Real Life Applications - ISOCS | International Society for ... Commercial E-nose Companies in 2005*...

Jan Mitrovics, JLM Innovation GmbH

From Research to Real LifeExperiences and practical demonstrations

• About JLM Innovation

• From research to real life

• Some history on electronic noses…

• How to develop a new sensor system?

• Application economics!

• Example 1 Instrumentation

• Example 2 Mass Market

• Exercises with chemical sensor systems

• Brief introduction to signal processing / data analysis

• MOXstick – Modulated single sensor

• Display of different systems

Structure of the talk

Jan Mitrovics, JLM Innovation GmbH 2

Founded in 2004 to help bridging the gap between R&D

and commercial exploitation.

20 years of experience in multi sensor systems in a broad

range of applications.

Products:

• Hardware platforms for various sensor technologies, interfaces and

networks

• Software platforms for data processing and control

Services:

• Custom hardware & software development based on a portfolio of

internal technology platforms

• Application support, feasibility studies, technology consulting,

market studies

About JLM Innovation GmbH and myself

3Jan Mitrovics, JLM Innovation GmbH

Efficient, easy to use solutions!

Added value, through access to

complementary technology.

Fair and open relationship.

Tailored solutions that meet your needs.

Prototype or small scale production.

Flexible licensing.

What to expect from us


Sensor systems and sensor networks based

on a broad range of sensor technologies

Hardware platforms

QMB, SAW,

MOS, EC, CP,

FET, IR, ...

5Jan Mitrovics, JLM Innovation GmbH 5

Lung Cancer Breath Analysis Instrument

LCaos Project


Data acquisition and analysis software for

sensor systems

• Software

(Windows, Mac,

IOS, Android)

• Embedded Software

Software platforms


From research to real life


€ 5M Nexus Report (in 2002)

€ 10-15M Wall Street Journal (1998)

€ 10M Greenberg (1998)

€ 12M Attempto Service GmbH (in 2001)

€ 50 M FutureTech Report

€ 145 M Gardner / Bartlett (in 2000)

€ 1200 M German Infotech (in 2004)

€ 4500 M The Economist (1998)• Source: Gardner 1st Workshop NOSE II

Estimates of Enose Market


9 Commercial E-nose Companies in 1997

10

Source: Gardner and Bartlett, Electronic Noses 1999, OUP


Step 1: build or buy an enose

Step 2: measure some stuff

Step 3: do magical data analysis

Step 4: write publications

Step 5: go to Step 2

Typical early enose research


17 Commercial E-nose Companies in 2002

12

Source: Vanneste, Handbook of Machine Olfaction (2002), Wiley-VCH


23 Commercial E-nose Companies in 2005*

13

Source: JLM Innovation / NOSE II, Market Survey Electronic Nose 2005

* With products on the market

Manufacturer Products TechnologiesAirsense GDA, PEN, i-PEN/MOD, KegControl,

Proc.Ctrl Nose, EDUMOS sensors, IMS, thermo desorption

Alpha M.O.S. Fox, Prometheus, Gemini, Kronos, Astree MOS, CP and QMB sensors, MS, GC, Electronic Tongue

AltraSens OdourVector QMBAppliedSensor Embedded modules MOS, FE and QMB sensorsDr. Födisch Umweltmesstechnik AG OMD 1.10; OMD 98 MOSElement FreshSense MOSEnvironics Oy Chempro 100, ... IMS, sensor arraysElectronic Sensor Technology Znose GC with SAW detectorFive Technologies GmbH QMB 6, MS-Sensor, SensiTOF QMB sensors, MSGERSTEL GmbH & Co. KG ChemSensor MS, GC, MOS, TDSIllumina Inc BeadStation Bead arraysLennartz electronic GmbH MOSES II MOS and QMB sensorsMeridiantek AG / Sensobi DL 1000, DL 1000 IS, DL 1000 IS Smoke SCP sensorsMicrosensor Systems Inc VaporLab, Hazmat, Eagle Monitor ... SAW sensors, GCPerkin Elmer QMB6 QMB sensorsQuartz Technology Limited QTS-1 QMB sensorsRST Rostock System-Technik GmbH SAM detect, ... MOS sensorsSACMI EOS 835 MOS sensorsScensive Technologies Ltd Bloodhound, ST214 CP and DLC sensorsSMart Nose Ltd SMart Nose MSSmith Detection /Cyrano Sciences Cyranose 320 Carbon black polymer sensorsSysca AG Kamina MOSTechnobiochip Libra Nose QMB sensors


• Quality assessment of food and beverages

very fragmented, low volume, various deployments

• Pharmaceutical and chemistry applications

very fragmented, low volume, various deployments

• Medical applications

Highly regulated, mid to high volumes, no deployments yet

• Safety and military applications

Mid volumes, various deployments

• Environmental and agricultural applications

Fragmented, low to mid volumes, few deployments

• Embedded Applications

Mid to high volumes, very cost sensitive, few deployments

Application Areas and Potential Markets


• Generic instruments, handheld instruments, dedicated systems and

embedded devices were covered

• More than 30 companies involved in electronic nose instrumentation

and technology

• Applications in food industry, chemistry and pharmaceutics,

medicine, safety and military, environmental and agricultural, and

automotive

• Market for generic instruments is very fragmented with low growth.

• Dedicated systems for medical and safety applications have a lot of

potential. Several companies are focused on these markets.

• Simple embedded devices are being introduces to automotive and

consumer markets with strong growth.

• For many application further improvement of the technology is

necessary.

Electronic NOSE survey 2005


A „We use a system, with sensing properties that

are vaguely known, measuring something of

vaguely known composition and use magical

data analysis to generate the result that we

want.“

Application approach: Which one is better?

B „We know (or research) the compounds of

interest and then build a sensor system specific

for those compounds.“


„A sounds great. We can start right away

and get going. Why go through more

hazzle, when we can find out with some

simple tests.“

Optimists answer


A is pure gambling. Our chance to succeed

is nil. I need more infomation, so please

go back and spend more research until we

have enough information to do B.

Pessimists answer


Obviously B is the only way to go.

We need verifiable well founded

methodology and B is the only way to

achieve that.

Please increase our budget. (We need more

fundamental research and fancier

equipment for a proper assessment.)

Scientific answer:


We just sold 1000 instruments to our

promising key customer.

Now get us the damn instruments.

By the way, if you could make them for half

the price, we could sell 10000

By the way, if you could make them for less

than 10 bucks, we could sell a million of

em (and it wouldn‘t matter if they worked).

Marketing answer


Marketing has identified a great window of

opportunity.

I know you guys are cookin something. I

don‘t understand the details, but we need

something that works.

Please put together a project plan with

budgets and a timeframe. We have a

board meeting on monday and need to

take a decision.

Management answer


It depends!

• On the amount of preexisting knowledge

(or the cost of acquiring it).

• On the available technologies.

• On the difficulty of the application.

A and B are not mutually exclusive. Often

both approaches are followed

simultaneously, or repetitively!

The only true, correct answer:


Odor (human perception) > generic

Complex mixture > simple mixture

Variable samples > stable samples

Variable matrix > stable matrix

Minor compound > major compound

Trace detection > high concentration

Unknown analytes > known composition

Reference samples hard to get > easily available

High thru put / short measurement time > no time constraint

Traceability required > not required

No false predictions > indication

Certifications > unregulated

Criteria for application difficulty


Typical error sources

24

Sample Measurement Evaluation

Pre treatment /Sampling

Sensors ElectronicsSpot sample Transportation /storage

Non represantative spot sample,

changing matrix,Influence of temperature, pressure,

humiditz, contaminations during sampling, transport and storage.

Contamination,

Influence of temperature,

pressure, carrier gas, amount and volume

on headspace

Drift,

poisoning, temperature,

pressure, carrier gas,

reference gas, gas flow.

Component

drift,temperature,

noise.Resolution or

measurement range too

small

Error in reference data

or recalibration data.Numerical effects.

Inadequate algorithms.


Economical potential

• Volume (number of deployments)

• Cost sensitivity / gross margin

• After sales

Development effort

Development risk

Initial costs for production

Sales cycle

Spin off opportunities

Application economics


Application Steps

26

Problem definition

Feasibility prephase

Training

and Validation

Deployment

Specifications / Requirements

Expert analysis

Tests with limited sample set

Extensive sample set (all classes,

good bad, cross sensitivities)

Reference methods

Expert know-how

Installation at (multiple) sites


Feasibility Study

-> Check for discrimination (e.g. PCA-Plot)

-> Assess main components / cross interference

=> No final answer for success!

Application Development

-> Optimization of method (technology, ...)

-> Validation

-> Reproducibility, Reliability, ...

=> Ready for implementation (or not...)

Feasibility vs. Application dev.


• First contact

• Second contact: Initiation of collaboration

Feasibility study

• Discussion + budget allocation

• Application development

• Acceptance + budget allocation

=> Repeat sales

Time to successful application

28

Months

0

2-3

6 – 12

> 6

6 – 12

======

Min 2

years


Classical Enose: Cost per sale

29

Instrument development

Instrument manufacturing

Marketing

Feasibility

Application development

Customer support

x M€ / sold instruments

x k€

Marketing cost / sold instr.

0.x k€

0.x M€ / (sold instr./ appl.)

x k€

Optimum: Few applications with high numbers

Real world:Average sale per customer: 1.x


Price Cost Manufacturing cost

-> Price = Customer Value

Be efficient

-> Don‘t waste your money on

-> impossible applications (technology fit)

-> non profit applications (nice to have)

Adopt your business modell

-> Concentrate on your strength

-> We need more specialization and open

exchange

Success strategies ?


Example 1:

Quality control of packaging material



Examples: PARFUM / ESCAPE

32

Philips-DAP, Netherlands

Philips-LEP, France

CSEM, Switzerland

Microsens, Switzerland

MOTECH, Germany

Neotronic Scientific, UK

Nestlé, Switzerland

AppliedSensor, Sweden

Gerstel, Germany

INRA, Dijon, France

Wall, Austria

Univ. Tübingen, Germany

M O T ECH



33

State of the Art:

Human Odour Panel

Automated investigation

with “Electronic Nose”

Better called application specific

sensor system (a triple s)


Scoring

34

Evaluation of odor and taste in comparison with a reference material. The difference is estimated according to the following category scale:

• 0: no difference with the reference

• 1: hardly perceptible difference (not definable)

• 2: slight difference (just definable)

• 3: strong difference (clearly definable)

• 4: very strong difference

Any packaging materials with a median score equal or greater than 2.5 is considered to be of doubtful quality and should be submitted to further analyses.



35

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Od

ou

r p

red

icti

on

by M

OS

ES

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Test data withre-calibration ofthe array

Training data

Odour prediction by human sensory panel

RMSE = 0.26 RMSE = 0.57

Od

ou

r p

red

icti

on

by M

OS

ES

II


GC/MS of Nestlé Packaging Material

36

5 10 15 20 25 30 35

0

1

2

3

4

5

Inte

ns

ity

[a

.u.]

Retention time [min]

more than 70% cyclohexane

in the headspace


GC/MS of Packaging Material, Bad Case


Quality control: Final Product

38

Application specific sensor system (a triple s)


• Odor quality assessment by panels delivers imprecise

data

• Solutions are possible in simple cases (few components)

• Reference analysis is critical

• Methods cannot easily be transferred to new sample

types

• Reference sample sets are expensive and hard to get

• Successful, validated method does not guaranty

commercial success

Quality Control: Lessons learned


• 2 consecutive projects, total duration more than 6

years

• Commercialization after the second project

• Effort for commercialization low in comparison to

research projects

• Prototypes during project based on commercial

products

• Low volume

• Small certification requirements

• No challenging production cost constraints

Project characteristics


Example 2:

Control of air intake in automobiles



Examples: CIA

42

VDO Germany

FIAT, Torino, Italy

Telecom Italia

Univ. Warwick, UK

Univ. Southampton, UK

Univ. Rome, Italy

Univ. Linköping, Sweden

Univ. Neuchâtel, Switzerl.

Univ. Tübingen, Germany


Automotive application

43

Automatic switching of the re-circulation flap• Increase of cabin air quality

• Safety issue

• Comfort aspect


• Development of prototype instruments containing

different sensor technologies

• Real life tests with the prototypes (test drives)

• Optimization of the sensor technologies for the

application

• Development of pattern recognition techniques

Methodology during project


• IAQ module from AppliedSensor

Automotive Product


• Duration 3 Years

• Outcome of the project

• Optimized prototype gas sensors

• Selected sensor technology

• Development methodology

• Effort for commercialization very high!

• Prototypes used in project not suitable for

commercialization (complete redevelopment)

• High certification requirements

• High volume, very cost sensitive

• New partners required

Project characteristics


• Certification / Design Wins / Product cycles

• Upscaling of production

• Establishing of a production process

• Reproducibility / Stability

• No recalibration

• No / limited factory calibration

• Cost, Cost, Cost

• Total solution cost (sensor + packaging + electronics +

signal processing + calibration)

• Better than good enough is wasting money!

Specific challenges of mass markets


General remarks / discussion


The SNet sensor network system:

4 Metal oxide sensors

32bit µC for onboard

data analysis

Ethernet and Zigbee

communication

Server software for

operation as sensor network

Display of chemical sensor systems


The MOXstick:

1 Metal oxide sensor, almost any available

MOx sensor can be used.

Temperature modulation and sensor

characterization

Plug and Play, no drivers required

Simple, but powerful PC software

Sensor arrays by combining

several Sticks / Instruments

Experiments with the MOXstick


Typical Steps During Data Analysis


final result

Feature

extractionmax, min, area, ...

Pattern

RecognitionPCA, LDA, ANN, ...

4

3

2

1

x

x

x

x

Pre-Processing: Filters


Filters are often applied to reduce noise in the signal

Clean signal

Noisy signal

MA3

MA30

IIR

Median5

xax x

ai

i i alt

,

1

xN

xi i jj

N

1

1,Moving Average

IIR

Pre-Processing: Linearization


Many simple pattern recognition algorithms rely on linear calibration curves.Non linearities in the transducer or electronics are often well known.→ Linearization during pre processing is a simple yet very effective method

Input e.g. concentration

Ou

tpu

t e

.g. V

olt

)(xfy

Input e.g. concentration

Lin

ea

rize

dO

utp

ut

)(1 yfconstylin

2

)ln(

yy

yy

yy

lin

lin

lin

Feature Extraction: Basic Features

The complete response curve contains a lot of redunancy.

In order to compute robust results, we want to remove as much

redundancy as possible, while preserving a maximum of information!


Se

nso

r sig

na

l

time

Maximum

MinimumBaseline

Signal at a

certain point

in time

Area

Pe

ak h

eig

ht

Feature Extraction

Criteria for good features

• High correlation to the desired information

• Low correlation to other features

• Robust

• Not susceptible to noise

• Reproducible (e.g. masks variations in flow,

temperature, memory effects …)

• Linear with changes in input signal


Common Features related to signal strength

Maximum:

• directly related to the size of the input value

Signal Height (Maximum – Baseline):

• removes offset and additive baseline drift from the sensor response

• typical use: QMB sensors, measurements with changing background

Relative Signal Height (Maximum – Baseline) / Baseline:

• weights the signal height with changes in baseline

• may be used to counteract linear drift effects, that affect baseline and sensitivity in

the same way

• scaling of different sensors to similar sensitivity

• typical use: metal oxide sensors sensor

These features are often combined with

averaging over a number of measurement points to reduce noise

ln(feature) or other linearisation functions



-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5

PC1: 66.5%

-4

-3

-2

-1

0

1

2

3

4

PC

2:

16

.4%

Anisol

Cyclohexanon

Propanol

Toluene

PCA: Basic concept

The PCA scores plot represents

a 2-dimensional view on the

data, in which

• the maximum of

variance is contained

• while preserving the

relative distances

between the objects.

• Often the amount of

information represented

by one principal component

is printed in the axis label.

• The principal components are

always sorted by their

information content, with PC1

beeing the most important one.


PCA: Calculations

In PCA X is decomposed (e.g. via SVD or NIPALS) to:

Where:

U is a matrix containing the Eigenvectors of XXT,

V is a matrix (called loadings) containing the Eigenvectors of XTX and

S is a diagonal matrix containing the square roots of the Eigenvalues λ.

Note that XTX corresponds to the covariance or correlation matrix of X

(depending on the scaling of the variables).

The scores for the objects in X are simply the Eigenvectors in U multiplied by S.

(The Eigenvectors in U and V are normalized to length one).

To calculate the scores t for a new object, we multiply its measurement vector x

with the loadings V :

Vxt

idiagS,USVXT


-1.2 -1.0 -0.8 -0.6 -0.4 -0.2 -0.0 0.2 0.4 0.6 0.8 1.0 1.2

PC1: 66.5%

-0.8

-0.6

-0.4

-0.2

-0.0

0.2

0.4

0.6

0.8

PC

2:

16

.4%

Q1

Q2Q3

Q4

Q5

Q6

Q7

Q8

S1

S2

S3 S4

S5

S6

S7

S8

PCA: Loadings plot

The loadings plot shows the

influence of each variable

onto the principal

components.

Variables not contributing will

be near the center.

Variables with similar

information content will be

represented next to each

other.


PCA: Biplot of scores and loadings

o In a biplot, scores and

loadings are shown (with

adequate scaling)

o By comparing the directions of

the loadings with the position

of objects it is possible to draw

conclusions on the suitability

of individual variables.

o In this plot the variables S2 –

S7 are in the direction of the

spread within the groups.

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5

PC1: 66.5%

-4

-3

-2

-1

0

1

2

3

4

PC

2:

16

.4%

Anisol

Cyclohexanon

Propanol

Toluene

Q1

Q2 Q3

Q4

Q5

Q6

Q7

Q8

S1

S2S3S4

S5

S6

S7

S8


PCA: Scree plot

o The scree plot shows the

amount of information

represented by each principal

component (bars)

o It is also possible to show the

information content covered by

the first x components (line)

o The information content of a

principal component is the size of its Eigenvalue λ, scaled in a

way, that the sum of all

Eigenvalues is 1.

0,00%

10,00%

20,00%

30,00%

40,00%

50,00%

60,00%

70,00%

80,00%

90,00%

100,00%

PC 1 PC 2 PC 3 PC 4 PC 5 PC 6 PC 7 PC 8 PC 9 PC 10 PC 11 PC 12 PC 13 PC 14 PC 15 PC 16

The scree plot helps to identify the number of

components, necessary to explain the data. In

above plot, the first 3 components amount to

more than 90% of all information.

Introduction to JLMlogSP

Life demonstration


63

Thanks for listening!


Real Life Applications - ISOCS | International Society for ... Commercial E-nose Companies in 2005*...

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Transcript of Real Life Applications - ISOCS | International Society for ... Commercial E-nose Companies in 2005*...