This report describes inverse spectral analysis of diffuse-reflectance spectra measured using Infrared Backscatter Imaging spectroscopy (IBIS). In IBIS, a tunable infrared laser illuminates a target while an infrared camera detects the backscatter. Target analytes are identified by analyzing the pattern of absorption dips in the detected backscatter and comparing them to the known or simulated reflectance spectra of hazardous materials. The backscatter spectrum is comparable to diffuse reflectance measured using a Fourier transform infrared (FTIR) spectrometer. The analysis methodology applied here entails iterative adjustment of spectra using phenomenological backgrounds. and estimation of absorbance using the Kubelka-Munk (KM) theory of diffuse reflectance. Applying spectrum-feature enhancement, measured with a field spectrometer, can provide a better estimation of dielectric response, which is for comparison to reference dielectric functions, for identification of target materials.
We present further development of an eye-safe, invisible, stand-off technique designed for the detection of target chemicals (such as explosives) in a single “Snapshot” frame. Broadband Fabry-Perot quantum cascade lasers (FP-QCLs) are employed in the Mid-LWIR (long-wave infrared) in the range of 7 to 12 μm, to interrogate the spectral features from analytes of interest. We have developed a custom-built broadband laser source in the Mid-LWIR range. This “white” broadband laser source enables stand-off detection in a single snapshot frame. High power FP-QCLs with wide spectral coverage were collimated and aligned toward the target several meters away. The “backscatter” and absorption signals from target chemicals are spectrally extracted by an LWIR spectrometer based on the spatial heterodyne spectroscopy (SHS) technique or by a grating spectrometer. Both spectroscopic methods offer full spectral coverage in each single frame from an IR imaging array. This presentation will cover the implementation and optimization of FP-QCLs for this broadband spectroscopic application. We discuss the collection and processing of SHS images to extract spectral information. Finally, we present results of measurements using specific analytes to demonstrate the application of the method to stand-off detection of targets such as explosives and other chemical threats.
We present the results of characterizing and using a quantum cascade laser as a broadband infrared source for real-time spectroscopy. Using a Fabry-Perot quantum cascade laser (FP-QCL), we illuminate samples with “white” light from 8.2μm to 10.2μm. This laser source and its operating conditions (25°C and 500ns pulses at 200kHz) were chosen to give broad spectral coverage and high power output (42.7mW average power, pulsed operation). We utilized a simple grating spectrometer together with a micro-bolometer focal plane array to capture each full spectrum in a single frame. Several samples were characterized using this apparatus in a transmission-style measurement and their real-time spectra were compared to their Fourier Transform Infrared (FTIR) spectra. The results show a good agreement between FTIR and the real-time grating spectrometer for several exemplar samples, including bandpass filters, isopropanol vapor, and samples of IR active materials such as PTFE and polystyrene.
We are developing machine learning algorithms to identify chemicals of interest by their diffuse infrared (IR) reflectance signatures. For capturing the signatures themselves, we are developing a cart-based mobile system for the detection of trace explosives on surfaces by active infrared (IR) backscatter hyperspectral imaging (HSI). We refer to this technology as Infrared Backscatter Imaging Spectroscopy (IBIS). A wavelength tunable multi-chip infrared quantum cascade laser (QCL) is used to interrogate a surface while an MCT focal plane array (FPA) collects backscattered images to comprise a hyperspectral image (HSI) cube. The HSI cube is processed and the extracted spectral information is fed into an algorithm to detect and identify chemical traces. The machine learning algorithm utilizes a 1-dimensional convolutional neural network (CNN) that has been trained on augmented FTIR diffuse reflectance spectra. In this manuscript, we implement a 1-D CNN to identify chemicals within an IBIS hypercube. This demonstrates a form of active chemical imaging where the CNN identifies a chemical within each pixel of an IBIS hypercube. Chemical imaging capability goes beyond point detection and identification to indicate where each chemical is within the field of view, as well as identifying multiple target chemicals simultaneously.
We are developing machine learning algorithms to identify chemicals of interest by their diffuse infrared (IR) reflectance signatures. For capturing the signatures themselves, we are developing a cart-based mobile system for the detection of trace explosives on surfaces by active infrared (IR) backscatter hyperspectral imaging (HSI). We refer to this technology as Infrared Backscatter Imaging Spectroscopy (IBIS). A wavelength tunable multi-chip infrared quantum cascade laser (QCL) is used to interrogate a surface while an MCT focal plane array (FPA) collects backscattered images to comprise a hyperspectral image (HSI) cube. The HSI cube is processed and the extracted spectral information is fed into an algorithm to detect and identify chemical traces. The machine learning algorithm utilizes a convolutional neural network (CNN) that has been trained on synthetic diffuse reflectance spectra. In this manuscript, we utilize a CNN to identify chemicals within an IBIS hypercube. We demonstrate a form of active chemical imaging where the CNN identifies a chemical within each pixel of an IBIS hypercube. Chemical imaging capability goes beyond point detection and identification to indicate where each chemical is within the field of view, as well as identifying multiple target chemicals simultaneously.
The ability to rapidly detect hazardous airborne chemicals in a complex chemical background with high fidelity remains a significant challenge. Separation through traditional Gas chromatography (GC) can significantly augment most detection technologies for high fidelity detection, but with the disadvantage of requiring the chemicals to elute off the column before detection can occur. This translates to added time for any decision-making process. Microfabrication of GC systems has reduced footprint and power consumption, but the end-of-column detection paradigm has remained. We present the first in-column detection system which probes the GC stationary phase, coated on an IR transparent column substrate, with an active infrared source. The optical evanescent field interacting with the stationary phase (US. Patent# 9,599,567, Navy Case number 211024-US1) allows for detection along the column without having to wait for complete elution. These spectral signatures, collected at different regions along the column, are analyzed by an algorithm to identify components in a complex mixture. We present results with an ATR-based system with a molded micro-GC column whose base comprises an optically transparent material coated with the stationary phase on proof of concept mixtures.
We present the further development of a cart-based system for infrared backscatter imaging spectroscopy (IBIS) designed to detect and analyze trace amounts of hazardous materials at proximal stand-off distances. A four-chip quantum cascade laser system quickly scans through the mid- to long-wave infrared (6 µm – 11 µm) wavelength range to illuminate samples contaminated with analyte. The backscattered light from the targets is collected with a liquid nitrogen cooled MCT focal plane array. Wavelengths are assigned to each frame collected with the MCT camera corresponding to the emission of the laser at the time of acquisition. This process builds a hyperspectral image cube containing spectral reflectance data for every pixel in the image. The experimental results of this cart-based infrared illumination and backscatter detection are presented. A single detection event can be completed in less than 1 second, and every pixel of the 128x128 camera array produces an individual spectrum. Advancements in this setup include mitigation of QCL beam wander and differentiating between nine analytes all present within the same one square inch target. Reference spectra of the target analytes are measured using a high resolution FTIR to validate the highly sensitive and chemically specific nature of the IBIS cart-based measurement. The sample was prepared to mimic real-world threats such as explosives and illicit drugs in trace amounts on relevant substrates.
In this paper, we discuss the characterization of several quantum cascade lasers (QCL) as candidates for a broadband infrared illumination source for use in single “snapshot” detection of hazardous materials. Each of the lasers discussed is a Fabry-Perot quantum cascade laser (FP-QCL) chosen for its peak emission within the mid- to long-wave infrared region of 7 µm to 12 µm. These lasers are commercially available from several vendors. The output of each laser was characterized using a high resolution FTIR spectrometer to record each laser’s emission spectrum under varying operating conditions such as driving current, QCL temperature, and operating modes (continuous wave or pulsed). Time-Resolved Spectroscopy (TRS) was performed on each laser’s pulsed driven output to provide further details on how the emission of each laser evolves on the nanosecond time scale. We specifically investigate and present spectra of FP-QCL packaged in sealed OEM configurations. These devices offer center wavelengths ranging from 8.9 µm to 10.5 µm. We present the results of changing operating conditions to optimize the QCL emission to provide high-power and broad spectral coverage. By combining two or more FP-QCL, we obtain spectral coverage of approximately 3 µm. The purpose of this study is to develop a high-power, broadband, “white light” illumination source to provide wide spectral coverage over the region of interest for standoff detection and analysis of potentially hazardous materials.
We present further development of an eye-safe, invisible, stand-off technique designed for the detection of target chemicals (such as explosives) in a single “snapshot” frame. Broadband Fabry-Perot quantum cascade lasers (FP-QCLs) are employed as active illumination sources, in the Mid-LWIR (long-wave infrared) in the range of 7 to 12 µm, to interrogate the spectral features from analytes of interest. We have developed a custom-built broadband laser source utilizing an OEM FP-QCL. This “white” broadband laser source enables stand-off detection in a single snapshot frame. Light from this source was collimated and aligned toward the target several meters away. The “backscatter” and absorption signals from target chemicals are spectrally extracted by an LWIR spectrometer based on the spatial heterodyne spectroscopy (SHS) technique. The SHS offers high throughput and full spectral coverage in each single frame from an IR imaging array. This manuscript will cover the implementation and optimization of FP-QCLs for this broadband spectroscopic application. We will also discuss the operation and processing of SHS images to extract spectral information. Finally, we will present results of measurements using specific analytes to demonstrate the application of the method to stand-off detection of targets such as explosives and other chemical threats.
Rapid scanning quantum cascade lasers are utilized in the detection of trace amounts of explosive materials. Infrared backscatter imaging spectroscopy employs a quick tuning infrared quantum cascade laser system to illuminate targets with mid-IR light, 6 – 11 μm in wavelength, and to perform spectroscopic measurements in less than one second. A narrow cone of the signal backscattered from targets at standoff distance is collected and imaged onto a liquid nitrogen cooled MCT focal plane array. This backscattered signal is processed into a hyperspectral image cube containing spectral and spatial information. The analysis of the experimental data measured with the system is discussed. This includes the processing of the raw camera frames (using signals from individual components of the system) into discrete wavelength bins, typically 0.01 μm in width. Spectra are generated by plotting the signal from regions of interest, typically clusters of adjacent pixels within the frames, as a function of the wavelength associated with the binned frames. These spectra are compared against the FTIR diffuse reflectance of the analytes on an equivalent substrate for identification. Methods to optimize signal to noise and produce identifications with high confidence are presented. For a single experiment, taking less than 1 second, with the camera running at full frame over 16,000 individual spectra are generated. Targets are prepared by sieving and also dry transfer to mimic real world threats, in trace amounts and on relevant substrates. Traces of explosives, as well as illicit drugs are investigated.
We present the development of an eye-safe, invisible, stand-off technique designed for the detection of target chemicals (such as explosives) in a single “snapshot” frame. Broadband Fabry-Perot quantum cascade lasers (FP-QCLs) in the wavelength range of 7 to 12 microns, are directed to a target to interrogate its spectral features. The “backscatter” return signals from target chemicals are spectrally discriminated by an LWIR spatial heterodyne spectrometer (SHS). The SHS offers high throughput and full spectral coverage in each single frame from an IR imaging array. This presentation will cover the performance and optimization of FP-QCLs for this broadband spectroscopic application. We will also discuss the operation and processing of SHS images to extract spectral information. Finally, we will present results of measurements using specific analytes to demonstrate the application of the method to stand-off detection of targets such as explosives and other chemical threats.
We are developing algorithms to identify chemicals of interest by their diffuse infrared (IR) reflectance signatures when they are deposited as particles on surfaces. For capturing the signatures themselves, we are developing a cart-based mobile system for the detection of trace explosives on surfaces by active infrared (IR) backscatter hyperspectral imaging (HSI). We refer to this technology as Infrared Backscatter Imaging Spectroscopy (IBIS). A wavelength tunable multi-chip infrared quantum cascade laser (QCL) is used to interrogate a surface while an MCT focal plane array (FPA) collects backscattered images to comprise a hyperspectral image (HSI) cube. The HSI cube is processed and the extracted spectral information is fed into an algorithm to detect and identify chemical traces. The algorithm utilizes a convolutional neural network (CNN) that has been pre-trained on synthetic diffuse reflectance spectra. In this manuscript, we present an approach to generate large libraries of synthetic infrared reflectance spectra for use in training and testing the CNN. We demonstrate advancements in the number of analytes, a method to generate synthetic substrate spectra, and the benefits of subtracting the substrate “background” to train and test the CNN on the resulting differential spectra.
We present a cart-based system based on infrared backscatter imaging spectroscopy (IBIS) for detecting and analyzing trace amounts of hazardous materials as particles on solid substrates. A system comprising four quantum cascade lasers rapidly scans through the mid-LWIR (6 μm – 11 μm) wavelength range to illuminate samples containing target analytes. The infrared backscatter signal is collected as a series of images to form a hyperspectral image cube. Each image is collected at a specified excitation wavelength using a liquid nitrogen cooled MCT focal plane array. The experimental results of this cart-based infrared illumination and backscatter detection are presented. Results compare imaged spectra over a range of different wavelength tuning speeds and different combinations of substrates and analytes. Camera frames are collected while the laser is sweeping through its wavelength range. A single complete analysis can be completed in less than 1 second. In every camera frame, each pixel of the 128x128 pixel camera array produces an individual intensity. These frames are then binned and assigned a discrete wavelength in steps, typically 0.01 μm, to produce a spectrum over 6 – 11 μm for each camera pixel. Target samples are prepared by sieving particles or by a dry transfer technique, to mimic particle size distributions associated with real world threats at trace levels, for explosives and illicit drugs on relevant substrates.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.