Optical-filter-based chemical sensors have the potential to dramatically alter the field of hazardous materials sensing. Such devices could be constructed using inexpensive components, in a small and lightweight package, for sensing hazardous chemicals in defense, industrial, and environmental applications. Filter-based sensors can be designed to mimic human color vision. Recent developments in this field have used this approach to discriminate between strongly overlapping chemical signatures in the mid-infrared. Reported work relied on using numerically filtered FTIR spectra to model the infrared biomimetic detection methodology. While these findings are encouraging, further advancement of this technique requires the collection and evaluation of directly filtered data, using an optical system without extensive numerical spectral analysis. The present work describes the design and testing of an infrared optical breadboard system that uses the biomimetic mammalian color-detection approach to chemical sensing. The set of chemicals tested includes one target chemical, fuel oil, along with two strongly overlapping interferents, acetone and hexane. The collected experimental results are compared with numerically filtered FTIR spectral data. The results show good agreement between the numerically filtered data model and the data collected using the optical breadboard system. It is shown that the optical breadboard system is operating as expected based on modeling and can be used for sensing and discriminating between chemicals with strongly overlapping absorption bands in the mid-infrared.

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.

Increasing the selectivity of sensors, while at the same time reducing their complexity, size and cost, are challenges to the sensing community. To this end, an area of exploration has been the development of filter-based chemical sensors. We have recently introduced an approach that utilizes multiple, broadband, infrared (IR) filters to enable discrimination of target chemicals, in the presence of potential interferents that have IR spectral signatures in a limited waveband. Our analysis technique, comparative discrimination spectral detection (CDSD), utilizes a set of broad IR transmission filters, to discriminate between a specific target chemical and multiple interferents with strongly overlapping IR spectra. We have demonstrated the ability of this technique to correctly distinguish between chemicals in the carbon -hydrogen stretch region of the IR absorption spectrum (2700 -3300 cm -1 ; 3.0 -3.7 µm).We present a numerical study exploring the choices of desired optical filter sets, and the resulting overall discrimination by these filter sets. Filter parameter choices, such as the peak transmission position and bandwidth, are fundamental in filter-based chemical sensing discrimination systems. In this paper, we describe a systematic numerical approach used to explore how optical filter properties, and filter overlap affect corresponding discrimination results. We describe the interaction between the overlapping spectra and various filter sets on both target and interferent chemicals. We discuss which filter parameters provide optimum selectivity for specific target chemicals and how this information can be utilized to select filters for future direct-filter sensors based on this methodology.

Detection of concealed hazardous materials is a pressing need for the global defense community. To address this need, the development of reliable and readily-deployable sensing devices is a key area of research. A multitude of infrared sensing techniques are being studied which allow for reliable sensing of concealed threats. Continued development in this field is working to increase the selectivity of such infrared sensors, while at the same time reducing their complexity, size and cost. We have recently developed a biomimetic optical filter based approach, based on human color vision, that utilizes multiple, broadband, overlapping infrared (IR) filters to clearly discriminate between hazardous target chemicals and interferents with very similar mid-IR spectral signatures. This technique was extensively studied in order to select filters which provide optimum selectivity for specific chemical sets. Using this knowledge, we designed and assembled a gas-phase sensor which uses three broadband mid-IR filters to detect and discriminate between a target chemical, fuel oil, and various interferents with strongly overlapping IR absorption bands in the carbon -hydrogen stretch region of the IR absorption spectrum 2700 cm -1 -3300 cm -1 (3.0 µm -3.7 µm).We present an overview of the design and performance of this filter-based system and explore the ability of this system to detect and discriminate between strongly overlapping target and interferent chemicals. The detection results using the filter-based system are compared to numerical methods to demonstrate the operation of this methodology. We present the results of experiments with both target and interferent chemicals present with chemicals both in and out of the detection set, and discuss future field development and application of this approach.