Automatic alignment of high-resolution NMR spectra using a Bayesian estimation approach

Zhou Wang, Seoung Bum Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Nuclear magnetic resonance (NMR) spectral analysis has recently become one of the major means for the detection and recognition of metabolic changes of disease state, physiological alteration, and natural biological variation. For the pattern recognition tasks in which two or more NMR spectra need to be compared, it is critical to properly align the spectra for the subsequent pattern recognition analysis. Previous spectral alignment methods do not consider any baseline intensity variation between the spectra and disregard the effect of noise. Here we formulate the spectra alignment problem in a Bayesian statistical framework, which allows us to simultaneously and efficiently estimate the spectral shift and the baseline intensity variation in the existence of independent additive noise. Experimental results with real high-resolution NMR spectral data from human plasma demonstrate the effectiveness and robustness of the proposed approach.

Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
Pages667-670
Number of pages4
Volume4
DOIs
Publication statusPublished - 2006
Externally publishedYes
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: 2006 Aug 202006 Aug 24

Other

Other18th International Conference on Pattern Recognition, ICPR 2006
CountryChina
CityHong Kong
Period06/8/2006/8/24

    Fingerprint

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture

Cite this

Wang, Z., & Kim, S. B. (2006). Automatic alignment of high-resolution NMR spectra using a Bayesian estimation approach. In Proceedings - International Conference on Pattern Recognition (Vol. 4, pp. 667-670). [1699929] https://doi.org/10.1109/ICPR.2006.295