analysis of port wines using the electronic tongue
DESCRIPTION
Analysis of port wines using the electronic tongue. Alisa Rudnitskaya 1 , Ivonne Delgadillo 2 , Andrey Legin 1 , Silvia Rocha 2 , Anne-Marie Da Costa 2 , Tomás Simoes 3 1 Chemistry Department, St. Petersburg University, Russia; www.elecronictongue.com - PowerPoint PPT PresentationTRANSCRIPT
Analysis of port wines using the electronic tongue
Alisa Rudnitskaya1, Ivonne Delgadillo2, Andrey Legin1,
Silvia Rocha2, Anne-Marie Da Costa2, Tomás Simoes3
1Chemistry Department, St. Petersburg University, Russia;
www.elecronictongue.com2Chemistry Department, University of Aveiro, Portugal
3Instituto do Vinho do Porto, Porto, Portugal
WCS-5, February 18-23 , 2006, Samara, Russia
A. Rudnitskaya et alSt. Petersburg University
Port wine making procedure
WCS-5, February 18-23 , 2006, Samara, Russia
A. Rudnitskaya et alSt. Petersburg University
Port wine producing region
WCS-5, February 18-23 , 2006, Samara, Russia
A. Rudnitskaya et alSt. Petersburg University
Port wine producing region
WCS-5, February 18-23 , 2006, Samara, Russia
A. Rudnitskaya et alSt. Petersburg University
Port wine styles
Ruby
Bottle aged
Ruby, Ruby reserve(2-3 years in the cask)
Tawny, Tawny reserve(min 6 years in the cask)
LBV(4-6 years in the cask)
Tawny with an Indication of Age(10, 20, 30 or 40 years in the cask)
Vintage(2-3 years in the cask)
Colheita(min 7 years in the cask)
Tawny
Cask aged
WCS-5, February 18-23 , 2006, Samara, Russia
A. Rudnitskaya et alSt. Petersburg University
Purpose of the study
Development of the rapid analytical methodology for the assessment of the port wine age
– Evaluation of the electronic tongue multisensor system (ET) for the determination of the port wine age
– Comparison between ET and conventional chemical analysis data for the determination of the port wine age
– Evaluation of the orthogonal signal correction for the data filtering
WCS-5, February 18-23 , 2006, Samara, Russia
A. Rudnitskaya et alSt. Petersburg University
Experimental
• Samples– 146 samples of port wine, in particular, wines aged in oak casks for 10, 20, 30 and 40
years, Vintage, LBV and Colheita (harvest) wines of age varying from 2 to 70 years.
– All port wine samples together with chemical analysis results were obtained from Instituto do Vinho Do Porto
• Measurements– Electronic tongue
• Sensor array of 28 potentiometric chemical sensors with both chalcogenide glass and polymeric membranes
• Direct measurements without sample preparation
– Chemical analysis using conventional analytical techniques (provided by Instituto de Vinho de Porto)
• 32 parameters including content of sugar (ºBé), ashes, reducible sugar, total SO2 and sulphates, tartaric and malic acids, alcohols (ethanol, methanol, glycerol, 1 and 2-butanol, 1-propanol, isopropanol, amyl and allyl alcohols), ethanal, ethyl acetate, volatile and total acidity, Foline index, density, dry extract, etc.
WCS-5, February 18-23 , 2006, Samara, Russia
A. Rudnitskaya et alSt. Petersburg University
ExperimentalData processing
– PCA
• Recognition of samples and data exploration
– PLS regression
• Calibration models for prediction of port wine age
• ET and chemical analysis data
• Raw and OSC filtered data
• Test set validation, 1/3 of the samples were used as tests
– OSC
• Applied for filtering of ET and chemical analysis data
– Software used
• Unscrambler v. 7.8 by CAMO AS
• SIMCA-P v.11.0 by Umetrics
WCS-5, February 18-23 , 2006, Samara, Russia
A. Rudnitskaya et alSt. Petersburg University
Orthogonal Signal Correction
– Wold S, Antti H, Lindgren F, Öhman J, Chemometrics Intell Lab. Syst. 44 (1998) 175-185
– Aim – removal of variation in X that is not correlated with Y prior to modeling
– to = Xwo, which is orthogonal to Y AND provides good modeling and prediction of X
po' = to‘X
XOSC = X – Σto*po‘
WCS-5, February 18-23 , 2006, Samara, Russia
A. Rudnitskaya et alSt. Petersburg University
PCAChemical analysis data
-8 -6 -4 -2 0 2 4 6 8 10 12 14
-4
-2
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44
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55
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1010
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47
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51
5252
57
60
64
67
67
67
69
Colour mapping in the order of port wine age
PC
2 (
16
%)
PC1 (47%)
• Good correlation between chemical analysis data and port wine age• Clustering according to port wine type – good separation between blended
tawnies and LBV and vintage wines
-6 -4 -2 0 2 4 6 8 10 12 14-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
PC
2 (
16
%)
PC1 (47%)
tawny colheita LBV & vintage
WCS-5, February 18-23 , 2006, Samara, Russia
A. Rudnitskaya et alSt. Petersburg University
PCAET data
• No good separation of port wines according to their age• Clustering according to port wine type• Significant temporary drift in the data
-100 -50 0 50 100 150
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-20
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20
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2
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22
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2
44 4
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44 4
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5
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66
7
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1010
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1010
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404040404040
40
4749
5152
5253
57
60
64
67
67 6769
Color mapping in the order of the port wine age
PC
2 (
15
%)
PC1 (68%)
-100 -50 0 50 100 150-100
-80
-60
-40
-20
0
20
40
60
80
PC
2 (1
5%)
PC1 (68%)
tawny colheita LBV & vintage
-100 -50 0 50 100 150
-100
-80
-60
-40
-20
0
20
40
60
80
11
1111111 1
2
22
2
2
22 2
2
2
22
2
22222
2
33
3
3
3
333
3
333 3
6666
6666
6
6
67
7
77
777
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7
7 77 777
77 7
778 8
8
8 88
8
88999
9
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1013
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202020
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212121
2121
2121
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23
2323
23
23
23
2323 23
Color mapping in the order of the measurements
PC
2 (
15
%)
PC1 (68%)
WCS-5, February 18-23 , 2006, Samara, Russia
A. Rudnitskaya et alSt. Petersburg University
Prediction of the port wine agePLS regression on the raw data
-6 -4 -2 0 2 4 6 8 10 12-5
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5252
57
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67
67
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Colour mapping in the order of the wine age
PC
2 (
13
%,
3%
)PC1 (47%, 88%)
ET Chemical analysis
PCs in the model - 2RMSEC 5.3RMSEP 5.4
PCs in the model - 4RMSEC 4.8RMSEP 5.8
-100 -50 0 50 100 150
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47
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52
52
5357
6064
67
6767
69
Colour mapping in the order of the port wine age
PC
2 (
8%
, 35
%)
PC1 (69%, 44%)
WCS-5, February 18-23 , 2006, Samara, Russia
A. Rudnitskaya et alSt. Petersburg University
OSC filtering of the data
0 1 2 30
1
2
3
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5
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9
10
RMSEP for the best model
RM
SE
P
Number of OSC factors removed
ET Chemdata
0 1 2 3 40
10
20
30
40
50
60
70
80
90
100
Remaining variance in X after OSC factors removal
Re
ma
inin
g S
S, %
Number of OSC factors removed
ET Chem. data
WCS-5, February 18-23 , 2006, Samara, Russia
A. Rudnitskaya et alSt. Petersburg University
OSC filtering of the dataRMSEP
1 2 3 4 5 64
6
8
10
12
14
RM
SE
P
Number of PCs
raw dataOSC factors removed
1 2 3 4
1 2 3 4 5 64
5
6
7
8
9
10
RM
SE
P
Number of PCs
raw dataOSC factors removed
1 2 3
ET data Chemical analysis data
WCS-5, February 18-23 , 2006, Samara, Russia
A. Rudnitskaya et alSt. Petersburg University
Effect of OSC filtering of ET data
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10 10
101010
1010
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101010
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10
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4040
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5357
6064
67
6767
69
ET raw dataColour mapping in the order of the port wine age
PC
2 (
8%
, 3
5%
)
PC1 (69%, 44%)
-120 -100 -80 -60 -40 -20 0 20 40 60 80 100-100
-80
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-40
-20
0
20
40
60
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100
2
2
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22
222
2
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2
2
22
2
44 4
4
4
4
44 4
4
4
4
44
4
44
555 6
66 7
910
1010 10
1010
10101010
10101010 10 10
101010
101010
1010
10
10 1010
10
10
10
1013
1414
1414
14
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20
2020
202020
2020
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202020
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3030
303030 303030
30
30 34
353637
40 40
4040
40404040 4040
47 4951
52
52
53 576064
67
67
6769
1 OSC factor removed from the raw dataColour mapping in the order of the port wine age
PC
2 (
15
%, 0
%)
PC1 (55%, 93%)
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-60
-40
-20
0
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100
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5 5
66
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9 10
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101010
10
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34
353637
40 40
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40
47 49
51
52
52
535760
64
67
67
6769
2 OSCs factor removed from the raw dataColour mapping in the order of the port wine age
PC
2 (
22
%, 0
%)
PC1 (61%, 93%)
-120 -100 -80 -60 -40 -20 0 20 40 60 80 100-100
-80
-60
-40
-20
0
20
40
60
80
100
2
2
2
222
2
22
2 2
2
22 2
2
2
24
4 4
44
44
44
44
444
444
5
5 56
6
6 791010
10 1010
1010
10101010 10
1010 10 101010
10
101010
1010
1010
10
1010
10
10
131414
14
1414
1416
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20
202020
202020 20
20
2020
2020
2020
2020
24
25
25
26
30
3030
3030 3030
30
3030
34353637
40 40
4040
40404040
4040
47 495152
5253
57
60
64
67676769
3 OSCs factors removed from the raw dataColour mapping in the order of the port wine age
PC
2 (
7%
, 0
%)
PC1(78%, 94%)
WCS-5, February 18-23 , 2006, Samara, Russia
A. Rudnitskaya et alSt. Petersburg University
Effect of OSC filtering on ET data
-120 -100 -80 -60 -40 -20 0 20 40 60 80 100-100
-80
-60
-40
-20
0
20
40
60
80
100
1 1
11
1111
11
222 22
2 2
2222 2
22
2 2222
33
33
3
33
3
33 3
33
66
66 66
6 6
6
66
7
7
77 7 7
7
7
77
7
77
77
77
777
88
88
88
8
8
8
9
9 9
99
99
99
9
9
999
910
10
1010
10
10
1013
13
13
1313
13
1313
13 13
202020
2020
20
20
2020
20
20
2020
21
21
21
2121
2121
21
21
23
232323
23 23
2323 23
23
3 OSC factors removed from the raw dataColour mapping in the order of the measurements
PC
2 (7
%, 0
%)
PC1 (78%, 94%)
WCS-5, February 18-23 , 2006, Samara, Russia
A. Rudnitskaya et alSt. Petersburg University
Conclusions
• Port wine age can be predicted using both electronic tongue and conventional chemical analysis data with the same precision of about 5 years.
• Electronic tongue response has shown a temporary drift in port wines, especially pronounced during first days of measuring session
• Data pretreatment using OSC was favorable for ET data successfully removing time dependence and producing improved calibration models
• Port wine sample can be separated according to their types using both ET and conventional chemical analysis data.