Please use this identifier to cite or link to this item:
https://doi.org/10.1016/j.aca.2007.12.005
DC Field | Value | |
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dc.title | A combination of spectral re-alignment and BTEM for the estimation of pure component NMR spectra from multi-component non-reactive and reactive systems | |
dc.contributor.author | Guo, L. | |
dc.contributor.author | Sprenger, P. | |
dc.contributor.author | Garland, M. | |
dc.date.accessioned | 2014-10-09T06:42:20Z | |
dc.date.available | 2014-10-09T06:42:20Z | |
dc.date.issued | 2008-02-04 | |
dc.identifier.citation | Guo, L., Sprenger, P., Garland, M. (2008-02-04). A combination of spectral re-alignment and BTEM for the estimation of pure component NMR spectra from multi-component non-reactive and reactive systems. Analytica Chimica Acta 608 (1) : 48-55. ScholarBank@NUS Repository. https://doi.org/10.1016/j.aca.2007.12.005 | |
dc.identifier.issn | 00032670 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/88438 | |
dc.description.abstract | The deconvolution of multi-component mixtures in NMR spectroscopy is a challenging problem due to the spectral non-linearities. In the present contribution, two data sets were studied (A) 10 samples of a four-component non-reactive mixture measured with 1H, 13C, 19F, 31P NMR and (B) a three-solute cyclo-addition reaction measured with 13C NMR. Both data sets were treated with a re-alignment procedure to correct for the non-stationary chemical shifts, followed by band-target entropy minimization (BTEM) analysis. For data set A, quite good spectral estimates of the two hydrogen-containing species, four carbon-containing species, two fluorine-containing species and two phosphorus-containing species were obtained from the multi-component data. For data set B quite good spectral estimates of all three carbon-containing reactants were obtained as well as their relative concentration profiles. The present contribution using model systems indicates the usefulness of re-alignment procedures for correcting non-stationary characteristics, prior to self-modeling curve resolution (SMCR), and the potential for investigating more complex problems. © 2007 Elsevier B.V. All rights reserved. | |
dc.description.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.aca.2007.12.005 | |
dc.source | Scopus | |
dc.subject | Band-target entropy minimization | |
dc.subject | Chemical shift | |
dc.subject | Chemometrics | |
dc.subject | Mixture analysis | |
dc.subject | Non-stationary signals | |
dc.type | Article | |
dc.contributor.department | CHEMICAL & BIOMOLECULAR ENGINEERING | |
dc.description.doi | 10.1016/j.aca.2007.12.005 | |
dc.description.sourcetitle | Analytica Chimica Acta | |
dc.description.volume | 608 | |
dc.description.issue | 1 | |
dc.description.page | 48-55 | |
dc.description.coden | ACACA | |
dc.identifier.isiut | 000253088800004 | |
Appears in Collections: | Staff Publications |
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