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.
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.