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. Copyright © 2005 John Wiley & Sons, Ltd.

[1]  M. Conradi,et al.  NMR detection of thermal damage in carbon fiber reinforced epoxy resins. , 2005, Journal of magnetic resonance.

[2]  L. Buydens,et al.  Diagnostic analysis of experimental artefacts in DOSY NMR data by covariance matrix of the residuals. , 2005, Journal of magnetic resonance.

[3]  R Huo,et al.  Improved DOSY NMR data processing by data enhancement and combination of multivariate curve resolution with non-linear least square fitting. , 2004, Journal of magnetic resonance.

[4]  A. M. Gil,et al.  Exploratory applications of diffusion ordered spectroscopy to liquid foods: an aid towards spectral assignment , 2004 .

[5]  Lutgarde M. C. Buydens,et al.  Assessment of techniques for DOSY NMR data processing , 2003 .

[6]  Yann Batonneau,et al.  Combined use of conventional and second-derivative data in the SIMPLISMA self-modeling mixture analysis approach. , 2002, Analytical chemistry.

[7]  A. Smilde,et al.  Constrained least squares methods for estimating reaction rate constants from spectroscopic data , 2002 .

[8]  Sarah C. Rutan,et al.  Multivariate curve resolution with non-linear fitting of kinetic profiles , 2001 .

[9]  Age K. Smilde,et al.  Sufficient conditions for unique solutions within a certain class of curve resolution models , 2001 .

[10]  John C. Lindon,et al.  Metabonomics: metabolic processes studied by NMR spectroscopy of biofluids , 2000 .

[11]  Golotvin,et al.  Improved baseline recognition and modeling of FT NMR spectra , 2000, Journal of magnetic resonance.

[12]  Chris W. Brown,et al.  Self-Modeling Mixture Analysis by Interactive Principal Component Analysis , 2000 .

[13]  A Heerschap,et al.  Automatic correction for phase shifts, frequency shifts, and lineshape distortions across a series of single resonance lines in large spectral data sets. , 2000, Journal of magnetic resonance.

[14]  Charles S. Johnson Diffusion Ordered Nuclear Magnetic Resonance Spectroscopy: Principles and Applications , 1999 .

[15]  T. Malliavin,et al.  Maximum Entropy Processing of DOSY NMR Spectra , 1998 .

[16]  W. Windig,et al.  Direct exponential curve resolution algorithm (DECRA): A novel application of the generalized rank annihilation method for a single spectral mixture data set with exponentially decaying contribution profiles , 1997 .

[17]  Desire L. Massart,et al.  Resolution of multicomponent overlapped peaks by the orthogonal projection approach, evolving factor analysis and window factor analysis , 1997 .

[18]  P. Griffiths,et al.  Global Least-Squares Analysis of Large, Correlated Spectral Data Sets: Application to Component-Resolved FT-PGSE NMR Spectroscopy , 1996 .

[19]  R. Tauler Multivariate curve resolution applied to second order data , 1995 .

[20]  Charles S. Johnson,et al.  Diffusion-ordered two-dimensional nuclear magnetic resonance spectroscopy , 1992 .

[21]  W. Windig,et al.  Interactive self-modeling mixture analysis , 1991 .

[22]  William H. Press,et al.  Numerical recipes , 1990 .

[23]  Gareth A. Morris,et al.  Compensation of instrumental imperfections by deconvolution using an internal reference signal , 1988 .

[24]  S. Provencher A constrained regularization method for inverting data represented by linear algebraic or integral equations , 1982 .

[25]  Miquel Esteban,et al.  Multivariate curve resolution with alternating least squares optimisation: a soft-modelling approach to metal complexation studies by voltammetric techniques , 2000 .

[26]  Van Gorkom LCM,et al.  Analysis of DOSY and GPC-NMR Experiments on Polymers by Multivariate Curve Resolution , 1998, Journal of magnetic resonance.

[27]  P. Stilbs,et al.  Fourier transform pulsed-gradient spin-echo studies of molecular diffusion , 1987 .