enose 3

E-nose

CHAPTER 1

E-NOSE OVERVIEW

Mimicking the nose is a challenging task. The human nose can smell 10,000 different odour

molecules mixed in air. Odour in a substance is due to certain volatile organic compounds

(VOCs), which easily evaporate and get carried by an air stream. An e-nose can smell and

estimate odours quickly though it has little or no resemblance to the human nose.A human nose

has receptors, which serve as binding sites for VOCs. A receptor is just a molecular structure on

the surface of the nerve cell to which an odorous molecule with the right shape binds. The

receptor and the binding molecule fit exactly as in a key and lock arrangement. These odour-

sensing nerve cells line the upper part of the cavity in the human nose.

Once an odour molecule binds to a receptor, a chain reaction follows which ultimately transmits

an electrical signal to the brain. A specific odour of coffee or wine is usually caused not by one,

but a mixture of hundreds of organic compounds. So, the brain has a mammoth task of

processing signals received from the nerve cells originating from the nose, to identify the nature

of smell. The exact working of the brain in processing these signals is yet to be fully understood.

An electronic nose can be defined as ‘an instrument which is comprised of an array of electronic

chemical sensors with partial specificity and an appropriate pattern recognition system, capable

of recognizing simple or complex odours (and other gaseous mixtures). The ability of an

electronic nose to rapidly discriminate between slight variations in complex mixtures makes the

techniques ideal for on-line process diagnostics and screening across a wide range of application

areas. An electronic nose is a machine that is designed to detect and discriminate among complex

odours using a sensor array. The sensor array of consists of broadly tuned (non-specific) sensors

that are treated with a variety of odour-sensitive biological or chemical materials. An odour

stimulus generates a characteristic fingerprint (or smell-print) from the sensor array. 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 identified. Thus, electronic

nose instruments are comprised of hardware components to collect and transport odours to the

sensor array – as well as electronic circuitry to digitize and stored the sensor responses for signal

processing. The two main components of an electronic nose are the sensing system and the

Dept. of Electronics and Communication, KITE, Jaipur

E-nose

automated pattern recognition system. The sensing system can be an array of several different

sensing elements (e.g., 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. Each chemical vapour

presented to the sensor array produces a signature or pattern characteristic of the vapour. By

presenting many different chemicals to the sensor array, a database of signatures is built up. This

database of labeled signatures is used to train the pattern recognition system.

The goal of this training process is to configure the recognition system to produce unique

classifications of each chemical so that an automated identification can be implemented. The

quantity and complexity of the data collected by sensors array can make conventional chemical

analysis of data in an automated fashion difficult. One approach to chemical vapour

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 vapour 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 vapour recognition, operation

consists of propagating the sensor data through the network. Since this is simply a series of

vector-matrix multiplications, unknown chemicals can be rapidly identified in the field.

Electronic noses that incorporate ANNs have been demonstrated in various applications. Some

of these applications will be discussed later in the paper. Many ANN configurations and training


Fig 1:Schematic diagram of an E-nose

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algorithms have been used to build electronic noses including back propagation-trained, feed-

forward networks; fuzzy ART maps; Cohune’s self-organizing maps (SOMs);learning vector

quantizers (LVQs); Hamming networks; Boltzmann machines; and Hopfield networks.

One of our prototype electronic noses, shown in Figure is composed of an array of nine tin oxide

vapor sensors, a humidity sensor, and a temperature sensor coupled with an ANN. Two types of

ANNs were constructed for this prototype: the standard multilayer feed- forward network trained

with the back propagation algorithm and the fuzzy ART map algorithm . During operation a

chemical vapor is blown across the array, the sensor signals are digitized and fed into the

computer, and the ANN (implemented in software) then identifies the chemical. This

identification time is limited only by the response time of the chemical sensors, which is on the

order of seconds. This prototype nose has been used to identify common household chemicals by

their odor .

Although each sensor is designed for a specific chemical, each responds to a wide variety of

chemicals. Collectively, these sensors respond with unique signatures (patterns) to different

chemicals. During the training process, various chemicals with known mixtures are presented to

the system. By training on samples of various chemicals, the ANN learns to recognize the

different chemicals. This prototype nose has been tested on a variety of household and office

supply chemicals including acetone, ammonia, ethanol, glass cleaner, contact cement, correction

fluid, iso-propanol,lighter fluid, methanol, rubber cement and vinegar. For the results shown in

the paper, five of these chemicals were used: acetone, ammonia, isopropanol, lighter fluid, and

vinegar. Another category, “none” was used to denote the absence of all chemicals except those

normally found in the air which resulted in six output categories from the ANN.


Fig 2: Sensor input and output

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Both networks were trained using randomly selected sample sensor inputs. The ANNs used here

were not trained to quantify the concentration level of the identified analytes,but were trained

with samples with different concentrations of the analytes. This allowed the ANN to generalize

well on the test data set. Performance levels of the two networks were basically equivalent

ranging from 89.7% to 98.2% correct identification on the test set depending on the random

selection of training patterns. Figures 4 and 5 illustrate the responses of the sensors and the ANN

classification for a variety of test chemicals presented to the ANNs. The ANN was able to

correctly classify the test samples with only small residual errors. While the ANN used here was

not trained to quantify the concentration level of the identified analytes, it was trained with

samples with different concentrations of the analytes. This allowed the ANN to generalize well

on the test data set. From the responses of the sensors to the analytes, one can easily see that the

individual sensors in the array are not selective. In addition, when a mixture of two or more

chemicals is presented to the sensor array, the resultant pattern (sensor values) may be even

harder to analyze . Thus, analyzing the sensor responses separately may not be adequate to yield

the classification accuracy achieved by analyzing the data in parallel


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CHAPTER 2

ODOUR SENSING

2.1 Sensing an Odorant

In a typical e-nose, an air sample is pulled by a vacuum pump through a tube into a small

chamber housing the electronic sensor array. The tube may be of plastic or stainless steel. A

sample-handling unit exposes the sensors to the odorant, producing a transient response as the

VOCs interact with the active material. The sensor response is recorded and delivered to the

signal-processing unit. Then a washing gas such as alcohol is applied to the array for a few

seconds or a minute, so as to remove the odorant mixture from the active material.

Fig 3: Block Diagram of E-nose

Finally, the reference gas is again applied to the array, to prepare it for a new measurement cycle.

The odorant is applied for a period equal to the response time of the sensor array. The washing

and reference gases are applied for the sensor array to recover and come to a reference point.

This duration is termed the recovery time.

2.2 Steps of odor recognition

The main steps of odor recognition can be briefly explained as follows:

• Heating the sample for a certain time generates the “smell”.

• The gas phase is sampled and transferred to a detection device which reacts to the

presence of various molecules.

• The difference in the sensor reactions is revealed using different statistical pattern

recognition techniques to classify the odors. From this pattern and from previous

human input (human training from sensory panels), the system predicts the mostly


Sample Handler

Arrays of gas sensors

Signal Processing system

Input output

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likely human response to the new pattern.

The electronic nose gives either a simple answer like “recognized”, “good”, or “bad” or a more

sophisticated response such as an odor intensity or a molecule concentration The terminology

can be simple and qualitative or more specific and quantitative.

2.3 Gas Sensors

The main advantages of the gas sensors are as follows.

1. High Speed

2. Reliable

3. Continuous real time monitoring of sites, etc.

The problems associated with human panels are individual variation, adaptation, fatigue,

infectious mental state, subjectivity and exposure to hazardous compounds. So, the e- nose can

create an odour profile that extends beyond the capabilities of the human panel or GC/MS

measurement techniques. The output of the e-nose can be the identity of the odorant, an estimate

concentration of the odorant or the characteristic properties of the odour as might be perceived

by a human.

Fundamental to the artificial nose is the idea that each sensor in the array has different

sensitivity. Also, the pattern of responses across all sensors in the array is used to identify and/or

characterize the odour.

CHAPTER 3


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INTRODUCTION TO SENSORS

3.1 SENSOR

A sensor is a device which can respond to some properties of the environment and transform the

response into an electric signal. The general working mechanism of a sensor is illustrated by the

following scheme :

Fig 4: Working mechanism of sensor

In the field of sensors, the correct definition of parameters is of paramount importance because

these parameters :allow the diffusion of more reliable information among researchers or sensor

operators, allow a better comprehension of the intrinsic behaviour of the sensors help to propose

new standards, give fundamental criteria for a sound evaluation of different sensor performances.

Response curves and sensitivity

Output signal:

The output signal is the response of the sensor when the sensitive material undergoes

modifications, in the following pattern:


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SENSITIVE MATERIAL (M)- TRANSDUCER- SENSOR (Vout)

Different types of response curves exist. The linear response is the easiest one; it is characterized

by the following equation Vout = aM + b. The other response is a non linear one; its equation is

Vout = f(M).

Output noise:

Noise measurement must be done if one wants to have an accurate definition of the sensor. The

noise is the output signal when the sensor does not measure any variations of the sensitive

material. The noise depends on the frequency (cf. graph). If we consider two sensors with the

same output noise but a different sensitivity (cf. graph), we can underline two statements:

Statement 1: the sensor which has the greatest sensitivity allows the detection of a lower M

Level.

Statement 2: the sensor which has the greatest sensitivity allows a better resolution with respect

to the other.


Fig 5: Response curve for Linear and Non linear sensor

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Resolution

The resolution is the measurement level which gives, at the output, a signal to noise ratio S/N)

Equal to 1.

In practice,(S/N) =3 or (S/N) = 9

We must distinguish between the resolution at the minimum M value and the resolution

elsewhere on the response curve .Moreover, it is essential to consider that the resolution value

follows the working point along the response curve and the boundary conditions.

Selectivity / Contents

The selectivity is the capacity of a sensor to be sensitive to a specific compound. The artificial

sensing techniques are often based on sensor arrays (electronic tongue and electronic nose, for

instance). In those cases, using less selective sensors is more interesting because one can detect a

larger field of compounds.

Reversibility / Contents


Fig 6: Sensor selection on the basis of selectivity and chemical species

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The reversibility is the aptitude of the sensing mechanism to follow (of course with a given

delay) the variation of the environment. It means that initial conditions must be obtained when

the input reaches initial values.

In pratice, reversibility is a requirement for continuous monitoring applications (e.g.

inenvironmental applications). However reversibility, as it requires week interactions between

sensors and analytes, can not be compatible with high selectivity which needs strong

interactions. When the sensors are non reversible,we can distinguish between :Regenerable

sensors : the initial conditions can be ripristinated through an additional chemical process

Disposable sensors which can be used only one time (e.g. medical sensors).

3.2 TYPES OF SENSORS

E-nose is classified based on the type of sensors used.

1. Conductivity Sensors

2. Piezoelectric Sensors

3. FET gas Sensors

4. Optical Sensors

5. Spectrometry based sensing methods

3.2.1 Conductivity Sensors


Fig 7: Ideal reversibility curve for sensors

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There are two types of conductivity sensors:

1) metal oxide

2) polymer

both of which exhibit a change in resistance when exposed to volatile organic compounds.

3.2.1.1 Metal Oxide Type


Fig 8: Conductivity Sensor

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3.2.1.1.1Working principle

These sensors are made of a ceramic former heated by a heating wire and coated by a

semiconducting film. These semiconductor sensors can sense gases by monitoring changes in the

conductance during the interaction of a chemically sensitive material with molecules that need to

be detected in the gas phase.

They are used to detect toxic and flammable gases in domestic and environmental applications

and for food aromas.

3.2.1.1.2 How to increase selectivity?

The metal oxides are generally less selective than many other sensor technologies. Selectivity

may be obtained using several methods:

• use of filters

• deposition of a suitable catalyst layer

• pulsing of sensor temperature in working conditions

• use of other semiconducting metal oxides

• control of the grain size

Preparation techniques for gas sensors

3.2.1.1.3 Metal oxide gas sensors can be subdivided into:


Fig 9: Metal Oxide Sensor

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Thick film devices (depositing a paste of material between two electrodes)

Advantages

easier to produce

Disadvantages

poor selectivity

depend on ambient

temperatures and relative

humidities

long stabilizing times after energization

large power consumption

Thin film devices: they use vapor deposition technologies in order to obtain a very thin film of

metal oxide between two electrodes.

Advantages

significantly higher sensitivity

lower power consuption per device

Disadvantages

more expensive

more difficult to produce

instable

Different deposition methods like PVD ( sputtering, thermal evaporation ... ), spray and sol-gel

techniques can be used for the preparation of thin film gas sensors.A new method called RGTO

enables to prepare mixed oxide thin films with high surface area and nanosized crystallites.These

sensors are manufactured by, among others, the Japonese company FIGARO.

Advantages and disadvantages of metal oxide semiconductor


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Advantages

• they are available because they are commercially produced

• they have high sensitivities to a range of organic vapors

• a variety of different types are available with broadly different sensitivities so that an

array can be constructed

• they are characterized by a relatively fast response, typically less than 10 seconds

Disadvantages

• their size

• they operate at elevated temperatures

• they are highly sensitive to compounds such as ethanol, CO2 or humidity

Typical offerings include oxides of tin, zinc, titanium, tungsten and iridium, doped with a noble

metal catalyst such as platinum or palladium, which operates at 200°C to 400°C.As a VOC

passes over the doped oxide material, the resistance between the two metal contacts changes in

proportion to the concentration of the VOC.

Advantages: Wide availability and low cost.

Disadvantages: These are prone to drift over periods of hours to days. So signal-processing

algorithm should be employed to counteract this property. These sensors are also susceptible to

irreversible binding by sulphur compounds.

Applications: The sensitivity ranges from 5 to 500 ppm. Used for sensing CO, NH3, or H2O.

3.2.2 Polymer Sensors


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Here the active material is a conducting polymer from such families as the polypyroles,

thiophenes, indoles or furans. Changes in the conductivity of these materials occur as they are

exposed to various types of chemicals, which bond with the polymer backbone. A given

compound’s affinity for a polymer and its effect on the polymer’s conductivity are strongly

influenced by the counter ions and functional group attached to the polymer backbone. Here the

response time is inversely proportional to the polymer’s thickness, which is usually in the range

of 10 to 20µm.

Advantages

The sensitivity varies from 0.1 ppm to 100 ppm.

No need of heaters, as they will operate in the ambient temperatures.

High portability.

Disadvantages

They are difficult to make.

Their responses also drift over time.

More susceptible to humidity

3.2.3 Piezoelectric Sensors


Fig 10: Metal Oxide Sensor

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These are of two types – QCM (Quartz Crystal Microbalance) and SAW (Surface Acoustic

wave devices). Here they are configured as mass change sensing devices.

3.2.3.1 QCM Type

It consists of resonating disc with metal electrodes on each side connected to read wire. The

device resonates at a characteristic frequency (10 to 30 MHz), when excited with an oscillating

signal. During manufacturing, a polymer coating is applied to the disc to serve as the active

sensing material. In operation, a gas sample is adsorbed at the surface of the polymer, increasing

the mass of the disk polymer device and thereby reducing the resonance frequency. The

reduction is inversely proportional to the odorant mass adsorbed by the polymer when the sensor

is exposed to a reference gas. The resonance frequency returns to the baseline value.

Advantages

High sensitivity

Remarkably linear over a wide dynamic range.

Sensitivity does not change with temperature.

Humidity response will depend upon the type of adsorbent material used.

Batch to batch variability is not a problem as a differential change measurement of

frequency change will remove the common mode noise.


Fig 11: Quartz Microbalance

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Disadvantages

When dimensions are scaled down to micrometer level the surface to volume ration will

increase and so the noise to signal ratio also will increase.

3.2.3.2 SAW Type

Here a surface wave travels over the surface of the device. So sensors operate at much higher

frequency and so can generate a large change in frequency. A typical SAW device operates in

hundreds of Megahertz, while 10 MHz is more typical for a QCM. But SAW devices can

measure changes in mass to the same order of magnitude as QCMs.

3.2.4 FET gas Sensors

The FET is a “metal "/ insulator / semiconductor structure in which the gate (the "metal ") can be

any conducting layer or medium. The FET is a semiconducting device which acts as an amplifier

(like a transistor). There are different FET configurations:

• The MOSFET: metal oxide semiconductor FET

• The SGFET: fabrication of a Suspended Gate on a metal-oxide-semiconductor

• The ISFET: ion sensitive FET

Advantages and disadvantages of the MOSFET

Advantages

• High sensitivity

• Small size

• Low cost

Disadvantages

The reproducibility and the sensibility of the sensor are not sufficient enough to use it in a real


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measuring system, particularly for a multiple-component gas mixture. These are based on the

principle that VOCs in contact with a catalytic metal can produce a reaction in the metal. The

reaction products can diffuse through the gate of a MOSFET to change the electrical properties

of the device. The sensitivity and selectivity of the device can be optimized by ranging the type

and thickness of the metal catalyst and operating them at different temperatures.

3.2.5 Optical Fibre Sensors

These utilise glass fibres with a thin chemically active material coating on their sides or ends. A

light source at a single frequency is used to interrogate the active materials, which responds with

the change in colour to the presence of VOCs.

The active material contains chemically active fluorescent dyes immobilized in an organic

polymer matrix. As VOCs interact with it, the polarity of the fluorescent emission spectrum.

Advantages

Cheap and easy to fabricate.

Arrays of fibre sensors have wide range of sensitivities.

Differential measurement is possible to avoid common mode noise.

Disadvantages

Complexity of the measuring system.

Limited lifetime to photo bleaching.

3.2.6 Spectrometry Based Sensors

Here a vapour trap is used to concentrate the VOCs and then it being injected into a spectrometer

that generates a spectral response characteristic of the vapour. Then the efficient signal

processing technique can be used for finding out the odorant. Here the disadvantage is that is the

use of highly complex electronic measuring device.

3.2.7 Potentiometric Chemical Sensors


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Potentiometric Chemical Sensors are based on the measurement of a potential under no current

flow. The measured potential may then be used to determine the analytical concentration of some

components of the analyte solution. For useful definitions please go to electrochemical terms and

concepts. There exist different types of potentiometric chemical sensors. A classification shows

the binding between them. This web-page will only develop the ion selective sensors (ISE) and

the biosensors.

3.2.7.1 Ion selective sensors (ISE)

An ISE produces a potential which is proportional to the concentration of an analyte. Making

measurements with an ISE is therefore a form of potentiometry. The most common ISE is the pH

electrode, which contains a thin glass membrane that responds to the H+ concentration in a

solution. Ion selective sensors are susceptible to several interferences. Samples and standards are

therefore diluted 1:1 with total ionic strength adjuster and buffer (TISAB).

The instrumentation of an ISE consists of the ion-selective membrane, an internal reference

electrode, an external reference electrode, and a voltmeter. Different sorts of ion selective

membranes exist: the glass, the chalcogenide and the crystal membrane. Research currently

focuses on chalcogenide membranes.

3.2.7.2 Biosensors

The principle: coupling enzymes and electrode reactions.


Fig 12: Biosensor

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The enzyme is used as a bio electro catalyst of the oxidation or reduction of a substrate. There

are 2 different ways of coupling enzymes and electrode reactions: with mediator or Mediator

less.

Example: measurement of the concentration of glucose.

Advantages and disadvantages of potentiometric chemical sensors

Advantages

• A wide range of available sensing materials and sensors.

• Wide variations of sensor properties, some unique features

• A wide knowledge about composition / properties relationship

• Simple installation. Easy, direct measurements.

• Different configurations (static, flow, bulk, micro).

• Easy applicability for automatic and / or industrial analysis.

• Low cost.

Disadvantages

• Insufficient selectivity of many sensors.

• The number of available sensors is still smaller than the number of analytes.

CHAPTER 4


E-nose

SIGNAL PROCESSING AND PATTERN RECOGNITION

The task of an electronic nose is to identify an odorant sample and perhaps to estimate its

concentration.The means are signal processing and pattern recognition. For an electronic nose

system this two steps may be subdivided into four sequential stages. They are pre-processing,

feature extraction, classification and decision-making. But first a data base of the expected

odorant must be compiled, and sample must be presented to the nose’s sensor array.

Preprocessing compensates for sensor drift compress the transient response of the sensor array,

and reduces sample to sample variations. Typical techniques are manipulation of sensor base

lines, normalization of sensor response ranges of all the sensors in an array (the normalization

constant may sometimes be used to estimate the odorant concentration), and compression sensor

transients.

Feature extraction has two purposes; they are to reduce the dimensionality of the measurement

space, and to extract information relevant for pattern recognition. To illustrate, in an electronic

nose with 32 sensors, the Odor class (confidence level) Feature vector Normalized measurements

Raw measurements Decision Making Classification Feature extraction preprocessing Sensor

array Feedback/adaptation measurement space has 32 dimensions. This space can cause

statistical problem if odor database contains only a few examples, typical in pattern recognition

applications because of the cost of data collection. Further more, since the sensors have

overlapping sensitivities there is high degree of redundancy in these 32 dimensions. Accordingly


Fig 13: Signal processing and pattern recognition process

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is it convenient to project the 32 on to a few informative and independent axes. This low

dimensional projection (typically 2 or 3 axes) has the added advantage that it can be more readily

inspected visually.

Feature extraction is generally performed with linear transformations such as the classical

principal component analyses (PCA) and linear discriminate analysis (LDA). PCA finds

projections of maximum variance and is the most widely used linear feature extraction

techniques. But it is not optimal for classification since it ignores the identity (class label) of the

odor examples in the database.

LDA, on the other hand, looks at the class label of each example. Its goal is to find projections

that maximize the distance between examples from different odorants yet minimize the distance

between examples of the same odorant. As in example, PCA may do better with a projection that

contain high variance random noise whereas LDA may do better with a projection that contain

subtle but maybe crucial, odor discriminatory information. LDA is therefore more appropriate

for classification purposes.

Several research groups have recently adopted some nonlinear transforms, such as Sammon

nonlinear maps and Kohonen self-organizing maps. Sammon maps attempt to find a 2D or 3D

mapping the preserves the distance between pairs of examples on the original 32 dimensional

space. Kohonen maps project the 32 dimensional space onto a two dimensional mesh of

processing elements called neurons.

Neighboring neurons are trained to respond to similar types of stimuli (odorants), a self-

organizing behavior motivated by neuro biological considerations. Neither of these techniques

makes use of class labels, so they are not optimal for pattern classification. Once the odor

examples have been projected on an appropriate low dimensional space the classification stage

can be trained to identify the patterns that are representative of each odor, when presented with

an unidentified odor, the classification stage will be able to assign to it a class label (identify the

odorant) by comparing its pattern with those complied during training. The classical methods of

performing the classification task are K nearest neighbor (KNN) Bayesian classifiers, and

Artificial Neural Network (ANN).


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An Artificial Neural Network (ANN) is an information- processing paradigm that is inspired by

the way nervous systems, such as the brain, process information. They key elements of this

paradigm are the novel structure of the information processing system. It is composed of a large

number of highly interconnected processing elements (neurons) working in unison to solve

specific problems. ANNs, like people, learn by example. An ANN is configured for a specific

application, such as pattern recognition or data classification 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 chemical or odors by example. A typical ANN classifier consists of two or

more layers of neurons that are connected with synaptic weights-real number multiplier that

connect that output of neuron to the inputs of neurons in the next layer. During the training the

Ann adapts the synaptic weights to learn the pattern of different odorants. After training when

presented with an unidentified odorant the ANN feeds its pattern through the different layers of

neurons and assigns the class label that provides the largest response.

ANNs have been applied to and increasing number of real world problems of considerable

complexity. Their most important advantage is in solving problems that are too complex for

conventional technologies; that is problems that do not have an algorithmic solution or for which

an algorithmic solution is too complex to be found. In general, because of their abstraction from

the biological brain, ANNs are well suited to problems that people are good solving but

computers are not. These problems include pattern recognition and forecasting. However, unlike

the human capability in pattern recognition, The ANNs capability is not affected by factors such

as fatigue, working conditions, emotional state and compensation.


E-nose

CHAPTER 5

ELECTRONIC NOSE INSTRUMENTATION

5.1 General measurement system

The basic element of a generalized electronic instrument system to measure odours are shown

schematically in the figure.

First there is an odour from the source material to the sensor chamber. There are two main ways

in which the odour can be delivered to the sensor chamber, namely head space sampling and

flow injection. In head space sampling, the head space of an odorant material is physically

removed from a sample vessel and inserted into the sensor chamber using either a manual or

automated procedure. Alternatively, a carrier gas can be used to carry the odorant from the

sample vessel into the sensor by a method called flow injection. The sensor chamber houses the

array of chosen odour sensors, e.g. Semi conducting polymer chemo resisters, etc. The sensor


Fig 14: Generalized electronic instrument system

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electronic not only convert the chemical signal into an electrical signal into an electrical signal

into an electrical signal into an electrical signal but also, usually, amplify and condition it.

This can be done using conventional analogue electronic circuitry (e.g. operational amplifiers)

and the output is then a set of an analogue outputs, such as 0 to 5v d.c. although a 4 to 20mA d.c.

current output of preferable if using a long cable. The signal must be converted into a digital

converter (e.g. a 12 – bit converter) followed by a multiplexer to produce a digital signal which

either interfaces to a serial port on the microprocessor (e.g. RS - 232) or digital bus (e.g. GPIB).

The microprocessor (e.g. an Intel 486 or Motorola 68HC11) is programmed to carry out a

number of tasks.


E-nose

CHAPTER 6

APPLICATIONS OF E-NOSE

The electronic nose finds lot of application in many fields. They have been used in a variety of

applications and could help solve problems in many fields including food product quality

assurance, health care, environmental monitoring, pharmaceuticals etc. The major applications

are

Food Industries Applications

Medical Applications

Environmental Monitoring

Pharmaceutical Industry applications

Safety and security Applications


E-nose

6.1 Food Industry Applications

Currently, the biggest market for electronic nose is in the food industry. In some

instances electronic noses can be used to augment or replace panels of human experts. In

food production especially when qualitative results will do. The applications of electronic

noses in food industry are numerous.

They include

Inspection of food by odour

Grading quality of food by odour

Fish inspection

Fermentation control

Checking mayonnaise for rancidity

Automated flavor control

Monitoring chees ripening

Beverage container inspection

Grading whiskey

Microwave over cooking control

6.2 Medical Applications

Since the sense of smell is an important sense it the physician, an electronic nose has

applicability as a diagnostic tool. An electronic nose can be used to analyze the odours from the

body and identify the possible problems. Odour in the breath can be indicative of gastrointestinal

problems, sinus problem, infection, diabetes, liver problems etc., infected wounds and tissues

will emit distinctive smell, which can be detected by the electronic nose. Odors coming from the

body fluids such as blood and urine can indicate liver and bladder problems. The electronic nose

will give the doctor a sixth sense. By sensing the smell of the breath doctor will be able to

identify the disease. As an example, it is found that the fruity, nail-varnish remover smell found

of the breathe of a diabetic about to enter a sever coma. The tin traces of illness-related

chemicals on your breath could indicate diseases such as schizophrenia when detected by a new

generation of electronic noses.


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6.3 Environmental Monitoring

The environmental applications of the electronic nose will include

Identification of toxic wastes.

Analysis of fuel mixtures.

Detection of oil leaks.

Identification of household odors.

Monitoring air quality.

Monitoring factor emission.

6.4 Pharmaceutical Industry Applications

In the pharmaceutical industry the electronic nose could be used to screen the incoming raw

materials, monitor production process, maintain security in storage and distribution areas,

quality assurance, testing the employees in critical occupations for drug use or abuse, use to

detect unpleasant smell in the industrial area.

5.5 Safety and security Application

The electronic nose can help in the safety and security applications.

They include

Hazardous alarms for toxic and biological agents

Screening airline passengers for explosive

Examining vehicles for drugs.

Monitoring indoor air quality.

Smart fire alarms.

Fire alarms in nuclear plants.

Biological and chemical detection in battlefield.

CHAPTER 6


E-nose

CONCLUSION

In the seminar, it is mentioned more accurately termed electronic arrays for chemical sensing and

identification. In this quick tour of the route from molecule to smell, it is helpful to correlate

many of the discrete physiological steps with engineering ones ranging from sampling, signal

processing and application all the way to neural computation.

REFERENCES


E-nose

1. www.smartnose.com

2. “The how and why of electronic nose”, IEEE Spectrum, Sep 1998

3. “The design of smelling”, IEEE Spectrum, Sep 1998

4. “The electronic nose in Lilliput”, IEEE Spectrum, Sep 1998

5. IEEE Spectrum January 1998

CURRICULUM VITAE


E-nose

Shweta Jain D-63 Vaishali Nagar, Jaipur E-mail: [email protected] Phone Number: 77378-27011

Career Objective

Position as an engineer in an Engineering company that lets me utilize my technical skill, interpersonal skill and extrovert personality for growth of the company.

Academic Qualifications

Sr.no.

Class Year Institute Percentage (%)

Board/University

1. B.Tech 2010-2014(pursuing)

Kautilya Institute Of Technology &

Engineering,Jaipur

76% Rajasthan Technical

University, Kota

2. Class XII 2010 Brightlands Girls Sr. Sec. School,Jaipur

82.6% CBSE

3. Class X 2008 Brightlands Girls Sr. Sec. School,Jaipur

88.2% CBSE

Training/Internship

Organization: BHEL, BhopalDepartment: Control Equipment Engineering DivisionDuration: 4 weeksUnder guidance: Mr. O P Singh, Manager (Design), CEE Division

Skills

Good communication skills. Good organization skills.

Computer skills

Programming Language : C, C++ languages Operating system : Windows 7, 98, 2003, XP, Vista

Projects


E-nose

Wireless Fire alarm system Propeller LED Display

Extra-Curricular Activities

Participated in National level Techfest “ENILAZE” Published articles in Kautilya Glimpses, FACE magazine. Volunteer in ETWON-2013, national level conference at KITE, Jaipur Participated in various national level seminars and conferences.

Personal Details

Name : Shweta JainFather’s Name : Mr. Sanjay JainD.O.B : 2nd March 1992Sex : FemaleMarital Status : SingleNationality : IndianLanguages known : Hindi, English.

Declaration

I hereby declare that the above information is true to the best of my knowledge and belief.

Place: ……… Date:…………


enose 3

Documents

Transcript of enose 3