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 components to identify it. An electronic nose consists of a mechanism for detection of chemicals, 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 done by
Moncrieff in 1961. This was really a mechanical nose. The first electronic nose was reported by Wilkens and Hatman in 1964 . 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 -
Get your grade
or your money back
using our Essay Writing Service!
"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 differentiate among complex
odours using an array of sensors. The array of sensors consists of a number of different (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 sample from this array of sensors. Patterns or samples 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 .
An E-nose basically consists of two main components the sensing system and the automated pattern recognition system(Neural network). The sensing system consists of an array of several different sensing elements such as chemical sensors, these sensing element measure the different properties of the sensed chemical. Each chemical vapor presented to the sensor array produces a distinct sample or pattern characteristic of the vapor. By presenting many different chemicals to the sensor array, a database of distinct sample or pattern is built up. This database is then used to configure the pattern recognition system. The main aim of this configuration is to configure the recognition system so that it can produce unique classifications of each chemical automatically.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 equal to the number of chemicals being monitored.
To analyze complex data and to recognize patterns,Artificial Neural Network (ANN)have been used, which 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 Neural 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 the functions similar to the olfactory nerves in the human olfactory system. Thus, the sensor array may be considered the heart of the electronic nose.
Always on Time
Marked to Standard
A good sensor should fulfill a number of criteria. The sensor should have highest sensitivity to the target group of chemical compounds intended for detection. Following are the different chemical sensors used in electronic nose.
Metal oxide sensors (MOS)
A Metal oxide semiconductor sensor is made from a thin film of metal oxide material usually tin oxide.It work by observing the electrical-resistance changes that occur when odorant molecules or vapors are adsorbed onto a semiconductor surface (Persaud and Dodd, 1982).The sensor surface absorbs the oxygen from the air and removes the electrons from the conduction band of the semiconductor (oxidation), 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.
The advantage of this sensor is that they have very high sensitivity and respond to oxidizing compounds like zinc-oxide, tin-dioxide, iron oxide etc and some reducing compounds, mainly nickel-oxide or cobalt-oxide,another advantage is low cost, low response to humidity and electronic simplicity .
2. Piezoelectric-based Surface Acoustic Wave Devices(SAW)
A surface acoustic wave sensor works of the basis of mass change on a piezoelectric crystal to indicate presence and concentration of odorant molecules.These sensors operate at higher frequency of oscillation.It consist of an input transducer,a chemical adsorbent film and an output transducer on piezoelectric quartz substrate.The input transducer launches an acoustic wave which travels through the chemical film and is detected by output transducer.
When the vapours from the sample are absorbed by the coating film then there is a reduction in frequency of the sensor by increasing the mass of the film and changes its elastic properties. The advantage of this sensors include high selectivity,high sensitivity,stability over wide temperature ranges,good reproducibility.
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.these sensors 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.
This Essay is
a Student's Work
This essay has been submitted by a student. This is not an example of the work written by our professional essay writers.Examples of our work
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
Electronic nose can be used as a diagnostic tool in medical field.In case of number of patients the infection with microorganisms produces a change on the smell of patient, which can be specially noticeable on the breath, in the urine or wounds or in the body fluids, can be detected by electronic nose and such changes can be used as an aid to diagnosis of disease.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
Presently the biggest application of electronic noses is in the food industry. In the food industry the electronic noses are used for 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.E-noses can also be used to verify if orange juice is natural, monitoring food and beverage odours , grading whiskey, inspection of beverage containers, checking plastic wrap for containment of onion odour.In food processing industry the qualitative assessment of food spoilage is made by human sensory panels that evaluate air samples and identify which food products are good or unacceptable ,but now a days most industries are interested in using electronic nose in place of human panels. Bacterial contamination of food and drinks can generate unpleasant odours and toxic substances which can also be identified by using E-noses. 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.
3. Electronic Noses for Plant Disease Diagnosis
Electronic nose is a rapid, sensitive, and specific technique which can also be used for detection and identification of plant pathogenic bacteria in plant diagnostic clinics and quarantine laboratories. 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 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
Electronic noses can also be used in different environmental monitoring applications that include analysis of fuel mixtures , detection of 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 find out 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.