Title: APPLICATION OF CHEMOMETRICS FOR DATA PROCESSING OF THE ELECTRONIC TONGUE
1APPLICATION OF CHEMOMETRICS FOR DATA PROCESSING
OF THE ELECTRONIC TONGUE
- Alisa Rudnitskaya, Andrey Legin, Kirill Legin,
Andrey Ipatov, Yuri Vlasov - Laboratory of Chemical Sensors, Chemistry
Department, St. Petersburg University, St.
Petersburg, Russia - http//www.electronictongue.com
2ELECTRONIC TONGUE RESEARCH GROUP
CHEMISTRY FACULTY RADIOCHEMISTRY
DEPARTMENT LABORATORY OF CHEMICAL SENSORS Head of
the Laboratory prof. Yuri Vlasov
- Project leader Dr. Andrey Legin
- Permanent Dr. Alisa Rudnitskaya
- staff Dr. Andrey Ipatov
- M.Sc. Boris Seleznev
- Associated researchers, currently 3 Ph.D.
students, several students a year -
-
3Research directions
1. New sensing materials
Solid-state materials (chalcogenide
glasses) Organic polymers Thin films
Electrochemical characteristics Cross-sensitivity
study Sensing mechanism
2. Chemical sensors
Multisensor arrays Chemometrics tools
Recognition Analysis
3. Sensor systems electronic tongue
4. Application of chemical sensors and sensor
systems
Industrial analysis Environmental control Medical
analysis Foodstuff analysis
4Advantages and drawbacks of potentiometric
chemical sensors
- Advantages
-
- 1. A wide range of available sensing materials
and sensors. - 2. Wide variations of sensor properties, some
unique features. - 3. A wide knowledge about composition/properties
relationship. - 4. Simple installation. Easy, direct
measurements. - 5. Different configuration (static, flow) and
size (bulk, micro). - 6. Easy applicability for automatic routine
analysis. - 7. Low cost.
-
- Drawbacks
-
- 1. Insufficient selectivity of many sensors.
- 2. The number of available sensors is far smaller
than the variety of analytes.
5Electronic tongue
- Electronic tongue is an analytical instrument
comprising an array of non-specific, poorly
selective, chemical sensors with partial
specificity (cross-sensitivity) to different
components in solution, and an appropriate
chemometrics tool (method of pattern recognition
and/or multivariate calibration) for the data
processing. Of primary importance is stability of
sensor behaviour and enhanced cross-sensitivity,
which is understood as reproducible response of a
sensor to as many species as possible. If
properly configured and trained (calibrated), the
electronic tongue" is capable to recognise
quantitative and qualitative composition of
multicomponent solutions of different nature.
6Potentiometric electronic tongue
7Electronic tongue laboratory version
8Composition of chemical sensor array for
electronic tongue
- Chalcogenide glass sensors
- As2S3, GeS2, AsSe with various additives
- Polymer based
- PVC, plastisizer and active substances
- Chrystalline based
- Ag2S with different additives, LaF3
- Totally up to 40 sensors
9Methods for the ET data processing
- Quantitative analysis (concentrations/parameters
prediction) - Modeling using MLR, PLS-regression, artificial
neural networks, N-PLS - Data exploration, recognition
- PCA
- Classification
- SIMCA, LDA, PLS-regression
10Electronic tongue applications
Types of analysis Classification and
discrimination (identification,
recognition) Quantitative analysis of multiple
components simultaneously Process control Taste
assessment and correlation with human perception
Objects Food - fruit juices, coffee, soft
drinks, milk, mineral water, wine, vodka,
cognac, meat, fish, onion Medical analysis -
dialyses solution for artificial kidney,
pharmaceuticals, urine Environmental -
groundwater, seawater, dirty water from
farms Industrial analysis - galvanic baths, waste
purification systems, control of
biotechnology processes
11Selected applications of the electronic tongue
- Discrimination of substances eliciting different
taste and different substances eliciting the
same taste - Determination of ultra low activity of transition
metals in seawater - Determination of ammonium and organic acids
content in the model growth media - New approach to the data for flow-injection
electronic tongue - determination of zinc and
lead concentration in mixed solutions
12Discrimination of taste substances
- Objective
- Discrimination of substances eliciting different
tastes (i.e. bitter, sweet and salty) and
substances eliciting the same taste - Samples 10mmolL-1 individual solutions of
substances - bitter quinine, caffeine, drugs A and B
- sweet acesulfam K, aspartame, sucrose
- salty sodium chloride, sodium benzoate, drug D
- Measurements
- ET comprising 20 sensors
- at least 3 replicas of each sample in random
order - Data processing
- discrimination
- LDA
- PCA
13Discrimination of taste substances
14Determination of ultra low activities of
transition metals
- Objective
- Determination of ultra low activities of
transition metals in waste waters and seawater - Solutions
- Individual and mixed binary buffered solutions of
Cu, Zn, Cd and Pb - Total concentration of metals 1 ?M to 0.3mM,
activity - 1nM to 0.1?M - Background of 0.01M of NaCl and 0.01M citrate, pH
8 - Measurements
- ET comprising 8 sensors
- Data processing
- Calibration and activity prediction of transition
metals - PLS-regression
15Determination of ultra low activities of
transition metalsMeasurements in individual
buffered solutions
16Determination of ultra low activities of
transition metals
17Determination of ammonium and organic acids
content in the model growth media
- Objective
- Quantification of main substances consumed /
produced during microorganisms growth
monitoring of the fermentation processes - Samples
- Set of 22 solutions modeling growth media
- Components MgSO4, KCl, KH2PO4, citrate,
pyruvate, oxalate, glucose, glycerol, mannitol,
erythritol, NH4Cl - Measurements
- ET comprising 8 sensors
- At least 3 replicas of each solution
- Data processing
- Calibration and concentration prediction w.r.t.
ammonium, oxalate and citrate - Artificial neural network
18Determination of ammonium and organic acids in
the growth media
19Determination of zinc and lead concentrations in
mixed solutions using flow-injection electronic
tongue
- Objectives
- Evaluate relevance of different types of signals
produced using flow-injection ET - Evaluate relevance of different multivariate
calibration methods for processing of the
flow-injection electronic tongue data
20Schematic of flow-injection electronic tongue
KNO3 0,1M
\
21Flow-through cell
22Sensor response parameters in FIA
ta- time before sample enters measuring cell ?
time of sample pass through the cell tb peak
width ?t- recovery time ? peak height
23Data produced by flow-injection ET
- 1. Peak height measured for each sensor
- one signal from each sensor, I x J
-
- 2. Time-dependent response for each sensor
- Unfolded data set, I x JK
- 3. Time-dependent response for each sensor
- 3-dimensional data set, I x J x K
Time
Sensors
Samples
24Calibration methods
- Data sets 1 and 2
- Partial least square regression
- Artificial neural network (back-propagation
neural network) - Data set 3
- N-way partial least square regression
25N-PLS regression
- PLS-regression X TP E Y TQ E
- N-PLS regression X TWj(Wk) E Y TQ E
-
26Experimental set-up
- ET 7 sensors with PVC plasticized membranes
- Set of mixed solutions containing zinc and lead
- Background solution - 0.1M KNO3
- Sensor potentials measured every 4 s for 2
minutes, 30 points for each solutions - Four replicas of each solutions
- Three types of data sets
- Data processing using PLS-1 and N-PLS regression
27Sensors response in the individual solutions of
zinc and lead
28Determination of zinc and lead in individual
solutions using flow-injection ET
- Calibration was done using PLS regression with
test set validation, only pick height being used
as sensor signals. - Concentration range of both zinc and lead 10-6
10-3 molL-1
29Sensors response in the mixed solutions of zinc
and lead
30Results of zinc and lead concentrations
prediction using three different types of data
sets
31X-loadings weights
32X-loadings weights
Time dependent response (3-d data)
33Conclusions
- Use of time-dependent response of flow-injection
ET instead of peak heights allows higher accuracy
of concentrations determination in mixed
solutions - Use of 3-dimensional data set and N-PLS
regression for calibration leads to simpler model
and the same prediction errors compared to
unfolded 2-dimensional data set and PLS
regression for calibration