This book provides a valuable information source for olfaction and taste which includes a comprehensive and timely overview of the current state of knowledge of use for olfaction and taste machines
* Presents original, latest research in the field, with an emphasis on the recent development of human interfacing
* Covers the full range of artificial chemical senses including olfaction and taste, from basic through to advanced level
* Timely project in that mobile robots, olfactory displays and odour recorders are currently under research, driven by commercial demand

Takamichi Nakamoto, Associate Professor with the Department of Electrical and Electronics Engineering, Tokyo Institute of Technology, Japan. Nakamoto received his Ph.D. degree in electrical and electronic engineering from Tokyo Institute of Technology, Tokyo, Japan. He worked for Hitachi in the area of VLSI design automation from 1984 to 1987. In 1987, he joined Tokyo Institute of Technology as a Research Associate. In 1993, he became an Associate Professor with the Department of Electrical and Electronics Engineering, Tokyo Institute of Technology. From 1996 to 1997, he was a Visiting Scientist at Pacific Northwest Laboratories, Richland, WA, USA. He has studied an odour sensing system for 25 years, an odour recorder for 13 years and an olfactory display for 7 years. He currently teaches multivariate data analysis and computer architecture to undergraduate students, and electronic measurement, a part of bio-sensing system and a part of advanced electronic matter to graduate students.

Preface xi

About the Contributors xiii

1 Introduction to Essentials of Machine Olfaction and Tastes 1
Takamichi Nakamoto

2 Physiology of Chemical Sense and its Biosensor Application 3
Ryohei Kanzaki, Kei Nakatani, Takeshi Sakurai, Nobuo Misawa and Hidefumi Mitsuno

2.1 Introduction 3

2.2 Olfaction and Taste of Insects 4

2.2.1 Olfaction 4

2.2.1.1 Anatomy of Olfaction 4

2.2.1.2 Signal Transduction of Odor Signals 6

2.2.1.3 Molecular Biology of Olfaction 7

2.2.2 Taste 8

2.2.2.1 Anatomy of Taste 8

2.2.2.2 Molecular Biology and Signal Transduction of Taste 9

2.3 Olfaction and Taste of Vertebrate 11

2.3.1 Olfaction 11

2.3.1.1 Anatomy of Olfaction 11

2.3.1.2 Transduction of Odor Signals 12

2.3.1.3 Molecular Biology of Olfaction 15

2.3.2 Taste 17

2.3.2.1 Anatomy of Taste 17

2.3.2.2 Transduction of Taste Signals 18

2.3.2.3 Molecular Biology of Taste 20

2.4 Cell¿Based Sensors and Receptor¿Based Sensors 21

2.4.1 Tissue¿Based Sensors 23

2.4.2 Cell¿Based Sensors 26

2.4.3 Receptor¿Based Sensors 30

2.4.3.1 Production of Odorant Receptors 34

2.4.3.2 Immobilization of Odorant Receptors 35

2.4.3.3 Measurement from Odorant Receptors 36

2.4.4 Summary of the Biosensors 41

2.5 Future Prospects 42

References 43

3 Large¿Scale Chemical Sensor Arrays for Machine Olfaction 49
Mara Bernabei, Simone Pantalei and Krishna C. Persaud

3.1 Introduction 49

3.2 Overview of Artificial Olfactory Systems 50

3.3 Common Sensor Technologies Employed in Artificial Olfactory Systems 53

3.3.1 Metal¿Oxide Gas Sensors 53

3.3.2 Piezoelectric Sensors 54

3.3.3 Conducting Polymer Sensors 55

3.4 Typical Application of "Electronic Nose" Technologies 58

3.5 A Comparison between Artificial and the Biological Olfaction Systems 58

3.6 A Large¿Scale Sensor Array 59

3.6.1 Conducting Polymers 60

3.6.2 Sensor Interrogation Strategy 62

3.6.3 Sensor Substrate 64

3.7 Characterization of the Large¿Scale Sensor Array 68

3.7.1 Pure Analyte Study: Classification and Quantification Capability 69

3.7.2 Binary Mixture Study: Segmentation and Background Suppression Capability 75

3.7.3 Polymer Classes: Testing Broad and Overlapping Sensitivity, High Level of Redundancy 76

3.7.4 System Robustness and Long¿Term Stability 77

3.8 Conclusions 79

Acknowledgment80 References 80

4 Taste Sensor: Electronic Tongue with Global Selectivity 87
Kiyoshi Toko, Yusuke Tahara, Masaaki Habara, Yoshikazu Kobayashi and Hidekazu Ikezaki

4.1 Introduction 87

4.2 Electronic Tongues 90

4.3 Taste Sensor 92

4.3.1 Introduction 92

4.3.2 Principle 93

4.3.3 Response Mechanism 93

4.3.4 Measurement Procedure 97

4.3.5 Sensor Design Techniques 98

4.3.6 Basic Characteristics 103

4.3.6.1 Threshold 106

4.3.6.2 Global Selectivity 106

4.3.6.3 High Correlation with Human Sensory Scores 108

4.3.6.4 Definition of Taste Information 109

4.3.6.5 Detection of Interactions between Taste Substances 110

4.3.7 Sample Preparation 111

4.3.8 Analysis 112

4.4 Taste Substances Adsorbed on the Membrane 116

4.5 Miniaturized Taste Sensor 117

4.6 Pungent Sensor 122

4.7 Application to Foods and Beverages 124

4.7.1 Introduction 124

4.7.2 Beer 124

4.7.3 Coffee 127

4.7.4 Meat 132

4.7.5 Combinatorial Optimization Technique for Ingredients and Qualities Using a GA 134

4.7.5.2 Ga 134

4.7.5.3 Constrained Nonlinear Optimization 137

4.7.6 For More Effective Use of "Taste Information" 137

4.7.6.1 Key Concept 138

4.7.6.2 Taste Attributes or Qualities become Understandable and Translatable When They Are Simplified 138

4.7.6.3 Simplification of Large Numbers of Molecules into a Couple of Taste Qualities Allows Mathematical Optimization 140

4.7.6.4 Summary 141

4.8 Application to Medicines 141

4.8.1 Introduction 141

4.8.2 Bitterness Evaluation of APIs and Suppression Effect of Formulations 141

4.8.3 Development of Bitterness Sensor for Pharmaceutical Formulations 143

4.8.3.1 Sensor Design 143

4.8.3.2 Prediction of Bitterness Intensity and Threshold 144

4.8.3.3 Applications to Orally Disintegrating Tablets 146

4.8.3.4 Response Mechanism to APIs 154

4.8.4 Evaluation of Poorly Water¿Soluble Drugs 156

4.9 Perspectives 160

References 163

5 Pattern Recognition 175
Saverio De Vito, Matteo Falasconi and Matteo Pardo

5.1 Introduction 175

5.2 Application Frameworks and Their Challenges 176

5.2.1 Common Challenges 176

5.2.2 Static In¿Lab Applications 177

5.2.3 On¿Field Applications 178

5.3 Unsupervised Learning and Data Exploration 180

5.3.1 Feature Extraction: Static and Dynamic Characteristics 180

5.3.2 Exploratory Data Analysis 184

5.3.3 Cluster Analysis 189

5.4 Supervised Learning 190

5.4.1 Classification: Detection and Discrimination of Analytes and Mixtures of Volatiles 192

5.4.2 Regression: Machine Olfaction Quantification Problems and Solutions 196

5.4.3 Feature Selection 200

5.5 Advanced Topics 202

5.5.1 System Instability Compensation 202

5.5.2 Calibration Transfer 208

5.6 Conclusions 210

References 211

6 Using Chemical Sensors as "Noses" for Mobile Robots 219
Hiroshi Ishida, Achim J. Lilienthal, Haruka Matsukura, Victor Hernandez Bennetts and Erik Schaffernicht

6.1 Introduction 219

6.2 Task Descriptions 220

6.2.1 Definitions of Tasks 220

6.2.2 Characteristics of Turbulent Chemical Plumes 222

6.3 Robots and Sensors 224

6.3.1 Sensors for Gas Detection 224

6.3.2 Airflow Sensing 225

6.3.3 Robot Platforms 226

6.4 Characterization of Environments 226

6.5 Case Studies 230

6.5.1 Chemical Trail Following 230

6.5.2 Chemotactic Search versus Anemotactic Approach 232

6.5.3 Attempts to Improve Gas Source Localization Robots 236

6.5.4 Flying, Swimming, and Burrowing Robots 238

6.5.5 Gas Distribution Mapping 239

6.6 Future Prospective 241

Acknowledgment242 References 242

7 Olfactory Display and Odor Recorder 247
Takamichi Nakamoto

7.1 Introduction 247

7.2 Principle of Olfactory Display 247

7.2.1 Olfactory Display Device 248

7.2.2 Olfactory Display Related to Spatial Distribution of Odor 250

7.2.3 Temporal Intensity Change of Odor 251

7.2.3.1 Problem of Smell Persistence 251

7.2.3.2 Olfactory Display Using Inkjet Device 254

7.2.4 Multicomponent Olfactory Display 256

7.2.4.1 Mass Flow Controller 256

7.2.4.2 Automatic Sampler 256

7.2.4.3 Solenoid Valve 258

7.2.4.4 Micropumps and Surface Acoustic Wave Atomizer 260

7.2.5 Cross Modality Interaction 261

7.3 Application of Olfactory Display 263

7.3.1 Entertainment 263

7.3.2 Olfactory Art 265

7.3.3 Advertisement 266

7.3.4 Medical Field 266

7.4 Odor Recorder 267

7.4.1 Background of Odor Recorder 267

7.4.2 Principle of Odor Recorder 268

7.4.3 Mixture Quantification Method 271

7.5.1 Odor Approximation 274

7.5.2 MIMO Feedback Method 276

7.5.3 Method to Increase Number of Odor Components 278

7.5.3.1 SVD Method 278

7.5.3.2 TwöLevel Quantization Method 280

7.5.4 Dynamic Method 283

7.5.4.1 Real¿Time Reference Method 284

7.5.4.2 Concurrent Method 287

7.5.5 Mixture Quantification Using Huge Number of Odor Candidates 289

7.6 Exploration of Odor Components 292

7.6.1 Introduction of Odor Components 292

7.6.2 Procedure for Odor Approximation 293

7.6.3 Simulation of Odor Approximation 295

7.6.4 Experiment on Essential Oil Approximation 297

7.6.5 Comparison of Distance Measure 301

7.6.6 Improvement of Odor Approximation 303

7.7 Teleolfaction 305

7.7.1 Concept of Teleolfaction 305

7.7.2 Implementation of Teleolfaction System 306

7.7.3 Experiment on Teleolfaction 307

7.8 Summary 308

References 309

8 Summary and Future Perspectives 315
Takamichi Nakamoto

Index 317