Publication

Folding and pattern recognition in two-dimensional NMR spectra

Geoffrey Bodenhausen
1988
Journal Articles

Abstract

The use is described of pattern recognition techniques to analyze folded (in w1) 2-dimensional NMR spectra obtained by using the COSY (correlation spectroscopy) technique. A pattern recognition procedure (P. and B., 1986) relying on the comparison of patterns in z-COSY and anti-z-COSY spectra is applied to both normal and reconstructed spectra of a 1:2 mixt. of the cis and trans isomers of 2-phenylcarboxylic acid Et ester as an illustrative example. [on SciFinder (R)]

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Geoffrey Bodenhausen
1988
Journal Articles

Abstract

Official source

Related concepts (23)

Two-dimensional nuclear magnetic resonance spectroscopy

Two-dimensional nuclear magnetic resonance spectroscopy (2D NMR) is a set of nuclear magnetic resonance spectroscopy (NMR) methods which give data plotted in a space defined by two frequency axes rather than one. Types of 2D NMR include correlation spectroscopy (COSY), J-spectroscopy, exchange spectroscopy (EXSY), and nuclear Overhauser effect spectroscopy (NOESY). Two-dimensional NMR spectra provide more information about a molecule than one-dimensional NMR spectra and are especially useful in determining the structure of a molecule, particularly for molecules that are too complicated to work with using one-dimensional NMR.

Nuclear magnetic resonance spectroscopy

Nuclear magnetic resonance spectroscopy, most commonly known as NMR spectroscopy or magnetic resonance spectroscopy (MRS), is a spectroscopic technique to observe local magnetic fields around atomic nuclei. This spectroscopy is based on the measurement of absorption of electromagnetic radiations in the radio frequency region from roughly 4 to 900 MHz. Absorption of radio waves in the presence of magnetic field is accompanied by a special type of nuclear transition, and for this reason, such type of spectroscopy is known as Nuclear Magnetic Resonance Spectroscopy.

Pattern recognition

Pattern recognition is the automated recognition of patterns and regularities in data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess (PR) capabilities but their primary function is to distinguish and create emergent pattern. PR has applications in statistical data analysis, signal processing, , information retrieval, bioinformatics, data compression, computer graphics and machine learning.

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