Optical Filter Selection for High Confidence Discrimination of Strongly Overlapping Infrared Chemical Spectra

Abstract

Optical filter-based chemical sensing techniques provide a new avenue to develop low-cost infrared sensors. These methods utilize multiple infrared optical filters to selectively measure different response functions for various chemicals, dependent on each chemical's infrared absorption. Rather than identifying distinct spectral features, which can then be used to determine the identity of a target chemical, optical filter-based approaches rely on measuring differences in the ensemble response between a given filter set and specific chemicals of interest. Therefore, the results of such methods are highly dependent on the original optical filter choice, which will dictate the selectivity, sensitivity, and stability of any filter-based sensing method. Recently, a method has been developed that utilizes unique detection vector operations defined by optical multifilter responses, to discriminate between volatile chemical vapors. This method, comparative-discrimination spectral detection (CDSD), is a technique which employs broadband optical filters to selectively discriminate between chemicals with highly overlapping infrared absorption spectra. CDSD has been shown to correctly distinguish between similar chemicals in the carbon-hydrogen stretch region of the infrared absorption spectra from 2800-3100 cm(-1). A key challenge to this approach is how to determine which optical filter sets should be utilized to achieve the greatest discrimination between target chemicals. Previous studies used empirical approaches to select the optical filter set; however this is insufficient to determine the optimum selectivity between strongly overlapping chemical spectra. Here we present a numerical approach to systematically study the effects of filter positioning and bandwidth on a number of three-chemical systems. We describe how both the filter properties, as well as the chemicals in each set, affect the CDSD results and subsequent discrimination. These results demonstrate the importance of choosing the proper filter set and chemicals for comparative discrimination, in order to identify the target chemical of interest in the presence of closely matched chemical interferents. These findings are an integral step in the development of experimental prototype sensors, which will utilize CDSD.

Discrimination Between Explosive Materials and Isomers Using a Human Color Vision-Inspired Sensing Method

“…In order to determine whether the human color vision sensing approach would be capable of discriminating between the explosives RDX, PETN, and HMX, we first calculated discrimination results using methods similar to those described previously. 21,26,27 Briefly, the integrated product of the average spectral values obtained from the FT-IR spectrum for each sample and each of the three optical filter spectra were calculated for each surface concentration of RDX along with the HMX and PETN samples. The integrated products of the three filters were then normalized such that the vector formed by these coordinates would have a unit length of one.…”

Demonstration of a Human Color Vision Mimic in the Infrared

“…The first step in the development of the CIE-IR chart is the selection of three IR optical filters, which are analogous to the three photopigments contained within the cone receptors in the human eye. , In order to provide discrimination between different chemicals, these optical filters must have significant spectral overlap with the vibrational absorption bands of the chemicals of interest. Such filters may be selected by any number of means; ,, in this study, a simulated annealing optimization approach was employed.…”

“…14,25 In order to provide discrimination between different chemicals, these optical filters must have significant spectral overlap with the vibrational absorption bands of the chemicals of interest. Such filters may be selected by any number of means; 8,26,27 in this study, a simulated annealing optimization approach was employed.…”

Biomimetic Optical-Filter Detection System for Discrimination of Infrared Chemical Signatures

“…Broadband optical filters for the IRFOB were selected by modeling the interaction of numerical filters (Gaussian) with experimentally generated IR single-channel power spectra of hexane, acetone, and fuel oil. , This resulted in an optimized numerical filter set consisting of three idealized Gaussian IR transmission filters which generated the greatest selectivity for the three chemicals of interest (Supporting Information). Once the optimized numerical filter set was identified, a search for commercially off the shelf (COTS) filters which matched the optimized numerical filter set as closely as possible was performed.…”