## Abstract Modeling NMRβbased metabolomics data often involves linear methods such as principal component analysis (PCA) and partial least squares (PLS). These methods have the objective of describing the main variance in the data and maximum covariance between the predictor variables and some res
Analysis of DOSY and GPC-NMR Experiments on Polymers by Multivariate Curve Resolution
β Scribed by Leon C.M. Van Gorkom; Thomas M. Hancewicz
- Publisher
- Elsevier Science
- Year
- 1998
- Tongue
- English
- Weight
- 112 KB
- Volume
- 130
- Category
- Article
- ISSN
- 1090-7807
No coin nor oath required. For personal study only.
β¦ Synopsis
Multivariate curve resolution ( MCR ) was successfully apdomain is analyzed with either a single-or a multiexponenplied to the analysis of DOSY experiments on polymer mixtures tial fit or ILT at each frequency channel. The analysis is and GPC-NMR experiments on industrial copolymer samples. performed independently of other frequency channels. Thus MCR generates pure factors of spectral and concentration prothese methods do not use intrinsic information in the DOSY files using, successively, principal factor analysis, Varimax rotadata that the entire spectral bandshape of each component is tion, and alternating least-squares optimization. The method attenuated similarly. This information has been incorporated described is robust and can be directly applied to DOSY and into the global least-squares analysis (CORE) of component-GPC-NMR data and one obtains 1 H NMR spectra of the individresolved FT-PFGSE NMR spectroscopy (5). In this analyual compounds with their corresponding diffusion or elution sis, for n components, n global self-diffusion coefficients are profiles, respectively.
π SIMILAR VOLUMES
## Abstract A method is presented that allows for retrieving 1D spectra of the individual components of a mixture from a sparsely acquired 2DβTOCSY spectrum. The decomposition of the 2DβTOCSY data into pure 1D traces is achieved using a nonβnegative matrix factorization algorithm, also known as mul