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Robust alignment of chromatograms by statistically analyzing the shifts matrix generated by moving window fast Fourier transform cross‐correlation
Author(s) -
Zhang Mingjing,
Wen Ming,
Zhang ZhiMin,
Lu Hongmei,
Liang Yizeng,
Zhan Dejian
Publication year - 2015
Publication title -
journal of separation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.72
H-Index - 102
eISSN - 1615-9314
pISSN - 1615-9306
DOI - 10.1002/jssc.201401235
Subject(s) - fourier transform , preprocessor , matrix (chemical analysis) , cross correlation , discrete fourier transform (general) , short time fourier transform , algorithm , window (computing) , computer science , artificial intelligence , mathematics , fourier analysis , chemistry , chromatography , statistics , mathematical analysis , operating system
Retention time shift is one of the most challenging problems during the preprocessing of massive chromatographic datasets. Here, an improved version of the moving window fast Fourier transform cross-correlation algorithm is presented to perform nonlinear and robust alignment of chromatograms by analyzing the shifts matrix generated by moving window procedure. The shifts matrix in retention time can be estimated by fast Fourier transform cross-correlation with a moving window procedure. The refined shift of each scan point can be obtained by calculating the mode of corresponding column of the shifts matrix. This version is simple, but more effective and robust than the previously published moving window fast Fourier transform cross-correlation method. It can handle nonlinear retention time shift robustly if proper window size has been selected. The window size is the only one parameter needed to adjust and optimize. The properties of the proposed method are investigated by comparison with the previous moving window fast Fourier transform cross-correlation and recursive alignment by fast Fourier transform using chromatographic datasets. The pattern recognition results of a gas chromatography mass spectrometry dataset of metabolic syndrome can be improved significantly after preprocessing by this method. Furthermore, the proposed method is available as an open source package at https://github.com/zmzhang/MWFFT2.