Distribution-based classification method for baseline correction of metabolomic 1D proton nuclear magnetic resonance spectra

Kuo Ching Wang, San Yuan Wang, Ching Hua Kuo, Yufeng J. Tseng

研究成果: 雜誌貢獻文章同行評審

16 引文 斯高帕斯(Scopus)

摘要

Baseline distortion in 1D 1H NMR data complicates the quantification of individual components of biofluids in metabolomic experiments. Current 1D 1H NMR baseline correction methods usually require manual parameter and filter tuning by experienced users to obtain desirable results from complex metabolomic spectra, thus becoming prone to correction variation and biased quantification. We present a novel alternative method, BaselineCorrector, for automatically estimating the baselines of 1D 1H NMR metabolomic data. By collecting the standard deviations of spectral intensities, using a moving window to slide through a spectrum, BaselineCorrector can model the distribution of noise standard deviation as a derived chi-squared distribution in each window and then determine optimal parameters for least-error classification of signal and noise. Due to the universal property of noise distributions, BaselineCorrector can robustly recognize the baseline segments in various spectra. In addition to the commonly used 1D NOESY and CPMG pulse sequences, BaselineCorrector also provides an algorithm for correcting diffusion-edited NMR spectra. Using its classification model, BaselineCorrector is able to preserve low signal peaks and correctly handle wide, overlapping peaks in complex metabolomic spectra.
原文英語
頁(從 - 到)1231-1239
頁數9
期刊Analytical Chemistry
85
發行號2
DOIs
出版狀態已發佈 - 一月 15 2013
對外發佈

ASJC Scopus subject areas

  • 分析化學

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