Todd A. Dickinson

Rapid analyte recognition in a device based on optical sensors and the olfactory system.

We report here the development of a new vapor sensing device that is designed as an array of optically based chemosensors providing input to a pattern recognition system incorporating artificial neural networks. Distributed sensors providing inputs to an integrative circuit is a principle derived from studies of the vertebrate olfactory system. In the present device, primary chemosensing input is provided by an array of fiber-optic sensors. The individual fiber sensors, which are broadly yet differentially responsive, were constructed by immobilizing molecules of the fluorescent indicator dye Nile Red in polymer matrices of varying polarity, hydrophobicity, pore size, elasticity, and swelling tendency, creating unique sensing regions that interact differently with vapor molecules. The fluorescent signals obtained from each fiber sensor in response to 2-s applications of different analyte vapors have unique temporal characteristics. Using signals from the fiber array as inputs, artificial neural networks were trained to identify both single analytes and binary mixtures, as well as relative concentrations. Networks trained with integrated response data from the array or with temporal data from a single fiber made numerous errors in analyte identification across concentrations. However, when trained with temporal information from the fiber array, networks using name or characteristic output codes performed well in identifying test analytes.

Current trends in `artificial-nose' technology

Basic principles derived from biological olfaction, such as combining semiselective sensor arrays with pattern recognition, have been used to develop instrumentation capable of broad-band chemical detection and quantification. Commercially available instruments are useful in areas including quality control in the food, beverage and fragrance industries, environmental monitoring, chemical-purity and -mixture analysis, and medical diagnostics. Ongoing research is aimed at the development of more-advanced instruments that are smaller, cheaper, faster and more stable and reliable. These second-generation instruments are likely to find an increasing number of applications, including the on-line monitoring of fermentation and other bioprocesses.

Generating Sensor Diversity through Combinatorial Polymer Synthesis

A new approach for rapid, simple generation of uniquely responding sensors for use in polymer-based sensor arrays has been developed. Polymerization reactions between different combinations of two starting materials have been found to lead to many new, unique sensors with responses not simply related to the proportion of the starting materials. This approach is demonstrated in two ways:u2009 (a) the use of discrete polymer sensing cones each comprised of a specific monomer combination and (b) the fabrication of a gradient sensor, containing all combinations between the starting and ending monomer concentrations. Gradient sensors were fabricated using two different binary monomer systems, with both systems showing regions of broadly diverse fluorescence responses to organic vapor pulses.

Optical sensor arrays for odor recognition

Optical sensor arrays containing fluorescent solvatochromatic dyes immobilized in a plurality of polymers generate information-rich responses upon exposure to organic vapors. The response profiles are used to train a variety of computational networks such that subsequent exposure of the array to the vapors enables them to be classified and/or quantified. A number of strategies can be taken to enhance sensitivity and to increase sensor diversity.

An olfactory neuronal network for vapor recognition in an artificial nose

Abstract. Odorant sensitivity and discrimination in the olfactory system appear to involve extensive neural processing of the primary sensory inputs from the olfactory epithelium. To test formally the functional consequences of such processing, we implemented in an artificial chemosensing system a new analytical approach that is based directly on neural circuits of the vertebrate olfactory system. An array of fiber-optic chemosensors, constructed with response properties similar to those of olfactory sensory neurons, provide time-varying inputs to a computer simulation of the olfactory bulb (OB). The OB simulation produces spatiotemporal patterns of neuronal firing that vary with vapor type. These patterns are then recognized by a delay line neural network (DLNN). In the final output of these two processing steps, vapor identity is encoded by the spatial patterning of activity across units in the DLNN, and vapor intensity is encoded by response latency. The OB-DLNN combination thus separates identity and intensity information into two distinct codes carried by the same output units, enabling discrimination among organic vapors over a range of input signal intensities. In addition to providing a well-defined system for investigating olfactory information processing, this biologically based neuronal network performs better than standard feed-forward neural networks in discriminating vapors when small amounts of training data are used.

Designing optical sensor arrays with enhanced sensitivity for explosives detection

We have previously developed an optically-based artificial nose to detect a wide variety of volatile organic compounds. An optical fiber sensor array is prepared containing a variety of differentially-reactive sensors comprised of polymer/dye combinations. When an analyte is presented in pulsatile form each sensor produces a unique fluorescence vs. time signature. The system employs neural network analysis to discriminate between many organic vapors using pattern recognition. Following an initial training step, the system can recognize 91 - 100% of a training set and greater than 84% of a test set of volatile organics. We are now attempting to detect explosives and explosive-like materials using this system. Prior work has shown that some sensors respond to compounds structurally similar to TNT (e.g. 2,4-DNT and 2,6-DNT) at saturated vapor concentrations. These preliminary results provide grounds for exploring the capacity of these and other new polymer/dye sensing combinations for detecting polynitro- compounds at low concentrations.

Fiber optic array sensors as an architecture for an artificial nose

Imaging optical fibers can be used in conjunction with 2D detectors such as CCD cameras to fabricate array sensors. These sensors contain spatially separated photopolymers containing analyte-sensitive fluorescent indicators on an imaging fiber tip. Spatial resolution of the indicators is maintained through the imaging fiber array and projected onto a CCD detector. Sensors have been fabricated using the conventional one analyte-one sensor paradigm. This approach has resulted in multianalyte sensors for blood gases, process control parameters, and environmental contaminants. An entirely different approach is also being taken. Sensing sites containing cross-reactive indicator regions are deposited on the end of the imaging fiber. The resulting array is then challenged with a variety of analytes. Pattern recognition algorithms are employed to train a neural network. The resulting sensor array can identify subsequent challenges with the analyte even after extended use.

Optical arrays and pattern recognition in the design of an artificial nose

Array sensors capable of multi-vapor discrimination have been developed that employ fiber optic bundles, CCD cameras, and artificial neural network processing. Sensors have been constructed both through spatial deposition of dye-containing photopolymers on an imaging fiber, and via individual polymer/dye coatings placed on individual single-core fibers and then bundled. Cross-reactive sensing regions are created by using a variety of polymers. The resulting array is then challenged with a variety of analytes. Vapor pulses give rise to temporal response patterns which are used to train a neural network. The final sensor array system can identify subsequent challenges with the analyte over extended periods of time with up to 100% accuracy. The sensor can also characterize analytes on the basis of functional groups and molecular weight, and is capable of identifying components of mixtures.