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
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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
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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
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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
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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.
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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
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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.
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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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