June 7-10, 2016
Regierungspräsidium am Rondellplatz, Karlsruhe, Germany
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Improved Methods and Tools for Identification of Mixture Components by NMR, G. Rheinwald, S. Golotvin, S. Pol, P. Wheeler, B. Pautler, T. SalbertDownload
Method and Application, Exhibition Hall, D1-A12
Improved Methods and Tools for Identification of Mixture Components by NMR
G. Rheinwald, S. Golotvin, S. Pol, P. Wheeler, B. Pautler, T. Salbert
Abstract: It is a common requirement in the preparation and analysis of many products to be able to easily identify and quantify the different components in mixtures of varying complexity. Specific examples include beverages, flavorings, drug formulation and counterfeit detection, as well as various consumer products.[1,2] A wide variety of analytical techniques have been applied to address this need which often depends on sample characteristics such as FT-IR, Raman spectroscopy, GC-MS & LC-MS. Nuclear Magnetic Resonance (NMR) spectroscopy offers a non-destructive approach that is information rich.
The analysis of NMR spectra can pose certain problems, not the least of which is the overlap of signals. Interpretation becomes more difficult through manual methods as the number of components grows and overlap increases. Various capabilities have been introduced over the years to assist with analysis, including database construction and query, deconvolution (peak fitting) and automated multiplet analysis. As the use of these methods increases, it becomes necessary to create an integrated tool-box to make this sort of work more consistent, accurate, and fast. Here we present a toolset to efficiently identify components in 1D NMR datasets which can be coupled with integrated DOSY analysis and advanced databasing. This implementation will allow for the analyst to work more effectively and allows for a better overall understanding the composition of mixtures by NMR.
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