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We are developing a proximal noncontact detection technique for trace amounts of hazardous materials using diffuse reflectance infrared spectral signatures. Spectroscopy is performed at a distance by tuning a quantum cascade laser system through its wavelength range (6-11 μm) as the active illumination source and collecting a portion of the diffusely reflected light from the target onto an MCT focal plane array. The signals from each collected frame are binned and processed into an image hypercube that contains spectral and spatial information. The primary motivation of this work is to protect the loss of life by detecting trace explosives on contaminated surfaces before attacks occur. Here, we present results from backscatter experiments on trace samples with low loading and fill factors on glass substrates and compare them with a conventional benchtop analysis technique, FTIR diffuse reflectance. The backscatter results illustrate the ability to detect explosives at 1 m distance at the nanogram level, which is beyond the capability of the benchtop FTIR diffuse reflectance measurement. [ABSTRACT FROM AUTHOR]

We are using active infrared (IR) spectroscopic imaging to detect trace explosives on surfaces at proximal distances up to a few meters. The technology comprises an IR quantum cascade laser (QCL) for illumination and an IR focal plane array (FPA) sensor to collect signal backscattered from surfaces of interest. By sweeping the wavelength of the QCL while collecting image frames with the FPA, we generate an active hyperspectral image (HSI) cube. The HSI cube contains both spatial and spectral information, where the spectrum of a pixel, or region of interest within the image, can be extracted and compared against a known threat library. These cubes are fed into a convolutional neural network (CNN) trained on purely synthetic data to identify chemicals in the field of view. The CNN identifies chemicals by their IR signature and identifies their location within the image. [ABSTRACT FROM AUTHOR]