About the meeting:  The „OLFACTORY BIORESPONSE III meeting” is the third conference in a series of meetings which started in 1995 at the Department of Pharmacology at the University of Erlangen, Germany.

The two previous meetings of this series of conferences have been received extremely well by all participants, largely because a major focus is on the interpersonal exchange between researchers.

The scientific focus of the meeting is on studies using electrophysiological and imaging techniques. Among other topics the 2003 meeting is going to highlight retronasal olfactory perception, olfaction in neurodegeneration, and qualitative olfactory dysfunction.

Zostały wykonane w ramach

magisterskiej pracy dyplomowej mgr inż. Beaty Krajewskiej

(nagroda II stopnia w Konkursie Ministra Środowiska "Nauka na rzecz ochrony środowiska i przyrody"

na najlepsze prace magisterskie przygotowane w polskich szkołach wyższych w 2003 roku)

Badania były jednym z etapów projektu badawczego:

„Intensywność zapachu. Prawa psychofizyczne i sztuczne sieci neuronowe”

(2001-2003; kierownik pracy: dr hab. inż. J. Kośmider).

Poniżej zamieszczono prezentację przygotowaną przez mgr inż. Beatę Krajewską

na OLFACTORY BIORESPONSE III,OLFACTORY BIORESPONSE III,

a po konferencji przedstawianą na Seminarium Doktoranckim WTiICh PS w języku polskim

(patrz – notatki prelegenta)

Joanna Kośmider, Beata Krajewska

Odour Monitoring Adopting GC-NN method

Technical University of Szczecin, Department of Chemical Engineering and Environmental Protection Processes, Laboratory for Odour Quality of the Air

Dresden Olfactory Bioresponse 2003

1. Introduction

2. Research methodology:a) sampling,

b) chromatographic analysis,c) sensory analysis,

d) artificial neural network application

3. Results of the researches

Plan of the presentation

4. Conclusions

INTRODUCTION

A property of a chemical compound or of mixtures of compounds depenent on the concentration to activate the sense of smell and then be able to start an odour sensation

Introduction

An individual sensation dependent on sensibility of human olfactory analyser and motivational factors

Odour - definition

Introduction

Each compound:• volatile in the conditions of the surroundings,• dissolvable in water,• dissolvable in fat,

• of eligible amount of molecules in the air (eligible concentration S),

• polar, while contacting protein receptors stimulating olfactory cells,

induces odour sensation of intensity I.

Introduction

I = k W–F · log (S/SPW)

Trials of combining strength of sensation (odour intensity,I) with strength of stimulus (odorant concentration, S),

psychophisical functions:1. Weber – Fechner law

I – strength of sensation (intensity), [ - ],kW–F – coefficient of proportionality (Weber – Fechner coefficient), [ – ],S – strength of stimulus (odourant concentration in air inducing odour sensation of intensity I), [mg/m3],SPW – odour sensation threshold, [mg/m3].

I = ks · S n

2. Stevens law

I – strength of sensation (intensity), [ - ], S – strength of stimulus (odourant concentration in air inducing odour sensation of

intensity I), [mg/m3],kS, n – empirical constants, [ – ].

Introduction

Odour– definition reffering to both pleasant and unpleasant olfactory sensations

Legal restrictions on odour emissions

Trials of regulating problems with odour quality of the air have been undertaken in different countries for more than 30 years:1. Japan (since 1972),2. Canada (Quebec, since 1980),

4. Germany,5. Poland.

3. Holland (since 1984),

Introduction

The most unambiguous and complex description of the problem was prepared by German legislation:Restrictions on odour emissions reffer to all industrial works irrespective of whether they are subject to the procedure of sanctioning their activity or not (different ways of executing the restricions in various regions).

The most advanced trials of regulating problems with odour difficulties - North Westphalia:

Guideline ‘Odours immission’ – frequency of occurance exceedings the threshold concentration of olfactory detectability of air pollutants (so called ‘odour hours’).

Introduction

Operational use of area Immission standardIW

habitable and miscellanous 0,1trade and industrial 0,15

Limiting frequencies of odour hours occurance (Germany)

Share of negative estimations in the total number of estimations

Prescriptions of German Agricultural Department

Operational use of soil

Limitary quantities

Odourants concentration

[ou/m3]

Frequency of evceedings

[% h in a year]

habitable areas 1 3miscellanous

dedication 1 5

rural areas 1 83 3

industrial areas1 10

3 5

Project of polish olfactory standard of air quality determines the highest admissible concentration elaborated by our researching group:

Type of area Class of hedonic quality of air

Time of exceedings TON30

= 0,1 ou/m3

[%h in a year]habitable and recreational

H0 5H1 3

industrial and rural

H0 10H1 8

• H0 – neutral or pleasant odour,• H1 – unpleasant odour

Introduction

Introduction

a. to determine olfactory difficulty of polluted air, especially in industrial areas where odourants emissions are much higher than in any others (with sensory analysis of samples of polluted air),

b. to verify the determined quantities with the standarised threshold values,

2. Air consists of mixtures of odourants to which quoted psychophisical laws are not applicable on the contrary to isolated compounds...

A fact that:

...provoked the idea of applying GC-NN system to evaluate odour intensity of mixtures of

compounds.

1. It is essential:

Aims of the work

1. Verifying potentiality of artificial neural networks to predict odour intensity of mixtures of compounds,

2. Determining existence of correlation between a feature of odour quality – odour intensity, I and 14 values describing the sample responsible for the odour (14 distinctive points of a chromatographic curve measured [mm] from an invariable basis, h1 - h 14)

3. Determining magnitude of training sets for ANN to achieve the best results (the smallest error measured with SD. RATIO, RMS Error and irrelative error)

Introduction

Introduction

SIMILARITY

ANNBIOLOGICAL NEURAL NETWORK

SIMILARITY

RESEARCH METHODOLOGY

Research methodology

Sensory analysis

Artificial neural network

Odour intensity,

I1

Chromatographic data, h1 - h14

SET OF DATA

Odour intensity obtained with

analitical methods, I2

Chromatographical analysis

• taking samples of pure air

Research methodology

a. SAMPLING

Stroehlein Gas Cylinder

Accumulatore

Heat-resistant foil sleeve

Polietylen hose

Materials

Research methodology

SAMPLING

• taking samples of pure air

Stroehlein Gas Cylinder

Accumulatore

Heat-resistant foil sleeve

Polietylen hose

• irrigating pure air samples with citrus oil

components

Draw-lift’s ZALIMP pump type 335BRychter type washerTwo foil sleeves

Materials

Research methodology

SAMPLING

• taking samples of pure air

Stroehlein Gas Cylinder

Accumulatore

Heat-resistant foil sleeve

Polietylen hose

• irrigating pure air samples with citrus

oil components

Draw-lift’s ZALIMP pump type 335BRychter type washerTwo foil sleeves

• injecting the pollutants: acetone, ethanol,

isopropanol, isoamyl acetate and dillutions of

the basic sample

Materials

Hamilton syringe 500 ml

pure air

Two foil sleeves containing:mixture of air and volatile citrus oil components

&

Research methodology

Schedule of measurements

Research methodology

b. CHROMATOGRAPHIC ANALYSIS in variable temperature conditions

GAS - CHROMATOGRAPH Chromatron GCHF 18.3:

six-permeable tap, sample loop of 5 cm3 capacity,tower 2 metres long with cross-section of 4 mm,packing: Chromosorb W NAW, 60 – 80 mesh, coated with 20% Carbowax 20 M, portative gas: nitrogen, pressure at the inlet 1,2 at, Flame Ionisation Detector, hydrogen pressure 0,4 at, air pressure 0,9 at, detection sensitivity of 30 · 108.

14 defining variables measured [mm] from an invariable basis make a part of a set of data

Defining variables, heights of sequential peaks of a chromatogram [mm]

h1 h2 h3 h4 h5 h6 h7 h8 h9 h10 h11 h12 h13 h14

7 7 7 7 7 7 7 7 7 7 7 7 7 7127 127 127 127 127 127 127 127 127 127 127 127 127 12724 24 24 24 24 24 24 24 24 24 24 24 24 24143 143 143 143 143 143 143 143 143 143 143 143 143 14312 12 12 12 12 12 12 12 12 12 12 12 12 12

Research methodology

c. SENSORY ANALYSIS

Number of a standard >10 10-9 9-8 8-7 7-6 6-5 5-4 4-3 3-2 2-1 <1

Sensibility threshold XOdour of a sample X

is a method of evaluating some features of a sample like odour intensity by a group of panelists• 12 students•15 sessions, 10-15 samples during one session

• Basic dilution: 8 cm3 of n-buthanol in 100 cm 3 H2O

• Step of diluting: 2,86

Research methodology

During ANN tests Network Creation Wizard function available in Statistica Neural Network (StatSoft) programme was used.

Multilayer Perceptron and Back Propagation method was applied.

Data set consisted of 14 defining variables (input layer of ANN) and one defined variable (output layer).

Research methodology

Three training sets were prepared:

Trainingvariables

586

Veryfyingvariables

50

Testing variables

50

Set 1

Trainingvariables

47

Veryfyingvariables

5

Testingvariables

5

Set 2

Trainingvariables

217

Veryfyingvariables

217

Testingvariables

57

Set 3

T ra in ing sets

h1 h2 h3 h4 h5 h6 h7 h8 h9 h10 h11 h12 h13 h14 I110 16 10 18 8 14 8 16 10 11 10 37 16 50 2,510 16 10 18 8 14 8 16 10 11 10 37 16 50 410 16 10 18 8 14 8 16 10 11 10 37 16 50 410 16 10 18 8 14 8 16 10 11 10 37 16 50 310 16 10 18 8 14 8 16 10 11 10 37 16 50 410 16 10 18 8 14 8 16 10 11 10 37 16 50 310 16 10 18 8 14 8 16 10 11 10 37 16 50 410 16 10 18 8 14 8 16 10 11 10 37 16 50 210 16 10 18 8 14 8 16 10 11 10 37 16 50 410 16 10 18 8 14 8 16 10 11 10 37 16 50 3.510 16 10 18 8 14 8 16 10 11 10 37 16 50 3

Train ingvariab les

586

Very fy ingvariab les

50

Testing variables

50

Set 1

An excerpt:

Research methodology

Train ingvariables

47

Very fy ingvariables

5

Testingvariables

5

Set 2

Research methodology

h1 h2 h3 h4 h5 h6 h7 h8 h9 h10 h11 h12 h13 h14 I17 127 24 143 12 22 11 67 31 55 44 188 44 248 510 82 20 93 11 12 10 56 28 51 40 152 38 190 510 12 10 48 11 20 12 58 28 34 30 160 40 212 59 10 80 36 10 32 13 56 26 36 48 136 38 182 512 65 16 38 11 168 28 66 29 35 30 164 40 224 510 96 20 222 16 40 14 50 25 57 41 148 36 196 410 131 26 207 11 11 11 49 26 45 42 154 41 207 4,813 45 10 10 10 128 26 42 22 36 29 107 32 217 57 99 21 196 15 67 18 13 19 30 26 110 29 240 47 32 8 41 8 28 12 39 18 21 20 112 29 194 59 159 27 111 12 25 13 25 20 41 32 95 26 168 5,5

An excerpt:

Research methodology

Trainingvariables

217

Veryfyingvariables

217

Testingvariables

57

Set 3

h1 h2 h3 h4 h5 h6 h7 h8 h9 h10 h11 h12 h13 h14 I15 27 12 187 10 22 10 39 19 49 37 142 34 312 55 27 12 187 10 22 10 39 19 49 37 142 34 312 55 27 12 187 10 22 10 39 19 49 37 142 34 312 55 27 12 187 10 22 10 39 19 49 37 142 34 312 5,55 27 12 187 10 22 10 39 19 49 37 142 34 312 5,58 76 19 58 7 7 7 34 17 35 29 121 29 242 58 76 19 58 7 7 7 34 17 35 29 121 29 242 58 76 19 58 7 7 7 34 17 35 29 121 29 242 58 76 19 58 7 7 7 34 17 35 29 121 29 242 58 76 19 58 7 7 7 34 17 35 29 121 29 242 58 76 19 58 7 7 7 34 17 35 29 121 29 242 58 76 19 58 7 7 7 34 17 35 29 121 29 242 5

An excerpt:

Research methodology

S.D. RATIO =

. .. .

DS DataDS Error

ANN error measurement

S.D. RATIO = śr

śr

X

B

SDSD

1

RMS Error =

q

xxq

iabiabi

1

212

a – number of a session of measurements,b – number of a test,i – following number of a studied feature of a pattern,q – total number of patterns in a test.

Irrelative error: proportional share of cases for which difference between sensory and ANN assessment was not graeater than 0,5 in total set of cases.

RESULTS OF THE RESEARCH

Results of the research

Set 1, test 14

Training stages

RMS

Erro

r

0,14

0,16

0,18

0,20

0,22

0,24

0,26

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

TrainingVerification

Set 1, test 14RMS Error - trainig: 0,1686; verification: 0,1552, test: 0,1678

Following number of test patterns

Inte

nsity

of o

dour

0,00,51,01,52,02,53,03,54,04,55,05,56,06,57,07,5

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50

I1I2

Set 1, test 14 y = 50 * 0,2 * normal (x; 0,80706; 0,607969)

| I1 - I2 |

Num

ber o

f obs

erva

tions

0123456789

1011121314151617181920

0,0 0,2 0,4 0,6 0,8 1,0 1,2 1,4 1,6 1,8 2,0 2,2 2,4 2,6 2,8 3,0 3,2 3,4

Set 1, test 14

Following number of test patterns

I1 -

I2

-3

-2

-1

0

1

2

3

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50

Exemplary test ofANN training

with data set 1

Exemplary test ofANN training

with data set 2

Results of the research

Set 2, test 1

Training stages

RMS

Erro

r

0,06

0,10

0,14

0,18

0,22

0,26

0,30

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

TraningVerification

Set 2, test 1RMS Error - traning: 0,1692; verification: 0,07621; test: 0,1305

Following number of test patterns

Inte

nsity

of o

dour

0,00,51,01,52,02,53,03,54,04,55,05,56,06,57,07,5

0 1 2 3 4 5 6

I1I2

Set 2, test 1 y = 5 * 0,1 * (x; 0,36272; 0,165112)

| I1 - I2 |

Num

ber o

f obs

erva

tions

0

1

2

3

0,0 0,5 1,0 1,5 2,0 2,5 3,0 3,5

Set 2, test 1

Following number of test patterns

I1 -

I2

-3,5

-3,0

-2,5

-2,0

-1,5

-1,0

-0,5

0,0

0,5

1,0

1,5

2,0

2,5

3,0

3,5

0 1 2 3 4 5 6

Exemplary test ofANN training

with data set 3

Results of the researchSet 3, test 5

Training stages

RMS

Erro

r

0,10

0,14

0,18

0,22

0,26

0,30

0,34

0,38

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Training

Verification

Set 3, test 5RMS Error - trening: 0,1394; weryfikacja: 0,1317; test: 0,1103

Following number of test patterns

Inte

nsity

of o

dour

0,00,51,01,52,02,53,03,54,04,55,05,56,06,57,07,5

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58

I1I2

Set 3, test 5 y = 57 * 0,1 * (x; 0,37785; 0,229896)

| I1 - I2 |

Num

ber o

f obs

erva

tions

0123456789

1011121314

0,0 0,5 1,0 1,5 2,0 2,5 3,0 3,5

Set 3, test 5

Following number of test pattern

I1 -

I2

-3,5

-3,0

-2,5

-2,0

-1,5

-1,0

-0,5

0,0

0,5

1,0

1,5

2,0

2,5

3,0

3,5

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58

CONCLUSIONS

You can conclude that:1. artificial neural networks can properly determine

intensity of air polluted with many compounds,

2. to conduct a training a series of 491 patterns of sensory – chromatographic characteristics of 57 samples evaluated by more than ten people are necessary,

3. it is favorable to remove from the series the estimations of those people whose olfactory sensibility differs considerably from the average,

4. it seems possible to use training series carrying less information of a sample composition for network training.

Conclusions

Thank you for attention