We are developing a portable, artificial olfactory system based on
multiple attributes of the sense of smell to identify air-borne odors,
including those associated with buried landmines. Brief (1-2 sec) air
samples are drawn over an array of optically-interrogated,
cross-reactive chemical sensors. These consist of polymers with high
sensitivity and relatively narrow specificity for nitroaromatics
(Timothy Swager, MIT), as well as those with broader responses, thus
permitting discrimination among substances that may be confused for
nitroaromatics. Biologically-based pattern matching algorithms
automatically identify odors as one of several to which the device has
been trained. In discrimination tests, after training to one
concentration of 6 odors, the device gave 95% correct identification
when tested at the original plus three different concentrations. Thus,
as required in real world applications, the device can identify odors
at multiple concentrations without explicitly training on each. In
sensitivity tests, the device showed 100% detection and no false
alarms for the landmine-related compound DNT at concentrations as low
as 500 pp-trillion (quantified by GC/MS) - 10 times lower than average
canine behavioral thresholds. To investigate landmine detection
capabilities, field studies were conducted at Ft. Leonard Wood, MO. In calibration tests, signals from buried PMA1A anti-personnel
landmines were clearly discriminated from background. In a limited 9
site "blind" test, PMA1A detection was 100% with false alarms of 40%. Although requiring further development, these data indicate that a
device with appropriate sensors and exploiting olfactory principles
can detect and discriminate low concentration vapor signatures,
including those of buried landmines.
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.
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.
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.
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