Abstract
Spectroscopic chemical classification based on adaptive, feature-specific measurements has been implemented and demonstrated to provide significant performance gain over traditional systems. The measurement scheme and the decision model are discussed. A prototype system with a digital micro-mirror device as the adaptive element has been constructed and validates the theoretical findings and simulation results.
Highlights
Spectroscopy-based chemical classification has a number of critical applications—for example, defense [1, 2], security [3, 4], and medicine [5, 6]
We have discussed a novel chemical detection scheme based on adaptive feature specific spectroscopy
Simulation results with regard to a pharmaceuticals library illustrated that adaptive feature specific spectrometer (AFSS) systems perform dramatically better than traditional systems
Summary
Spectroscopy-based chemical classification has a number of critical applications—for example, defense [1, 2], security [3, 4], and medicine [5, 6]. Traditional spectroscopic approaches, struggle in low signal situations where a small number of signal photons are apportioned across many detectors Many of these critical applications fall in this low signal category due to the weak analyte signatures associated with such tasks. There is frequently a constant need for rapid identifications due to the serious repercussions associated with these application areas, which further limits signal acquisition time In this manuscript, an alternate spectrometer architecture is discussed in detail. Specific design choices for the AFSS were made at a practical level in order to test our simulations with a working laboratory system. This architecture can be applied to any context to which one might apply traditional spectroscopy. We believe the demonstrated performance gains to be qualitatively independent of choices such as wavelength range, spectral resolution, decision framework, error thresholds, and spectral library content
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