Citation: WANG Hai-Peng, CHU Xiao-Li, CHEN Pu, LIU Dan, LI Jing-Yan, XU Yu-Peng. Research and Application Progress of Algorithms for Spectral Baseline Correction[J]. Chinese Journal of Analytical Chemistry, ;2021, 49(8): 1270-1281. doi: 10.19756/j.issn.0253-3820.201679 shu

Research and Application Progress of Algorithms for Spectral Baseline Correction

Research Institute of Petroleum Processing, SINOPEC, Beijing 100083, China

Corresponding author: CHU Xiao-Li, cxlyuli@sina.com
Received Date: 15 November 2020
Revised Date: 29 March 2021

Fund Project: Supported by the National Key Research & Development Program of China (No.2017YFB0306501).

The baseline drift is common in the measurement process of Raman spectroscopy, middle infrared (MIR) spectroscopy, near infrared (NIR) spectroscopy, laser-induced breakdown spectroscopy (LIBS) and other spectral instrumentation, which will seriously deteriorate the result of subsequent qualitative and quantitative analysis of spectra. To obtain accurate and clear results, some effective baseline correction algorithms have been implemented to correct the baseline of spectra before subsequent qualitative and quantitative analysis, especially combined with chemometric methods. Baseline correction algorithms are mainly derivative method, iterative polynomial fitting, piecewise fitting, moving window smoothing, wavelet transform (WT), penalized least squares and robust baseline estimation (RBE). These algorithms can eliminate the adverse effects of baseline drift on quantitative and qualitative analysis to a large extent, but each of them has some shortcomings in certain aspects. In recent years, in response to the drawbacks of the aforementioned algorithms, some improved and novel algorithms for baseline correction have been proposed one after another. Improved algorithms are mainly adaptive minmax polynomial fitting fluorescence background subtraction algorithms, interval linear fitting based on subspace vector angle, dynamic moving Savitzky-Golay algorithms, selection method of optimum decomposition in wavelet transform based on energy distribution, adaptive iteratively reweighted penalized least squares (airPLS), etc. Novel algorithms include fluorescence photo-bleaching difference approach (FBDA), algorithms based on morphological operators, synchronous fitting algorithms of pure spectrum and baseline based on sparse representation, etc. These algorithms have not only improved the quality of spectra but also further enhanced the accuracy and robustness of subsequent quantitative and qualitative analysis based on spectra. This article systematically reviews basic algorithms, improved algorithms and novel algorithms for spectral baseline correction, as well as their application research progress, and the development prospect of algorithms for spectral baseline correction is discussed.
- Chemometrics,
- Baseline correction,
- Raman spectra,
- Laser-induced breakdown spectroscopy,
- Iterative polynomial fitting,
- Piecewise fitting,
- Penalized least squares,
- Review
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