R. Andrew McGill

A comparison study of chemical sensor array pattern recognition algorithms

Probabilistic neural networks (PNN), learning vector quantization (LVQ) neural networks, back-propagation artificial neural networks (BP-ANN), soft independent modeling of class analogy (SIMCA), Bayesian linear discriminant analysis (BLDA), Mahalanobis linear discriminant analysis (MLDA), and the nearest-neighbor (NN) pattern recognition algorithms are compared for their ability to classify chemical sensor array data. Comparisons are made based on five qualitative criteria (speed, training difficulty, memory requirements, robustness to outliers, and the ability to produce a measure of uncertainty) and one quantitative criterion (classification accuracy). Four sample data sets from our laboratory, involving simulated data and polymer-coated surface acoustic wave chemical sensor array data, are used to estimate classification accuracies for each method. Among the seven algorithms in this study and the four data sets, the neural network based algorithms (LVQ, PNN, and BP-ANN) have the highest classification accuracies. When considering the qualitative criteria, the LVQ and PNN approaches fare well compared to BP-ANN due to their simpler training methods. The PNN is recommended for applications where a confidence measure and fast training are critical, while speed and memory requirements are not. LVQ is suggested for all other applications of chemical sensor array pattern recognition.

Rational materials design of sorbent coatings for explosives : applications with chemical sensors

A series of chemoselective polymers had been designed and synthesized to enhance the sorption properties of polymer coated chemical sensors for polynitroaromatic analytes. To evaluate the effectiveness of the chemoselective coatings, a polynitroaromatic vapor test bed was utilized to challenge polymer coated surface acoustic wave (SAW) devices with different explosive vapors. Dinitrotoluene detection limits were determined to be in the <100 parts per trillion ranges. ATR-FTIR studies were used to determine the nature of the polymer-polynitroaromatic analyte interactions, and confirm the presence of hydrogen-bonding between polymer pendant groups and the nitro functional groups of polynitroaromatic explosive materials.

The design of functionalized silicone polymers for chemical sensor detection of nitroaromatic compounds

Abstract The solubility properties of a series of nitroaromatic compounds have been determined and utilized with known linear solvation energy relationships to calculate their sorption properties in a series of chemoselective polymers. These measurements and results were used to design a series of novel chemoselective polymers to target polynitroaromatic compounds. The polymers have been evaluated as thin sorbent coatings on surface acoustic wave (SAW) devices for their vapor sorption and selectivity properties. The most promising materials tested, include siloxane polymers functionalized with acidic pendant groups that are complimentary in their solubility properties for nitroaromatic compounds. The most sensitive of the new polymers exhibit SAW sensor detection limits for nitrobenzene (NB) and 2,4-dinitrotoluene in the low parts per billion (ppb) and low parts per trillion (ppt) concentration range, respectively. Polymers with favorable physicochemical properties exhibit low water vapor sorption, and rapid signal kinetics for NB, reaching 90% of signal response in 4 s. Studies with an in situ infrared spectroscopy technique are used to determine the mechanism of interaction between nitroaromatic compounds and the chemoselective polymer.

The “NRL-SAWRHINO”: a nose for toxic gases

Abstract At the Naval Research Laboratory (NRL), surface acoustic wave (SAW) chemical sensor systems have been in development since 1981. The primary focus has been the detection and identification of chemical agents and other toxic gases or vapors. In the recently developed “NRL-SAWRHINO” system (Rhino, Gr. Nose), a self-contained unit has been developed capable of autonomous field operation. An automated dual gas sampling system is included, for immediate and periodic detection capability. The latter, utilizes a trap-and-purge miniature gas chromatographic column, which serves to collect, concentrate, and separate vapor or gas mixtures prior to SAW analysis. The SAWRHINO includes all the necessary electronic and microprocessor control, SAW sensor temperature control, onboard neural net pattern recognition capability, and visual/audible alarm features for field deployment. The SAWRHINO has been trained to detect and identify a range of nerve and blister agents, and related simulants, and to discriminate against a wide range of interferent vapors and gases.

Multiway analysis of preconcentrator-sampled surface acoustic wave chemical sensor array data

New data processing methods for preconcentrator-sampled surface acoustic wave (SAW) sensor arrays are described. The preconcentrator-sampling procedure is used to collect and concentrate analyte vapors on a porous solid sorbent. Subsequent thermal desorption provides a crude chromatographic separation of the collected vapors prior to exposure to the SAW array. This article describes experiments to test the effects of incorporating retention information into the pattern-recognition procedures and to explore the feasibility of multiway classification methods. Linear discriminant analysis (LDA) and nearest-neighbor (NN) pattern-recognition models are built to discriminate between SAW sensor array data for four toxic organophosphorus chemical agent vapors and one agent simulant collected under a wide variety of conditions. Classification results are obtained for three types of patterns: (a) first-order patterns; (b) first-order patterns augmented with the time of the largest peak; and (c) second-order patterns with the use of the SAW frequency for each sensor over a broad time window. Classification models for the second-order patterns are also developed with the use of unfolded and multiway partial least-squares discriminants (uPLSD and mPLSD) and NN and LDA of the scores from unfolded and multiway principal-component analysis (uPCA and mPCA). It is determined that classification performance improves when information about the desorption time is included. Treating the preconcentrator-sampled SAW sensor array as a second-order analytical instrument and using a classification model based upon either uPLSD, uPCA-LDA, or NN results in the correct identification of 100% of the patterns in the prediction set. With the second-order patterns, the other pattern-recognition algorithms only do slightly worse.

Trace gas absorption spectroscopy using functionalized microring resonators

We detect trace gases at parts-per-billion levels using evanescent-field absorption spectroscopy in silicon nitride microring resonators coated with a functionalized sorbent polymer. An analysis of the microring resonance line shapes enables a measurement of the differential absorption spectra for a number of vapor-phase analytes. The spectra are obtained at the near-infrared overtone of OH-stretch resonance, which provides information about the toxicity of the analyte vapor.

Evaluation of SAW chemical sensors for air filter lifetime and performance monitoring

Abstract A miniature surface acoustic wave (SAW) chemical sensor has been utilized to monitor the progression and breakthrough of the nerve agent simulant dimethylmethylphosphonate (DMMP) through a porous carbon filter-bed. The SAW sensor was successfully operated in carbon filter-beds under high air flow rates and a variety of humidity conditions with no active temperature control applied to the filter bed or SAW sensor. The SAW sensor successfully monitored the progression of DMMP through the filter-bed, from low to high vapor concentrations. The inclusion of the SAW sensor in the middle or at the end of the filter-bed did not degrade the performance of the filter-bed.

Stand-off detection of trace explosives by infrared photothermal imaging

We have developed a technique for the stand-off detection of trace explosives using infrared photothermal imaging. In this approach, infrared quantum cascade lasers tuned to strong vibrational absorption bands of the explosive particles illuminate a surface of interest, preferentially heating the explosives material. An infrared focal plane array is used to image the surface and detect a small increase in the thermal intensity upon laser illumination. We have demonstrated the technique using TNT and RDX residues at several meters of stand-off distance under laboratory conditions, while operating the lasers below the eye-safe intensity limit. Sensitivity to explosives traces as small as a single grain (~100 ng) of TNT has been demonstrated using an uncooled bolometer array. We show the viability of this approach on a variety of surfaces which transmit, reflect or absorb the infrared laser light and have a range of thermal conductivities. By varying the incident wavelength slightly, we demonstrate selectivity between TNT and RDX. Using a sequence of lasers at different wavelengths, we increase both sensitivity and selectivity while reducing the false alarm rate. At higher energy levels we also show it is possible to generate vapor from solid materials with inherently low vapor pressures.

Trace gas Raman spectroscopy using functionalized waveguides

Weak scattering and short optical interaction lengths have, until this work, prevented the observation of trace gas Raman spectra using photonic integrated circuitry. Raman spectroscopy is a powerful analytical tool, and its implementation using chip-scale waveguide devices represents a critical step toward trace gas detection and identification in small handheld systems. Here, we report the first Raman scattering measurements of trace gases using integrated nanophotonic waveguides. These measurements were made possible using highly evanescent rib waveguides functionalized with a thin cladding layer designed to reversibly sorb organophosphonates and other hazardous chemical species. Raman spectra were collected using 9.6 mm-long waveguides exposed to ambient trace concentrations of ethyl acetate, methyl salicylate, and dimethyl sulfoxide with one-sigma limits of detection in 100 s integration times equal to 600 ppm, 360 ppb, and 7.6 ppb, respectively. Our analysis shows that the functionalized waveguide Raman efficiency can be enhanced by over nine orders of magnitude compared to traditional micro-Raman spectroscopy, paving the way toward a sensitive, low-cost, miniature, spectroscopy-based trace gas sensor inherently suitable for foundry-level photonic integrated circuit manufacturing.

Infrared photothermal imaging of trace explosives on relevant substrates

We are developing a technique for the stand-off detection of trace explosives on relevant substrate surfaces using photo-thermal infrared (IR) imaging spectroscopy (PT-IRIS). This approach leverages one or more compact IR quantum cascade lasers, tuned to strong absorption bands in the analytes and directed to illuminate an area on a surface of interest. An IR focal plane array is used to image the surface and detect small increases in thermal emission upon laser illumination. The PT-IRIS signal is processed as a hyperspectral image cube comprised of spatial, spectral and temporal dimensions as vectors within a detection algorithm. The ability to detect trace analytes on relevant substrates is critical for stand-off applications, but is complicated by the optical and thermal analyte/substrate interactions. This manuscript describes recent PT-IRIS experimental results and analysis for traces of RDX, TNT, ammonium nitrate (AN) and sucrose on relevant substrates (steel, polyethylene, glass and painted steel panels). We demonstrate that these analytes can be detected on these substrates at relevant surface mass loadings (10 μg/cm2 to 100 μg/cm2) even at the single pixel level.