This article presents a simulation-driven process to design an infrared camera system that is tuned to specific analytes of interest based on “molecular factor computing”.
There are many factors involved in optimizing discrimination using optical filtering aids, including, but not limited to, the detector response, optical throughput of the system, optical properties of the samples, and optical properties of the materials for sensitizing films/filters. There are nearly infinite possible setups for the system, which means it is neither cost nor time efficient to physically test each one. In lieu of this, we developed routines in MATLAB (The Mathworks, Natick, MA) that simulate the camera output, per pixel, given specific conditions. Beginning with measured spectra of calibration samples or standards and using an objective function or figure of merit (FOM) to measure simulated performance, these routines evaluate large numbers of combinations of chemical films as filters for discrimination based on linear discriminant analysis (LDA). In this study, the FOM was the Fisher ratio between a neat fabric and one stained with either a polymer film or blood. (publisher abstract modified)
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