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Informationen zum Autor FOO-TIM CHAU, PhD , is a Professor in the Department of Applied Biology and Chemical Technology at Hong Kong Polytechnic University. YI-ZENG LIANG, PhD , is a Professor in the College of Chemistry and Chemical Engineering at Central South University, China. JUNBIN GAO, PhD , is a Professor in the Department of Mathematics at Huazhong University of Science and Technology. He is currently visiting the University of Southhampton. XUE-GUANG SHAO, PhD , is a Professor at the University of Science and Technology in China. Klappentext Wavelet Transformations and Their Applications in Chemistry pioneers a new approach to classifying existing chemometric techniques for data analysis in one and two dimensions, using a practical applications approach to illustrating chemical examples and problems. Written in a simple, balanced, applications-based style, the book is geared to both theorists and non-mathematicians.This text emphasizes practical applications in chemistry. It employs straightforward language and examples to show the power of wavelet transforms without overwhelming mathematics, reviews other methods, and compares wavelets with other techniques that provide similar capabilities. It uses examples illustrated in MATLAB codes to assist chemists in developing applications, and includes access to a supplementary Web site providing code and data sets for work examples. Wavelet Transformations and Their Applications in Chemistry will prove essential to professionals and students working in analytical chemistry and process chemistry, as well as physical chemistry, spectroscopy, and statistics. Zusammenfassung The authors are pioneering a new approach to classifying existing chemometric techniques for data analysis in one and two dimensions, using a practical applications approach to illustrating chemical examples and problems. Written in a simple, balanced, applications-based style, the book will appeal to both theorists and non-mathematicians. Inhaltsverzeichnis Preface xiii Chapter 1 Introduction 1 1.1. Modern Analytical Chemistry 1 1.1.1. Developments in Modern Chemistry 1 1.1.2. Modern Analytical Chemistry 2 1.1.3. Multidimensional Dataset 3 1.2. Chemometrics 5 1.2.1. Introduction to Chemometrics 5 1.2.2. Instrumental Response and Data Processing 8 1.2.3. White, Black, and Gray Systems 9 1.3. Chemometrics-Based Signal Processing Techniques 10 1.3.1. Common Methods for Processing Chemical Data 10 1.3.2. Wavelets in Chemistry 11 1.4. Resources Available on Chemometrics and Wavelet Transform 12 1.4.1. Books 12 1.4.2. Online Resources 14 1.4.3. Mathematics Software 15 Chapter 2 One-dimensional Signal Processing Techniques in Chemistry 23 2.1. Digital Smoothing and Filtering Methods 23 2.1.1. Moving-Window Average Smoothing Method 24 2.1.2. Savitsky-Golay Filter 25 2.1.3. Kalman Filtering 32 2.1.4. Spline Smoothing 36 2.2. Transformation Methods of Analytical Signals 39 2.2.1. Physical Meaning of the Convolution Algorithm 39 2.2.2. Multichannel Advantage in Spectroscopy and Hadamard Transformation 41 2.2.3. Fourier Transformation 44 2.2.3.1. Discrete Fourier Transformation and Spectral Multiplex Advantage 45 2.2.3.2. Fast Fourier Transformation 48 2.2.3.3. Fourier Transformation as Applied to Smooth Analytical Signals 50 2.2.3.4. Fourier Transformation as Applied to Convolution and Deconvolution 52 2.3. Numerical Differentiation 54 2.3.1. Simple Difference Method 54 2.3.2. Moving-Window Polynomial Least-Squares Fitting Method 55 2.4. Data Compression 57 2.4.1. Data Compression Based on B-Spline Curve Fitting 57 2.4.2. Data Compression Based on Fourier Transformati...
List of contents
PREFACE.
CHAPTER 1: INTRODUCTION.
1.1. Modern Analytical Chemistry.
1.2. Chemometrics.
1.3. Chemometrics-Based Signal Processing Techniques.
1.4. Resources Available on Chemometrics and Wavelet Transform.
CHAPTER 2: ONE-DIMENSIONAL SIGNAL PROCESSING TECHNIQUES IN CHEMISTRY.
2.1. Digital Smoothing and Filtering Methods.
2.2. Transformation Methods of Analytical Signals.
2.3. Numerical Differentiation.
2.4. Data Compression.
CHAPTER 3: TWO-DIMENSIONAL SIGNAL PROCESSING TECHNIQUES IN CHEMISTRY.
3.1. General Features of Two-Dimensional Data.
3.2. Some Basic Concepts for Two-Dimensional Data from Hyphenated Instrumentation.
3.3. Double-Centering Technique for Background Correction.
3.4. Congruence Analysis and Least-Squares Fitting.
3.5. Differentiation Methods for Two-Dimensional Data.
3.6 Resolution Methods for Two-Dimensional Data.
CHAPTER 4: FUNDAMENTALS OF WAVELET TRANSFORM.
4.1. Introduction to Wavelet Transform and Wavelet Packet Transform.
4.2. Wavelet Function Examples.
4.3. Fast Wavelet Algorithm and Packet Algorithm.
4.4. Biorthogonal Wavelet Transform.
4.5. Two-Dimensional Wavelet Transform.
CHAPTER 5: APPLICATION OF WAVELET TRANSFORM IN CHEMISTRY.
5.1. Data Compression.
5.2. Data Denoising and Smoothing.
5.3. Baseline/Background Removal.
5.4. Resolution Enhancement.
5.5. Combined Techniques.
5.6. An Overview of the Applications in Chemistry.
APPENDIX VECTOR AND MATRIX OPERATIONS AND ELEMENTARY MATLAB.
A.1. Elementary Knowledge in Linear Algebra.
A.2. Elementary Knowledge of MATLAB.
INDEX.
Report
"Statisticians, biochemists, engineers, and health researchers willbenefit a lot from this wonderful book." ( Journal of StatisticalComputation and Simulation , November 2005)
"...quite useful for persons who apply signal processing methodsin chemistry." ( Technometrics , May 2005)
"...my overall impression of the text is favorable...Iwould recommend this book to chemists who are interested in usingwavelets in their research and to faculty..." ( Journal ofthe American Chemical Society , February 23, 2005)
"I recommend this book to chemists who are interested in usingwavelets in their research and to faculty who would like to teachgraduate students about signal processing..." ( AnalyticalChemistry , February 1, 2005)
"The presentation of information makes it easy for reader tofind the relevant information. The text is well-written andunderstandable." ( E-STREAMS , October 2004)
