elen-tool: on-line measurement tool for automatic control of must fermentation process in wine...
TRANSCRIPT
ELEN-TOOL: on-line measurement tool for automatic control of must
fermentation process in wine industry
Mid-Term Meeting10 Oct 2003
Development of Sensor SystemsArray of non-specific gas sensors (electronic nose)Array of non-specific liquid sensors (electronic tongue)Biosensors
Preliminary measurements on “Domain du Moreau” winesPreliminary tests on must samples provided by ErmacoraSet-up of further experimentsDesign of demonstrator
What are electronic nose and tongue
They are arrays of non-specific sensors operating in air and liquid respectively.
Each sensor captures the presence of a multitude of compounds in the measured sample.
With a suitable procedure of data analysis is possible to retrieve both qualitative and quantitative information about the measured sample.
They are subject to strong “matrix effects” this means that calibrations extensions has to be done with great care.
They can recognize classes of samples This is must of this wine at a certain evolution
And can quantify some relevant compounds The concentration of sugars in this must is mg/l
They needs of an accurate calibration
How do they work? (I)
An array of sensors is like a system of equations
Sensors coefficients (sensitivities) towards the various compounds have to be different (Ai≠ Bi≠ … ≠ Ki)
The knowledge of all the coefficients and a large number of sensors would allow the measurement of a high number of compounds
Most of the knowledge about sensor arrays comes from calibration
s1 A1 c1 A2 c2 A3 c3 .... An cn
s2 B1 c1 B2 c2 B3 c3 .... Bn cn
....
sm K1 c1 K2 c2 K3 c3 .... Kn cn
How do they work? (II)
In practice in calibration only few compounds are known so the array equation becomes:
Where and are unknown quantities randomly variable Statistics allow the evaluation of ci if the randomly variables are
normally distributed. Nonetheless, sensors signals contains more information than
the ci, this extra of information allows more discrimination than analytical parameters.
s1 A1 c1 A2 c2 A3 c3 s2 B1 c1 B2 c2 B3 c3 ....
....
sm K1 c1 K2 c2 K3 c3 ....
Development of Electronic Nose
Electronic nose is developed by Technobiochip
It is based on metalloporphyrins coated Quartz Microbalances
A sensor technology introduced at the University of Rome “Tor Vergata”
2 µmCo -TPP (20 µg)
N HN
NNH
R
R R
R
R
RR
R
R'
R'
R'
'R Me
Electronic Nose measurement procedure
reference
sample
f
Data Analysis
0
50
100
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300
350
sensor 1 sensor 2 sensor 3 sensor 4 sensor 5 sensor 6
SE
NS
OR
SIG
NA
L
• Differential measurements between sample and a reference atmosphere
• The “fingeprint” can be analyzed for qualitative and quantitative analysis
Development of Electronic Tongue
Electronic tongue is developed at the University of Rome “Tor Vergata”
It is based on potentiometric technique
The voltage drop across a working electrode and a reference electrode (Ag/AgCl) is measured through a high input impedance amplifier.
Electrodes are glassy carbon surface functionalized by electropolymers of metalloporphyrins
Development of Biosensors
Biosensors are developed at the University of Rome “Tor Vergata”
They are based on amperometric technique
The current flowing across working and counter electrodes is measured when a voltage drop is applied across a reference and counter electrodes.
Electrodes are modified with enzymes to catalyze reactions producing electrons at the electrode surface.
Development of Biosensors
Available now: Glucose, ethanol
Available for the project: Malic acid, lactic acid
Not available Antocyans and generic polyphenols This kind of biosensors are still object of research and are
not reliable for a final product A generic measurement of colour can be proposed
Electronic Nose preliminary tests on “Domaine de Moreau” wines
Wines shipped from Domaine de Moreau to Technobiochip
Colombelle, Madiran ‘94, Madiran ‘00, Pacherenc Wines have been measured with the following set-up in
order to avoid the humidity contribution. Five Measurements for each wine repeated two times per
day, the second hours after the bottle opening.
ENose
H2O
wine
ambient air
reference
sample
ENose results on DdM wines
PLS-DA model
Oxygenation does not produce the same effect on all wines
100% of classification measuring in only one condition
-6 -5 -4 -3 -2 -1 0 1 2 3 4-1
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25
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27 28 29 30
31
32 33
34
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LV 1 (96.37%)
LV 2
( 2
.15%
)
Scores Plot
madiran ‘94
madiran ‘00
pacherenc
colombelleM94 9 0 0 0M00P 1 5 0 0P 0 0 10 1C 0 0 0 9
percvin = 94.2857%
Preliminary measurements at “Brava Lab.”
Electronic nose, electronig tongue, and biosensors have been tested together on a set of musts delivered by Ermacora
Measurements took place at the Brava Laboratories Brava La. Provided the measurement of a set of analytical
parametersAnalytical parameters:
AV: volatile acidity [g of acetic acid per l]
RS: reducing sugars [%]TAV: volumic alcoholic title [%]Total polyph.: total polyphenols
[mg of catechins per l]I.C.: colour intensityT.C.: colour toneAnth: Antocyans [mg/l]L-malic acid [g/l]L-lactic acid [g/l]3-alkyl-2-methoxypyrazines
[ng/l]
Musts:MerlotCabernet FrancsCabernet Sauvignon
3 consecutive daysRefosco
A wine as reference
Brava Lab dataa lot of missing data
Measurement with sensors
Electronic tongue Each must and wine have been measured at various
concentrations in distilled water Electronic Nose
Each must and wine measured several times in the same conditions illustrated for DdM wines
Biosensors One value of glucose and ethanol given for each must and
wine
Experimental problems resulted in some missing data
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2.5
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14 15 16 17
PC 1 (49.86%)
PC
2 (
39.2
7%)
Scores Plot
merlotrefosco
Cabernet franc
Cabernet sauv 1d
Vino E0
Cabernet sauv 3d
Classification of must and wine from analytical parameters
PCA score plot
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4 5
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PC 1 (49.86%)
PC
2 (
39.2
7%)
Biplot: (o) normalized scores, (+) loads
merlotrefosco
Cabernet franc
Cabernet sauv 1d
Vino E0
Cabernet sauv 3d
VA
RS
TP
IC
An
MA
LA
Classification of must and wine from analytical parameters
PCA biplot
Fermentation trend
Finished product
Fermentation produce:increase of TP, IC, AnReduction of RS
Finished wine is characterized by:High LA, low MA and VA
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18 19
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PC 1 (43.96%)
PC
2 (
27.3
6%)
Scores Plot
merlot
refosco
Cabernet franc
Cabernet sauv 1d Vino E0
Cabernet sauv 3d
Cabernet sauv 2d
<0.02ml
1 ml
0.1 ml
Classification of must and wine from electronic tongue
Concentration effect
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PC 1 (58.30%)
PC
2 (
28.4
9%)
Scores Plot
merlot
refosco
Cabernet franc
Cabernet sauv 1d
Vino E0
Cabernet sauv 3d
Cabernet sauv 2d
Classification of must and wine from electronic tongue
Normalized to remove concentration effect
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200
400
600
800
1000
1200
Latent Variable
RM
SE
CV
(o)
RMSECV vs. LV
VARSTPICANMALA
Model performance
Parameter RMSEC RMSECVVA g/l 0.08 0.12RS % 1.90 2.84TP mg/l 205.14 339.79IC 2.99 4.69AN mg/l 207.23 330.49MA g/l 0.23 0.39LA g/l 0.26 0.43
RMSEC: root mean square error in calibrationRMSECV: root mean square error in validation
Estimation of analytical parameters from electronic tongue
PLS Model Leave-One-Out cross validated
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1000
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0 1 2 30
1
2
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-1 0 1 2-1
0
1
2
VA RS TP
IC AN MA
LA
Estimation of analytical parameters from electronic tongue
Scatter plots from PLS model
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PC 1 (52.23%)
PC
2 (
45.6
2%)
Biplot: (o) normalized scores, (+) loads
merlot refosco
Cabernet franc
Cabernet sauv 1d
Vino E0
Cabernet sauv 3d
VA
RS
TP
IC
An
MA
LA
Classification of samples from estimated parameters
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PC 1 (52.23%)
PC
2 (45.62%)
Biplot: (o) normalized scores, (+) loads
merlotrefosco
Cabernet franc
Cabernet sauv 1d
Vino E0
Cabernet sauv 3d
VA
RS
TP
IC
An
MA
LA
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10 11 12 13
14 15 16 17
1
2
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4 5
6
7
PC 1 (49.86%)
PC
2 (
39.2
7%)
Biplot: (o) normalized scores, (+) loads
merlotrefosco
Cabernet franc
Cabernet sauv 1d
Vino E0
Cabernet sauv 3d
VA
RS
TP
IC
An
MA
LA
measuredestimated
Comparison of classifications
• Score plot of estimated parameters is a mirror reflection of that calculated from the actual parameters
• Parameters maintain their mutual relationship and significance, and as consequence musts and wine reciprocal positions are maintained
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PC 1 (95.43%)
PC
2 (
3.8
2%)
Scores Plot
merlot
refosco
Cabernet franc
Cabernet sauv 1d
Cabernet sauv 3d
Cabernet sauv 2d
Classification of must and wine from electronic nose
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100
200
300
400
500
600
700
Latent Variable
RM
SE
CV
(o)
RMSECV vs. LV
VARSTPICANMALA Model performance
Parameter RMSEC RMSECVVA g/l 0.12 0.14RS % 1.43 1.71TP mg/l 207.85 346.08IC 1.54 1.81AN mg/l 58.92 71.14MA g/l 0.13 0.18LA g/l 0.00 0.01
Estimation of analytical parameters from electronic nosePLS Model Leave-One-Out cross validated
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VA RS TP
IC AN MA
LA
Estimation of analytical parameters from electronic nose
Scatter plots from PLS model
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PC 1 (60.15%)
PC
2 (
20.1
6%)
Biplot: (o) normalized scores, (+) loads
merlot
refosco
Cabernet franc
Cabernet sauv 1d
Cabernet sauv 3d
VA
RS
TP
IC
An
MALA
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11 12
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19 20
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PC 1 (65.62%)
PC
2 (
24.0
3%)
Biplot: (o) normalized scores, (+) loads
merlot
refoscoCabernet franc
Cabernet sauv 1d
Cabernet sauv 3d
VA
RS
TP
IC
An
MA
LA
measured estimated
Comparison of classifications
0 2 4 6 8 10 120
50
100
150
200
250
300
350
400
450
500
Latent Variable
RM
SE
CV
(o)
RMSECV vs. LV
y1y2y3y4y5y6y7
Model performance
Parameter RMSEC RMSECVVA g/l 0.09 0.20RS % 1.11 2.38TP mg/l 78.40 241.29IC 1.38 3.21AN mg/l 52.74 117.05MA g/l 0.02 0.07LA g/l 0.00 0.02
Classification of must and wine from electronic nose + tongue
0 0.5 10
0.5
1
0 10 20 300
10
20
30
0 2000 4000 60000
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0 500 1000 15000
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1500
1 1.5 2 2.51
1.5
2
2.5
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0.2
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VA RS TP
IC AN MA
LA
Estimation of analytical parameters from electronic
nose+tongueScatter plots from PLS model
Biosensors trials
Two biosensors have been tested for glucose and ethanol Although these two values are limited with respect to the
amount od compounds in must they add a reference to ETongue and ENose data.
Here, only one measure of biosensors for must is available, while ETongue measured the same sample three times, so biosensors variability is not included and the data are surely over-estimated
This evaluation is presented as test on final data treatment.
0 2 4 6 8 10 120
100
200
300
400
500
600
Latent Variable
RM
SE
CV
(o)
RMSECV vs. LV
y1y2y3y4y5y6y7
Model performance
Parameter RMSEC RMSECVVA g/l 0.02 0.15RS % 0,10 0.66TP mg/l 29,16 107.11IC 0,32 2.01AN mg/l 20.57 89.88MA g/l 0.04 0.17LA g/l 0.04 0.18
Classification of must and wine from electronic tongue +
biosensors
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0.5
1
0 10 20 300
10
20
30
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4000
6000
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0 500 1000 15000
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Estimation of analytical parameters from electronic
nose+tongue
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0
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PC 1 (49.88%)
PC
2 (
39.3
6%)
Biplot: (o) normalized scores, (+) loads
merlotrefosco
Cabernet franc
Cabernet sauv 1d
Vino E0
Cabernet sauv 3d
VA
RS
TP
IC
An
MA
LA
Classification from data estimated by etongue+biosensors set
Comparison of the performances between the data sets
RMSECV of regression models
Parameter ET EN ET+EN ET+biosVA g/l 0.12 0.14 0.20 0.15RS % 2.84 1.71 2.38 0.66TP mg/l 339.79 346.08 241.29 107.11IC 4.69 1.81 3.21 2.01AN mg/l 330.49 71.14 117.05 89.88MA g/l 0.39 0.18 0.07 0.17LA g/l 0.43 0.01 0.02 0.18
EN did not measure wine so lactic acid results more accurate for EN containing datasets.
Biosensors made only one measure per sample, they introduce a great stability in data
Conclusions from preliminary tests
All sensors shown enough sensitivity to capture all the variables of the problem
Accuracies are sufficient (for each data-set) to re-draw the classification obtained with the analytical parameters, namely the system is able to follow the fermentation process with the accuracy of an analytical determination
The calibration database has to be extended with proper experiments including anomalies.
Set-up of further experiments
Conclusion of Cormons experiments Brava is required to characterize and deliver to Technobiochip
e University of Rome musts at the end of their fermentation for a final evaluation
The main goal of further experiments is to extend the calibration dataset
wineries will be requested to deliver “stabilized” musts to University of Rome
Musts will be “re-vitalized” with a proper protocol measured and characterized
Musts fermentation evolution will be artificially modified introducing defects in order to calibrate sensors towards anomalous musts.
Design of demonstrator
An integrated ENose and ETongue system ready to be placed on-line to fermentation vessels will be designed and fabricated by: Technobiochip, Labor, and University of Rome.
The proposed concept is the following
Fermentationvessel
Distilledwater
Enose + Etongue pump
pump
pump
sample
reference
exhaust
MeasurementCell (V≈100ml)
T control