration of the Mueller Matrix Spectrometer Engine of Spectrophotopolarimeter in its POL - PEM : Ψ : PEM - POL Configuration and Measured Scattergrams a

Tables Icon

Table 2 Matching Matrices Computed from an Unsupervised SOM Neural Network Model Built from the Core Mueller Matrix Database Measured by the Spectrophotopolarimeter a

Equations (10)

Equations on this page are rendered with MathJax. Learn more.

E ( x , t ) = ( ε 1 E 1 + ε 2 E 2 ) e i ( k x ω t ) ,
E 1 = a 1 e i δ 1 , E 2 = a 2 e i δ 2
s 0 | ε 1 E | 2 + | ε 2 E | 2 = a 1 2 + a 2 2 ,
s 1 | ε 1 E | 2 | ε 2 E | 2 = a 1 2 a 2 2 ,
s 2 2 Re [ ( ε 1 E ) * ( ε 2 E ) ] = 2 a 1 a 2 cos ( δ 2 δ 1 ) ,
s 3 2 Im [ ( ε 1 E ) * ( ε 2 E ) ] = 2 a 1 a 2 sin ( δ 2 δ 1 ) ,
M i j = ½ tr σ i J σ j J ,
M = 1 2 ( | J | 2 + | J | 2 + | J | 2 + | J | 2 | J | 2 | J | 2 + | J | 2 | J | 2 2 Re ( J J * + J J * ) 2 Im ( J J * J J * ) | J | 2 + | J | 2 | J | 2 | J | 2 | J | 2 | J | 2 | J | 2 + | J | 2 2 Re ( J J * J J * ) 2 Im ( J J * + J J * ) 2 Re ( J J * + J J * ) 2 Re ( J J * J J * ) 2 Re ( J J * + J J * ) 2 Im ( J J * J J * ) 2 Im ( J J * J J * ) 2 Im ( J J * + J J * ) 2 Im ( J J * + J J * ) 2 Re ( J J * J J * ) ) .
I = I d c + I a c ( n , k = 0 2 J n ( ζ 0 ) cosnκ m t , J k ( ζ 0 ) coskκ m r , J n ( ζ 0 ) J k ( ζ 0 ) cos ( m t ± m r ) ) + I a c ε ( n , k = 3 ( n , k > 2 J n ( ζ 0 ) J k ( ζ 0 ) cosnκ m t cosnκ m r ) ) ,
ρ i j = 2 1 / 2 [ M i j 2 + M i j 2 ] 1 / 2 { cos [ arctan ( M i j / M i j ) ] sin [ arctan ( M i j / M i j ) ] } ,

Metrics