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An electronic nose is a device that mimics the human olfactory sensory system . It identifies and detects the specific components of an odor or Vapours and analyzes its chemical makeup to identify it. An electronic nose consists of a mechanism for chemical detection, such as an array of electronic sensors, and a mechanism for pattern recognition, such as a neural network. In this paper, we briefly describe an electronic nose, and discuss applications of electronic noses in the environmental, medical,plant diagnosis and food industries.
The earliest work on the development of an instrument to detect odors is dedicated
to Moncrieff in 1961. This was really a mechanical nose and the first electronic noses were reported by Wilkens and Hatman in 1964 ,Dravieks and Trotter (modulation of contact potential by odorants), in 1965. In 1982, however, the concept of electronic nose as an intelligent chemical array sensor system was presented by Persaud and Dodd of the Warwick Olfaction Research Group. The expression 'Electronic Nose' (EN),however, appeared for the first time in 1988 and Gardner and Bartlett (1992) give the following definition -
"An electronic nose is an instrument which comprises an array of electronic
chemical sensors with partial specificity and an appropriate pattern recognition system,capable of recognizing simple and complex odors".
Current research is undertaken by an interdisciplinary collaboration between the Sensors Research Laboratory led by Prof. Julian Gardner in the Centre for Nanotechnology & Microengineering and the Electrochemical Group led by Prof. Philip Bartlett at the University of Southampton.
Electronic Nose is a smart instrument that is designed to detect and discriminate among complex
odours using an array of sensors. The array of sensors consists of a number of broadly tuned (nonspecific) sensors that are treated with a variety of odour-sensitive biological or chemical materials.This instrument provides a rapid, simple and non-invasive sampling technique, for the detection and identification of a range of volatile compounds . Several types of sensory material are currently used in artificial nose technology such as metal oxide, conductive polymers, piezoelectric crystal and fibre optics .An odour stimulus generates a characteristic fingerprint from this array of sensors. Patterns or fingerprints from known odours are used to construct a database and train a pattern recognition system so that unknown odours can subsequently be classified and/or identified .
Typically an electronic nose consists of two main components the sensing system and the automated pattern recognition system(Neural network). The sensing system can be an array of several different sensing elements (i.e. chemical sensors), where each element measures a different property of the sensed chemical, or it can be a single sensing device (e.g., spectrometer) that produces an array of measurements for each chemical, or it can be a combination of both. Each chemical vapor presented to the sensor array produces a signature or pattern characteristic of the vapor. By presenting many different chemicals to the sensor array, a database of signatures is built up. This database of labeled signatures is used to configure the pattern recognition system. The goal of this configuration is to train the recognition system to produce unique classifications of each chemical so that an automated identification can be implemented.One approach to chemical vapor identification is to build an array of sensors, where each sensor in the array is designed to respond to a specific chemical. With this approach, the number of unique sensors must be at least as great as the number of chemicals being monitored. It is both expensive and difficult to build highly selective chemical sensors.
Artificial neural networks (ANNs), which have been used to analyze complex data and to recognize patterns, are showing promising results in chemical vapor recognition. When an ANN is combined with a sensor array, the number of detectable chemicals is generally greater than the number of sensors. Also, less selective sensors which are generally less expensive can be used with this approach. Once the ANN is trained for chemical vapor recognition, operation consists of propagating the sensor data through the network.
Following figure illustrates the basic structure of an electronic nose.
Figure.1 Simple structure of e-nose
COMPONENTS OF E-NOSE:
The Sensing system of an electronic nose consists of sensor array. sensor array in an electronic nose performs very similar functions to the olfactory nerves in the human olfactory system. Thus, the sensor array may be considered the heart and most important component of the electronic nose. The instrument is completed by interfacing with the computer central processing unit (CPU), recognition library and recognition software that serve as the brain to process input data from the sensor array for subsequent data analysis.
A good sensor should fulfill a number of criteria. First, the sensor should have highest sensitivity to the target group of chemical compound(s) intended for detection and with a threshold of detection similar to that of the human nose. Following are the different chemical sensors used in electronic nose.
Metal oxide sensors (MOS)
A Metal oxide semiconductor can be used as sensors by observing the electrical-resistance changes that occur when vapors are adsorbed onto a semiconductor surface (Persaud and Dodd, 1982).Sensors are typically prepared by depositing a thin porous film of a metal-oxide
material (usually tin oxide) onto an electrically heated ceramic pellet and annealing at
high temperatures . Oxygen in the air adsorbs onto the sensor surface, removing electrons from the conduction band of the semiconductor, thereby increasing its electrical resistance. The interaction of reducing gases with the surfaceadsorbed oxygen decreases this electron trapping, leading to characteristic increases in electrical conductance of the sensor. In order to reduce response and recovery times, metal-oxide sensors are typically run at elevated temperatures (up to 400 OC).
Metal-oxide sensors have very high sensitivity and respond to oxidizing compounds (zinc-oxide, tin-dioxide, titanium-dioxide, iron oxide) and some reducing compounds, mainly nickel-oxide or cobalt-oxide .
2. Piezoelectric-based Surface Acoustic Wave Devices(SAW)
The SAW device is made of a relatively thick plate of piezoelectric materials (ZnO and
lithium niobate, etc.) with interdigitated electrodes to excite the oscillation of the surface
wave. The SAW is stimulated by applying an alternating current (AC) voltage to the
fingers of the interdigitated electrode to lead to a deformation of the piezoelectric
crystal surface. The SAW devices are usually operated in one of two configurations
such as a delay line and a resonator. In common gas sensors using SAW device with a
dual delay line structure, one arm of the delay line is coated with the sorbent
membrane, the other acts as a reference to reduce the change of environmental
conditions such as temperature drift and other effects. In the resonator configuration
the same electrode pair acts as transmitter and receiver, with the surface acoustic
wave being reflected back to the electrodes by a groove or ridge formed on the crystal
surface. In both cases, the propagation of SAW is affected by changes in the properties
of the piezoelectric crystal surface and this is exploited in gas sensing application.
Optical sensor systems are somewhat more complex than typical sensor-array systems having
transduction mechanisms based on changes in electrical resistance. Optical sensors work by means of light modulation measurements and consist of an assortment of technologies ranging from diverse light sources with optical fibers to various photodiode and light-sensitive photodetectors. Various operational modes have been developed that measure changes in absorbance, fluorescence, light polarization, optical layer thickness, or colorimetric dye response. The simplest optic sensors use color- changing indicators, such as metalloporphyrins, to measure absorbance with a LED and photodetector system upon exposure to gas analytes.
Two specialized types of optical sensors are the colorimetric and fluorescence sensors. Colorimetric sensors use thin films of chemically-responsive dyes as a colorimetric sensor array. Fluorescence sensors detect fluorescent light emissions from the gas analyte at a lower wavelength and are more sensitive than colorimetric sensor arrays.
4. Conducting polymer sensors
Conducting polymer sensors are another type of sensors. A conductive polymer (CP) sensor has a semi-conducting polymer film coated to adsorb specific species of molecules. When chemical vapours come into contact with the absorbent, the chemicals absorb into the polymers, causing them to swell. The swelling changes the resistance of the electrode, which can be measured and recorded. The amount of swelling corresponds to the concentration of the chemical vapour in contact with the absorbent. The process is reversible, but some hysteresis can occur when exposed to high concentrations.
Compared with metal oxides, organic polymers are much more diverse and can impart
a wide variety of functionalities to sensors. In the case of conducting polymers, the
molecular-interaction capabilities of a polymer can be selectively modified by
incorporating different counterions during polymer preparation or by attaching
functional groups to the polymer backbone . Another advantage of conducting polymers is that they operate at room temperatures
5. Mass selective sensors
Mass selective sensors use the proven technology of mass spectrometers. The principle of the mass spectrometer is well known for detection of chemicals in the vapour phase. Sampled gas mixtures are ionised, and charged molecular fragments are produced. These fragments are sorted in a mass filter according to their mass to charge ratio. The ions are detected as electrical signals with an electron multiplier or a Faraday plate. Mass selective sensors record without previous separation the total ion current over a defined period of time. Most commercial instruments use a quadrupole mass spectrometer. The mass range is typically from 1 to 200 amu.
Mass selective sensors seems in particular to be advantageous when dominant matrices, e.g. water or alcohol, have to be analyzed. In such a case the corresponding peak(s) can be eliminated and thus the significance of the remaining mass peaks is increased. Mass selective sensors are also favorable when quantitative information about specific compounds e.g. pollutants or well defined off-odors are required.
Apart from other technical requirements the reproducibility of the results is also influenced by a representative sampling procedure. The concentration of volatiles in a headspace depends on several factors. In order to yield constant partial gas pressure and thus constant results of repetitive measurements all these factors have to maintain constant. The main factors as known from headspace GC are:
Type of carrier gas, its quality and relative humidity
Pressure of carrier gas
Measurements can be performed by either bringing the sensoric part of an electronic nose in the atmosphere which has to be analyzed or by transferring a representative fraction of the headspace to the sensor array. In this case again two opportunities can be distinguished: the static method uses a fixed sample volume which is brought to the sensor and remains there statically during a measuring cycle. Alternatively, a constant stream of the headspace is drawn across the sensor during a complete measuring cycle.
Sensitivity can be increased by several techniques, e.g. purge and trap technology, which is commonly known from gas chromatography.
3.Artificial neural network
The Artificial neural network (ANN) in the best known and most evolved analysis techniques
utilized in statistical software packages for commercially available electronic noses.
ANN is an information processing System that was inspired by the way the biological nervous systems, such as the brain, processes the information. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems . ANNs are like people which learn by example. An ANN is configured for an application such identifying chemical vapours through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well. For the electronic nose, the ANN learns to identify the various chemicals or odors by example.
Figure.1 shows the structure of an artificial neural network. It consists of three interconnected layers of neurons. The computing neurons (hidden and output layers) have a non-linear transfer function.
Figure.2 Structure of neurons
The basic unit of an artificial neural network is the neuron. Each neuron receives a number of inputs, multiplies the inputs by individual weights, sums the weighted inputs, and passes the sum through a transfer function, which can be, e.g., linear or sigmoid (linear for values close to zero, flattening out for large positive or negative values). An ANN is an interconnected network of neurons. The input layer has one neuron for each of the sensor signals, while the output layer has one neuron for each of the different sample properties that should be predicted. Usually, one hidden layer with a variable number of neurons is placed between the input and output layer. During the ANN training phase, the weights and transfer function parameters in the ANN are adjusted such that the calculated output values for a set of input values are as close as possible to the known true values of the sample properties. The model estimation is more complex than for a linear regression model due to the non-linearity of the model. The model adaptation is made using the so-called back-propagation algorithm involving gradient search methods, where each weight is changed in proportion to the error it is causing.
1. Electronic Noses for Medicine
In many cases, infection with microorganisms produces a change on the smell of person, which can be specially noticeable on the breath, in the urine or the stool, such changes have been commonly used as an aid to diagnosis of disease and some countries smelling the patient or the body fluids of patient was,and still is, an important tool in diagnosis .The diagnosis power of odour in medicine is vary old practice which in being rediscovered due to new advances in gas sensor technology and artificial intelligence. Several diseases have been noted in the past to produce odour or volatiles characteristic of the disease state. Intelligence gas sensor technology has been applied in several areas of clinical practice, from bacteria detection UTI, Mycobacterium tuberculosis (TB) and gastric diagnosis ,as well as, detection of certain bacterial pathogen infections in clinical specimens such as vaginal fluids, urine and leg ulcer specimens .
Electronic Noses for the Food Industry
Currently, the biggest market for electronic noses is the food industry. Applications of electronic noses in the food industry include quality assessment in food production, inspection of food quality by odour, control of food cooking processes, inspection of fish, monitoring the fermentation process, checking rancidity of mayonnaise, verifying if orange juice is natural, monitoring food and beverage odours , grading whiskey, inspection of beverage containers, checking plastic wrap for containment of onion odour, and automated flavor control to name a few. In the food-processing industry quality assurance systems need to be rapid and range from organoleptic measurement to microbiological surveys. Generally, qualitative assessment of food spoilage is made by human sensory panels that evaluate air samples and discriminate which food products are good or unacceptable. Bacterial contamination of food and drinks can generate unpleasant odours and toxic substances. Therefore, different industries are interested in the application of the electronic nose both for monitoring of storage quality degradation and for detecting microbial contaminants . Early detection of milk spoilage as well as different concentrations of spoilage bacteria and yeasts was also investigated. The results of these studies showed that, using an electronic nose system, it could distinguish between volatile
profiles of different species inoculated in milk-based media after two and five hours of incubation . As well as bacteria many fungal species were described to play an important role in the degradation of foodstuffs. Different species have been isolated from food and some studies have been performed on different fungal species isolated from cereal grain and mouldy bread. Electronic nose was used for detection of these contaminations in many cases. In some instances electronic noses can be used to augment or replace panels of human experts. In other cases, electronic noses can be used to reduce the amount of analytical chemistry that is performed in food production especially when qualitative results will do.
3. Electronic Noses for Plant Disease Diagnosis
Electronic nose is a rapid,sensitive, and specific technique which could be utilized for detection and identification of plant pathogenic bacteria in plant diagnostic clinics and quarantine laboratories.The discrimination of seven species of plant pathogenic bacteria (Acidovorax avenae subsp. citrulli, Agrobacterium tumefaciens, Clavibacter michiganensis subsp. michiganensis, Erwinia amylovora, Pseudomonas syringae pv. tomato, Ralstonia solanacearum, and Xanthomonas campestris pv. vesicatoria) by measuring the volatile compounds produced from pure cultures has been performed using an Electronic nose and Discriminant Function Analysis [. Many microbes have effects on forest health and ecosystem functions because they include causal agents of tree mortality, forest diseases, wood decay and lumber defects of importance in ecosystem and timber management, and in the manufacture of forest products. Within the field of forest pest management, electronic nose has proven useful in detection of bacterial wet wood infections in cottonwood, the detection and identification of fungal forest pathogens (e.g. Ceratocystis fagacearum), and the discrimination of wood decay fungi in wood samples . Some Fungi such as Aspergillus species is one of the most important factors that influence deterioration of library and museum materials. Electronic nose was used for detection and differentiation ox xerophilic Aspergillus/Eurotiom species on different types of paper samples in library, as well as for detection of the growth of moulds in library, archives and museum.
4.Electronic Noses for Environmental Monitoring
Environmental applications of electronic noses include analysis of fuel mixtures , detection oil leaks , testing ground water for odors,identification of household odors . Potential applications include identification of toxic wastes, air quality monitoring, and monitoring factory emissions.
Chemical industries should be able to use these handheld detectors to easily pinpoint the locations of odors. Because current leak detection and monitoring techniques are resource intensive and cumbersome, leading chemical companies are presently evaluating the technology for use in the development of products to detect leaks in pipelines and storage containers.
Opportunities for electronic-nose technology applications are creating much interest in the areas of environmental surveillance. Toxic spills could be identified, as well as levels of air pollution. Spotting explosives and counterfeit drugs or products could be made easier.
The E-nose is a prime example of a successful application of artificial neural network. It uses many of the concepts from biological olfaction including the sniffing, chemical detection, and odor recognition processes. This technology provides a lot of advantages and so in future the traditional job of human nose can be done by Electronic nose. In particular the possibility to evaluate odors objectively without getting tired is a great step forward. Thus, this new type of instrumentation should steadily open new fields of application in all fields.
The development of electronic-nose technology and its applications has generated tremendous interest in different fields. The devices may become just as ubiquitous as the mobile phones they resemble.