Olfactory Bioresponse III: Interdisciplinary Meeting on Electrophysiological and Imaging Studies

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. Badania były jednym z etapów projektu badawczego: „Intensywność zapachu. Prawa psychofizyczne i sztuczne sieci neuronowe” (2001-2003; kierownik pracy: dr hab. inż. J. Kośmider). 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)

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

6. Technical University of Szczecin, Department of Chemical Engineering and Environmental Protection Processes, Laboratory for Odour Quality of the Air Joanna Kośmider, Beata Krajewska Odour Monitoring Adopting GC-NN method Dresden Olfactory Bioresponse 2003

7. Plan of the presentation • Introduction • Research methodology: • sampling, • chromatographic analysis, • sensory analysis, • artificial neural network application • Results of the researches • Conclusions

8. INTRODUCTION

9. Introduction Odour - definition • 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 • An individual sensation dependent on sensibility of human olfactory analyser and motivational factors

10. Introduction Each compound: • volatile in the conditions of the surroundings, • dissolvable in water, • dissolvable in fat, • polar, while contacting protein receptors stimulating olfactory cells, • of eligible amount of molecules in the air(eligibleconcentration S), induces odour sensation of intensity I.

11. Introduction Trials of combining strength of sensation (odour intensity,I) with strength of stimulus (odorant concentration, S), psychophisical functions: • Weber – Fechnerlaw I = k W–F· log (S/SPW) I– strength of sensation(intensity), [ - ], kW–F– coefficient of proportionality (Weber – Fechner coefficient), [ – ], S– strength of stimulus (odourant concentration in airinducing odour sensation of intensity I), [mg/m3], SPW – odour sensation threshold, [mg/m3]. • Stevens law I = ks · S n I– strength of sensation(intensity), [ - ], S– strength of stimulus (odourant concentration in airinducing odour sensation of intensity I), [mg/m3], kS, 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: • Japan (since 1972), • Canada (Quebec, since 1980), • Holland (since 1984), • Germany, • Poland.

13. Introduction The most unambiguous and complex description of the problem was prepared by German legislation: Restrictions on odour emissions reffer to all industrialworks 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 Limiting frequencies of odour hours occurance (Germany) Share of negative estimations in the total number of estimations Prescriptions of German Agricultural Department

15. Introduction Project of polish olfactory standard of air quality determines the highest admissible concentration elaborated by our researching group: H0 – neutral or pleasant odour, H1 – unpleasant odour

16. Introduction A fact that: • It is essential: • 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), • to verify the determined quantities with the standarised threshold values, • Air consists of mixtures of odourants to which quoted psychophisical laws are not applicable on the contrary to isolated compounds... ...provoked the idea of applying GC-NN system to evaluate odour intensity of mixtures of compounds.

17. Introduction Aims of the work • Verifying potentiality of artificial neural networks to predict odour intensity of mixtures of compounds, • Determining existence of correlation between a feature of odour quality – odour intensity,Iand 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) • Determining magnitude of training sets for ANN to achieve the best results (the smallest error measured with SD. RATIO, RMS Error and irrelative error)

18. Introduction ANN BIOLOGICAL NEURAL NETWORK SIMILARITY S I M I L A R I T Y

19. RESEARCH METHODOLOGY

20. Research methodology Chromatographical analysis Sensory analysis Odour intensity, I1 SET OF DATA Chromatographic data, h1 - h14 Artificial neural network Odour intensity obtained with analitical methods, I2

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

22. Research methodology SAMPLING Materials Polietylen hose Stroehlein Gas Cylinder taking samples of pure air Heat-resistant foil sleeve Accumulatore Draw-lift’s ZALIMP pump type 335B irrigating pure air samples with citrus oil components Rychter type washer Two foil sleeves

23. Research methodology SAMPLING Materials Polietylen hose Stroehlein Gas Cylinder taking samples of pure air Heat-resistant foil sleeve Accumulatore Draw-lift’s ZALIMP pump type 335B irrigating pure air samples with citrus oil components Rychter type washer Two foil sleeves Hamilton syringe 500 ml Two foil sleeves containing: injecting the pollutants: acetone, ethanol, isopropanol, isoamyl acetateand dillutions of the basic sample mixture of air and volatile citrus oil components & pure air

24. Research methodology Schedule of measurements

25. Research methodology • 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, 14 defining variables measured [mm] from an invariable basis make a part of a set of data • 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.

26. Research methodology • SENSORY ANALYSIS 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

27. Research methodology During ANN tests Network Creation Wizard function available in Statistica NeuralNetwork (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. Research methodology An excerpt:

30. Research methodology An excerpt:

31. Research methodology An excerpt:

32. Research methodology ANN error measurement S.D. RATIO = 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. Results of the research Exemplary test of ANN training with data set 2

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

37. CONCLUSIONS

38. Conclusions You can conclude that: • artificial neural networks can properly determine intensity of air polluted with many compounds, • to conduct a training a series of 491 patterns of sensory – chromatographic characteristics of 57 samples evaluated by more than ten people are necessary, • it is favorable to remove from the series the estimations of those people whose olfactory sensibility differs considerably from the average, • it seems possible to use training series carrying less information of a sample composition for network training.

39. Thank you for attention