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Book Details
Abstract
Providing a definitive reference source on novel methods in NMR acquisition and processing, this book will highlight similarities and differences between emerging approaches and focus on identifying which methods are best suited for different applications. The highly qualified editors have conducted extensive research into the fundamentals of fast methods of data acquisition in NMR, including applications of non-Fourier methods of spectrum analysis. With contributions from additional distinguished experts in allied fields, clear explanations are provided on methods that speed up NMR experiments using different ways to manipulate the nuclei in the sample, modern methods for estimating the spectrum from the time domain response recorded during an NMR experiment, and finally how the data is sampled. Starting with a historical overview of Fourier Transformation and its role in modern NMR spectroscopy, this volume will clarify and demystify this important emerging field for spectroscopists and analytical chemists in industry and academia.
The book serves as a step stone for professional NMR scientists who are interested in adopting or developing fast data collection approaches for their own application and research.
The assembled book for fast NMR is a timely contribution from all expert authors and editors. It serves the NMR community with a new direction and a solid starting point for further fast NMR development.
Kang Chen
Table of Contents
| Section Title | Page | Action | Price |
|---|---|---|---|
| Cover\r | Cover | ||
| Preface | viii | ||
| Foreword | v | ||
| Contents | xi | ||
| Chapter 1 Polarization-enhanced Fast-pulsing Techniques | 1 | ||
| 1.1 Introduction | 1 | ||
| 1.1.1 Some Basic Considerations on NMR Sensitivity and Experimental Time | 2 | ||
| 1.1.2 Inter-scan Delay, Longitudinal Relaxation, and Experimental Sensitivity | 3 | ||
| 1.2 Proton Longitudinal Relaxation Enhancement | 6 | ||
| 1.2.1 Theoretical Background: Solomon and Bloch–McConnell Equations | 6 | ||
| 1.2.2 Proton LRE Using Paramagnetic Relaxation Agents | 7 | ||
| 1.2.3 Proton LRE from Selective Spin Manipulation | 8 | ||
| 1.2.4 Amide Proton LRE: What Can We Get? | 11 | ||
| 1.2.5 LRE for Protons Other Than Amides | 14 | ||
| 1.3 BEST: Increased Sensitivity in Reduced Experimental Time | 14 | ||
| 1.3.1 Properties of Band-selective Pulse Shapes | 14 | ||
| 1.3.2 BEST-HSQC versus BEST-TROSY | 18 | ||
| 1.3.3 BEST-optimized 13C-detected Experiments | 22 | ||
| 1.4 SOFAST-HMQC: Fast and Sensitive 2D NMR | 24 | ||
| 1.4.1 Ernst-angle Excitation | 24 | ||
| 1.4.2 SOFAST-HMQC: Different Implementations of the Same \rExperiment | 26 | ||
| 1.4.3 UltraSOFAST-HMQC | 28 | ||
| 1.5 Conclusions | 29 | ||
| References | 30 | ||
| Chapter 2 Principles of Ultrafast NMR Spectroscopy | 33 | ||
| 2.1 Introduction | 33 | ||
| 2.1.1 One- and Two-dimensional FT NMR\r | 34 | ||
| 2.2 Principles of UF NMR Spectroscopy | 35 | ||
| 2.2.1 Magnetic Field Gradients | 35 | ||
| 2.2.2 Generic Scheme of UF 2D NMR Spectroscopy | 38 | ||
| 2.2.3 Spatial Encoding | 39 | ||
| 2.2.4 Decoding the Indirect Domain Information | 40 | ||
| 2.2.5 The Direct-domain Acquisition | 42 | ||
| 2.3 Processing UF 2D NMR Experiments | 43 | ||
| 2.3.1 Basic Procedure | 43 | ||
| 2.3.2 SNR Considerations in UF 2D NMR | 45 | ||
| 2.4 Discussion | 46 | ||
| Acknowledgements | 47 | ||
| References | 47 | ||
| Chapter 3 Linear Prediction Extrapolation | 49 | ||
| 3.1 Introduction | 49 | ||
| 3.2 History of LP Extrapolation in NMR | 50 | ||
| 3.2.1 Broader History of LP | 51 | ||
| 3.3 Determining the LP Coefficients\r | 51 | ||
| 3.4 Parametric LP and the Stability Requirement | 52 | ||
| 3.5 Mirror-image LP for Signals of Known Phase | 53 | ||
| 3.6 Application | 54 | ||
| 3.7 Best Practices | 57 | ||
| Acknowledgements | 58 | ||
| References | 58 | ||
| Chapter 4 The Filter Diagonalization Method | 60 | ||
| 4.1 Introduction | 60 | ||
| 4.2 Theory | 64 | ||
| 4.2.1 Solving the Harmonic Inversion Problem: 1D FDM | 64 | ||
| 4.2.2 The Spectral Estimation Problem and Regularized Resolvent Transform | 68 | ||
| 4.2.3 Hybrid FDM | 70 | ||
| 4.2.4 Multi-D Spectral Estimation and Harmonic Inversion Problems | 71 | ||
| 4.2.5 Spectral Estimation by Multi-D FDM | 72 | ||
| 4.2.6 Regularization of the Multi-D FDM | 76 | ||
| 4.3 Examples | 78 | ||
| 4.3.1 1D NMR | 78 | ||
| 4.3.2 2D NMR | 82 | ||
| 4.3.3 3D NMR | 86 | ||
| 4.3.4 4D NMR | 91 | ||
| 4.4 Conclusions | 93 | ||
| Acknowledgements | 93 | ||
| References | 94 | ||
| Chapter 5 Acquisition and Post-processing of Reduced Dimensionality NMR Experiments | 96 | ||
| 5.1 Introduction | 96 | ||
| 5.2 Data Acquisition Approaches | 98 | ||
| 5.3 Post-processing and Interpretation | 100 | ||
| 5.4 HIFI-NMR | 102 | ||
| 5.5 Brief Primer on Statistical Post-processing | 102 | ||
| 5.6 HIFI-NMR Algorithm | 103 | ||
| 5.7 Automated Projection Spectroscopy | 107 | ||
| 5.8 Fast Maximum Likelihood Method | 110 | ||
| 5.9 Mixture Models | 111 | ||
| 5.10 FMLR Algorithm | 112 | ||
| 5.11 Conclusions and Outlook | 114 | ||
| References | 114 | ||
| Chapter 6 Backprojection and Related Methods | 119 | ||
| 6.1 Introduction | 119 | ||
| 6.2 Radial Sampling and Projections | 120 | ||
| 6.2.1 Measuring Projections: The Projection-slice Theorem | 120 | ||
| 6.2.2 Quadrature Detection and Projections | 123 | ||
| 6.3 Reconstruction from Projections: Theory | 125 | ||
| 6.3.1 A Simple Approach: The Lattice of Possible Peak Positions | 125 | ||
| 6.3.2 Limitations of the Lattice Analysis and Related Reconstruction Methods | 128 | ||
| 6.3.3 The Radon Transform and Its Inverse | 130 | ||
| 6.3.4 The Polar Fourier Transform and the Inverse Radon Transform | 134 | ||
| 6.3.5 Reconstruction of Higher-dimensional Spectra | 135 | ||
| 6.3.6 The Point Response Function for Radial Sampling | 137 | ||
| 6.3.7 The Information Content and Ambiguity of Radially Sampled Data | 149 | ||
| 6.4 Reconstruction from Projections: Practice | 151 | ||
| 6.4.1 The Lower-value Algorithm | 151 | ||
| 6.4.2 Backprojection Without Filtering | 153 | ||
| 6.4.3 The Hybrid Backprojection/ Lower-value Method | 154 | ||
| 6.4.4 Filtered Backprojection | 156 | ||
| 6.4.5 Other Proposed Approaches to Reconstruction | 157 | ||
| 6.5 Applications of Projection– Reconstruction to Protein NMR | 158 | ||
| 6.6 From Radial to Random | 161 | ||
| 6.7 Conclusions | 166 | ||
| Acknowledgements | 167 | ||
| References | 167 | ||
| Chapter 7 CLEAN | 169 | ||
| 7.1 Introduction | 169 | ||
| 7.2 Historical Background: The Origins of CLEAN in Radioastronomy | 170 | ||
| 7.3 The CLEAN Method | 171 | ||
| 7.3.1 Notation | 171 | ||
| 7.3.2 The Problem to be Solved | 174 | ||
| 7.3.3 CLEAN Deconvolves Sampling Artifacts via Decomposition | 176 | ||
| 7.3.4 Obtaining the Decomposition into Components | 177 | ||
| 7.3.5 The Role of the Gain Parameter | 179 | ||
| 7.3.6 Reconstructing the Clean Spectrum | 180 | ||
| 7.4 Mathematical Analysis of CLEAN | 181 | ||
| 7.4.1 CLEAN and the NUS Inverse Problem | 181 | ||
| 7.4.2 CLEAN as an Iterative Method for Solving a System of Linear Equations | 185 | ||
| 7.4.3 CLEAN and Compressed Sensing | 193 | ||
| 7.5 Implementations of CLEAN in NMR | 200 | ||
| 7.5.1 Early Uses of CLEAN in NMR | 200 | ||
| 7.5.2 Projection–reconstruction NMR | 203 | ||
| 7.5.3 CLEAN and Randomized Sparse Nonuniform Sampling | 203 | ||
| 7.6 Using CLEAN in Biomolecular NMR: Examples of Applications | 207 | ||
| 7.7 Conclusions | 217 | ||
| Acknowledgements | 218 | ||
| References | 218 | ||
| Chapter 8 Covariance NMR | 220 | ||
| 8.1 Introduction | 220 | ||
| 8.2 Direct Covariance NMR | 221 | ||
| 8.3 Indirect Covariance NMR | 226 | ||
| 8.3.1 Principle | 226 | ||
| 8.3.2 Unsymmetrical Indirect Covariance (UIC)and Generalized Indirect Covariance (GIC) NMR | 226 | ||
| 8.3.3 Signal/Noise Ratio in Covariance Spectra | 228 | ||
| 8.3.4 Artifact Detection | 231 | ||
| 8.3.5 Applications of Indirect Covariance NMR | 236 | ||
| 8.3.6 Optimizing Spectra for Best Application to Covariance | 239 | ||
| 8.3.7 Applications of Covariance Processing in Structure Elucidation Problems | 242 | ||
| 8.4 Related Methods | 245 | ||
| 8.5 Conclusions and Further Directions | 246 | ||
| 8.6 Computer-assisted Structure Elucidation (CASE)and the Potential Influence of Covariance Processing | 247 | ||
| References | 249 | ||
| Chapter 9 Maximum Entropy Reconstruction | 252 | ||
| 9.1 Introduction | 252 | ||
| 9.2 Theory | 253 | ||
| 9.3 Parameter Selection | 257 | ||
| 9.4 Linearity of MaxEnt Reconstruction | 258 | ||
| 9.5 Non-uniform Sampling | 259 | ||
| 9.6 Random Phase Sampling | 260 | ||
| 9.7 MaxEnt Reconstruction and Deconvolution | 262 | ||
| 9.7.1 J-coupling | 262 | ||
| 9.7.2 Linewidths | 263 | ||
| 9.8 Perspective and Future Applications | 263 | ||
| References | 265 | ||
| Chapter 10 Compressed Sensing ℓ-Norm Minimisation in Multidimensional NMR Spectroscopy\r | 267 | ||
| 10.1 Introduction | 267 | ||
| 10.2 Theory | 269 | ||
| 10.3 Algorithms | 271 | ||
| 10.3.1 Greedy Pursuit | 273 | ||
| 10.3.2 Convex Relaxation Methods | 275 | ||
| 10.3.3 Non-convex Minimisation | 277 | ||
| 10.3.4 Other Approaches | 278 | ||
| 10.4 Implementation and Choice of Stopping Criteria | 278 | ||
| 10.5 Terminology | 282 | ||
| 10.6 Current Applications | 283 | ||
| 10.7 Applications to Higher Dimensional Spectroscopy | 291 | ||
| 10.8 Future Perspectives | 299 | ||
| 10.9 Conclusion | 300 | ||
| References | 300 | ||
| Subject Index | 304 |