Most people believe that our perception depends heavily on sight and hearing, and therefore underrate our sense of smell. As this sense is rather subjective, for a long time it was considered a matter of preference within the framework of arts and poetry. Our association of feelings and emotions with scents has made fragrance a multi-billion dollar industry. Continuing advancements in neuroscience have led to great progress in understanding and imitating this sense, and recent technological and scientific developments have made it a hot topic.

New Findings in Biology

Olfaction was long considered a uniquely mammalian trait. Scientists have disproven this by showing that many intertebrates can smell. For example, birds were thought to be unable to smell, although they have nostrils in their bills. John Audubon, a famous nineteenth-century bird artist, reached this mistaken conclusion by observing vultures confronted with a covered and an uncovered animal corpse, he concluded that they could not smell. The minute weight of the birds’ olfactory bulb consolidated this widespread misconception. Recent research shows that birds use smell when finding and distinguishing food, choosing proper nesting sites and mates, and following avian navigation routes. Ken Stager, an orinthogist at Los Angeles County Natural History Museum, used turkey vultures to disprove Audubon's vulture experiment. Marine biologist Betsy Bang, who measured the olfactory bulbs and tissues in the brains of 151 bird species, calculated the olfactory bulb's mass as being between 3% to 37% of the brain's entire mass. This shows that the ratio, and not the weight, determines a bird's ability to smell.

Other examples are as follows:

•Pigeons perceive small amounts of odorants. If their olfactory bulbs are blocked, they become lost.

•Certain seabirds (e.g., white chinned petrels) are sensitive to the chemical emitted by their main food (plankton), and so follow an olfactory path over the sea.

•European starlings smell the best region for their nesting site.

•Chickens detect inedible bugs (e.g., bright-colored bad-tasting caterpillars) through smell and sight.

•Salmon return to their hatching sites years later by using the unique olfactory memory of these sites left in their brains.

Smelling is far more developed in mammals, especially dogs and cats, which can sense parts per billion or trillion and can identify millions of different odorants. Science would benefit greatly if such abilities could be reproduced in sensors. But first, how does the human nose smell?

Perceiving Odors

Scientists divide human olfaction into steps. First, a potential odorant emits an odor's basic elements: volatile organic compounds (VOCs). We perceive an odor when molecules are transformed into an odor by binding the receptor proteins.(1) After binding with certain types of VOCs, these receptor proteins cause depolarization. The electrical charges produce unique signals, which the epithelium's sensory cells transmit to our neural network (axons).

These signals then are carried to a cluster of neural networks in the brain (glomeruli).(2) Ultimately, the impulse reaches the hypothalamus and describes the scent through a process of classification and identification.

Quantifying Scents

Human odor panels or gas chromatography and mass spectroscopy (GC/MS) are used to identify odors. Such quantification is problematic, however, because it is hard to quantify the VOC's perception in the nose as a unit of odor. Quantifying mass, volume, temperature, light intensity, and the molecular concentration of a soluble substance in a solution are reasonably objective and can be measured as a multiple of a standard unit.

But a standard olfactory measure does not exist, for it varies according to time and environment. Odor concentration is expressed as a multiple of a threshold: 50% of human "sniffers" must detect-not necessarily identify-it. This threshold is defined by the American Society for Testing and Materials (ASTM), and is accepted as the absolute threshold of odor perception. It takes 5 or 10 odor units for the human panel to identify the odor. GC/MS also can identify the odor's chemical composition.

Electronic Nose

After developments in electronic sight and hearing, scientists sought similar progress in odor perception. Research began at the University of Warwick (Coventry, England) in the 1980s. Its participants coined the term "electronic nose," now commonly known as "e-nose."(3) Their progress made it a commercial commodity with many applications.

E-noses have moved from being metal oxide devices, to conducting polymers, and now to laptop-size or pocket-size odor sensors. The Swiss Federal Institute of Technology (Zurich) has made one the size of a wrist-watch. However, current e-nose use is largely restricted to labs and military applications. Scientists are trying to match or surpass the human sense of smell's accuracy and sensitivity, after which they will work on surpassing that of the canine species.

Uses and Advantages

E-noses have a wide application in agriculture. Since they can detect minute differences, an e-nose using polymer materials can determine whether a tomato is sun-ripened, picked green, or internally damaged, and whether apple juice comes from a concentrate or is authentic but pasteurized.

Volunteers often test such products. But who wants to determine if corn oil is rancid or canola oil is oxidized? E-noses, having no such "qualms," detect changed odors in oil samples and provide far more accurate reports.

In animal science and poultry, e-noses provide detailed reports about spoiled food. Judy Arnold, a microbiologist in Athens, GA, researches food quality for the Agricultural Research Service (ARS).

In 1998, researchers discovered that e-noses can detect gases produced by spoiled poultry products. They claim that an e-nose can determine freshness, period of time in a refrigerator, and the amount of fat in white meat. Such an objective evaluation benefits poultry farmers and producers by eliminating returns of "funny-smelling" poultry. E-noses also can detect meat's decay rate and bacteria, overall quality and freshness, the composition of mixed meat-part products (e.g., processed meat), and how long a ham has been dry-cured. Given this, the e-nose's ability to examine a bundle of scents makes it very useful. It can perform hundreds of preliminary assessments that would occupy a chemist for months.

The military uses e-noses to detect land mines and traces of chemical-biological weapons. This is important, for over 100 million land mines litter 62 war-torn countries. Although dog-sniffers are useful, this practice is inhumane (dogs are often injured) and impractical (they need lots of training).

E-noses also are better than metal detectors and ground-penetrating radar and infrared imaging-the former detects even tiny pieces of metal, whereas the latter often images pebbles. As e-noses can identify traces of TNT or similar explosives to the 100 parts per quadrillion level, their detection rate is far more accurate and efficient. Nomadics, a Still-water, OK-based company, produces a cigar-box-sized e-nose for this purpose. Tufts University produces an optical e-nose that is designed and functions much like a mammalian nose.

Environmentalists use e-noses to analyze air. For instance, e-noses can report the chemical makeup of odors emitted by a farm's store of manure, detect the compounds causing that odor, help minimize leaks, and determine a new diet that will decrease such odors. With their ability to detect toxic VOCs and compounds leaking from a factory's or waste site's storage areas, e-noses will help environmentalists force industry to change its practices. The major difficulty here remains sampling, as concentrations vary with time and place.

Caltech has used Department of Defense funding to develop a device that identifies odors in seconds. Its 32 components swell like sponges when exposed to a particular vapor, and its resistance (hence conductivity) changes accordingly. As it can detect any type of odor, doctors at the Children's Hospital in Los Angeles are studying medical applications. Currently, it is applied to patients' breath to help diagnose upper respiratory infections.

The major advantages of e-noses over human noses in these areas are objectivity; ability to measure odors over long real-time periods; and immunity to fatigue, infection, mental state, hazardous material, and adaptation (gradual loss of sensitivity).

How E-noses Work

E-noses have three functional components: a sample handler, a gas sensor array, and a signal processing system. Its output identifies the odorant, estimates its concentration, and relates its characteristic properties. A sensor recognizes different types and concentrations of odors through its arrays, each of which has a different sensitivity. The resulting combination provides the response pattern that enables the e-nose to identify odorants.

In a typical e-nose, a vacuum pump pulls the first air sample into the tube housing the electronic sensor arrays. The air sampling unit exposes the odorant to the sensor, after which VOCs interact with the surface and the sensor's active material until reaching a steady state. The sensor's response is recorded and transmitted to the signal-processing unit. When completed, a washing gas cleanses the sensor. After the reference gas is applied to the unit, the sensor is ready to measure again.

E-nose Technologies

The sensor is the e-nose's key element, and the sensor type is its defining characteristic. There are 5 types of e-nose sensors, as follows:

Optical sensors: Optical fiber sensors work through fluorescence and chemoluminescence. The tube's glass fibers contain a thin encoated active material in their sides and at both ends. As VOCs interact with the organic matrix's chemical dyes, the dye's fluorescent emission changes the spectrum. These changes then are measured and recorded for different odorous particles.

Fiber arrays with different dye mixtures can be used as sensors. These are fabricated by dipcoating (binding a plastic solution to a substrate), micro electromechanical system (MEMS), and precision machining. The main advantage is that this adjustable tool can filter out noise. Also, since many dye forms are available in biological research, sensors are cheap and easy to fabricate. But the instrumentation control systems are complex, which adds to the cost, and have a limited lifetime due to photo bleaching (the sensing process slowly consumes the fluorescent dyes).

Optical sensors are sensitive and can measure low ppb (parts per billion); however, they are still in the researach stage of development.

Spectrometry-based Sensors: This group consists of a molecular spectrum-based gas chromatography (GC), an atomic mass spectrum-based mass spectrometry (MS), and a transmitted light spectrum-based light spectrum (LS). The first two can analyze the odor's components accurately, which is a plus. However, their use of a vapor trap to increase concentration can alter the odor's characteristics. LS devices do not consume the sample, but do require tunable quantum-well devices. GC and MS devices are commercially available, while LS devices are only at the research stage. All spectrometry-based sensors are fabricated by MEMS and precision machining, and can measure odors to a low ppb level.

The GC tube decomposes the odorant into its molecular constituents, and MS forms a mass spectrum for each peak. The spectra then is compared to a large precompiled database of spectral peaks to classify and identify odorants.

MOSFET (Metal-oxide-silicon field-effect-transistor): The basic principle here is capacitive charge coupling. In other words, VOCs react with the catalytic metal and thereby alter the device's electrical properties. The device's selectivity and sensitivity can be fine-tuned by varying the metal catalyst's thickness and composition. MOSFETs are micro-fabricated and commercially available, but can measure only parts per million. They can be manufactured by electronic interface circuits, which minimizes batch-to-batch variation. However, the gas produced by the VOC-metal reaction must penetrate the MOSFET's gate.

Conductivity Sensors: The sensor types used here are metal oxide or conducting polymer. Both operate on the principle of conductivity, for their resistance changes as they interact with VOCs. Metal oxide sensors are common, commercially available, inexpensive, and easy to produce (they are micro-fabricated). Their sensitivity ranges from 5-500 ppm. However, they only operate at high temperatures (200°C to 400°C).

In conducting polymer sensors, VOCs bond with the polymer backbone and change the polymer's conductivity (resistance). They are micro-fabricated together with electroplating and screen printing, are commercially available, and can measure from .1 to 100 ppm. They operate at room temperature, yet are very sensitive to humidity. Moreover, it is hard to electropolymerize the active material, which makes batch-to-batch variation inevitable. Sometimes VOCs penetrate the polymer chain, which means that the sensor must be returned to its neutral and reference state-a very time-consuming process.

Piezoelectric Sensors: These devices, which measure any change in mass, come in two varieties: quartz crystal microbalance (QCM) and surface acoustic wave (SAW) devices.

QCM sensors have a resonating disk and metal electrodes on each side. While applying the gas sample to the resonator's surface, the polymer surface absorbs VOCs from the environment. Thus its mass increases, which increases resonance frequency. As the U.S. Navy has long used QCMs, this technology is familiar, developed, and commercially available. A QCM sensor is fabricated by screen-printing, wire bonding, and MEMS. Althoug it can measure a 1.0 Ng mass change, its MEMS fabrication and interface electronics is a major disadvantages. QCM sensors are quite linear in mass changes, their sensitivity to temperature can be adjusted, and their response to water can vary for the material used.

MEMS techniques should be handled carefully, for the surface-to-volume ratio increases drastically as dimensions approach the micrometer levels. Measurement accuracy is lost when the increasing surface-to-volume ratio begins to degrade the signal-to-noise ratio. This problem occurs in most micro-fabricated devices. SAW devices have much higher frequencies. Since 3-D MEMS processing is unnecessary, SAW devices are cheaper. As with QCM devices, many polymer coatings are available. The differential devices can be quite sensitive. However, interface electronics require more complex electronics than those of conductivity sensors for both QCM and SAW sensors. Also, as the active membrane ages, resonance frequencies can drift and so must be detected for frequency by time. SAW devices are commercially available and sensitive to mass changes at the 1.0 pg level.

Pattern Recognition

Any e-nose's primary task is to identify an odorant and perhaps measure its concentration. After the signal processing step comes the crucial step of pattern recognition: preprocessing, feature extraction, classification, and decision-making. A database of odors must be formed for comparison purposes.

Preprocessing accounts for sensor drifts and reduces sample-to-sample variation. This can be done by normalizing sensor response ranges, manipulating sensor baselines, and compressing sensor transients.

Feature extraction involves dimensionality reduction, a crucial step for statistical data analysis, since the database's examples usually are subject to financial constraints. The higher dimensionality caused by sensor arrays is reduced to relevant pattern-recognition information and thus extracts only significant data. As most dimensions are correlated and dependent, it is better to reduce dimensionality to a few informative axes.

Feature extraction usually is accomplished by classical principal component analysis (PGA) or linear discriminant analysis (LDA). PCA is a linear transformation that finds the maximum variance projections and the most widely used technique for feature extraction. But as PCA ignores class labels, it is not an optimal technique for odor recognition.

LDA seeks to maximize the distance between class label examples and minimize the within distance, and thus is a more appropriate approach. LDA is also a linear transformation. For instance, LDA might better discriminate subtle but crucial odor projections, whereas PCA can remove the high variance random noise in a projection.(4)

The classification stage identifies odors. Classical classification techniques are KNN (k nearest neighbors), Bayesian classifiers, and ANN (artificial neural networks]. KNN with, say, 5 nearest points will find the 5 closest matches from the precompiled database. The closest match will be assigned as the tested material's odorant class.

Bayesian classifiers first assign a posterior probability to the classes in the lower dimension and then pick the class that maximizes the predetermined probability distribution. ANN is closer to biological odor recognition. After being trained by the odor database, it is exposed to the unknown odorant in order to recognize the largest applicable response odorant class. The classifier estimates the class and places a confidence level on it.

In decision-making, risks and application-specific knowledge are considered in order to modify the classification. All decisions are reported-even a nonmatch.

Conclusion

As this article indicates, we can expect great progress in this area. And with each step forward, science and technology will continue to point toward the Greatest Artist's most subtle designs and allow us to appreciate them better.

Footnotes

1. There are over 100 million receptor proteins of about 1,000 different types.
2. A human olfactory bulb contains approximately 2,000 glomeruli.
3. The terms "electronic nose" and "e-nose" are incorrect, for these devices cannot be considered "real" noses. The correct terminology should be "electronic arrays for chemical sensory and identification." However, "e-nose" has gained wide acceptance in the literature since it first appeared during a 1991 NATO workshop in Reykjavik, Iceland.
4. Such nonlinear transformations as Sammon nonlinear maps and Kohonen self organizing maps also are used in feature extraction. These preserve the distance between pairs of examples when reducing dimensionality to 2 or 3.

References

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