1. ELEN-TOOL: on-line measurement tool for automatic control of must fermentation process in wine industry Mid-Term Meeting 10 Oct 2003  Development of Sensor Systems Array of non-specific gas sensors (electronic nose) Array of non-specific liquid sensors (electronic tongue) Biosensors  Preliminary measurements on “Domain du Moreau” wines  Preliminary tests on must samples provided by Ermacora  Set-up of further experiments  Design of demonstrator

2. 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

3. 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 (A i ≠ B i ≠ … ≠ K i )  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

4. 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 c i if the randomly variables are normally distributed.  Nonetheless, sensors signals contains more information than the c i, this extra of information allows more discrimination than analytical parameters.

5. 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” N HN NNH R RR R R R R R R' 'R Me

6. Electronic Nose measurement procedure reference sample ff Data Analysis Differential measurements between sample and a reference atmosphere The “fingeprint” can be analyzed for qualitative and quantitative analysis

7. 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

8. 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.

9. 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

10. 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 H2OH2O wine ambient air reference sample

11. 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 madiran ‘94 madiran ‘00 pacherenc colombelle M94 9 0 0 0 M00P 1 5 0 0 P 0 0 10 1 C 0 0 0 9 percvin = 94.2857%

12. 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 parameters  Analytical 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 intensity T.C.: colour tone Anth: Antocyans [mg/l] L-malic acid [g/l] L-lactic acid [g/l] 3-alkyl-2-methoxypyrazines [ng/l]  Musts: Merlot Cabernet Francs Cabernet Sauvignon 3 consecutive days Refosco A wine as reference

13. Brava Lab data a lot of missing data

14. 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

15. -3-20123 -2.5 -2 -1.5 -0.5 0 0.5 1 1.5 2 2.5 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 PC 1 (49.86%) PC 2 (39.27%) Scores Plot merlot refosco Cabernet franc Cabernet sauv 1d Vino E0 Cabernet sauv 3d Classification of must and wine from analytical parameters PCA score plot

16. merlot refosco 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, An Reduction of RS Finished wine is characterized by: High LA, low MA and VA

17. -4-3-201234 -5 -4 -3 -2 0 1 2 3 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 PC 1 (43.96%) PC 2 (27.36%) 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

18. -4-3-20123456 -4 -3 -2 0 1 2 3 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 PC 1 (58.30%) PC 2 (28.49%) 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

19. Model performance ParameterRMSECRMSECV VA g/l0.080.12 RS %1.902.84 TP mg/l205.14339.79 IC2.994.69 AN mg/l207.23330.49 MA g/l0.230.39 LA g/l0.260.43 RMSEC: root mean square error in calibration RMSECV: root mean square error in validation Estimation of analytical parameters from electronic tongue PLS Model Leave-One-Out cross validated

20. Estimation of analytical parameters from electronic tongue Scatter plots from PLS model

21. -0.6-0.5-0.4-0.3-0.2-0.100.10.20.30.4 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1 2 3 4 5 6 7 PC 1 (52.23%) PC 2 (45.62%) 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

22. -0.6-0.5-0.4-0.3-0.2-0.100.10.20.30.4 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1 2 3 4 5 6 7 PC 1 (52.23%) PC 2 (45.62%) Biplot: (o) normalized scores, (+) loads merlot refosco Cabernet franc Cabernet sauv 1d Vino E0 Cabernet sauv 3d VA RS TP IC An MA LA -0.4-0.3-0.2-0.100.10.20.30.40.50.6 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1 2 3 4 5 6 7 PC 1 (49.86%) PC 2 (39.27%) Biplot: (o) normalized scores, (+) loads merlot refosco Cabernet franc Cabernet sauv 1d Vino E0 Cabernet sauv 3d VA RS TP IC An MA LA measured estimated 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

23. merlot refosco Cabernet franc Cabernet sauv 1d Cabernet sauv 3d Cabernet sauv 2d Classification of must and wine from electronic nose

24. Model performance ParameterRMSECRMSECV VA g/l0.120.14 RS %1.431.71 TP mg/l207.85346.08 IC1.541.81 AN mg/l58.9271.14 MA g/l0.130.18 LA g/l0.000.01 Estimation of analytical parameters from electronic nose PLS Model Leave-One-Out cross validated

25. Estimation of analytical parameters from electronic nose Scatter plots from PLS model

26. -0.8-0.6-0.4-0.200.20.40.6 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 PC 1 (60.15%) PC 2 (20.16%) Biplot: (o) normalized scores, (+) loads merlot refosco Cabernet franc Cabernet sauv 1d Cabernet sauv 3d VA RS TP IC An MA LA -0.8-0.6-0.4-0.200.20.40.6 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 PC 1 (65.62%) PC 2 (24.03%) Biplot: (o) normalized scores, (+) loads merlot refosco Cabernet franc Cabernet sauv 1d Cabernet sauv 3d VA RS TP IC An MA LA measured estimated Comparison of classifications

27. Model performance ParameterRMSECRMSECV VA g/l0.090.20 RS %1.112.38 TP mg/l78.40241.29 IC1.383.21 AN mg/l52.74117.05 MA g/l0.020.07 LA g/l0.000.02 Classification of must and wine from electronic nose + tongue

28. 00.51 0 1 0102030 0 10 20 30 0200040006000 0 2000 4000 6000 051015 0 5 10 15 050010001500 0 500 1000 1500 11.522.5 1 1.5 2 2.5 00.20.4 0.1 0.2 0.3 0.4 0.5 VA RS TP IC AN MA LA Estimation of analytical parameters from electronic nose+tongue Scatter plots from PLS model

29. 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.

30. Model performance ParameterRMSECRMSECV VA g/l0.020.15 RS %0,100.66 TP mg/l29,16107.11 IC0,322.01 AN mg/l20.5789.88 MA g/l0.040.17 LA g/l0.040.18 Classification of must and wine from electronic tongue + biosensors

31. Estimation of analytical parameters from electronic nose+tongue

32. Classification from data estimated by etongue+biosensors set

33. Comparison of the performances between the data sets RMSECV of regression models ParameterETENET+ENET+bios VA g/l0.120.140.200.15 RS %2.841.712.380.66 TP mg/l339.79346.08241.29107.11 IC4.691.813.212.01 AN mg/l330.4971.14117.0589.88 MA g/l0.390.180.070.17 LA g/l0.430.010.020.18  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

34. 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.

35. 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.

36. 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 Fermentation vessel Distilled water Enose + Etongue pump sample reference exhaust Measurement Cell (V≈100ml) T control