Diagnostic analysis of experimental artefacts in DOSY NMR data by covariance matrix of the residuals

A review of blind source separation in NMR spectroscopy

Fourier transform is the data processing naturally associated to most NMR experiments. Notable exceptions are Pulse Field Gradient and relaxation analysis, the structure of which is only partially suitable for FT. With the revamp of NMR of complex mixtures, fueled by analytical challenges such as metabolomics, alternative and more apt mathematical methods for data processing have been sought, with the aim of decomposing the NMR signal into simpler bits. Blind source separation is a very broad definition regrouping several classes of mathematical methods for complex signal decomposition that use no hypothesis on the form of the data. Developed outside NMR, these algorithms have been increasingly tested on spectra of mixtures. In this review, we shall provide an historical overview of the application of blind source separation methodologies to NMR, including methods specifically designed for the specificity of this spectroscopy.

Contribution made by multivariate curve resolution applied to gel permeation chromatography-Fourier transform infrared data for an in-depth characterization of styrene-butadiene rubber blends

We evaluate the contribution made by multivariate curve resolution-alternating least squares (MCR-ALS) for resolving gel permeation chromatography-Fourier transform infrared (GPC-FT-IR) data collected on butadiene rubber (BR) and styrene butadiene rubber (SBR) blends in order to access in-depth knowledge of polymers along the molecular weight distribution (MWD). In the BR-SBR case, individual polymers differ in chemical composition but share almost the same MWD. Principal component analysis (PCA) gives a general overview of the data structure and attests to the feasibility of modeling blends as a binary system. MCR-ALS is then performed. It allows resolving the chromatographic coelution and validates the chosen methodology. For SBR-SBR blends, the problem is more challenging since the individual elastomers present the same chemical composition. Rank deficiency is detected from the PCA data structure analysis. MCR-ALS is thus performed on column-wise augmented matrices. It brings very useful insight into the composition of the analyzed blends. In particular, a weak change in the composition of individual SBR in the MWD's lowest mass region is revealed.

Correction of systematic errors in CORE processing of DOSY data

DOSY data for mixtures are commonly processed either by single channel methods (e.g. HR-DOSY) or multichannel methods (e.g. CORE). Both aim to separate the signals from species of different molecular sizes by their diffusion coefficients; the result is displayed either as a 2D plot (as in HR-DOSY) or as individual spectra (as in CORE). Both types of methods are sensitive to any systematic errors in the experimental data. The effects of, and remedies for, two such sources of error, spatially non-uniform pulsed field gradients (PFGs) and instrument instability, are demonstrated for CORE processing, using a corrected form of the Stejskal-Tanner equation and reference deconvolution, respectively.

Real-life applications of the MULVADO software package for processing DOSY NMR data

MULVADO is a newly developed software package for DOSY NMR data processing, based on multivariate curve resolution (MCR), one of the principal multivariate methods for processing DOSY data. This paper will evaluate this software package by using real-life data of materials used in the printing industry: two data sets from the same ink sample but of different quality. Also a sample of an organic photoconductor and a toner sample are analysed. Compared with the routine DOSY output from monoexponential fitting, one of the single channel algorithms in the commercial Bruker software, MULVADO provides several advantages. The key advantage of MCR is that it overcomes the fluctuation problem (non-consistent diffusion coefficient of the same component). The combination of non-linear regression (NLR) and MCR can yield more accurate resolution of a complex mixture. In addition, the data pre-processing techniques in MULVADO minimise the negative effects of experimental artefacts on the results of the data. In this paper, the challenges for analysing polymer samples and other more complex samples will also be discussed.