Electronic Noses
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f all human senses our ability to detect odors may be the least appreciated, and it is certainly the least understood. Even the broad outlines of how airborne molecules trigger our sense of smell remain speculative. As we go to press, a recent report indicates that odors are detected by the action of a large family of genes expressing a host of different receptor proteins exclusively in the nose (Axel, R.; Buck, L. Cell 1991, 65, 175-87). As dogs have d e m o n s t r a t e d for centuries, there is significant analytical information to be obtained from "sniffing" t h e e n v i r o n m e n t . Two talks at the Pittsburgh Conference in Chicago (see EDITORS' COLUMN, p. 551 A) highlighted efforts by analytical chemists to electronically sample the air. One effort provides a model and the other provides protection for mammalian noses. The Warwick Nose Electrochemist Philip Bartlett of the University of Warwick in Coventry, England, generated considerable excitement at the meeting with his description of an array of 12 S n 0 2 sensors for discriminating the aromas of commercial products such as foods, beverages, or perfumes. Although such a system has potential practical applications in industries where odor is important, Bartlett and his Warwick collaborators, George Dodd, Julian Gardner, and Harold Shurmer, also see their electronic nose as a route to understanding how the human nose functions.
Most odorants are a select group of small, hydrophobic molecules with masses of up to 300 Da, which is typical of volatile molecules. Some nonvolatiles, such as oils, can be detected as aerosols. Complex s t r u c t u r e function r e l a t i o n s h i p s h a v e been developed to classify odorants. Generally, many odor-producing molecules include one or two polar functional groups, often containing an oxygen or sulfur atom.
FOCUS Classification of odors is made difficult by the fact that they can be described only in subjective terms such as "minty," "fishy," or "pungent." Odor r e s p o n s e s lack d e s c r i p t o r s equivalent to the salty, bitter, sweet, and sour sensations found with taste. Moreover, the vast majority of natural odors are complex mixtures of odorants in which subtle variations in the relative ratios may, for instance, distinguish t h e bouquet of one cabernet sauvignon wine from another. On the other hand, the overwhelming part of an odor may be attributable to a compound present at only trace levels. Three properties define olfactory response: the threshold value (minimum amount detected), intensity (response), and type of odor (physical and chemical properties of the analytes). Unfortunately, the relationships among these properties complicate
the development of a true artificial nose. For example, odorants with low intensities have low vapor pressures and hence low gas-phase concentrations. To detect these molecules, the nose must display a low threshold of response. The opposite could be true for a high-intensity odorant. The situation becomes more complex when there is a combination of low- and high-intensity odorants. As the mixture evaporates or dissipates, the number of low-intensity molecules falls below the threshold value and its aroma changes to one dominated by the high-intensity (high vapor pressure) molecules. In t h e Warwick artificial nose, complex odorants are detected with an array of commercially available sensors (Figure 1). The sensors are based on a design known as t h e Taguchi gas sensor, which detects analytes on a 3-mm-long ceramic tube coated with S n 0 2 . The S n 0 2 is doped with different additives, such as Pt, to improve and vary the response. To optimize the semiconductor's response, the sensor tube is heated to - 350 °C. Response times are usually about 10 s, and sensor recovery requires another 60 s. Twelve of these sensors, each having different characteristics, are suspended inside a 20-L flask. A stirring paddle disperses vapor samples within the flask, and the entire system is cooled by immersing the flask in a 25 °C constant-temperature water bath. The output signals of the sensors are multiplexed together and can be
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Electrical connections
Perspex cover \
Sensors
de Motor Injection port Clamp
.Flask Mitral cell
Olfactory bulb Water bath
Stirrer Bone (Cribriform plate)
Figure 1. The Warwick electronic nose. Olfactory receptor
Courtesy P. Bartlett, University of Warwick.
used to calculate the gas concentra tion. Signals from the sensors are sampled regularly and then analyzed by using pattern recognition algo rithms as described below. The S n 0 2 sensors can detect mole cules down to 100 ppb levels. How ever, Bartlett warns that it is not the concentrations of odorants that are important, but the ability to classify complex aromas such as ground cof fee. "It's an engineering type of ap proach. If you ran the odor associat ed with coffee t h r o u g h a GC you could find as m a n y as 50 com pounds." The sensor array serves as an elec tronic analogue for the mammalian olfactory cells that respond to odorants. These cells, located at the top of the nasal cavity, are covered by a thin (10-100 μπι) layer of mucosa. In humans, this mucosal layer occupies only about 2.5 cm 2 in each nostril. Within this layer 50 million to 100 million receptor cells plus additional column-shaped supporting (or sustentacular) cells are found (see Fig ure 2). The 1-μπι-wide receptor cells act as both transducers—responding to odor stimuli—and neurons—trans mitting the signal to the brain. How these cells respond to odors remains a mystery. "There are different theo ries on how the cells work, involving changes in the phospholipid layer or enzyme cycles," explains Bartlett. At the transducer end, several fin ger-like microvilli project out toward the nasal cavity. The cell thins at the other end and converges to a single, uncoated (unmyelinated) nerve axon. The axon passes through the bone of
Olfactory axons Supporting cell
Figure 2. Diagram of human olfactory cells in the nose and nerve connections into the brain. Adapted from Physiology, Berne, R. M.; Levy, M. N., Eds.; C. V. Mosby: St. Louis, MO, 1988, p. 194.
the skull, directly into the olfactory bulbs in the brain. Odor detection takes it toll on the receptor cells, which continually die and are replaced by new ones. (They are the only neurons that regener ate.) This means that the electronic sensors may also need regular r e placement, says Bartlett. F u r t h e r more, it appears t h a t the receptor cells are basically identical and do not discriminate among odorants. Translating molecular t r a n s d u c tion into different sensations may oc cur inside the olfactory bulb where signals from the receptor cells con verge. For every 10 000-20 000 pri mary receptor neurons, each second ary or mitral cell neuron receives a single signal. (This convergence of stimuli may be indicative of a "sec ondary system processing the smell sensations into odor-specific signals. That function, say the Warwick sci entists, is an analogous step to their data analysis procedures [Figure 3].) The mitral cells, in turn, pass their signals to neurons of the limbic sys tem found in the cerebral cortex. The artificial nose relies on statis tical pattern recognition algorithms for analysis of its sensor data. For in stance, the Warwick system was ex posed to 1-2 μΙ_. of tobacco smoke from four different brands of ciga rettes. ("One of the worst things to put into a tin oxide sensor," admits
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Bartlett.) The raw data from the sen sors produced a scatter diagram of outputs. By dividing the coordinates of each output by the standard devi ation of its set, the modified values fall into clusters that clearly distin guish the different cigarette brands. With 12 sensors producing data, analysis could require looking for patterns in 12-dimensional space. To avoid that situation, principal com ponent analysis methods have been used to find which combination of v a r i a b l e s m a k e s t h e most sense.
(Tin oxide, polymer)
Sensor array
(Current to voltage amp + A/D converter)
Transducer
Hardware
Processor
Software/ (Microprocessor) hardware
Figure 3. Information flow in the Warwick electronic nose. Courtesy P. Bartlett, University of Warwick.
Data from just three or four sensors that show sufficient variations are used to discriminate between different a r o m a s . Processed d a t a from these key sensors are then plotted against each other, generating the recognition patterns. Pure materials or single odorants are easier to classify than complex mixtures. Sensor outputs are compared and converted to normalized vectors that eliminate the initial concentration dependence of the signal. The vectors are then compared with a known pattern of parameters that either clearly identifies the odorant or provides a most likely candidate. The Warwick r e s e a r c h e r s h a v e also begun testing an artificial neural network program for processing sensor data. This type of approach could offer a more flexible and, because of the parallelism, faster method of analysis. In addition, it might b e t t e r mimic m a m m a l i a n n e u r o n processing of odor stimuli. Following s t a n d a r d n e u r a l network architecture, the program feeds the inputs of the 12 sensors into a hidden layer that processes the data and then generates a number of outputs that appear in a third layer. The network trains on all the data sets (using the back propagation paradigm) except one, which is used to test the system. "Training is a slow business t h a t takes several hours," says Bartlett. Early results demonstrated that this approach can discriminate between various organic alcohols. The electronic nose has already been used to "sniff" and evaluate commercial products such as coffee, beer, and lager ale. To decide which aromas are most appetizing, the analyzed data are then compared with ratings from h u m a n experts. It is hoped that with enough correlations the electronic nose might become an "expert" and rate new aromas. The Warwick researchers are also exploring alternative sensor designs. Although commercial availability of detectors is a benefit, says Bartlett, they come at the cost of signal drift and a heater that draws up to about 1 W of power. Preliminary experiments have been performed with a conducting polymer chemiresistor sensor. In these studies a gold electrode is covered with a conducting polymer, such as a polypyrrole, polyaniline, or polythiophene. These electrodes can be customized by varying the functional groups on the polymers, offering a wide range of responses. In addition, they operate at room temperatures
Reference device
Sensor array
Oscillator and mixer circuitry
Thermoelectric cooler
Heat sink (case)
Figure 4. Schematic of the core sensor system. The four-sensor array and the separate reference device are maintained at constant temperature by the thermoelectric cooler. Courtesy J. Grate, Naval Research Laboratory.
and can be fabricated to sizes that are < 1 mm 2 . The Warwick researchers are also investigating small S n 0 2 sensors incorporated into an integrated circuit chip. They hope that with this newer technology their entire system will eventually be portable. A nose for toxic gases Quite a different problem in vapor detection was presented by Jay Grate of the Naval Research Laboratory (NRL) in Washington, DC. Grate and his colleagues are using sensor technology to detect chemical warfare gases. Basically, they are creating a rugged black box device that can be used in combat conditions to alert troops to the presence of either nerve or blistering agents (e.g., m u s t a r d gas) at low concentrations. Furtherm o r e , to p r e v e n t s o l d i e r s from "dumping the device out of the helicopter," it must not be subject to false alarms. To accomplish this, NRL scientists have built what Grate calls a "smart sensor system" in which four surface acoustic wave (SAW) devices are used to sense the toxic agents (as shown in Figure 4). SAW devices operate by generating mechanical oscillations with a chara c t e r i s t i c frequency in t h e MHz range across a thin piece of piezoelectric material such as quartz. By coating the oscillating material with a substance that adsorbs the analyte, the SAW device will change in frequency as material collects on the surface. The change in frequency is related to a standard partition coefficient, giving the ratio of analyte in the vapor and stationary phases. NRL researchers have been working with a 158-MHz SAW device that consists of a quartz substrate with Al
inter digital fingers covering the surface. To protect this layer, a - 20-nm layer of S i 0 2 is applied over the surface. The chemically selective coating is then added to the glass overcoat. Tests with stimulants at NRL and with chemical agents at contract laboratories have shown that ethyl cellulose and poly(epichlorohydrin) are the best sensor coatings for blistering agents and that fluoropolyol works best for nerve gases. The fourth detector coating, poly(ethylenimine), monitors humidity levels. The response p a t t e r n s generated by the sensors are evaluated by pattern recognition algorithms to determine if the detected vapors are hazards requiring an alarm or merely background gases that should be ignored. The sensor coatings are applied with an airbrush, and the thickness of each c o a t i n g is m o n i t o r e d by changes in the SAW device's oscillation frequency. The depth of the applied layer depends on the material's density, but typically measures 4 0 80 nm. Vapor diffusion into the sensors' coatings is temperature dependent. A fifth, unmodified SAW device acts as a reference and provides a constant frequency. Thus frequency values are determined as differences. "The reference [SAW device] is on a separate chip," says Grate, "so that there is no crosstalk between sensors. Lower frequency difference signals are useful because they are easily c o u n t e d by TTL [ t r a n s i s t o r transistor logic] circuits." To improve the system's sensitivity and selectivity by as much as 50fold, samples can be preconcentrated before analysis (Figure 5). Two tubes loaded with Tenax collect air samples and are then desorbed by heating the
ANALYTICAL CHEMISTRY, VOL. 63, NO. 10, MAY 15, 1991 · 587 A
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Small pump Sensor array
Preconcentrator tube
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Large pump Figure 5. Schematic of the sampling system. Courtesy J. Grate, Naval Research Laboratory.
Tenax at either 2- or 14-min inter vals. The latter collection time allows analyses down to pptr levels.
"Analysis of data is accomplished by statistical pattern recognition us ing linear determinants with weight ed vectors," explains Grate. NRL has also explored neural networks for data analysis and has found that this method produces results equivalent to those of the statistical analysis. The entire system can successfully detect chemical agents in the pres ence of water, jet or diesel fuel, jet exhaust, cigarette smoke, and other potential interferences. At this point, claims Grate, their system meets or exceeds the standards that the mili tary has set for a poison gas sensor. The system will alert operators to nerve gas at levels < 0.01 mg/m 3 and to mustard gas at < 0.1 mg/m 3 . Grate and his co-workers, in coop eration with Virginia-based Microsensor Systems, Inc., have assembled the entire system into a compart ment the size of a shoebox. The sys tem runs on either 115 V ac or 12-15 V dc, providing sample results every 2 min. Three pumps pull air into the sampler—two 800 mL/min pumps are attached to the Tenax preconcen t r a t o r t u b e s , a n d a 100 m L / m i n pump is used for real-time analysis.
The sensor data are analyzed by sta tistical analysis, which, says Grate, "is really easy to implement on a microprocessor." The entire system is portable and is now being tested for military applications. However, the technology could be useful wherever toxic vapors might collect. Other advanced sensor systems for detecting vapors are being developed. Thus, just as analytical devices have extended our vision into wavelengths invisible to the eye and our hearing into frequencies undetectable by the ear, new analytical devices might also be able to extend our sense of smell. Alan R. Newman
Suggested reading
Ballantine, Jr., D. S.; Wohltjen, H. Anal. Chem. 1989, 61, 704 A. Gardner, J. W.; Bartlett, P. N.; Dodd, G. H.; Shurmer, H. V. In NATO ASI Se ries; Schild, D., Ed.; Springer-Verlag: Berlin, 1990; Vol. H 39. Grate, J. W.; Snow, A. W.; Ballantine, Jr., D. S.; Wohltjen, H.; Abraham, M. H.; McGill, Α.; Sasson, P. Anal. Chem. 1988, 60, 869. Grate, J. W.; Klusty, M.; Barger, W. R.; Snow, A. W. Anal. Chem. 1990, 62, 1927.
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