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

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

2. 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)

3. The Department of presents…

4. Towards the Electronic Nose Emerging Interdisciplinary Challenges Robi Polikar October 16, 2002

5. 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

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

7. IntroductionMotivation & 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:

8. 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)

9. Mammalian Nose Vs. Electronic Nose Mammalian Nose Electronic 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

10. Electronic Nose Systems

11. 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

12. Pattern Recognitiontechnologies • 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

13. Commercially Available Systems

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

15. 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

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

17. Block Diagram of theExperimental Setup

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

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

20. How Does Odor Signallook Like?

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

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

23. Pattern (In)separabilityin Mixture VOC Problem ETHANOL TOLUENE XYLENE OCTANE TCE Sensor 3 Sensor 2 Sensor 1

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

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

26. PRINCIPLE COMPONENT ANALYSISA Comparison XYLENE ETHANOL OCTANE TOLUENE TCE TOLUENE XYLENE OCTANE TCE ETHANOL

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

28. x1 wJi J xd . . . . . The Multilayer PerceptronNeural Network d inputnodes H hidden layer nodes x1 c outputnodes z1 x2 ……... .. Wkj netk Wji netj zk …….... netj .. yj zc … x(d-1) netk i=1,2,…d j=1,2,…,H k=1,2,…c xd

29. ResultsSingle 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 !

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

31. Conclusions Towards the Electronic Nose • 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++)

32. Questions