Robust DOSY NMR data analysis

Abstract Multivariate methods based on curve resolution outperform single channel methods in the analysis of diffusion-ordered spectroscopy (DOSY) NMR data in terms of accuracy and ease of interpretation, especially for systems with large spectral overlap. In this paper, the focus is on the robustness of two multivariate methods, classical multivariate curve resolution (MCR) and MCR combined with non-linear least squares regression (MCR–NLR). Three important factors that influence the analysis are investigated: Peak shifts, phase shifts, and the difference of diffusion coefficients. Using controlled disturbances of a data set of a mixture of three discrete components, ATP, glucose, and SDS, it is shown that both multivariate methods outperform SPLMOD, one of the standard single channel methods. In particular, MCR–NLR is the more accurate as well as the more robust of the two multivariate methods. This implies that MCR–NLR is less sensitive to the quality of the data and may give good results in cases where other methods fail.