Peak alignment of urine NMR spectra using fuzzy warping

Wen Wu, Michal Daszykowski, Beata Walczak, Brian C. Sweatman, Susan C. Connor, John N. Haselden, Daniel J. Crowther, Rob W. Gill, Michael W. Lutz

Research output: Contribution to journalArticlepeer-review

57 Citations (Scopus)


Proton nuclear magnetic resonance (1H NMR) spectroscopic analysis of mixtures has been used extensively for a variety of applications ranging from the analysis of plant extracts, wine, and food to the evaluation of toxicity in animals. For example, NMR analysis of urine samples has been used extensively for biomarker discovery and, more simply, for the construction of classification models of toxicity, disease, and biochemical phenotype. However, NMR spectra of complex mixtures typically show unwanted local peak shifts caused by matrix and instrument variability, which must be compensated for prior to statistical analysis and interpretation of the data. One approach is to align the spectral peaks across the data set. An efficient and fast warping algorithm is required as the signals typically contain ca. 32 000-64 000 data points and there can be several thousand spectra in a data set. As demonstrated in our study, the iterative fuzzy warping algorithm fulfills these requirements and can be used on-line for an alignment of the NMR spectra. Correlation coefficients between the aligned and target spectra are used as the evaluation function for the algorithm, and its performance is compared with those of other published warping methods.

Original languageEnglish
Pages (from-to)863-875
Number of pages13
JournalJournal of Chemical Information and Modeling
Issue number2
Early online date24 Jan 2006
Publication statusPublished - 1 Mar 2006

ASJC Scopus subject areas

  • General Chemistry
  • General Chemical Engineering
  • Computer Science Applications
  • Library and Information Sciences


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