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Published in:
Introduction and Bioaerosol Detection Terminology Read chapter Read first chapter

2014 | OriginalPaper | Chapter

13. Spectrally Resolved Laser-Induced Fluorescence Lidar Based Standoff Biodetection System

Authors : Jean-Robert Simard, Sylvie Buteau, Pierre Lahaie

Published in: Bioaerosol Detection Technologies

Publisher: Springer New York

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Abstract

Over the years, rapidly monitoring wide areas for the presence of threatening bioaerosols has become an important objective for defense and public security. This chapter describes an important contending technology showing valuable capability to achieve that goal: Spectrally resolved laser-induced fluorescence lidars. After an introduction to this subject, the fundamental lidar theory associated with this specific technology is derived. Then, the robustness, specificity, and sensitivity of this technique to recognize the class of bioaerosols from a remote position are discussed. Subsequently, a statistical multivariate method based on the Mahalanobis distance to classify bioaerosols from their collected fluorescence induced spectral data is detailed. Finally, a conclusion reviews the key issues associated with this inelastic lidar technology as an important component of a complete threatening bioaerosol defense suite.

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previous chapter Introduction to Stand-Off Detection of Biological Warfare Agents
next chapter Standoff Aerosol Size Determination based on Multiple-Field-Of-View of Elastic Scattering
Footnotes
1
In our work, we have chosen to target this specific fluorophore because of its important role in leaving organisms. Additional arguments for this choice are the good atmospheric transmission and the commercial availability of energetically efficient lasers (tripled YAG) in that spectral band.
 
2
It is important to note the time delay that exists between the moments when the laser light is fired and reach the volume element dV where the spectral radiance is generated and the moment it is detected by the lidar. This delay depends on the time of flight between this volume element and the lidar instrument. This time variable will be introduced later in this document.
 
3
The definition of βλ in Eq. (13.3) can be generalized as a summation over the different types of scatterers present in the probed volume to model aerosol mixture lidar detection.
 
4
For single optical axis lidar, this axis is defined by the laser path. When the lidar transmitter optical alignment is optimized, the collector field-of-view should be centered on the laser path axis.
 
5
These electronic counts may be those produced by the readout electronics of a Photo Multiplier Tube (PMT) output, a CCD camera, or any other electronic transducer components. It should be derived from the sum of counts resulting from a detected photon distributed over several (spectral) detector elements if spectrometric detection is performed. It is also important to note that if the electronic transducer is performing photon counting detection, κ is equal to 1 by definition.
 
6
In this equation, the spectral resolution limited by the dispersive element is not introduced for simplification. This effect can be visualized as a convolution of the collected spectra with the spectral impulse response of that dispersive element.
 
7
The scatterer concentration in the cloud column is averaged either over the whole range interval DR defined by the lidar design or the cloud thickness itself. If the lidar range interval is greater than the region where the targeted cloud is located, the effective lidar range interval is determined by the cloud column, reflecting that a fixed number of scatterers contributes to the return signal.
 
8
In addition to the end members of a library, an extra concurrent end member may be associated with the measured LIF spectrum of background aerosols. This is particularly advantageous in a stable and homogeneous atmosphere and facilitates the detection of the arrival of new aerosols that may be associated with end members of the library. This is further discussed in section “Spectral classification of bioaerosols”.
 
9
Note that the wavelength intervals defining the atmospheric windows are representatives. There are severe attenuation bands contained within the NIR, SWIR and MWIR windows and must be taken into account in all Lidar designs, especially spectrometric Lidars.
 
10
It is also common to ‘switch-on’ the gain of the Lidar detector only for the time taken by the light to reach the maximum range and back. This has the advantages to avoid common detection electronic saturations at the moment the laser pulse leaves the Lidar emitter.
 
11
It is important to note that the extent of the validity of this choice for the distribution and associated error theory is still to be demonstrated with experimental results. However, it is anticipated that this initial modeling choice will provide valuable tools to assess the capability of LIF based standoff biodetection technology.
 
12
This background sampling is performed regularly at different ranges during the surveillance procedure.
 
13
In this approach, the changes in laser power and in the transmission for different ranges between the two background samplings are neglected in the interpolation of the spectral mean and covariance.
 
Literature
1.
Evans BTN, Roy G, and Ho J (1990) The detection and mapping of biological simulants V: statistical investigation of LCM limitation. Report DREV-4616/91 (Defence Research Establishment, Valcartier, Canada)
2.
Tiee JJ, Hof DE, Karl RR, Martinez RJ, Quick CR, Cooper DI, Eichinger WE, Holtkamp DB (1992) Remote detection of biological particles and chemical plumes using UV fluorescence lidar. Paper presented at the 16th International Laser Radar Conference, Langley Research Center (NASA), USA, Part 1, p.189-192 (SEE N92-29228 20–35)
3.
Simard J-R, Roy G, Mathieu P, Larochelle V, McFee J, Ho J (2004) Standoff sensing of bioaerosols using intensified range-gated spectral analysis of laser-induced fluorescence. In: IEEE Trans. on Geoscience and Remote Sensing 42(4): 865–874
4.
Li J-K, Asali EC, Humphrey AE, Horvath JJ (1991) Monitoring cell concentration and activity by multiple excitation fluorometry. Biotechnology Progress 7(1): 21–27CrossRef
5.
Chen RF (1990) Fluorescence of proteins and peptides. In: G. G. Guilbault (ed) Practical Fluorescence, 3rd ed. Marcel Dekker, New York
6.
7.
Dawson RB (1985) Data for biochemical research (3rd ed.). Clarendon Press, Oxford, 122
8.
Lakowicz JR, Szmacinski H, Nowaczyk K, Johnson ML (1992) Fluorescence lifetime imaging of free and protein-bound NADH. Proc. Natl. Acad. Sci. U.S.A. 89 (4): 1271–1275CrossRef
9.
Measures RM (1984) Laser-Remote-Sensor Equations. In: Laser Remote Sensing: Fundamentals and Applications. John Wiley & Sons, New York
10.
Hill SC, Pinnick RG, Niles S, Pan YL, Holler S, Chang RK, Bottinger J, Chen BT, Orr CS, Feather G (1999) Real-time measurement of fluorescence spectra from single airborne biological particles. Field Analytical Chemistry and Technology, 3:221–239
11.
Buteau S, Simard J-R, Rowsell S, Roy G (2010) Bioaerosol standoff detection and correlation assessment with concentration and viability point sensors. SPIE Europe Security and Defense: Optics and Photonics for Counterterrorism and Crime Fighting, SPIE 7838: 78380J1–78380J12
12.
Manninen A, Putkiranta M, Saarela J, Rostedt A, Sorvajärvi T, Toivonen J, Marjamäki M, Keskinen J, Hernberg R (2009) Fluorescence cross sections of bioaerosols and suspended biological agents. Applied Optics, 48(22): 4320–4328CrossRef
13.
Bronk BV, Reinisch L (1993) Variability of Steady-State Bacterial Fluorescence with Respect to Growth Conditions. Appl. Spectroscopy, 47:436–440CrossRef
14.
Heaton H I (2005) Principal-components analysis of fluorescence cross-section spectra from pathogenic and stimulant bacteria. Applied Optics, 44(30):6486–6495CrossRef
15.
Laflamme C, Simard J-R, Buteau S, Lahaie P, Nadeau P, Déry B, Houle O, Mathieu P, Roy G, Ho J, Duchaine C (2011) Effect of growth media and washing on the spectral signatures of aerosolized biological simulants. Applied optics, 50(6):788–796CrossRef
16.
Hill SC, Mayo MW, Chang RK (2009) Fluorescence of Bacteria, Pollens, and Naturally Occurring Airborne Particles: Excitation/Emission Spectra. ARL-TR–4722
17.
Agranovski V, Ristovski Z, Hargreaves M, Blackall PJ, Morawska L (2003) Performance evaluation of the UV APS: Influence of physiological age of airborne bacteria and bacterial stress. Journal of Aerosol Science, 34:1711–1727CrossRef
18.
Christesen DS, Ong KK (1998) Fluorescence spectroscopy of biological agents - 1. Bacillusa Anthracis. Edgewood Research Development and Engineering Center report, ERDEC-TR–466
19.
Steinvall O, Jonsson P, Kullander F (2007) Performance for a standoff biological warfare agent detection lidar. Proc. SPIE 6739: 673912.1-673912.14
20.
Buteau S, Simard J-R, Déry B, Roy G, Lahaie P, Mathieu P, Ho J, McFee J (2006) Bioaerosols Laser-Induced Fluorescence provides specific robust signatures for standoff detection.Proc. SPIE 6378: 637813.1-637813.12
21.
Buteau S, Stadnyk L, Rowsell S, Simard J-R, Ho J, Déry B (2007) Spectrally resolved Laser-Induced Fluorescence for bioaerosols standoff detection. Proc SPIE 6756: 08.1-08.10
22.
Jensen GJ (2007) Effect of atmospheric background aerosols on biological agent detectors. Final technical report, Science Application International Corporation, VA, USA
23.
Buteau S, Lahaie P, Rowsell S, Rustad G, Baxter K, Castle M, Foot V, Vanderbeek R, Warren R, Marquardt J, Baynard T (2008) Final Report for Task Group (RTG-55) on Laser based stand-off detection of biological agents. NATO RTO SET098 TG55 final report
24.
Farsund O, Rustad G, Kasen I, Haavardsholm TV (2010) Required spectral resolution for bioaerosol detection algorithms using standoff laser induced fluorescence measurements. IEEE Sensor Journal, 10(3):355–661CrossRef
25.
Hopkins RJ, Barrington SJ, Castle MJ, Baxter KL, Felton NV, Jones J, Griffiths C, Foot V, Risbey K (2007) UV-LIF lidar for standoff BW aerosol detection. Proc. SPIE 7484:748409-748409-11
26.
Buteau S, Ho J, Simard J-R, Lahaie P, McFee J, Roy G, Mathieu P, Déry B (2008) Bioaerosol standoff monitoring using intensified range-gated laser-induced fluorescence spectroscopy. In: Kim, Young J., Platt, Ulrich (Eds.) Advanced Environmental Monitoring, Chap. 16, Springer-Verlag, p. 203–216.
27.
Cheng YS, Barr EB, Fan BJ, Hargis PJ, Rader DJ, O’Hern TJ, Torczynski JR, Tisone GC, Preppernau BL, Young SA and Radlioff RJ (1999) Detection of bioaerosol using multiwavelength UV fluorescence spectroscopy. Aerosol Science and Technology 30:186–201
28.
Sivaprakasam V, Huston A, Scotto C, Eversole J (2004) Multiple UV wavelength excitation of bioaerosols. Opt. Express 12(19):4457
29.
Huang HC, Pan YL, Hill SC, Pinnick RG, Chang RK (2008) Real-time measurement of dual-wavelength laser-induced fluorescence spectra of individual aerosol particles. Opt. Express 16(21):16523–16528
30.
Pan YL, Hill SC, Pinnick RG, Huang H, Bottiger JR, Chang RK (2010) Fluorescence spectra of atmospheric aerosol particles measured using one or two excitation wavelengths: Comparison of classification schemes employing different emission and scattering results. Opt. Express 18:12436–12457
31.
Mierczyk Z, Kopczyński K, Zygmunt M, Wojtanowski J, Młynczak J, Gawlikowski A, Młodzianko A, Piotrowski W, Gietka A, Knysak P, Drozd T, Muzal M, Kaszczuk M, Ostrowski R, Jakubaszek M (2011) Fluorescence/depolarization lidar for mid-range stand-off detection of biological agents. Proc. SPIE 8037:80371J.1–9
32.
Carrano J, Jeys T, Cousins D, Eversole J, Gillespie J, Heally D, Licata N, Loerop W, O’Keefe M, Samuels A, Schultz J, Walter M, Wong N, Billote B, Munley M, Reich E, Roots J (2005) Chemical and Biological Sensor Standards Study. Defense Advanced Research Projects Agency, Department of Defense, DTIC, Fort Belvoir, VA
33.
Buteau S, Simard J-R, Rowsell S (2009) Bioaerosol standoff detection simultaneously refereed with particle concentration (ppl) and viability units (ACPLA). Proc. SPIE 7484:748408.1–12
34.
National Research Council of the national academies (2008) A framework for assessing the health hazard posed by bioaerosols. The National Academies Press, Washington, D.C.
35.
Valdes JJ, Mohr J, Mackay R, Tunia E, Mara A (2010) Total Agent per Liter of Air With Particle Size Distribution (TALAp): A New Unit of Measure for the Test and Evaluation of Biodetectors. International Test and Evaluation Association Journal, 31:417–425
36.
Buteau S, Simard J-R, Rowsell S, Roy G (2010) Bioaerosol standoff detection and correlation assessment with concentration and viability point sensors. Proc. SPIE 7838:78380J.1–12
37.
38.
Ratnesar-Shumate S, Wagner ML, Kerechanin C, House G, Brinkley KM, Bare C, Baker NA, Quizon R, Quizon J, Proescher A, Van Gieson E, Santarpia JL (2011) Improved Method for the Evaluation of Real-Time Biological Aerosol Detection Technologies. Aerosol Science and Technology, 45:635–644
39.
Theiler J, Wohlberg B (2012) Detection of spectrally sparse anomalies in hyperspectral imagery. Paper presented at IEEE Southwest symposium on Image Analysis and Interpretation (SSIAI), Santa Fe, NM, USA, Apr 2012
40.
Poor HV (1994) An introduction to signal detection and estimation. 2nd edn. Springer-Verlag, New-York
41.
Kelly EJ (1986) An adaptive detection algorithm. In: IEEE Transactions on aerospace and electronic systems, AES-22(2):115–127
42.
Reed IS, Yu X (1990) Adaptive Multiple band CFAR detection of an optical pattern with unknown spectral distribution. In: IEEE Transactions on acoustics, Speech and Signal processing, 38(10): 1760–1770
Metadata
Title
Spectrally Resolved Laser-Induced Fluorescence Lidar Based Standoff Biodetection System
Authors
Jean-Robert Simard
Sylvie Buteau
Pierre Lahaie
Copyright Year
2014
Publisher
Springer New York
Book
Bioaerosol Detection Technologies

Print ISBN: 978-1-4419-5581-4

Electronic ISBN: 978-1-4419-5582-1

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-1-4419-5582-1_13

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