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Transcript of © All Rights Reserved, Robi Polikar, Electrical and Computer Eng. Rowan University, Glassboro, NJ...
© All Rights Reserved, Robi Polikar, Electrical and Computer Eng. Rowan University, Glassboro, NJ 08028
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)
The Department of
presents…
Emerging Interdisciplinary Challenges
Robi PolikarOctober 16, 2002
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
Introduction:Emerging Interdisciplinary Challenges
.
.
.
.
.
Olfactory Physiology
Organic Chemistry
Signal Processing
Pattern Recognition Computational Learning
ElectronicNose
Chemical Sensors /Analytical Chemistry
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:
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)
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
Electronic Nose Systems
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
Pattern Recognitiontechnologies
Statistical pattern recognition (SPR)Bayes classifiersDiscriminant analysis (DA)Maximum likelihood estimatePrincipal component analysis (PCA)
Non-parametric techniquesArtificial neural networks (ANN)Fuzzy logic (FL)Rule-based / expert systems
Com
merc
ially
Availa
ble
S
yst
em
s
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
A
WFF
26103.2
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
Advantages Disadvantages
Thickness sensitivity resistance, phase lag, attenuation
Softness response time, reversibility
Attenuation
Stiffness Attenuation Reversibility
Temperature
Softness and hence response time
sorption and hence
sensitivity.
VOCs and Coatings Used
O
Apiezon (grease, not a polymer)
APZ
Poly(isobutylene) PIB
Poly(diethyleneglycoladipate)
DEGA
Sol-gel SG
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)
Block Diagram of theExperimental Setup
Experimental Setup
Switching Box
Mass FlowController
NetworkAnalyzer
VOC inbubbler
NitrogenVOC
PC
SensorCell
EXPERIMENTAL SETUP
Mass FlowController
Network Analyzer
Gas Bubbler
SensorCell
Mass FlowMeter
SwitchingBox
Post-Itnotes
How Does Odor Signallook Like?
• Existence of dominant VOCs
• Approach: Identify dominant VOC first, and identify secondary VOC based on the identification of the dominant VOC.
Problems With Problems With Identification Of MixturesIdentification Of Mixtures
APZ: Apiezon, PIB: Polyisobutelene, DEGA:Poly(diethyleneglycoladipate),
SG: Solgel, OV:Poly(siloxane), PDPP: Poly (diphenoxylphosphorazene)
Pattern Separability Issues
(a) Well separated patterns and (b) densely packed / overlapping patterns
Pattern (In)separabilityin Mixture VOC Problem
Sensor 1Sensor 2
Sen
sor
3
ETHANOL
TOLUENE
TCE
OCTANE
XYLENE
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
.
.
.
.
.
Nonlinear Cluster Transformation
Outlier Removal
Cluster Translation
Nonlinear Cluster Transformation
C
jijii C 1
1mmMS
Generalized regression neural networks Similar to RBF networks Do not require iterative training Successful in multidimensional function approximation
PRINCIPLE COMPONENT ANALYSISA Comparison
ETHANOL
TOLUENE
TCE
OCTANE
TOLUENE
OCTANE
XYLENE
ETHANOL
TCE
XYLENE
Artificial Neural Networks
SignalsOutput signal based on a
weighted average of input signals
.
.
.
.
.
Toluene
Xylene
From sensors(six)
The Multilayer PerceptronNeural Network
d
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i=1,2,…dj=1,2,…,Hk=1,2,…c
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x2
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d inputnodes H hidden
layer nodes
c outputnodes
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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 !
ResultsBinary Mixture of VOCs
Dominant VOC Performance: 96%
Secondary VOC Performance: 96%
196 (50%) patterns used for training and remaining 196 used for testing.
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++)
Questions