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 xiAbout the Contributors xiii1 Introduction to Essentials of Machine Olfaction and Tastes 1Takamichi Nakamoto2 Physiology of Chemical Sense and its Biosensor Application 3Ryohei Kanzaki, Kei Nakatani, Takeshi Sakurai, Nobuo Misawa and Hidefumi Mitsuno2.1 Introduction 32.2 Olfaction and Taste of Insects 42.2.1 Olfaction 42.2.1.1 Anatomy of Olfaction 42.2.1.2 Signal Transduction of Odor Signals 62.2.1.3 Molecular Biology of Olfaction 72.2.2 Taste 82.2.2.1 Anatomy of Taste 82.2.2.2 Molecular Biology and Signal Transduction of Taste 92.3 Olfaction and Taste of Vertebrate 112.3.1 Olfaction 112.3.1.1 Anatomy of Olfaction 112.3.1.2 Transduction of Odor Signals 122.3.1.3 Molecular Biology of Olfaction 152.3.2 Taste 172.3.2.1 Anatomy of Taste 172.3.2.2 Transduction of Taste Signals 182.3.2.3 Molecular Biology of Taste 202.4 Cell‐Based Sensors and Receptor‐Based Sensors 212.4.1 Tissue‐Based Sensors 232.4.2 Cell‐Based Sensors 262.4.3 Receptor‐Based Sensors 302.4.3.1 Production of Odorant Receptors 342.4.3.2 Immobilization of Odorant Receptors 352.4.3.3 Measurement from Odorant Receptors 362.4.4 Summary of the Biosensors 412.5 Future Prospects 42References 433 Large‐Scale Chemical Sensor Arrays for Machine Olfaction 49Mara Bernabei, Simone Pantalei and Krishna C. Persaud3.1 Introduction 493.2 Overview of Artificial Olfactory Systems 503.3 Common Sensor Technologies Employed in Artificial Olfactory Systems 533.3.1 Metal‐Oxide Gas Sensors 533.3.2 Piezoelectric Sensors 543.3.3 Conducting Polymer Sensors 553.4 Typical Application of “Electronic Nose” Technologies 583.5 A Comparison between Artificial and the Biological Olfaction Systems 583.6 A Large‐Scale Sensor Array 593.6.1 Conducting Polymers 603.6.2 Sensor Interrogation Strategy 623.6.3 Sensor Substrate 643.7 Characterization of the Large‐Scale Sensor Array 683.7.1 Pure Analyte Study: Classification and Quantification Capability 693.7.2 Binary Mixture Study: Segmentation and Background Suppression Capability 753.7.3 Polymer Classes: Testing Broad and Overlapping Sensitivity, High Level of Redundancy 763.7.4 System Robustness and Long‐Term Stability 773.8 Conclusions 79Acknowledgment80 References 804 Taste Sensor: Electronic Tongue with Global Selectivity 87Kiyoshi Toko, Yusuke Tahara, Masaaki Habara, Yoshikazu Kobayashi and Hidekazu Ikezaki4.1 Introduction 874.2 Electronic Tongues 904.3 Taste Sensor 924.3.1 Introduction 924.3.2 Principle 934.3.3 Response Mechanism 934.3.4 Measurement Procedure 974.3.5 Sensor Design Techniques 984.3.6 Basic Characteristics 1034.3.6.1 Threshold 1064.3.6.2 Global Selectivity 1064.3.6.3 High Correlation with Human Sensory Scores 1084.3.6.4 Definition of Taste Information 1094.3.6.5 Detection of Interactions between Taste Substances 1104.3.7 Sample Preparation 1114.3.8 Analysis 1124.4 Taste Substances Adsorbed on the Membrane 1164.5 Miniaturized Taste Sensor 1174.6 Pungent Sensor 1224.7 Application to Foods and Beverages 1244.7.1 Introduction 1244.7.2 Beer 1244.7.3 Coffee 1274.7.4 Meat 1324.7.5 Combinatorial Optimization Technique for Ingredients and Qualities Using a GA 1344.7.5.2 Ga 1344.7.5.3 Constrained Nonlinear Optimization 1374.7.6 For More Effective Use of “Taste Information” 1374.7.6.1 Key Concept 1384.7.6.2 Taste Attributes or Qualities become Understandable and Translatable When They Are Simplified 1384.7.6.3 Simplification of Large Numbers of Molecules into a Couple of Taste Qualities Allows Mathematical Optimization 1404.7.6.4 Summary 1414.8 Application to Medicines 1414.8.1 Introduction 1414.8.2 Bitterness Evaluation of APIs and Suppression Effect of Formulations 1414.8.3 Development of Bitterness Sensor for Pharmaceutical Formulations 1434.8.3.1 Sensor Design 1434.8.3.2 Prediction of Bitterness Intensity and Threshold 1444.8.3.3 Applications to Orally Disintegrating Tablets 1464.8.3.4 Response Mechanism to APIs 1544.8.4 Evaluation of Poorly Water‐Soluble Drugs 1564.9 Perspectives 160References 1635 Pattern Recognition 175Saverio De Vito, Matteo Falasconi and Matteo Pardo5.1 Introduction 1755.2 Application Frameworks and Their Challenges 1765.2.1 Common Challenges 1765.2.2 Static In‐Lab Applications 1775.2.3 On‐Field Applications 1785.3 Unsupervised Learning and Data Exploration 1805.3.1 Feature Extraction: Static and Dynamic Characteristics 1805.3.2 Exploratory Data Analysis 1845.3.3 Cluster Analysis 1895.4 Supervised Learning 1905.4.1 Classification: Detection and Discrimination of Analytes and Mixtures of Volatiles 1925.4.2 Regression: Machine Olfaction Quantification Problems and Solutions 1965.4.3 Feature Selection 2005.5 Advanced Topics 2025.5.1 System Instability Compensation 2025.5.2 Calibration Transfer 2085.6 Conclusions 210References 2116 Using Chemical Sensors as “Noses” for Mobile Robots 219Hiroshi Ishida, Achim J. Lilienthal, Haruka Matsukura, Victor Hernandez Bennetts and Erik Schaffernicht6.1 Introduction 2196.2 Task Descriptions 2206.2.1 Definitions of Tasks 2206.2.2 Characteristics of Turbulent Chemical Plumes 2226.3 Robots and Sensors 2246.3.1 Sensors for Gas Detection 2246.3.2 Airflow Sensing 2256.3.3 Robot Platforms 2266.4 Characterization of Environments 2266.5 Case Studies 2306.5.1 Chemical Trail Following 2306.5.2 Chemotactic Search versus Anemotactic Approach 2326.5.3 Attempts to Improve Gas Source Localization Robots 2366.5.4 Flying, Swimming, and Burrowing Robots 2386.5.5 Gas Distribution Mapping 2396.6 Future Prospective 241Acknowledgment242 References 2427 Olfactory Display and Odor Recorder 247Takamichi Nakamoto7.1 Introduction 2477.2 Principle of Olfactory Display 2477.2.1 Olfactory Display Device 2487.2.2 Olfactory Display Related to Spatial Distribution of Odor 2507.2.3 Temporal Intensity Change of Odor 2517.2.3.1 Problem of Smell Persistence 2517.2.3.2 Olfactory Display Using Inkjet Device 2547.2.4 Multicomponent Olfactory Display 2567.2.4.1 Mass Flow Controller 2567.2.4.2 Automatic Sampler 2567.2.4.3 Solenoid Valve 2587.2.4.4 Micropumps and Surface Acoustic Wave Atomizer 2607.2.5 Cross Modality Interaction 2617.3 Application of Olfactory Display 2637.3.1 Entertainment 2637.3.2 Olfactory Art 2657.3.3 Advertisement 2667.3.4 Medical Field 2667.4 Odor Recorder 2677.4.1 Background of Odor Recorder 2677.4.2 Principle of Odor Recorder 2687.4.3 Mixture Quantification Method 2717.5.1 Odor Approximation 2747.5.2 MIMO Feedback Method 2767.5.3 Method to Increase Number of Odor Components 2787.5.3.1 SVD Method 2787.5.3.2 Two‐Level Quantization Method 2807.5.4 Dynamic Method 2837.5.4.1 Real‐Time Reference Method 2847.5.4.2 Concurrent Method 2877.5.5 Mixture Quantification Using Huge Number of Odor Candidates 2897.6 Exploration of Odor Components 2927.6.1 Introduction of Odor Components 2927.6.2 Procedure for Odor Approximation 2937.6.3 Simulation of Odor Approximation 2957.6.4 Experiment on Essential Oil Approximation 2977.6.5 Comparison of Distance Measure 3017.6.6 Improvement of Odor Approximation 3037.7 Teleolfaction 3057.7.1 Concept of Teleolfaction 3057.7.2 Implementation of Teleolfaction System 3067.7.3 Experiment on Teleolfaction 3077.8 Summary 308References 3098 Summary and Future Perspectives 315Takamichi NakamotoIndex 317