Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful nondestructive technique for exploring the structure of matter. In recent years, NMR instrumentation has become increasingly sophisticated, and the software used to acquire and process NMR data continues to expand in scope and complexity. This software has always been difficult to understand, and, until now, it seemed likely to remain that way.
NMR Data Processing examines and explains the techniques used to process, present, and analyze NMR data. It provides a complete account of the fundamentals of spectrum analysis and establishes a framework for applying those fundamentals to real NMR data. It also details, in clear and concise language, the basic principles underlying the complex software needed to analyze the data.
Two chapters are devoted to the fundamentals and applications of discrete Fourier transform (DFT) in NMR, which was crucial to the development of modern NMR spectroscopy. A large part of the book focuses on increasingly important non-DFT methods, which obtain higher sensitivity and resolution. Other topics covered include:
* Data formats
* Processing for multidimensional experiments
* Parametric modeling of NMR signals
* Standard techniques-apodization, zero-filling, the Hilbert transform
* Artifacts-aliasing, leakage, solvent signals
* Advanced processing techniques-LP, MaxEnt, Bayesian analysis
Jeffrey C. Hoch and Alan S. Stern conclude their in-depth look at this rapidly growing field by exploring methods for analyzing processed data, including visualization, quantification, and error analysis. Readers are provided with a solid foundation for developing new methods of their own.
NMR Data Processing is an important tool for students learning basic principles for the first time, technicians troubleshooting data processing problems, and professional researchers developing new techniques. It will help all NMR users acquire a true grasp of the methods behind the process, avoid the pitfalls of misapplication and misinterpretation, and exploit the full power of NMR software.
The book contains predominantly black-and-white illustrations, with some color illustrations.
Examines spectrum analysis in relation to the processing of nuclear magnetic resonance (NMR) data. Also studies fundamental principles inherent in the reduction of raw data into a form of use to further scientific investigation. Chapters include an historical introduction, fundamentals of the Discrete Fourier Transform (DFT), applying the DFT to NMR, linear prediction, maximum entropy reconstruction as an alternative to DFT, emerging methods, and visualization, quantification, and error analysis. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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More About This Textbook
Overview
NMR DATA PROCESSING
Jeffrey C. Hoch and Alan S. Stern
Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful nondestructive technique for exploring the structure of matter. In recent years, NMR instrumentation has become increasingly sophisticated, and the software used to acquire and process NMR data continues to expand in scope and complexity. This software has always been difficult to understand, and, until now, it seemed likely to remain that way.
NMR Data Processing examines and explains the techniques used to process, present, and analyze NMR data. It provides a complete account of the fundamentals of spectrum analysis and establishes a framework for applying those fundamentals to real NMR data. It also details, in clear and concise language, the basic principles underlying the complex software needed to analyze the data.
Two chapters are devoted to the fundamentals and applications of discrete Fourier transform (DFT) in NMR, which was crucial to the development of modern NMR spectroscopy. A large part of the book focuses on increasingly important non-DFT methods, which obtain higher sensitivity and resolution. Other topics covered include:
* Data formats
* Processing for multidimensional experiments
* Parametric modeling of NMR signals
* Standard techniques-apodization, zero-filling, the Hilbert transform
* Artifacts-aliasing, leakage, solvent signals
* Advanced processing techniques-LP, MaxEnt, Bayesian analysis
Jeffrey C. Hoch and Alan S. Stern conclude their in-depth look at this rapidly growing field by exploring methods for analyzing processed data, including visualization, quantification, and error analysis. Readers are provided with a solid foundation for developing new methods of their own.
NMR Data Processing is an important tool for students learning basic principles for the first time, technicians troubleshooting data processing problems, and professional researchers developing new techniques. It will help all NMR users acquire a true grasp of the methods behind the process, avoid the pitfalls of misapplication and misinterpretation, and exploit the full power of NMR software.
The book contains predominantly black-and-white illustrations, with some color illustrations.
Editorial Reviews
Booknews
Examines spectrum analysis in relation to the processing of nuclear magnetic resonance (NMR) data. Also studies fundamental principles inherent in the reduction of raw data into a form of use to further scientific investigation. Chapters include an historical introduction, fundamentals of the Discrete Fourier Transform (DFT), applying the DFT to NMR, linear prediction, maximum entropy reconstruction as an alternative to DFT, emerging methods, and visualization, quantification, and error analysis. Annotation c. Book News, Inc., Portland, OR (booknews.com)Product Details
Related Subjects
Table of Contents
Fundamentals of the Discrete Fourier Transform.
Using the DFT: Application to NMR.
Linear Prediction.
Maximum Entropy Reconstruction in NMR: An Alternative to DFT.
Emerging Methods.
Visualization, Quantification, and Error Analysis.
References.
Index.