Infrared thermal imaging using lock-in and molecular factor computing methods for the detection of blood on a dark, acrylic fabric is shown. Contrast differences between the clean fabric and the fabric stained with blood diluted as low as 1:100 are reported. We have also demonstrated that this method can be used to discriminate between a bloodstain and four common interfering agents (bleach, rust, cherry soda, and coffee) to other blood detection methods. These results indicate that this system could be useful for crime scene investigations by focusing nondestructive attention on areas more likely to be suitable for further analysis.

We present 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.

We combine a thermal light source with a conventional thermal infrared camera, alternating current (AC) detection methods, and chemical filtering of the infrared (IR) light to generate several imaging modalities in a simple manner. We demonstrate that digital lock-in amplifier techniques can increase the chemical contrast in an active thermal infrared image using both reflectance and thermal re-emission. We show this method is useful for visualizing thin coatings on fabrics that are invisible to the eye. We also take advantage of a "like-detects-like" chemical filtering approach to chemical selectivity for the purpose of chemical identification using a broadband thermal detector.