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ARTIFICIAL CHEMICAL SENSES -ELECTRONIC TONGUE & ELECTRONIC NOSE
ARTIFICIAL CHEMICAL SENSES -ELECTRONIC TONGUE & ELECTRONIC NOSE
Patrycja CiosekPatrycja Ciosek
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Human senses
PhysicalVisionHearingTouch
ChemicalSmellTaste
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Bionics
study modeling analysis
Of functioning of biological organisms
Construction of analogous functioning devices
For theoretical-cognitive orders
•Fusion of biological and artificial organs => implants, biomaterials...
• devices simulating biological constructions and systems => artificial kidney, electronic tongue....
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
ELECTRONIC TONGUE / ELECTRONIC NOSE (ETongue, ENose)
systems for automatic analysis and recognition (classification) of liquids or gasesElectronic tongues - liquid samplesElectronic noses – gaseous samplesThe result of analysis: the identification of the sample, an estimation of its concentration or its characteristic propertiesmany advantages: problems associated with human senses, like individual variability, impossibility of on-line monitoring, subjectivity, adaptation, infections, harmful exposure to hazardous compounds, mental state, are no concern of it.Synonyms: artificial tongue, taste sensor, artificial nose, olfactory system
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Electronic tongue / Electronic nose
A SYSTEM FOR AUTOMATIC ANALYSIS AND CLASSIFICATION (RECOGNITION)
OF SAMPLES
AN ARRAY OF
CHEMICAL SENSORS
PATTERN RECOGNITION
SYSTEM
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Sensor arrays
The first electronic
nose
1982
sensor array for
analysis of
liquid samples
1985 90’ and now
ELECTRONIC
TONGUE
Application of more
sophisticated numerical
methods -
Quantitative analysis
Recognition of
complex samples -
Qualitative analysis
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
ApplicationsFoodstuffs Industry
food quality control during processing and storage (water, wine,coffee, milk, juice…)optimization of bioreactorscontrol of ageing process of cheese, whiskeyautomatic control of taste
Medicinenon-invasive diagnostics (patient's breath, analysis of urine, sweat, skin odour)clinical monitoring in vivoidentification of unpleasant odour of pharmaceuticals
Quality control of air in buildings, closed accommodation (i.e. space station, control of ventilation systems)
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
ApplicationsSafety
searching for chemical/biological weaponsearching for drugs, explosivesfriend-or-foe identification
Environmental pollution monitoringmonitoring of agricultural and industrial pollution of air and wateridentification of toxic substancesleak detection
Chemical Industryproducts purityin the future - detection of functional groups, chiral distinction
Legal protection of inventions - digital "fingerprints" of taste and odours
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
SENSING METHODS APPLIEDConductivity sensors (MOSFET, CP)
Piezoelectric sensors (QMB, SAW)
Optical sensors
Potentiometric sensors Measurements of conductivity Voltamperommetry Optical sensors Biosensors E-NOSE
E-TONGUE
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Pattern recognition
The electronic tongue or nose system performance is dependent on the quality of functioning of its pattern recognition block. Various techniques and methods can be used separately or together to perform the recognition of the samples. After measurement procedure the signals are transformed by a preprocessing block. The results obtained are inputs for Principal Components Analysis, Cluster Analysisand/or Artificial Neural Network.
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Data Analysis - Data matrixEach sample is characterized by unique and typical set of data, forming "fingerprint" of an analyte in m-dimensional pattern space.
=
n mn jn
i mi ji
mj
xxx
xxx
xxx
X
. . .. . .. .. . . . . . . . . .
. . .. . .. .. . . . . . . . . .
. . .. . .
1
1
111 1
=
n mn jn
i mi ji
mj
xxx
xxx
xxx
X
. . .. . .. .. . . . . . . . . .
. . .. . .. .. . . . . . . . . .
. . .. . .
1
1
111 1
Following sensor signals
Following samples
Pattern
vector
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Data Analysis - Preprocessing
linear transformation on the data matrix is performed (without changing the dimensionality of the problem) in order to enhance qualitative information. Typical techniques: manipulation of sensor baseline, normalization, standarization and scaling of response for all the sensors in an array.
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Data Analysis - Preprocessing
0.0
0.0
1.0
1.0Specific transformations
Meancentering Autoscaling....
0.0
0.0
15.0
2.0
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Principal Components Analysis and Cluster Analysis
A multi-sensor system produces data of high dimensionality - hard to handle and visualizePrincipal Component Analysis (PCA) and Cluster Analysis (CA) are multivariate pattern analysis techniques reducing dimensionality of the problem and reducing high degree of redundancy.
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Principal Components Analysis (PCA)linear feature-extraction technique finding most influential,
new directions in the pattern space, explaining as much of the variance in the data set as possible.
This new directions - called principal components - are the base for a new data matrix. Usually 2 or 3 of them are
sufficient to transfer more than 90% of the variation of the samples.
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Data Analysis –Principal Component Analysis (PCA)
X1
X2
1 DIMENSION
2 DIMENSIONS
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Cluster Analysis (CA)The base principle of Cluster Analysis is the
assumption of close position of similar samples in multidimensional pattern space.
Similarity between each 2 samples is calculated as a function of the distance between them - usually in Euclidean sense - and displayed on a dendrogram.
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Artificial Neural Network (ANN)information processing structures imitating behaviour of human brainmain advantages: adaptive structure, complex interaction between input and output data, ability to generalize, parallel data processing and handling incomplete or high noise level data =>useful pattern recognition toolmany possible architectures and algorithms available in the literaturethe most common in measurement applications is feed-forward network and back-propagation learning algorithm.
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Artificial Neural Network (ANN)
The base units of artificial neural networks are neurons and synapses. Neurons are organized in layers and connected by synapses. Their task is to sum up their inputs and non-linear transfer of the result, which is then transmitted via synapsis
with modification by means of the synapsis weights - this signal, in turn, is the input for the next layer of the network
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Artificial Neural Network (ANN)
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Artificial Neural Network (ANN)
network is forced to provide desired outputs corresponding to a determined inputadjusting the synapses' weights
The production phase
The testing phase
The learning phase
verification of the generalization capability of network
network is capable of providing outputs corresponding to any input
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Electronic tongue developed at Warsaw University of Technology
Sensorarray
Ion-selective electrodes (ISEs)
•Selective•Partially selective
Data analysisExtraction of information from multidimensional measurement data
•PCA
•ANN
•SIMCA (Soft Independent Modeling of Class Analogy)
•PLS (Partial Least Squares)
•More...
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Juice brand recognition
All brands
One brand(Hortex)
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Juice measurements – PCA & ANN
Mean squared error of neural net processing3.09 * 10-4
% of correct classifications92.0
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Miniaturized flow-throughelectronic tongue
Reference electrode
inletoutlet
implants
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Beverages recognition
86,3Beer
86,7Orange juice
96,7Milk
% of correct classifications
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Commercial Systems
Aromascanner (Aromascan, UK)Fox Intelligent Nose (Alpha MOS, France)Taste sensing system SA401
(Anritsu Corp., Japan)
cost :
20 000 – 100 000 $
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Comparison of natural and artificial chemical senses
Data analysisLimited databasePossible to integrate with other
instruments (for example fusion of e-tongue&e-nose)
Data analysisWide databaseHigh integration with other senses
Signal processing – one sensor –one signal
Signal processing – data synthesis
SensorsSelective/partially selectiveLow redundancyChemical transductionSignal: steady signal
ReceptorsNon-selectiveHigh redundancyBiochemical transductionSignal: pattern of spikes
Sample delivery - continuousSample delivery - periodicalArtificialNatural
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
References1. P. Ciosek, Z. Brzózka, W. Wróblewski, Classification of beverages using a reduced
sensor array, Sens. Actuators B, 103 (2004), 76-83.2. P. Ciosek, E. Augustyniak, W. Wróblewski, Polymeric membrane ion-selective and
cross-sensitive electrodes-based electronic tongue for qualitative analysis of beverages, Analyst, 129 (2004), 639-644.
3. P. Ciosek, Z. Brzózka, W. Wróblewski, E. Martinelli, C. Di Natale, A. D’Amico, Direct and two stage data analysis procedures based on PCA, PLS-DA and ANN for ISE-based electronic tongue – effect of supervised feature extraction, Talanta, in press
4. P. Ciosek, W. Wróblewski, The analysis of sensor array data with various pattern recognition techniques, Sens. Actuators B, in press
5. D'Amico A., Di Natale C., Paolesse R., Portraits of gasses and liquids by arrays of nonspecific chemical sensors: trends and perspectives, Sensors and Actuators B, 68 (2000), 324
6. Toko K., Taste sensors with global selectivity, Materials Science and Engineering, C4 (1996), 69
7. Vlasov Y., Legin A., Non-selective chemical sensors in analytical chemistry: from "electronic nose" to "electronic tongue", Journal of Analytical Chemistry, 361 (1998), 255
8. Krantz-Ruckler C., Stenberg M., Winquist F., Lundstrom I., Electronic tongues for environmental monitoring based on sensor arrays and pattern recognition: a review, Analytica Chimica Acta, 426 (2001), 217
9. Winquist F., Holmin S., Krantz-Ruckler C., Wide P., Lundstrom I., A hybrid electronic tongue, Analytica Chimica Acta, 406 (2000), 147
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
LinksCommercially available e-noses/e-tongues:
http://www.alpha-mos.com/
http://www.detect-measure.com/neo.htm
http://www.osmetech.plc.uk/
http://www.appliedsensor.com/
http://www.airsense.com
http://cyranosciences.com/
http://estcal.com/
http://www.hkr-sensor.de/
http://www.lennartz-electronic.de/
Chemometrics:
http://ull.chemistry.uakron.edu/chemometrics/
http://www.spectroscopynow.com/Spy/basehtml/SpyH/1,1176,2-0-0-0-0-home-0-0,00.html/
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Patrycja CiosekArtificial Chemical Senses - Electronic tongue & Electronic nose
Summary
Novel method in chemical analysisApplication of chemometrics, mathematics, statistics, artificial intelligenceElimination of subjective, human estimation