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14 June 2004 In vivo applications of a molecular computing-based high-throughput NIR spectrometer
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Modern hyperspectral imaging is able to collect exceptional amounts of information at astonishing speed. Reducing these data from physical fields to high-level, useful information is difficult. Integrated computational imaging (ICI) is a process in which image information is encoded as it is sensed to produce information better suited for high-speed digital processors. Both spatial and spectral features of samples can be encoded in ICI. When spectral images are simultaneously obtained and encoded at many different wavelengths, the process is called hyperspectral integrated computational imaging (HICI). Lenslet arrays and masks are ideal for encoding spatial features of an image. This process is used here to analyze motion and metabolism in freely moving rats. Complex molecular absorption filters can be used as mathematical factors in spectral encoding to create a factor-analytic optical calibration in a high-throughput spectrometer. This process is used here for remote sensing of ethanol concentrations. In this system, the molecules in the filter effectively compute the calibration function by weighting the signals received at each wavelength over a broad wavelength range. One or two molecular filters are sufficient to produce a detector voltage that is proportional to an analyte concentration in the image field. Because a single detector voltage can reveal analyte concentration, HICI is able to calculate chemical images orders of magnitude more rapidly than conventional chemometric approaches.
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Lisa A. Cassis, Bin Dai, Aaron Urbas, and Robert A. Lodder "In vivo applications of a molecular computing-based high-throughput NIR spectrometer", Proc. SPIE 5329, Genetically Engineered and Optical Probes for Biomedical Applications II, (14 June 2004);

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