Stray light characterization of an InGaAs anamorphic hyperspectral imager.

Compact hyperspectral sensors potentially have a wide range of applications, including machine vision, quality control, and surveillance from small Unmanned Aerial Vehicles (UAVs). With the development of Indium Gallium Arsenide (InGaAs) focal plane arrays, much of the Short Wave Infra-Red (SWIR) spectral regime can be accessed with a small hyperspectral imaging system, thereby substantially expanding hyperspectral sensing capabilities. To fully realize this potential, system performance must be well-understood. Here, stray light characterization of a recently-developed push-broom hyperspectral sensor sensitive in the 1 microm -1.7 microm spectral regime is described. The sensor utilizes anamorphic fore-optics that partially decouple image formation along the spatial and spectral axes of the instrument. This design benefits from a reduction in complexity over standard high-performance spectrometer optical designs while maintaining excellent aberration control and spatial and spectral distortion characteristics. The stray light performance characteristics of the anamorphic imaging spectrometer were measured using the spectral irradiance and radiance responsivity calibrations using uniform sources (SIRCUS) facility at the National Institute of Standards and Technology (NIST). A description of the measurements and results are presented. Additionally, a stray-light matrix was assembled for the instrument to improve the instrument's spectral accuracy. Transmittance of a silicon wafer was measured to validate this approach.

Machine vision detection of bonemeal in animal feed samples.

There is growing public concern about contaminants in food and feed products, and reflection-based machine vision systems can be used to develop automated quality control systems. An important risk factor in animal feed products is the presence of prohibited ruminant-derived bonemeal that may contain the BSE (Bovine Spongiform Encephalopathy) prion. Animal feed products are highly complex in composition and texture (i.e., vegetable products, mineral supplements, fish and chicken meal), and current contaminant detection systems rely heavily on labor-intensive microscopy. In this study, we developed a training data set comprising 3.65 million hyperspectral profiles of which 1.15 million were from bonemeal samples, 2.31 million from twelve other feed materials, and 0.19 million denoting light green background (bottom of Petri dishes holding feed materials). Hyperspectral profiles in 150 spectral bands between 419 and 892 nm were analyzed. The classification approach was based on a sequence of linear discriminant analyses (LDA) to gradually improve the classification accuracy of hyperspectral profiles (reduce level of false positives), which had been classified as bonemeal in previous LDAs. That is, all hyperspectral profiles classified as bonemeal in an initial LDA (31% of these were false positives) were used as input data in a second LDA with new discriminant functions. Hyperspectral profiles classified as bonemeal in LDA2 (false positives were equivalent to 16%) were used as input data in a third LDA. This approach was repeated twelve times, in which at each step hyperspectral profiles were eliminated if they were classified as feed material (not bonemeal). Four independent feed materials were experimentally contaminated with 0-25% (by weight) bonemeal and used for validation. The analysis presented here provides support for development of an automated machine vision to detect bonemeal contamination around the 1% (by weight) level and therefore constitutes an important initial screening tool in comprehensive, rapid, and practically feasible quality control of feed materials.