sistemes multisensors aplicats al control de qualitat dels...

Sistemes multisensors aplicats al control de qualitatdels vins

Cecilia JiménezInstitut de Microelectrònica de Barcelona (IMB-CNM) CSIC

NATIONAL CENTER OF MICROELECTRONICS

(CNM)

D+T Microelectrónica, A.I.E.

BARCELONA INSTITUTE OF

MICROELECTRONICS (IMB)

MADRID INSTITUTE OF

MICROELECTRONICS (IMM)

SEVILLA INSTITUTE OF

MICROELECTRONICS (IMSE)

LARGE SCALE FACILITY

(Integrated Clean Room)

SPANISH COUNCIL FOR SCIENTIFIC RESEARCH (CSIC)

Barcelona Barcelona InstituteInstitute ofof Microelectronics (IMBMicroelectronics (IMB--CNM), CSICCNM), CSIC

2011 IMB Budget: 11.5 M€External funding: 50 %

IMB STAFF (2012)

• Researchers 66

• Ph.D. Students 57

• Clean room 43

• Support services 28

• Admin. & general serv. 20

• Visitors 5

TOTAL: 219

Research at IMB:Research at IMB:

µ/n-sensors

µ/n-actuators

µ/n-structures & passives

Signal processing

Powerdevices

Pac

kagi

ng &

in

terc

onne

ct

Embedded softwareCharacterization & Test

Micro/Nano-System

(µ)powersources

• Micro – Nanotechnologies

• Nanofabrication and functional properties of nanostructures

• Transducers for chemical and biochemical sensing

• Micro-Nano-Bio systems

• Integrated circuits and systems

• Power devices and systems

Technology focus : MICRO & Technology focus : MICRO & NANO INTEGRATED SYSTEMSNANO INTEGRATED SYSTEMS

Table of contents

• Introduction to Multisensor systems or Electronic Tongues

•Microelectrodes used– ISFET based sensors

– Thin film metal microelectrodes

– Optical integrated systems

• Chemometric tools

• Results– Sensor signals & treatment of data

– Classification of wines: • Grape variety/ vintage year/ geographical origin

– Quantitative analysis

• Conclusions









• Conclusions

Electronic Tongue: analogy to gustative organ

RecognitionElement Obtained Data TreatmentObtained Data Results

Nerve endings of the

tongue

Transmission ofnerve pulse

Brain

Signals (electrochemical,

absorbance, …)

Chemometric processing tools

(PCA, NNs, PLS)

Tastesense

• sweet

• salty

• bitter

• sour

• umami

QualitativeAnalysis

Classification

QuantitativeAnalysis

Nerve endings of the

tongue

Array of sensors

(crosselectivity)

Sabor: Sensación debida a conjunto de substanciasCategorías :

• Salado : NaCl• Dulce : glucosa, fructosa• Amargo : quinina, cafeína• Acido : HCl, ácido acético• “Umami ”: glutamato monosódico (MSG) Sweet Salt Sour Bitter

Neu

ral

Re

sp

on

se

Sucrose Test

Sweet Salt Sour Bitter

Neu

ral

Re

sp

on

se

HCl Test


NaCl Test


Quinine Test

Respuesta de sistema gustativo a distintos compuestos

Respuesta de sistema gustativo a distintos compuestos

Respuesta de conjunto de sensores a distintos compue stosRespuesta de conjunto de sensores a distintos compue stos

Toko, K., Taste sensor. Sensors and Actuators B-Chemical, 2000. 64(1-3): p. 205-215.

Electronic Tongue: analogy to gustative organ

Table of contents









• Conclusions









• Conclusions

ISFET

IDEs

Type of transducers:

• ISFET based sensors : pH, ions Na, K, Ca, Cl, NO3, etc

• Microelectrodes and UMEAs (Voltamperometry): Cl2and COD, heavy metals, molecules

• Interdigitated electrodes (IDEs): conductivity, dielectric properties.

• Multisensor chips (compatibilization of fabricationtechnologies)

Type of transducers:

• ISFET based sensors : pH, ions Na, K, Ca, Cl, NO3, etc

• Microelectrodes and UMEAs (Voltamperometry): Cl2and COD, heavy metals, molecules

• Interdigitated electrodes (IDEs): conductivity, dielectric properties.

• Multisensor chips (compatibilization of fabricationtechnologies)

Advantages

• Low cost (mass produced)

• Low energy consumption

• Reproducible

• Miniaturization (µTAS or LOC)

• Circuit integration

Advantages

• Low cost (mass produced)

• Low energy consumption

• Reproducible

• Miniaturization (µTAS or LOC)

• Circuit integration

UMEA

4-electrode Cell

Multisensor chip on BESOI

82% yielding perwafer

amperometric

Fabricated with microelectronic technology

Solution inlets/outlets

Sensors

Solution inlets/outlets

Microelectrodes used

ISFET and CHEMFETs

-0,6

-0,5

-0,4

-0,3

-0,2

-0,1

0

0,1

0,2

0,3

0,4

0 2 4 6 8 10 12 14

pH

Vo

(mV

)

Detection of:

• pH

• Cations Ca 2+, K+, Na+

• Anions: Cl-, CO32-

Detection of:

• pH

• Cations Ca 2+, K+, Na+

• Anions: Cl-, CO32-

Pt/Au Microelectrodes

Standard technology Si/SiO 2/metal

• Conductivity/impedance

• Redox Potential (ORP)

• Dissolved Oxygen (O2)

• Cloride (Cl2)

• Heavy Metals

• Electrochemical oxygen demand (EOD)

• Conductivity/impedance

• Redox Potential (ORP)

• Dissolved Oxygen (O2)

• Cloride (Cl2)

• Heavy Metals

• Electrochemical oxygen demand (EOD)

Au/Pt microelectrodesIDS

4B1

y = 0.1003x + 7.4826R2 = 0.9986

0

200

400

600

800

1000

1200

0 2000 4000 6000 8000 10000 12000Conductividad (mS/cm)

Pot

enc

ial,

mV

145 µm/cm - 11,2 mS/cm

4 bars

Conductivitycalibration curve

3-cell Au/Pt microelectrodes

Amperometric Microelectrodes

Ip = K x CEq. Randles

Oxidation- Reduction reactions: Fe2+

0 1 2 3 4 5 6 7 8 9 10

-3

-2

-1

0

i -i 0 (

µA)

Concentracion O2 (ppm)

Experimento 9 Experimento 7 Experimento 8

Electrodo 3360-02-03

-0.4 -0.2 0.0 0.2 0.4 0.6 0.8

0

100

200

300

400

500

600

700 i (µA) = 0.256 * Gluc. conc (ppm O2) + 68.00

r = 0.998

0 400 800 12000

100

200

300

400

i / µ

A

Glucose concentration / ppm O2

i / µ

A

E / V vs Ag/AgCl Baseline 200 µl glucose 400 µl glucose 600 µl glucose

Disolved O2 sensor Glucose sensor

Peroxidase Biosensors

Biosensors based on nanomaterials

NS O

OO

O

N SO

OO

O

+

NS O

OO

O

NS O

OO

O

NS O

OO

O

S NH

O

S NH

O

S NH

O

NS O

OO

O

N SO

OO

O

+

Gold UMEA

4 mM DTSP/DMONS O

OO

O

NS O

OO

O

NS O

OO

O

HRP

S NH

O

S NH

O

S NH

O

S NH

O

S NH

O

S NH

O

Immunosensor approach for the detection of rabbit IgG

Carbon nanotube-polystyrene composite electrodes

O

NH

O

NH

O

HO

O

HO

O

NH

O

NH

O

HO

O

HO

Biological recogntion event usinganti-rabbit IgG peroxidase conjugateCovalent immobilization of rabbit IgG

-4.5

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

0 20 40 60 80

Time / s

Cur

ren

t / µ

A

031030100

[rIgG] (ng/mL)

a

b

c

de

abcde

-4.5

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

0 20 40 60 80

Time / s

Cur

ren

t / µ

A

031030100

[rIgG] (ng/mL)

a

b

c

de

abcde

Calibration:• I/µA = 0.44*log[rIgG] - 2.47

r= 0.99; n= 4Linear range:• 3-100 ng/mL IgG rabbitLimit of detection: • 3 ng/mL

UMEA modified with Au nanoparticles

10 times higher sensitivity/ Au microelectrode10 times higher sensitivity/ Au microelectrode

500

600

700

800

900

1000

1100

1200

1300

1400

1 3 5 7 9 11 13

pH

Vou

t (m

V)

2201-3-005

2201-3-006

2201-5-005

2201-5-006

Monolithic Integration on BESOI substrate6 NMOS ISFET1 IDEs1 Temperature Diode

I = 100 µA

0.525

0.550

0.575

0.600

0.625

0.650

0.675

20 30 40 50 60 70 80

PCB-1

PCB-2

PCB-3

PCB-4

PCB-5

PCB-6

PCB-7

PCB-8

PROMEDIO

T (ºC)

V (V)

Electrically Isolated byTrench (SOI Substrate)

pH ISFET

P-n diode

IDE

Multisensor chips

Optical integrated systems

Characteristics:

• Method of fabrication is fast and easy.

• Monolithic integration (µTAS).

• Compatible with aqueous media.

• Biocompatible, chemically stable and non-toxic.

• Absorbance between 200 and 1000 nm

Characteristics:

• Method of fabrication is fast and easy.

• Monolithic integration (µTAS).

• Compatible with aqueous media.

• Biocompatible, chemically stable and non-toxic.

• Absorbance between 200 and 1000 nmPDMS

Prism configuration MIR configuration

Polydimethylsiloxane

• Instrumento analítico para control de calidad de alimentos• Formado por:

• Sistema de muestreo• Conjunto de sensores químicos• Sistema de procesamiento de datos: reconocimiento de patrones o calibración multivariante (PCA, Redes Neuronales)

�AplicacionesAlimentación, bebidas, cosmética, farmacéutica y medio ambiente� Control de calidad y fraude� Selección de tipos de alimentos� Determinación de D.O. en vinos� Análisis cuantitativo

�AplicacionesAlimentación, bebidas, cosmética, farmacéutica y medio ambiente� Control de calidad y fraude� Selección de tipos de alimentos� Determinación de D.O. en vinos� Análisis cuantitativo

fluídica

Software para:control de bombas/válvulasVisualización señalTratamiento señal

Sistema multisensor

Table of contents









• Conclusions









• Conclusions

Graphic representationin the new axes

PC 1 (60 %)

PC 2 (30 %)

Group 1

Group 3Group 4

Group 2

Principal Component Analysis (PCA)

Y

XZ

Directions of maximumvariation, PCs

PCA

Original Data

Representation in twodimensions

Change of the axes

Reduction of variables

Method for pattern recognition

Partial-Least Squares (PLS)

∑=

+=k

k

kk xbby1

0

Method of multivariate calibration. Linear equation expressedgenerally like this:

Reduction of variables

RegressionComponents or factors

Standard Method in Chemometrics.

Wine Samples

Calibration set : 2/3 of the total number of samples

Prediction set : the rest 1/3 of samples. At least, one sampleof each variety

Table of contents









• Conclusions









• Conclusions

-870

-860

-850

-840

-830

-820

-810

-8000 1000 2000 3000 4000 5000 6000

Time (s)E

(m

V)

ISF

ET

pH

pH ISFET forthe white wine set.

Sensor signals: Electrochemical Microsensors

0

10

20

30

40

50

60

70

80

0 100 200 300 400

Time (s)

Inte

nsity

(µA

)

MerlotCabernetGarnatxaTrepat

EOD sensor response from the red wine set.

-30

-20

-10

0

10

20

30

40

50

-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

E (V) vs. Ag/AgCl

Inte

nsity

(µA

)

MacabeuParelladaChardonnayXarel·lo

I. +1.31 V

I. -0.38 V

I. +0.65 V

I. +1.01 V

-30

-20

-10

0

10

20

30

40

50

-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

E (V) vs. Ag/AgCl

Inte

nsity

(µA

)

MacabeuParelladaChardonnayXarel·lo

I. +1.31 V

I. -0.38 V

I. +0.65 V

I. +1.01 V

Amperometric measurements for the white wine set.

Absorbance spectra obtained with the opticalsensor using DI water as reference

-0.1

0.1

0.3

0.5

0.7

0.9

300 350 400 450 500 550 600 650 700 750

Wavelength (nm)

Abs

orba

nce

Merlot 100%Cabernet 100%Pinot 100%

Abs. 420 nm

Abs. 520 nm

Abs. 620 nm

-0.1

0.1

0.3

0.5

0.7

0.9

300 350 400 450 500 550 600 650 700 750

Wavelength (nm)

Abs

orba

nce

Merlot 100%Cabernet 100%Pinot 100%

Abs. 420 nm

Abs. 520 nm

Abs. 620 nm

Sensor Signals: Optical sensors

-0.07

-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

202

249

296

343

389

435

481

526

571

616

661

705

750

793

837

880

923

966

1008

1051

1092

Mac 09/07 Abs Mitja

Par 78/07 Abs Mitja

Char 04/07 Abs Mitja

Xar 63/07 Abs Mitja

Red wines White wines

• Matrix of Data• Variables vs samples• We perform a PCA: all samples are

used for the training• Weight of samples: all are normalized to

1/sdv

• Matrix of Data• Variables vs samples• We perform a PCA: all samples are

used for the training• Weight of samples: all are normalized to

1/sdv

Data treatment

PCA analysis

• Scores distribution of all the samples all through the 2 C

• Loadings distribution of the variables (weight of each variable)

• Scores distribution of all the samples all through the 2 C

• Loadings distribution of the variables (weight of each variable)

•PLS 1. One model for each variable

• Comparison of our data with standard

method

•PLS 1. One model for each variable

• Comparison of our data with standard

method

Data treatment

PLS analysis

•One model for Ca detection

•Error ~ 0 for 2 PC

•Regression treatment

•One model for Ca detection

•Error ~ 0 for 2 PC

•Regression treatment

training set

test set

Classification of wines

-4

-3

-2

-1

0

1

2

3

4

-6 -4 -2 0 2 4 6

PC 1 (35%)

PC

2 (

17%

)

Chardonnay

Xarel·loParellada

MacabeuPenedes

MacabeuGarraf

-4

-3

-2

-1

0

1

2

3

4

-6 -4 -2 0 2 4 6

PC 1 (35%)

PC

2 (

17%

)

Chardonnay

Xarel·loParellada

MacabeuPenedes

MacabeuGarraf

white

-4

-3

-2

-1

0

1

2

3

4

-6 -4 -2 0 2 4

PC 1 (38%)

PC

2 (

27%

)

Merlot 08

Carbenet S.

Garnatxa

Trepat

Merlot 07

-4

-3

-2

-1

0

1

2

3

4

-6 -4 -2 0 2 4

PC 1 (38%)

PC

2 (

27%

)

Merlot 08

Carbenet S.

Garnatxa

Trepat

Merlot 07

red

Classification according to:• Grape variety• vintage year• geographical origin

Classification according to:• Grape variety• vintage year• geographical origin

PCA for monovarietal wines

Classification of wines

60 mixtures of

Cabernet Sauvignon, Pinot Noir and Merlot 0, 25, 50, 75, 85, 100%

Wine classification accordingto the percentage of grape

variety

Wine classification accordingto the percentage of grape

variety

Quantification ofeach variety

Quantification ofeach variety

0 20 40 60 80 100

0

20

40

60

80

100

Obt

aine

d %

Pin

ot N

.

Expected % Pinot N.

y = (0.96±0.08)x + (1.62±3.75)r2 = 0.976

0 20 40 60 80 100

0

20

40

60

80

100

Obt

aine

d %

Mer

lot

Expected % Merlot

y = (0.94±0.11)x + (3.01±4.68)r2 = 0.949

-4 -3 -2 -1 0 1 2 3

-3

-2

-1

0

1

2

3

4

PN >= 75 % CS >= 75 % MT >= 75 % Mescles 50:50 Mescles ternàries

PC

2 (

15 %

)

PC 1 (20 %)

(A)

-4 -3 -2 -1 0 1 2 3

-3

-2

-1

0

1

2

3

4

PN >= 75 % CS >= 75 % MT >= 75 % Mescles 50:50 Mescles ternàries

PC

2 (

15 %

)

PC 1 (20 %)

(A)

PLS for Chemical Wine Parameters

Sample VAD Total acidity pH Ca2+ Mg2+

Glycerol Macabeu 09/07 2.82 −3.7 −0.79 −0.7 −4.9 6.0

Parellada 77/07 0.07 9.5 −3.55 5.1 −0.1 2.0

Chardonnay 02/07 3.32 −2.7 1.16 5.7 −2.2 −4.3

Xarel·lo 44/07 −0.62 1.0 −4.76 4.0 −2.8 12.3

Sample VAD Total acidity pH Ca2+ K+

Glycerol 118/07 Trepat 4.45 0.6 -2.20 -5.1 -4.0 8.8

95/07 Garnatxa -4.05 -7.7 1.34 -6.1 6.7 2.3

49/07 Merlot -2.31 -2.6 -0.91 3.9 -0.7 3.8

45/08 Merlot -6.52 8.0 2.03 -2.4 -3.2 -5.6

105/07 Cabernet 0.99 -9.0 -4.01 8.7 -8.5 -2.7

Relative errors comparing with standard methods of analysis:

White wine

Red wineError < 5 % Error < 10 %

Quantitative analysis

VAD: volatil alcoholic degree

PLS for Optical Wine Paramenters

Sample Intensity of color Tonality Macabeu 09/07 4.67 1.09

Parellada 77/07 4.56 3.66

Chardonnay 02/07 0.87 2.48

Xarel·lo 44/07 7.29 −4.23

Sample Intensity of color Tonality CieLab a *

118/07 Trepat -89.74 2.20 -5.16

95/07 Garnatxa -44.57 -3.14 7.90

49/07 Merlot -32.31 4.30 8.98

45/08 Merlot 17.43 3.17 -7.16

105/07 Cabernet -10.22 -5.65 3.41

Relative errors comparing with standard methods of analysis:

Error < 10 %

Quantitative analysis

White wine

Red wine

• A multisensor system with different kind of sensors: potentiometric,

voltamperometric, conductimetric, optical � hybrid electronic tongue

• The system is capable of discriminating wines samples not only according to the

grape variety, but also to the origin and even the vintage.

• The system is able to quantify several parameters. The relative errors obtained are

below 10%.

• The ET could be applied in the cellars as a system for rapid detection, close to the

production and as an alarm system.

Conclusions

Acknowlegments:

Spanish R & D National Program (MICINN Project TEC2007-68012-C03-01/03)

ACC10 network GTQ-TECNIO

INCAVI: Santiago Minguez, Fina Capdevila

GTQ members specially: Manuel Gutierrez, Andreu Llobera

Tech. Univ. Braunschweig: S.Demming, S. Büttgenbach

Acknowledegements

Cecilia Jimé[email protected]

Sistemes multisensors aplicats al control de qualitatdels vins

sistemes multisensors aplicats al control de qualitat dels...

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