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DOSY (diffusion-ordered spectroscopy) is one of the most commonly employed methods for identifying compounds in mixtures by nuclear magnetic resonance (NMR). However, it struggles to resolve component spectra when there is severe signal overlap and/or diffusion coefficients are very similar. In order to improve the ability of DOSY to distinguish between different species, here, relaxation has been incorporated into diffusion experiments, as a further dimension. This results, to a first approximation, in a locally trilinear data set which, in contrast with a bilinear data set (e.g., a standard DOSY data set), can be decomposed with multivariate statistical methods such as PARAFAC (parallel factor analysis). This enables overlapping multiplets from different species, and by extension whole spectra, to be separated.
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Nilsson et al. (2009) studied this question.
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