1. 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.

4. 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).

5. Poniżej zamieszczono prezentację przygotowaną przez mgr inż. Beatę Krajewską OLFACTORY BIORESPONSE III, na OLFACTORY BIORESPONSE III, a po konferencji przedstawianą na Seminarium Doktoranckim WTiICh PS w języku polskim (patrz – notatki prelegenta)

6. 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

7. 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

8. INTRODUCTION

9.  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

10. 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.

11. 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), [ - ], k W–F – coefficient of proportionality (Weber – Fechner coefficient), [ – ], S – strength of stimulus (odourant concentration in air inducing odour sensation of intensity I), [mg/m 3 ], SPW – odour sensation threshold, [mg/m 3 ]. I = k s · 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/m 3 ], k S, n – empirical constants, [ – ].

12. 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),

13. 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’).

14. Introduction Operational use of area Immission standard IW habitable and miscellanous0,1 trade and industrial0,15 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/m 3 ] Frequency of evceedings [% h in a year] habitable areas13 miscellanous dedication 15 rural areas 18 33 industrial areas 110 35

15. 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 TON 30 = 0,1 ou/m 3 [%h in a year] habitable and recreational H05 H13 industrial and rural H010 H18 H0 – neutral or pleasant odour, H1 – unpleasant odour Introduction

16. 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:

17. 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, h 1 - 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

18. SIMILARITYSIMILARITY ANN BIOLOGICAL NEURAL NETWORK SIMILARITY

19. RESEARCH METHODOLOGY

20. Research methodology Sensory analysis Artificial neural network Odour intensity, I 1 Chromatographic data, h 1 - h 14 SET OF DATA Odour intensity obtained with analitical methods, I 2 Chromatographical analysis

21. taking samples of pure air Research methodology a.SAMPLING Stroehlein Gas Cylinder Accumulatore Heat-resistant foil sleeve Polietylen hose Materials

22. 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 335B Rychter type washer Two foil sleeves Materials

23. 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 335B Rychter type washer Two 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 &

24. Research methodology Schedule of measurements

25. Research methodology b.CHROMATOGRAPHIC ANALYSIS in variable temperature conditions GAS - CHROMATOGRAPH Chromatron GCHF 18.3:  six-permeable tap,  sample loop of 5 cm 3 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 · 10 8. 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] h1h1 h2h2 h3h3 h4h4 h5h5 h6h6 h7h7 h8h8 h9h9 h10h10 h11h11 h12h12 h13h13 h14h14 77777777777777 127 24 143 12

26. Research methodology c.SENSORY ANALYSIS Number of a standard >1010-99-88-77-66-55-44-33-22-1<1<1 Sensibility threshold X Odour 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 cm 3 of n- buthanol in 100 cm 3 H 2 O Step of diluting: 2,86

27. 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).

28. Research methodology Three training sets were prepared:

29. h1h1 h2h2 h3h3 h4h4 h5h5 h6h6 h7h7 h8h8 h9h9 h 10 h 11 h 12 h 13 h 14 I1 101610188148161011103716502,5 101610188148161011103716504 101610188148161011103716504 101610188148161011103716503 101610188148161011103716504 101610188148161011103716503 101610188148161011103716504 101610188148161011103716502 101610188148161011103716504 101610188148161011103716503.5 101610188148161011103716503 An excerpt: Research methodology

30. h1h1 h2h2 h3h3 h4h4 h5h5 h6h6 h7h7 h8h8 h9h9 h 10 h 11 h 12 h 13 h 14 I1 71272414312221167315544188442485 1082209311121056285140152381905 1012104811201258283430160402125 910803610321356263648136381825 12651638111682866293530164402245 10962022216401450255741148361964 101312620711 49264542154412074,8 134510 1282642223629107322175 7992119615671813193026110292404 7328418281239182120112291945 9159271111225132520413295261685,5 An excerpt:

31. Research methodology h1h1 h2h2 h3h3 h4h4 h5h5 h6h6 h7h7 h8h8 h9h9 h 10 h 11 h 12 h 13 h 14 I1 5271218710221039194937142343125 5271218710221039194937142343125 5271218710221039194937142343125 5271218710221039194937142343125,5 5271218710221039194937142343125,5 876195877734173529121292425 876195877734173529121292425 876195877734173529121292425 876195877734173529121292425 876195877734173529121292425 876195877734173529121292425 876195877734173529121292425 An excerpt:

32. Research methodology S.D. RATIO = ANN error measurement S.D. RATIO = RMS Error = 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.

33. RESULTS OF THE RESEARCH

34. Results of the research Exemplary test of ANN training with data set 1

35. Exemplary test of ANN training with data set 2 Results of the research

36. Exemplary test of ANN training with data set 3 Results of the research

37. CONCLUSIONS

38. 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

39. Thank you for attention