Proceedings of the 1999 LEEE
International Symposium on Intelligent ControYIntelligent Systems and Semiotics
Cambridge, MA September 15-17.1999
Mimicking Biology:
Applications of Cognitive Systems to Electronic Noses
Paul E. Keller
Battelle Pacific Northwest Division, P.O. Box 999, Richland, WA 99352, USA
[email protected]
Abstract
artificial system and physiology is necessary to achieve a
reliable, subjective, and analytically acceptable system.
The electronic nose draws its inspiration from biology.
Both the electronic nose and the biological olfactory
system consist of an array of chemical sensing elements
and a pattern recognition system. This paper reviews the
basic concepts of electronic noses and their relationship to
biologiczl olfaction. Different approaches to chemical data
analysis including statistical methods, standard artificial
neural network approaches, and those based on advanced
biological models of the.olfaction are described. Finally, a
prototype system is reviewed.
2. Ideas from the Biological Nose
The mammalian olfactory system uses a variety of
chemical sensors, known as olfactory receptors, combined
with signal processing in the olfactory bulb w d automated
pattern recognition in the olfactory cortex of the brain. No
one receptor type alone identifies a specific odor. It is the
collective set of receptors combined with pattern
recognition that results in the detection and identification
of each odor. Figure 1 illustrates the major operations of
the mammalian olfactory system. The operations can be
broken into sniffing, reception, detection, recognition, and
cleansing. Figure 2 illustrate the major components of the
olfactory system.
1. Introduction
The standard approach to odor analysis is to employ a
human sensory panel, which is a group of people with
highly trained senses of smelI. The disadvantages of
human sensory panels include subjectivity, poor
reproducibility (i.e., results fluctuate depending on time of
day, health of the panel members, prior odors analyzed,
fatigue, etc.), time consumption, and large labor expense.
Also, human sensory panels can not be used to assess
hazardous odors, work in continuous production, or remote
operation.
7. Cleaning
6. Action
I
\
t
5. Identification o’facfo
4. Transmission
3. Stimulus
2. Reception and
Binding
Analytical chemistry instruments such as gas
chromatographs (GC) and mass spectrometers (MS) have
been used to analyze both hazardous and non-hazardous
odors. GC and G C M S systems can require a significant
amount of human intervention to perform the analysis and
then relate the analysis to something useable.
1. Sniffing
Olfactory
Receptors
x -
Odorant
Figure 1. This figure illustrates themajor processes of the
olfactory system. Through sniffing, odor molecules arrive at the
olfactory receptors stimulate an electro-chemical response that is
transmitted to the olfactory bulb and ultimately the olfactory
cortex for identification.
The main motivation for electronic noses is the
development of qualitative, low-cost, real-time, and
portable methods to perform reliable, objective, and
reproducible measures of volatile compounds and odors.
In order to develop an electronic nose, it is useful to
examine the physiology behind olfaction since biological
olfactory systems contain many of the desired properties
for electronic noses. Also, the contrast between an
0-7803-5665-9/99/$10.00 0 1999 IEEE
Mammalian Nose
The olfaction process begins with sniffing which brings
odorant molecules from the outside world into the nose.
With the aid of turbinates (bony structures in the nose
which produce turbulence), sniffmg also mixes the odorant
447
There are no individual olfactory receptors or portions of
the brain that recognize specific odors. It is the brain that
associates the collection of olfactory signals with the odor.
Finally, in order for the nose to respond to new odors, the
olfactory receptors must be cleansed. This involves
breathing fresh air and the removal of odorant molecules
from the olfactory receptors.
molecules into a uniform concentration and delivers these
molecules to the mucus layer lining the olfactory
epithelium in the upper portion of the nasal cavity. Next,
the odorant molecules dissolve in this thin mucus layer
which then transports them to the cilia (hair like fibers) of
the olfactory receptor neurons. The mucus layer also
functions as a filter to remove larger particles.
3. The Electronic Nose Concept
An electronic nose is composed of a chemical sensing
device and an automated pattern recognition system. This
combination of broadly tuned sensors coupled with
sophisticated information processing makes the electronic
nose a powerful instrument for odor analysis. The sensing
system can be an array of chemical sensors where each
sensor measures a different property of the sensed
chemical, or it can be a single sensing device (e.g., gas
chromatograph, spectrometer) that produces an array of
measurements for each chemical, or it can be a hybrid of
both. Each odorant or volatile compound presented to the
sensor array produces a signature or characteristic pattern
of the odorant.
Olfactorv Tract
7. Cleaning
6. Action
Figure 2. This figure illustrates the maior components of the
senses of olfaction-and taste in the huma. The major olfactory
components are the olfactory receptors (sensors), the olfactory
bulb (signal preprocessing), and the olfactory cortex (odor
identification). The VNO is the vomeronasal organ and is
associated with pheromone detection.
5 . Identification
Electronic Nose
Display
Labels or
c/ustey
,m,
,
Artificial
Neural
Network
4. Transmission
3. Stimulus
Reception involves binding the odorant molecules to the
olfactory receptors. These olfactory receptors respond
chemically with the odorant molecules. This process
involves temporarily binding the odorant molecules to
proteins that transport the molecules across the receptor
membrane. Once across the boundary, the odorant
molecules chemically stimulate the receptors. Receptors
with different binding proteins are arranged randomly
throughout the olfactory epithelium.
2. Reception and
Binding
Chemical
Sensors
Sensor
1. Sniffing
Odorant
Figure 3. This figure illustrates themajor processes of the
electronic noses. Odor molecules arrive at the chemical sensor
array stimulate an electrical response that is transmitted to the
pattern recognition system and ultimately to an output display or
actuation.
The chemical reaction in the receptors produces an
electrical stimulus. ’ These electrical signals from the
receptor neurons are then transported by the olfactory
mons through the cribiform plate (a perforated bone that
separates the cranial cavity fiom the nasal cavity within the
skull) to the olfactory bulb (a structure in the brain located
just above the nasal cavity).
The configuration of an electronic nose for a specific
application requires the collection of a set of sensor data
(odor signatures) fiom relevant odorants. This odor
signature database is built up of by presenting many
different odorants to the sensor array. Then the database is
used to train or configure the recognition system to
produce unique classifications or clusterings of each
odorant so that an automated identification can be
implemented. Like biological systems, electronic noses
are qualitative in nature and do not give precise
From the olfactory bulb, the receptor response information
is transmitted to the olfactory cortex where odor
recognition takes place. After this, the information is
transmitted to the limbic system and cerebral cortex.
448
B. Artificial Neural Network Approaches
Electronic noses that incorporate ANNs have been
demonstrated in various applications. When an ANN is
combined with a sensor array, the number of detectable
chemicals is generally greater than the number of unique
sensor types. Also, less selective sensors which are
generally less expensive can be used with this approach.
Once the ANN is trained for odor or volatile compound
recognition, the operation consists of propagating the
sensor data through the network. Since this is simply a
series of vector-matrix multiplications, odors can be
rapidly analyzed qualitatively.
concentrations.
Unlike biological systems, current
electronic noses are usually trained to identify only a few
different odors or volatile compounds. Also, current
systems lack the temporal dynamics found in biological
systems and neuromorphic models. .
4. Chemical Sensor Technology
Although the broad selectivity of the chemical sensors in
an electronic nose is compensated by advanced
information processing, the sensors still must meet key
design parameters for the system.
These include
sensitivity,
speed
of
operation,
cost,
size,
manufacturability, the ability to operate in diverse
environments, and the ability to be automatically and
quickly cleaned. The sensors must be able to adsorb (i.e.,
collect and hold) large numbers of molecules of a
particular species to produce a measurable change in the
sensor. After the odorant is identified, the process must be
reversed through a cleaning process. The choice of
chemical sensors to meet these requirements is large and
includes
metal-oxide
semiconductors,
conductive
polymers, conducting oligomers, non-conducting polymers
with embedded conductors, surface acoustic wave devices,
bulk acoustic wave devices, quartz crystal microbalances,
chemical field effect transistors, fiber optic sensors, and
discotic liquid crystal sensors. In addition, GCs and
spectrometers can also be used alone or in combination
with these chemical sensors.
ANNs as well as statistical techniques can be divided into
supervised and unsupervised approaches. Supervised algorithms used in electronic noses include backpropagationtrained feed-forward networks [2], learning vector
quantizers, and fuzzy ARTmaps [3]. Figure 4 illustrates
the output of a prototype electronic nose which maps
sensor values to specific labels and is trained by a supervised approach [4]. An unsupervised algorithm does not
require predetermined odor classes for training and essentially performs clustering of the data into similar groups
based on the measured attributes or features that serve as
inputs to the algorithm. Unsupervised ANNs used in
electronic noses include self-organizing maps (SOMs) [5]
and adaptive resonance theory networks. Figure 5
illustrates a map produced by an SOM to show the
relationships between various odors.
5. Pattern Recognition Technology
Electronic noses rely on lower cost but broadly tuned
sensors inspired by biology. Therefore, a natural approach
is to couple the sensors with a physiologically inspired
pattern recognition method. This can include the use of
conventional statistical methods, artificial neural networks
(ANNs), and neuromorphic models.
A. Statistical Approaches
Many statistical techniques are either analogous or
complementary to ANNs and are often combined with
ANNs to produce classifiers and clusterers that are more
robust than those made from individual techniques. These
statistical approaches include principal components
analysis (PCA), partial least squares, discriminant analysis,
discriminant factorial analysis (DFA), and cluster analysis
(CA). PCA breaks apart data into linear combinations of
orthogonal vectors based on axes that maximize variance.
To reduce the amount of data, only the axes with large
variances are kept in the representation [l]. DFA is a
multivariate technique which determines a set of variables
which best discriminates one group of objects from
another.
Figure 4. This figure illustrates the output of an electronic nose
configured to label odors with specific labels with a supervised
ANN classification algorithm (in this case backpropagation). The
lower graph represents the sensor values and the upper graph
illustrates the assigned labels.
C. Neuromorphic Approaches
Neuromorphic approaches center on building fully
plausible models of olfaction based on biology and
implementing them in electronics. Of all the senses,
olfaction is the least understood. In addition, there has
449
been poor communication between theoreticians and
experimentalists. As such, there is a lack of realistic
mathematical models of biological olfaction, and the area
of neuromorphic models of the olfactory system lags
behind vision, auditory, and motor control models.
One of the advanced features of neuromorphic models is
the incorporation of temporal dynamics to handle
identification of combinations of odors. Part of the need
for temporal dynamics in the model relates to the location
of olfactory receptors in the nose and the propagation
delays associated with these spatial differences. The other
need is that different chemicals have different volatility
rates, which produces varying concentrations over time.
At least two universities are actively trying to implement
neuromorphic models of olfaction in electronic systems.
These include an effort by Tim Pearce at the University of
Leicester in the UK and an effort by Rodney Goodman at
Caltech in the USA [7].
6 . Protoype
In late 1993 and early 1994, we developed a simple
electronic nose prototype to test pattern recognition
techniques that are necessary for building fieldable
electronic nose systems [4,8]. A photograph of the
prototype is illustrated in Figure 6 and the computer
interface to the electronic nose is shown in Figure 4.
Figure 5 ; This figure illustrates a self-organizing map of odors
representing their topological relationships.
Olfactory information is processed in both the olfactory
bulb and in the olfactory cortex. Figure 2 illustrates the
main information processing structures within the brain.
The olfactory bulb performs the signal preprocessing of
olfactory information including recoding, remapping, and
signal compression. The olfactory bulb also handles cases
where an odor presented for a long time produces habituation. The olfactory cortex performs pattern classification
and recognition of the sensed odors. Once identified, odor
information is transmitted to the hippocampus, limbic
system, and the cerebral cortex. The connection to the
hippocampus explains why odor can sub-consciously
evoke memories. Conscious perception of the odor and
how to act on the odor takes place in the cerebral cortex.
There are two main competing models of olfactory coding
[6]. The selective receptor model comes from recent
experimental results in molecular biology. It can be
thought of as an odor mapper. This approach draws an
analogy to visual systems with the idea of receptive fields
of olfactory receptors and mitral cells in the olfactory bulb.
Functionally identical olfactory receptors project to the
same glomeruli in the olfactory bulb. This results in
unique glomeruli for each unique odor.
Figure 6. This figure is a photograph of the prototype showing the
sampling box on top of the electronics case and desktop
computer. In the background on the computer monitor is the
graphical output of the prototype.
The system works by placing an odorant in the sampling
box that contains a mixing fan and a sensor array. The
volatile compounds off-gassing from the sample are blown
over the sensor array. This process both transports odorant
molecules to the sensors and produces a uniform mixture
of odorant molecules across the sensor array so that each
sensor is interacting with the same concentration of
odorant molecules. This process is analogous to the
physiological process of sniffing in the biological nose.
The other approach is a non-selective receptor,
distributive-coding model that comes from data collected
by electrophysiology and imaging of the olfactory bulbs.
Experimental data have been collected supporting both of
these contradictory hypotheses, so additional research is
necessary to resolve this conflict.
450
The sensors physically respond to the odorant molecules
through a chemical reduction process. In the prototype, an
array of nine tin-oxide vapor sensors, a humidity sensor,
and a temperature sensor were used. The tin-oxide sensors
are commercially available Taguchi-type gas sensors
obtained from Figaro Co. Ltd. [ 9 ] . Exposure of a tin-oxide
sensor to a volatile compound produces a large change in
its electrical resistance. This is analogous to the reception
and detection process in the olfactory receptors.
The electrical signals from the sensors are then sent from
the sampling box to an analog-to-digital converter within a
desktop computer. The digitized sensor values were then
accessible to the ANN pattern recognition software for
real-time odor identification.
Next, the odors were identified by ANNs implemented as
software modules on the desktop computer. Two types of
ANNs were constructed for this prototype: the standard
backpropagation-trained feed-forward network and the
fuzzy ARTmap algorithm. The identification time in the
prototype was limited only by the response time of the
chemical sensors, which was on the order of seconds.
Figure 4 illustrates the user display on the prototype. It
shows both the instantaneous sensor values along with the
output of the ANN and listing of the identified odorant.
The final step in the process is sensor cleansing. For tinoxide sensors an oxidation process does this. The
sampling box is opened to outside air removing the
volatiles. Heaters within the sensors aid in the oxidation
process which usually lasts 30-60 seconds. This process is
longer when high concentrations are used. Although
useful in demonstration systems and for specific
applications, tin-oxide sensors are limited and not
recommended for general-purpose odor identification.
7. Conclusion
The electronic nose is a prime example of a biologically
inspired device and a successful application of artificial
neural network technology. Many rudimentary concepts
from biological olfaction including the sniffing, chemical
detection, and odor recognition processes are mimicked by
electronic noses. The first generation of electronic noses,
including our prototype and the current commercial
systems, is useful in specific odor applications such as
detection of food spoilage and specific odors in controlled
environments. Future generations of electronic noses that
incorporate more sophisticated models of the biological
olfactory system (i.e., neuromorphic) will be more flexible,
be able to work in less controlled environments, and be
able to detect and analyze a wide variety of odors.
Acknowledgements
The development of cognitive systems was supported by
an internal core technology investment by Battelle
Memorial Institute. The prototype work with electronic
noses was supported by the Laboratory Directed Research
and Development program at Pacific Northwest National
Laboratory (PNNL). PNNL is a multiprogram national
laboratory operated by Battelle for the U.S. Department of
Energy under Contract DE-AC06-76RLO 1830.
Information about artificial neural network developments
at Battelle and the Pacific Northwest National Laboratory
is available on the World Wide Web at:
http ://www.em sl .pn 1.gov :2080Iproj/neuron/neural/
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