Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1181456 | Chemometrics and Intelligent Laboratory Systems | 2007 | 11 Pages |
Abstract
Statistical analysis of metabolomic datasets can lead to erroneous interpretation of results due to misalignment of the data. Therefore pre-processing methods for peak alignment and data averaging (binning or bucketing) to improve data quality have been used. Here we introduce adaptive binning. The undecimated wavelet transform is used in an improved method for correcting variation in chemical shifts in nuclear magnetic resonance spectroscopy data. Adaptive binning using theoretical and metabolomics NMR spectra significantly increases the ratio of inter-class to intra-class variation and increases data interpretability when compared to conventional binning.
Related Topics
Physical Sciences and Engineering
Chemistry
Analytical Chemistry
Authors
Richard A. Davis, Adrian J. Charlton, John Godward, Stephen A. Jones, Mark Harrison, Julie C. Wilson,