Baseline Correction for NMR Spectroscopic Metabolomics Data Analysis
1 Department of Applied Science, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
2 Division of Biostatistics, School of Medicine, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
BMC Bioinformatics 2008, 9:324 doi:10.1186/1471-2105-9-324Published: 29 July 2008
We propose a statistically principled baseline correction method, derived from a parametric smoothing model. It uses a score function to describe the key features of baseline distortion and constructs an optimal baseline curve to maximize it. The parameters are determined automatically by using LOWESS (locally weighted scatterplot smoothing) regression to estimate the noise variance.
We tested this method on 1D NMR spectra with different forms of baseline distortions, and demonstrated that it is effective for both regular 1D NMR spectra and metabolomics spectra with over-crowded peaks.
Compared with the automatic baseline correction function in XWINNMR 3.5, the penalized smoothing method provides more accurate baseline correction for high-signal density metabolomics spectra.