2. © All Rights Reserved, Robi Polikar, Electrical and Computer Eng. Rowan University, Glassboro, NJ 08028

3. Did you ever measure a smell? Can you tell whether one smell is just twice strong as another? Can you measure the difference between one kind of smell and another? It is very obvious that we have very many different kinds of smells, all the way from the odor of violets and roses to asafetida. But until you can measure their likeness and differences, you can have no science of odor. If you are ambitious to find a new science, measure a smell. Alexander Graham Bell (1914)

4. The Department of presents…

5. Emerging Interdisciplinary Challenges Robi Polikar October 16, 2002

6. Outline  Introduction: emerging interdisciplinary challenges  Motivation and background  The mammalian olfactory system vs. the electronic nose  Commercially available electronic nose systems  Quartz crystal microbalances  Experimental setup  Identification of volatile organic compounds (VOCs)  An uncooperative database / sensitivity / selectivity issues  Dealing with an uncooperative database  Automated Identification  Neural Networks  Conclusions  Questions, comments and suggestions

7. Introduction: Emerging Interdisciplinary Challenges.......... Olfactory Physiology Organic Chemistry Signal Processing Pattern Recognition Computational Learning Electronic Nose Chemical Sensors / Analytical Chemistry

8. Introduction Motivation & Background  Food industries: detection of food quality / wholesomeness  Airport security: drug smuggling, detection of explosives  Anti-personnel land-mine detection  Detection of household chemicals  Detection of hazardous gases  VX, CO, radon, etc  Detection of volatile organic compounds  Wastewater odor control Many industries, institutions and organizations can benefit from a device capable of identifying odors:

9. Selectivity & Sensitivity Issues Humans can identify 10000 types of odors at varying sensitivity levels. 10000 odors are considered to be combination of a few basic types of odors: floral, musky, camphorous, pepperminty, ethereal, pungent (stinging), and putrid (rotten). Another group of researchers believe that this number is actually around 50. More recently, it has been suggested that there are actually over 1000 smell genes in the nose, each of which encodes a unique receptor protein. Sensitivity: 5.83 mg/L of ethyl ether, 3.30 mg/L of chloroform, 0.0000004 mg/L of methyl mercaptan (1/25 trillionth of a gram)

10. Mammalian Nose Vs. Electronic Nose Mammalian NoseElectronic Nose Receptor neuron Sensor / transducer Odorant binding protein Coating 10000000 receptors 6-30 sensors (array) Glomeruli Signal processing module Brain Pattern recognition module Sens. 1 part per trillion 1 part per million Selec. 10000~20000 odors <50 odors

11. Electronic Nose Systems

12. Sensor Technologies  Metal Oxide Semiconductor sensors (MOS)  Chemical Field Effect Transistors (ChemFET)  Conducting Polymers (CP)  Fiber Optical Sensors (FOS)  Quartz Crystal Microbalances (QCM)  Surface Acoustic Wave devices (SAW)  Mass Spectrometry  Gas Chromatography

13. Pattern Recognition technologies  Statistical pattern recognition (SPR)  Bayes classifiers  Discriminant analysis (DA)  Maximum likelihood estimate  Principal component analysis (PCA)  Non-parametric techniques  Artificial neural networks (ANN)  Fuzzy logic (FL)  Rule-based / expert systems

14. Commercially Available Systems

15. Quartz Crystal Microbalances & Gas Sensing Bare piezoelectric crystal Central part of the crystal coated with first gold, and then polymer material Electrode on front Electrode on back Crystal holder

16. Coating Selection Considerations For desired levels of selectivity and sensitivity Thickness, softness / stiffness, reversibility, operation temperature Viscoelastic properties: thermal expansion, swelling due to sorption, film resonance Solubility parameters: coating – analyte interactions AdvantagesDisadvantages  Thickness  sensitivity  resistance,  phase lag,  attenuation  Softness  response time,  reversibility  Attenuation  Stiffness  Attenuation  Reversibility  Temperature  Softness and hence  response time  sorption and hence  sensitivity.

17. VOCs and Coatings Used O Apiezon (grease, not a polymer) APZ Poly(isobutylene)PIB Poly (diethyleneglycoladipate) DEGA Sol-gelSG Poly(siloxane)OV275 Poly (diphenoxylphosphorazene) PDPP 12 individual VOCs at 7 different concentrations (84 patters). 24 Binary Mixtures of VOCs at 16 different concentrations (384 patterns)

18. Block Diagram of the Experimental Setup

19. Experimental Setup Switching Box Mass Flow Controller Network Analyzer VOC in bubbler Nitrogen VOC PC Sensor Cell

20. EXPERIMENTAL SETUP Mass Flow Controller Network Analyzer Gas Bubbler Sensor Cell Mass Flow Meter Switching Box Post-It notes

21. How Does Odor Signal look Like?

22. Existence of dominant VOCs Approach: Identify dominant VOC first, and identify secondary VOC based on the identification of the dominant VOC. Problems With Identification Of Mixtures APZ: Apiezon, PIB: Polyisobutelene, DEGA:Poly(diethyleneglycoladipate), SG: Solgel, OV:Poly(siloxane), PDPP: Poly (diphenoxylphosphorazene)

23. Pattern Separability Issues (a) Well separated patterns and (b) densely packed / overlapping patterns

24. Pattern (In)separability in Mixture VOC Problem Sensor 1 Sensor 2 Sensor 3 ETHANOL TOLUENE TCE OCTANE XYLENE

25. Identification of VOCs Preprocessing Increasing Pattern Separability Neural Network Training Neural Network Validation VOC Identification Raw Sensor Readings (6-D) Filtering, Normalization, De-trending, etc. Fuzzy nose (FNOSE), Feature range stretching, or Nonlinear cluster transformation Multilayer perceptron LEARN++ (for incremental learning) Classification..........

26. Nonlinear Cluster Transformation Outlier Removal Cluster Translation Nonlinear Cluster Transformation  Generalized regression neural networks  Similar to RBF networks  Do not require iterative training  Successful in multidimensional function approximation

27. PRINCIPLE COMPONENT ANALYSIS A Comparison ETHANOL TOLUENE TCE OCTANE TOLUENE OCTANE XYLENE ETHANOL TCE XYLENE

28. Artificial Neural Networks Signals Output signal based on a weighted average of input signals.......... Toluene Xylene From sensors (six)

29. The Multilayer Perceptron Neural Network …….... ……... W ji W kj net j i=1,2,…d j=1,2,…,H k=1,2,…c xdxd x (d-1) x2x2 x1x1 d input nodes H hidden layer nodes c output nodes zczc z1z1.. J x1x1 xdxd w Ji net k net j ….. zkzk net k yjyj..........

30. Results Single VOC Identification  7 patterns obtained for each VOC, corresponding to seven different concentration values between 70 ppm and 700 ppm.  Thirty (30) of the total 12*7=84 patterns were used to train the neural network.  Remaining patterns were used to validate the performance of the network  All 54 validation patterns were identified correctly !

31. Results Binary Mixture of VOCs Dominant VOC Performance: 96% Secondary VOC Performance: 96% 196 (50%) patterns used for training and remaining 196 used for testing.

32. Conclusions  QCM technology along with neural network identification can be used as an efficient tool for electronic nose applications  Challenges:  Identification of components in mixtures  Identification of gases at very low concentrations (ppb levels ?)  Adverse environmental conditions (temperature, humidity, etc.)  New sensor technologies for improved sensitivity and selectivity  Incremental learning of additional odorant (Algorithm: Learn++)

33. Questions