A new automatic baseline correction method based on iterative method

Automatic Baseline Recognition for the Correction of Large Sets of Spectra Using Continuous Wavelet Transform and Iterative Fitting

“…Although this method often does not outperform the human eye and is therefore not the most suitable for treating very tricky baselines, it appears to be a very good choice for the quick and reproducible processing of a large number of not particularly difficult signals. It is also more automatic than methods that recognize the baseline using CWT and iterative thresholding, 25,17 in which the user must indicate the scale parameter or at least the presence of broad peaks. Moreover, these methods need a rather large difference in curvature between peaks and baseline, or they may not converge (although the user may toggle a different recognition strategy in the presence of broad peaks).…”

“…Various authors tackled this issue by proposing automated or semi-automated algorithms for baseline correction that reduce human intervention. Effective solutions were devised using iterative polynomial fitting, 13 penalized quantile spline regression, 14 adaptive least squares/ Whittaker smoother, [15][16][17] moving average-peak stripping, [18][19][20] local second derivative, 12 and morphological or geometrical approaches. 21,22 The performance of these methods differ in terms of accuracy, computational speed, amount of human intervention, and types of spectra to which they can be applied; these goals are usually conflicting.…”

“…In the most notable application, 25 it was employed together with an iterative thresholding on the power spectrum to recognize baseline points, which were subsequently fitted with a Whittaker smoother. This method was later improved 17 to better recognize broad peaks and baseline points in low and congested regions. The reported spectra were corrected almost completely automatically, but the user was still required to manually indicate the presence of broad peaks and set a smoothness parameter for the fitting function.…”

A novel strategy for quantitative analysis of the formulated complex system using chromatographic fingerprints combined with some chemometric techniques

“…However, baseline variation and peak shift are two main problems need to be addressed by more advanced chemometric data-processing techniques [26]. Research on baseline correction has become a focused field of data-processing and many superior algorithms were developed [27][28][29][30][31]. Considering the fact that peak shift is the biggest problem to extract accurate sample information from chromatographic profiles using contemporary chemometric and statistical techniques, chemometricians have made great efforts to develop alignment strategies for chromatograms.…”

Distribution-Based Classification Method for Baseline Correction of Metabolomic 1D Proton Nuclear Magnetic Resonance Spectra

“…Golotvin et al 19 identified signal-free regions by calculating the difference between maximal and minimal intensities within a given segment and then used an algorithm to compare this difference with the standard deviation of noise multiplied by a threshold constant, the selection of which determined the quality/ accuracy of the method. Bao et al 28 combined three methods derived from the works of Cobas et al, Golotvin et al, and Dietrich et al and a peak shape function to recognize the signalfree regions. An iterative weighted function was used to correct the negative signal regions and construct the baseline curves in Bao's work.…”