Intelligent and Biosensors Intelligent and Biosensors Edited by Vernon S. Somerset Intech IV Published by Intech Intech Olajnica 19/2, 32000 Vukovar, Croatia Abstracting and non-profit use of the material is permitted with credit to the source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. Publisher assumes no responsibility liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained inside. After this work has been published by the Intech, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. © 2010 Intech Free online edition of this book you can find under www.sciyo.com Additional copies can be obtained from: publication@sciyo.com First published January 2010 Printed in India Technical Editor: Teodora Smiljanic Intelligent and Biosensors, Edited by Vernon S. Somerset p. cm. ISBN 978-953-7619-58-9 Preface The term intelligent sensor (or smart sensor) has been used in the sensor industry to describe sensors that provide not only measurements, but also functionality to specific measurements. There are three characteristics that define an intelligent sensor: i) firstly, it contains a sensing element that measures one or more physical parameter; ii) secondly, it has a computational element that analyses the measurements made by the sensing element; iii) thirdly, it contains a communication interface enabling interaction with the outside world in order to exchange information with other components in a larger system. Furthermore, intelligent sensors allow networks of sensors to connect to each other, locally or around the globe in order to accomplish specific tasks. The use of intelligent sensors have revolutionised the way in which we gather data from the world around us, also how we extract useful information from that data, and the manner in which we use the newly obtained information for various operations and decision making. The field of Electrochemical sensors have shown that various methods can be employed in transducer modification in order to produce analytical probes that can be applied for the analysis of clinical, industrial, food and environmental samples. One specific type of electrochemical sensor that has received serious research attention over several decades is the Biosensor. A Biosensor can be defined as a compact analytical device containing biological material that is closely associated with a physico-chemical transducer, to produce either discrete or continuous digital electronic signals that are proportional to a single analyte or a related group of analytes. In this book the particular emphasis is on biosensors for the detection of organophosphorous and carbamate pesticide compounds. These pesticide compounds are known for their toxic effects due to their ability to irreversibly modify the catalytic serine residue in acetylcholinesterases (AChE) and subsequent inhibition of the AChE effectively prevents nerve transmission by blocking the breakdown of the transmitter choline. This book is an attempt to highlight the current research in the field of Intelligent and Biosensors, thereby describing state-of-the-art techniques in the field and emerging new technologies, also showcasing some examples and applications. The focus of the first eight chapters is on Intelligent Sensors. In Chapter 1 we are introduced to the work of Chen and co-workers on the design of a smart jacket and a power supply for neonatal monitoring with wearable sensors. This work has shown how it is possible to improve the comfort and quality of life for the child by elimination of the adhesive electrodes and by the elimination of wires. In Chapter 2, we are introduced to a comprehensive survey of signal processing, feature extraction/selection and classification methods used to provide the readers with guidelines on design brain-computer interfaces (BCIs). This work by Al-Ani and Trad have shown that the exploration of new methods in BCI design would be strongly driven by new properties that will have to be taken into VI consideration in the real future applications of BCIs. In Chapter 3, Sashima and Kurumatani proposes some views of what a mobile sensor fusion platform can contribute to the field and two types of fusion architecture, e.g. “mobile sensing architecture” and “stable sensing architecture” are described with a prototype platform of the mobile sensing architecture introduced. In Chapter 4, the focus is on the assessment of the biomineralization capacity of polyamidoamine (PAMAM) dendrimers amino- and carboxylic-terminated immobilized on solid supports. This work by Stancu is aimed as the first attempt of investigation of biomaterials-induced biomineralization through the label-free Surface Plasmon Resonance Imaging (SPRi). In Chapter 5, the work of Rangelova and co-workers discusses the use of soft computing techniques for modelling the inputoutput dependency of a dopamine biosensor that takes into account the simultaneous influence of pH and temperature over the output current. In Chapter 6, Gargiulo and co-workers describes a long term, wearable personal monitoring system that is wireless, low power and uses convenient dry electrodes. The use of this system for electrocardiogram (ECG) and athlete monitoring has also been demonstrated. In Chapter 7, the work by De Silva and co-workers presents a framework to transfer the natural gestural behaviours of a human agent to a robot through a robust imitation algorithm. The novelty of their proposed algorithm is the use of symbolic postures to generate the gestural behaviours of a robot without using any training data or trained model. The idea behind using symbolic postures is that a robot is flexibly able to generate its own motion. In Chapter 8, the author Bae focuses our attention on a newly designed sensor or structure of an in-vitro giant magnetoresistance (GMR) biosensor with a specially designed magnetic shield layer (MSL). The physical sensing characteristics of the in-vitro GMR biosensor with an immobilized single FNSA are also discussed to explore its feasibility to a single molecular based disease diagnostic biosensor system. The work in the following chapters focus on Biosensors for the detection of various analytes. In Chapter 9, Somerset and co-workers describe the application of a mercaptobenzothiazole-on-gold biosensor system for the analysis of organosphosphorous and carbamate pesticide compounds. The aim of this work was to improve the detection limit of these insecticides with an AChE biosensor, applied to various water miscible organic solvents. In Chapter 10, the work of Cortina-Puig and co-workers focuses on AChE biosensors as a rapid and simple alternative method for the detection of organophosphorous insecticides. They indicate that such sensors should be small, cheap, simple to handle and able to provide reliable information in realtime with or without minimum sample preparation. In Chapter 11, the work of Stoytcheva highlights the fact that the analytical potential of electrochemical biosensors for the detection of organophosphorous insecticides is obvious, despite the fact that they still demonstrate limited application in the quantification of real samples. In Chapter 12, Srivastava and co-workers focus our attention on the first continuous, electrochemical biosensor for real-time, rapid measurement of Neuropathy Target Esterase (NEST (or NTE) esterase activity. The biosensor was fabricated by coimmobilizing NEST protein and tyrosinase enzyme on an electrode using the layer by layer assembly approach. In Chapter 13, the work of Nien and co-workers showcase two systems. In the first system, a poly(3,4-ethylenedioxythiophene) (PEDOT) modified electrode was used as a matrix to entrap glucose oxidase and was integrated in a flow system for sensing chip applications. In the second system, the proposed electrode fabricated by multilayer structures successfully works as a glucose biosensor in the oxygen- independence solution, and the anode of the biofuel cell operates not only on glucose VII solution but also on real blood of human beings. In Chapter 14, Budai discuss the fabrication of single- and multibarrel carbon fiber (CF) microelectrodes, the covalent modifications of the carbon surface as well as the applications of CF microelectrodes in recording spikes from neurons, electrochemical or biosensor signals from various tissues. This chapter further discuss the novel use of CF microelectrodes as oxygen detectors usable in vitro and in vivo applications. In Chapter 15, the work of Reshetilov and co-workers focuses on microbial biosensors and showcase that the properties of microbial sensors are in many respects analogous to the properties of enzyme biosensors. In Chapter 16, the work of Mateo-Martí and Pradier focuses on DNA biosensors with specific attention on a new artificial nucleic acid, PNA, as a highly specific probe. They also provide an overview of some surface analysis techniques that have been successfully applied to the detection of PNA-DNA hybridisation. In Chapter 17, the work of Yakhno and co-workers demonstrate the unique use of an uncoated quartz resonator in the diagnostics of multi-component liquids without detection of their content. This is a new type of analytical instrument, based on non-linear non-equilibrium processes in drying drops, so called selforganization. The main feature of this approach is that phase transitions in drying drops were registered and used as the informative parameter. In Chapter 18, Konuk and co-workers introduce and ALAD (δ- Aminolevulinic Acid Dehydratase) biosensor and indicates that the expression of ALAD activity gives us a clear indication of the severity of the effect of Pb pollution along the pollution gradient. In Chapter 19, the work of Vidic focuses on a bioelectronic nose based on olfactory receptors indicating that the development of sensor technology incorporating natural olfactory receptors provides the basis for a bioelectronic nose mimicking the animal olfactory system. Such devices can be used for qualitative and quantitative identification and monitoring of a spectrum of odorants with much higher selectivity and sensibility than the present electronic devices. It is envisaged that this book will provide valuable reference and learning material to other researchers, scientists and postgraduate students in the field. The references at the end of each chapter serve as valuable entry points to further reading on the various topics discussed and should provide guidance to those interested in moving forward in the field of Intelligent and Biosensors. My sincere gratitude is expressed to the contributing authors for their hard work, time and effort in preparing the different chapters, because without their dedication this book would not have been possible. Editor Vernon S. Somerset Cape Town, South Africa Contents Preface V 1. Intelligent Design for Neonatal Monitoring with Wearable Sensors 001 Wei Chen, Sibrecht Bouwstra, Sidarto Bambang Oetomo and Loe Feijs 2. Signal Processing and Classification Approaches for Brain-computer Interface 025 Tarik Al-ani and Dalila Trad 3. Toward Mobile Sensor Fusion Platform for Context-Aware Services 067 Akio Sashima, Takeshi Ikeda, and Koichi Kurumatani 4. SPR Imaging Label-Free Control of Biomineral Nucleation!? 083 Stancu Izabela-Cristina 5. Soft Computing Techniques in Modelling the Influence of pH and Temperature on Dopamine Biosensor 099 Vania Rangelova, Diana Tsankova and Nina Dimcheva 6. Non-invasive Electronic Biosensor Circuits and Systems 123 Gaetano Gargiulo, Paolo Bifulco, Rafael A. Calvo, Mario Cesarelli, Craig Jin, Alistair McEwan and André van Schaik 7. The Extraction of Symbolic Postures to Transfer Social Cues into Robot 147 P. Ravindra S. De Silva, Tohru Matsumoto, Stephen G. Lambacher, Ajith P. Madurapperuma, Susantha Herath and Masatake Higashi 8. In-Vitro Magnetoresistive Biosensors for Single Molecular Based Disease Diagnostics: Optimization of Sensor Geometry and Structure 163 Seongtae Bae 9. Mercaptobenzothiazole-on-Gold Organic Phase Biosensor Systems: 3. Thick-Film Biosensors for Organophosphate and Carbamate Pesticide Determination 185 V. Somerset, P. Baker and E. Iwuoha X 10. Analysis of Pesticide Mixtures using Intelligent Biosensors 205 Montserrat Cortina-Puig, Georges Istamboulie, Thierry Noguer and Jean-Louis Marty 11. Enzyme vs. Bacterial Electrochemical Sensors for Organophosphorus Pesticides Quantification 217 Margarita Stoytcheva 12. Neuropathy Target Esterase Biosensor 231 Devesh Srivastava, Neeraj Kohli, Rudy J. Richardson, Robert M. Worden, and Ilsoon Lee 13. Amperometric Enzyme-based Biosensors for Lowering the Interferences 245 Po-Chin Nien, Po-Yen Chen and Kuo-Chuan Ho 14. Carbon Fiber-based Microelectrodes and Microbiosensors 269 Dénes Budai 15. The Microbial Cell Based Biosensors 289 Reshetilov A.N., Iliasov P.V. and Reshetilova T.A. 16. A Novel Type of Nucleic Acid-based Biosensors: the Use of PNA Probes, Associated with Surface Science and Electrochemical Detection Techniques 323 Eva Mateo-Martí and Claire-Marie Pradier 17. Uncoated Quartz Resonator as a Universal Biosensor 345 Tatiana Yakhno, Anatoly Sanin, Vyacheslav Kazakov, Olga Sanina, Christina Vacca, Frank Falcione, and Vladimir Yakhno 18. ALAD (-aminolevulinic Acid Dehydratase) as Biosensor for Pb Contamination 363 Muhsin Konuk, İbrahim Hakkı Ciğerci and Safiye Elif Korcan, 19. Bioelectronic Noses Based on Olfactory Receptors 377 Jasmina Vidic [...].. .1 Intelligent Design for Neonatal Monitoring with Wearable Sensors Wei Chen1, Sibrecht Bouwstra1, Sidarto Bambang Oetomo1,2 and Loe Feijs1 1Department of Industrial Design, Eindhoven University of Technology, of Neonatology, Máxima Medical Center, Veldhoven, The Netherlands 2Department 1 Introduction Neonatal monitoring refers to the monitoring... and exposure to pain and stress is illustrated in Fig 1, according to Anand and Scalzo (Anand & Scalzo, 2000) These negative stimuli can interfere with the normal growth and development of the neonates and hamper the parent-child interaction (Als et al., 2003) Thus, it is essential to develop comfortable care solutions for NICU and follow-up Fig 1 Schematic diagram of the effects of neonatal pain and. .. mutual inductance of 1. 32 μH when the secondary winding is centred directly above the primary winding, i.e the best- 13 Intelligent Design for Neonatal Monitoring with Wearable Sensors Parameter Dimensions Turns per layer Layers Thickness Track width Track spacing Inductance Resistance (DC) Resistance (2.5 MHz) Primary Winding Value 10 0 mm x 12 0 mm 10 turns 1 100 μm 1 mm 1 mm 17 .5 μH 2.48 Ω 3.47 Ω... is transferred From 14 Intelligent and Biosensors Table 3, it can be seen that for a certain load power, the best-case PowerBoy toy placement has a higher induced voltage than the worst-case placement Load power value 200 mW 450 mW 700 mW Best PowerBoy toy placement iA = 1. 53 A (peak) iB = 13 mA (peak) VL = 31. 1 V (peak) iA = 1. 42 A (peak) iB = 31 mA (peak) VL = 29 V (peak) iA = 1. 29 A (peak) iB = 54... A − jω M ABiB , (1) jω M ABi A = jωLBiB + iB / jωC B + RBiB + ZL iB (2) Here, ω is the radial frequency of the current VA and iA are the primary voltage and current, respectively The secondary current is given as iB, and the induced secondary winding voltage is VB RA and LA, and RB and LB are the internal resistances and self inductances of the primary and secondary windings, SA and SB, respectively... spiral winding with 12 0 mm length and 10 0 mm width The primary and secondary windings are shown in Fig 14 (a) and (b) Table 2 summarizes their physical dimensions and electrical properties (a) (b) Fig 14 (a) Primary rectangular spiral winding, and (b) secondary hexagon spiral winding 4.2.3 Mutual inductance values & calculated power transfer The mutual inductance between the primary and secondary windings,... weeks and 5 days and one of 31 weeks and 6 days, both admitted in the NICU Veldhoven The ECG is sensed by three textile electrodes in regular configuration and the data is acquired with a GE Heathcare Solar® 8000M The unprocessed digital data of derivation II was obtained from a network and imported and filtered in MATLAB A notch, high pass and low pass filter are applied to remove the 50 Hz and higher... is partly picked up by the secondary coil The primary circuit and secondary circuit are separated by an air gap (incubator mattress) CA RA LA VA iA MAB VB RB CB + ZL VL LB - Fig 13 Principle of inductive contactless energy transfer + iB - 12 Intelligent and Biosensors In this way, power can be transferred wirelessly Assuming steady-state sinusoidal voltages and currents, the inductive link from Fig 13 ... Worst PowerBoy toy placement iA = 1. 53 A (peak) iB = 23 mA (peak) VL = 17 .5 V (peak) iA = 1. 42 A (peak) iB = 57 mA (peak) VL = 16 V (peak) iA = 1. 27 A (peak) iB = 10 0 mA (peak) VL = 13 .8 V (peak) Table 3 Power transfer results for different winding placements and load power 4.2.4 Magnetic field values The magnetic fields created by the currents circulating in the primary and secondary windings are estimated... transmission and an adjustable size for different patients which enable clinical reliability tests 10 Intelligent and Biosensors 4 Power supply design for neonatal monitoring 4 .1 Design concept A key question for health monitoring with wearable sensors is how to obtain reliable electrical power for the sensors, signal amplifiers, filters and transmitters The deployment of new sensing and monitoring . Wearable Sensors Wei Chen 1 , Sibrecht Bouwstra 1 , Sidarto Bambang Oetomo 1, 2 and Loe Feijs 1 1 Department of Industrial Design, Eindhoven University of Technology, 2 Department of Neonatology,. Thick-Film Biosensors for Organophosphate and Carbamate Pesticide Determination 18 5 V. Somerset, P. Baker and E. Iwuoha X 10 . Analysis of Pesticide Mixtures using Intelligent Biosensors. and Kuo-Chuan Ho 14 . Carbon Fiber-based Microelectrodes and Microbiosensors 269 Dénes Budai 15 . The Microbial Cell Based Biosensors 289 Reshetilov A.N., Iliasov P.V. and