Transcript Слайд 1 - Хемометрика в России

APPLICATION 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
CHEMISTRY FACULTY
RADIOCHEMISTRY DEPARTMENT
LABORATORY OF CHEMICAL SENSORS
Head of the Laboratory prof. Yuri Vlasov
ELECTRONIC TONGUE RESEARCH GROUP
Project leader
Dr. Andrey Legin
Permanent
staff
Dr. Alisa Rudnitskaya
Dr. Andrey Ipatov
M.Sc. Boris Seleznev
Associated researchers, currently 3 Ph.D. students, several students a year
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WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004
Research directions
1. New sensing materials
Solid-state materials (chalcogenide glasses)
Organic polymers
Thin films
2. Chemical sensors
Electrochemical characteristics
Cross-sensitivity study
Sensing mechanism
3. Sensor systems – electronic tongue
Multisensor arrays
Chemometrics tools
Recognition & Analysis
4. Application of chemical sensors and sensor systems
Industrial analysis
Environmental control
Medical analysis
Foodstuff analysis
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Advantages and drawbacks of potentiometric chemical
sensors
·
Advantages
1.
2.
3.
4.
5.
6.
7.
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 configuration (static, flow) and size (bulk, micro).
Easy applicability for automatic routine analysis.
Low cost.
·
Drawbacks
1. Insufficient selectivity of many sensors.
2. The number of available sensors is far smaller than the variety of analytes.
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Electronic tongue
Electronic tongue is an analytical instrument comprising an array of nonspecific, poorly selective, chemical sensors with partial specificity (crosssensitivity) 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.
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WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004
Potentiometric electronic tongue
multiplexor
measuring device
sensor
array
V
reference
electrode
computer
analysed
solution
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WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004
Electronic tongue – laboratory version
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WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004
Composition 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
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WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004
Methods 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
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WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004
Electronic 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
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WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004
Selected 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
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WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004
Discrimination 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
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WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004
Discrimination of taste substances
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bitter
sweet
salty
4
3
120
NaCl
80
PC2 (26%)
Root2 (8%)
2
1
0
40
0
-1
-40
Na benzoate
Drug D
-2
-80
-3
-6
-4
-2
0
2
4
6
8
10
12
-200 -150 -100
14
Root1 (92%)
150
-50
0
50
20
200
Aspartame
10
Quinine
PC2 (2%)
50
PC2 (11%)
150
Drug B
100
0
100
PC1 (73%)
Drug A
-50
0
ASK
-10
-100
-150
-250 -200 -150 -100
Sugar
-20
Caffeine
-50
0
50
PC1 (81%)
100
150
200
250
-250
-200
-150
-100
-50
0
50
100
150
PC1 (98%)
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Determination 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
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WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004
Determination of ultra low activities of transition metals
Measurements in individual buffered solutions
320
P1
P2
240
-240
200
-280
160
-320
120
E, mV
E, mV
280
Ch2 june
Ch2 august
Ch3 june
Ch3 august
-360
-10.0 -9.5 -9.0 -8.5 -8.0 -7.5 -7.0 -6.5
-400 -6.0 -5.5 -5.0 -4.5
-240
2+
loga(Zn )
-280
-480
-320
-11
-10
-9
-8
E, mV
-440
-7
-6
loga(Cd)-360
-5
Ch3 june
Ch3 august
Ch2 june
Ch2 august
-400
-440
-480
-9.5
-9.0
-8.5
-8.0
-7.5
-7.0
-6.5
-6.0
-5.5
-5.0
loga(Pb)
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Determination of ultra low activities of transition metals
Ion
Copper
Zinc
Cadmium
Lead
Copper
Lead
Added, nM
1.48
8.13
107
5.01
11.7
19.1
2.6
15.1
52.5
2.0
12.0
53.7
3.2
10
85
3
11
66
79
Found, nM
St. Deviation Average relative error, %
Binary Cu-Zn solutions
1.7
0.2
23
8
4
110
30
5.4
0.2
11
11
1
17
2
Binary Cd-Pb solutions
3.0
0.7
24
15
6
49
16
2.3
0.5
25
10
4
50
14
Binary Cu-Pb solutions
3.3
1.3
22
10
2
97
22
2.3
0.5
13
4
26
56
12
69
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Determination 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
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Determination of ammonium and organic acids in the
growth media
Sample № Added, mM Predicted, mM St.Deviation Average relative error, %
Ammonium
2
2.0
2.1
0.4
4
2.8
2.5
0.3
6
4.8
4.9
0.1
6
14
6.0
6.01
0.04
16
12.0
11.8
0.3
18
13.2
13.57
0.05
19
14.0
13.59
0.02
Oxalate
6
7.8
7
1
8
13.0
14
1
9
20.7
21.0
0.3
6
10
28.5
28
1
16
33.7
33.8
0.6
18
44.0
43.9
0.4
20
49.2
51
2
Citrate
6
1.7
2.1
0.1
8
2.6
2.5
0.2
10
3.3
2.9
0.2
8
13
3.6
3.11
0.03
16
3.8
4.0
0.1
18
4.3
4.5
0.1
20
5.0
5.0
0.2
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WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004
Determination 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
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Schematic of flow-injection electronic tongue
KNO3
0,1M
\
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Flow-through cell
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Sensor response parameters in FIA
T
ta- time before sample
enters measuring cell
H(mV)
Т – time of sample pass
through the cell
tb – peak width
t- recovery time
Н – peak height
tb
ta
t(c)
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Data produced by flow-injection ET
Sensors
1 2 … J
Samples
1. Peak height measured for each sensor:
one signal from each sensor, I x J
1
2
…
I
2. Time-dependent response for each sensor:
Unfolded data set, I x JK
Samples
Sensor 1
Sensor 2
…
Sensor J
t1 t2 … tk t1 t2 … tk … t1 t2 … tk
1
2
…
I
3. Time-dependent response for each sensor:
Samples
3-dimensional data set, I x J x K
Sensors
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Calibration 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
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N-PLS regression
PLS-regression:
X = TP’ + E;
Y = TQ’ + E
N-PLS regression:
X = TWj(Wk)’ + E;
Y = TQ’ + E
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WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004
Experimental set-up
•
ET – 7 sensors with PVC plasticized membranes
•
Set of mixed solutions containing zinc and lead;
Sample 1 2 3 4
5 6 7 8 9
pZn
5 4 5 4 5.3 5 5.3 4 5.3
pPb
5.3 4 5 5.3 5.3 4 5 5 4
•
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
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WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004
Sensors’ response in the individual solutions of zinc and
lead
Sensor 1
Sensor 1
320
300
310
290
pZn
280
6
5
4
3
290
280
E, mV
E, mV
270
pPb
300
6
5
4
3
260
250
270
260
250
240
240
230
230
220
220
210
0
50
100
150
210
200
0
50
Time, s
150
200
Time, s
Sensor 4
Sensor 4
350
380
340
pZn
360
6
5
4
3
340
pPb
330
6
5
4
3
320
310
320
300
E, mV
E, mV
100
300
290
280
270
280
260
260
250
240
240
0
50
100
150
200
Time, s
0
50
100
150
200
Time, s
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WSC – 3, Pushkinskie Gory, Russia, February 16-20, 2004
Determination 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
Slope Offset Correlation RMSE
Zinc
Calibration 0.99
0
0.99
0.03
Validation 1.04 -0.10
0.99
0.07
Lead
Calibration 0.99
0.01
0.99
0.05
Validation 0.94
0.11
0.99
0.08
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Sensors’ response in the mixed solutions of zinc and
lead
Sensor 4
Sensor 1
270
320
pPb = 5
pZn
6
5
4
260
250
pPb = 5
pZn
6
5
4
310
300
240
E, mV
E, mV
290
230
280
270
260
220
250
240
210
-10
0
10
20
30
40
50
60
70
80
90
100
230
110
0
Time, s
Sensor 1
10
20
30
40
50
60
70
80
90
100
110
Time, s
Sensor 4
280
310
PZn = 5
pPb
6
5
4
270
260
290
280
E, mV
250
E, mV
300
PZn = 5
pPb
6
5
4
240
270
230
260
220
250
210
240
230
200
0
10
20
30
40
50
60
Time, s
70
80
90
100
110
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0
20
40
60
Time, s
80
100
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Results of zinc and lead concentrations’ prediction using
three different types of data sets
Zinc
Slope Offset Correlation RMSE
Data set
Calibration 0.81
0.87
0.90
0.23
PLS, Data set 1 (peaks’ heights)
Validation 0.65
1.75
0.92
0.27
Calibration 0.88
0.58
0.94
0.19
PLS, Data set2 (3-D unfolded)
Validation 0.78
0.97
0.99
0.12
Calibration 0.87
0.60
0.93
0.20
N-PLS, Data set 3(3-D)
Validation 0.78
1.03
0.99
0.13
Lead
Slope Offset Correlation RMSE
Data set
Calibration 0.91
0.43
0.95
0.17
PLS, Data set 1 (peaks’ heights)
Validation 0.77
1.11
0.95
0.19
Calibration 0.99
0.06
0.99
0.06
PLS, Data set2 (3-D unfolded)
Validation 1.03 -0.17
0.97
0.14
Calibration 0.94
0.30
0.97
0.14
N-PLS, Data set 3(3-D)
Validation 0.94
0.32
0.97
0.13
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X-loadings weights
Peak heights
Time dependent response (unfolded data)
0.8
0.30
5
0.6
0.25
0.4
0.20
0.15
4
7
0.0
-0.2
6
1
PC2
PC2
0.2
5-32
5-28
5-36
5-40
5-24
5-44
5-48
5-52
4-32
4-28
5-56 4-36
4-40
4-24 5-20
5-60
4-445-641-32 1-28 1-24
1-20
4-48 5-68
1-36
5-16
5-8
5-12
4-52
5-72
4-20
5-4
1-16
7-44 7-407-36 7-32
1-4
1-12
1-8
4-12
4-8
7-28
4-16
4-56 1-40 5-76
4-4
7-48
4-601-44 5-80
7-52
7-24
5-84
4-64
1-48
1-112
2-8
1-108
1-120 2-20
7-56
2-16
1-104
1-100
2-12
5-88 7-60
1-116
1-52
4-68
1-88
1-92
2-24
1-96
7-20
2-4
1-84
1-56
7-16
6-4
5-92
1-80
7-8
1-76
4-72
5-96
7-4
1-68
7-12
1-64
6-24
1-72
5-100
2-28
1-60
6-16
7-68
7-64
6-12
2-32
6-8
6-28
6-20
5-104
5-116
5-112
6-32
5-120
5-108
6-120
4-76
6-108
7-108
7-116
2-120
7-112
7-120
6-36
6-112
6-96
7-72
7-104
2-116
7-84
6-104
2-100
6-116
2-112
2-36
6-100
7-76
4-80
7-92
6-92
6-40
7-80
7-100
6-88
2-108
7-96
4-104
2-104
3-20
3-4
4-120
7-88
2-96
3-16
4-84
4-116
4-112
6-80
6-84
3-8
4-108
6-44
6-72
3-24
4-100
3-28
6-76
4-96
6-56
6-48
2-92
3-12
2-40
6-68
2-44
4-88
6-64
6-52
6-60
3-32
2-80
4-92
2-84
3-36
2-88
3-40
3-112
3-120
3-100
3-108
3-96
3-92
3-116
3-104
3-80
3-88
3-72
2-76 3-60
3-64
2-68
2-48
3-52
3-48
3-56
3-76
2-72
3-84
3-44
2-56
3-68
2-52
2-64
2-60
3
0.10
0.05
0.00
-0.4
-0.05
2
-0.6
-0.10
-0.8
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
-0.16
-0.14
-0.12
PC1
-0.10
-0.08
-0.06
-0.04
-0.02
0.00
0.02
PC 1
0,3
PC1
PC2
0,2
X-loadings
0,1
0,0
-0,1
-0,2
1-120
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2-120
3-120
4-120
Variables
5-120
6-120
7-120
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X-loadings weights
Time dependent response (3-d data)
X-loadings in the order of sensors
X-loadings in the order of time
0.8
0.42
2
76
0.41
0.6
0.40
1
3
0.4
0.38
0.37
PC2
PC2
0.39
54
0.0
6
0.36
0.35
0.34
0.2
-0.2
72
60
56
4
108
20
92
16
104
24
40
100
3696 88 120
52
4844
32
7
0.33
0.370
0.375
0.380
0.385
0.390
PC1
-0.4
0.00
0.05
0.10
0.15
80
8
112
116
28
0.20
12
68
0.25
84
0.30
64
0.35
0.40
PC1
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Conclusions
• 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
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