Application of Linear Prediction and Rapid Acquisition to Nuclear Magnetic Resonance

Authors

  • Edward T. Chainani Department of Chemistry and National Chemistry Instrumentation Center, School of Science and Engineering, Ateneo de Manila University, Quezon City
  • Fabian M. Dayrit Department of Chemistry and National Chemistry Instrumentation Center, School of Science and Engineering, Ateneo de Manila University, Quezon City
  • Luis G. Sison Department of Electrical and Electronics Engineering, University of the Philippines, Diliman, Quezon City

DOI:

https://doi.org/10.26534/kimika.v17i2.57-63

Keywords:

NMR, linear prediction, driven-equilibrium Fourier transfonn, singular value decomposition

Abstract

A method for the acquisition of 1-dimensional nuclear magnetic resonance (NMR) data, which obtains· signals more rapidly than the conventional method is presented. However, because the data are truncated, data processing by Fourier  transformation is overcome by alternative spectral estimation methods. Linear prediction (LP) is used to reconstruct the spectrum from the incomplete time-domain magnetic resonance data. A pulse sequence modified from the driven-equilibrium Fourier transform (DEFT) implements truncated acquisition with forced return to equilibrium. This combination of truncated acquisition and LP processing is a novel way of acquiring and processing NMR data. The technique is demonstrated using a 31P NMR acquisition where the conventional procedure required 17 h whereas the proposed method took only 45 min.

Downloads

How to Cite

Chainani, E. T., Dayrit, F. M., & Sison, L. G. (2001). Application of Linear Prediction and Rapid Acquisition to Nuclear Magnetic Resonance. KIMIKA, 17(2), 57–63. https://doi.org/10.26534/kimika.v17i2.57-63

Issue

Vol. 17 No. 2 (2001)

Section

Research Articles

License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).