ELEN TOOL - ANOVA studi.com€¦ · ELEN TOOL - Fermentation Control & Monitoring System UNI ISO...

ELEN TOOL - Fermentation Control & Monitoring System

• 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

ELEN-TOOL: on-line measurement tool for automatic control of must fermentation process in wine industry


Fermentation vessel

Distilled water

Enose + Etongue pump

pump

pump

sample

reference

exhaust

Measurement Cell (V≈100ml)

T control

• An integrated ENose and ETongue system ready to be placed on-line to fermentation vessels will be designed and fabricated by University of Rome.

• The proposed concept is the following

Design of Demonstrator



• Electronic nose 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

N NH

R

R R

R

R

R R

R

R'

R'

R'

'R Me

Development of Electronic Nose


-8

-6

-4

-2

0

2

4 -1.5

-1

-0.5

0

0.5

1

1.5

1

2

3

4

5

6 7 8 9 10

11 12

13 14 15

16 17

18

19 20 21

22 23 24 25

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


• 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 Electronic Tongue


-4

-3

-2

-1

0

1

2

3

4 -5

-4

-3

-2

-1

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

UNI ISO 9001: 2000

Laboratorio MIUR di Ricerca e Sviluppo nel Campo dell’Ingegneria della Conoscenza e delle Applicazioni Software Centro Direzionale is.G1 80143 – 80143 Napoli (ITALY) – Tel 0039 081 750.25.35 www.anova.it [email protected]

Final Meeting December 21st 2004

WP4 – Integration and Industrialisation of sensors

End Users’ Software Interface End-User’s Software interface design,

implementation and testing

ANOVA - Knowledge Engineering Lab

G. Mappa


UNI ISO 9001: 2000


End-User… , Who?

Oenologist

Winemaking responsible in charge

External Laboratory Analyst

Oenological apparatus maker


The potential Elen-Tool End-User

UNI ISO 9001: 2000


End-User… , Why?

Long time & laborious evaluation of

the fermentation parameters

No reproducibility of fermentation

process quality

High cost for highly skilled oenologist

operators


The End-User’s Interface required


UNI ISO 9001: 2000


End-User… , What?

Visual multidimensional data

presentation (Control Panel)

Visualizing eNose/eTongue Sensory

evaluation

Early Warning from Trends Monitoring

Wine Quality Picturing

The End User’s Interface Value Added

End User Needs based & Requirements

for fermentation process control

Best Lay-Out & Functionality for a user-

friendly EUI

Sw Expansibility & Incrementability

Sensors System Standard Integration &

Communication

EUI Design Criteria & Specs

UNI ISO 9001: 2000



How developing sw module & getting the target

UNI ISO 9001: 2000



1° Level: Data Fusion/Matching &

Neural-Fuzzy Net Platform (Knowledge

Base – PRS Data exchange)

2° Level: Fermentation Process Real-

Time Visualization & Tracking

EUI Implementation End User’s Interface Structure Implementation


UNI ISO 9001: 2000


Diagnosis

eNose/eTongue

Apparatus

(PRS)

Basic Process Param.

(pH, T, °Brix, ecc.)

Wine GRAPES

Target

Critical Oenological

Parameters

– LAB Data

QUALITATIVE

Parameters

Must Fermentation

Process Evolution

Foreseen Must/Wine

Quality Characteristics

1° Level: Data Fusion/

Matching & Neural-Fuzzy Net

Platform (Knowledge Base – PRS

Data exchange)

2° Level: Fermentation Process

Real-Time Visualization & Tracking

DB

Early

Warnings


UNI ISO 9001: 2000


From Grapes …

Must Quality Index

UNI ISO 9001: 2000



… to Wine … Good wine cannot be made from bad grapes…

Must Quality Index

10

11

12

13

14

15

16

17

1 2 3 4 5 6 7 8 9 10 11 12 13

Days

°Bri

x

Pyrazi

nes

L-Malic

Ac.

L-Lactic

Ac.

T

A

V.

R

S Antocy

ans.

I.

C

.

AV

p

H

T

.

C

.

Tot.

Polyphenols

Refere

nce

Must/

Wine

Must/

Wine

Sample

Wine Quality Index

Chromatic Intensity Vegetal Aroma

Astringency

Reference

Must/Wine Must/Wine

Sample

Must Characterization Index - MCI


UNI ISO 9001: 2000


Neural-Fuzzy Net platform …


UNI ISO 9001: 2000


eNose/eTongue matching …


UNI ISO 9001: 2000



UNI ISO 9001: 2000


UNI ISO 9001: 2000



EUI is a Sw product, working both as

front-end of eNose/eTongue sensors

system and as stand-alone tool for

process control & DSS

EUI may be customized and

integrated to all process standard on-

line instrumentation system

Conclusions Bad wine can be made from good grapes…

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