A fully automated iterative moving averaging (AIMA) technique for baseline correction

Abstract

Baseline correction is one of the pre-processing steps in the analysis of metabolite signals from chemometric analytical instruments. Fully automated baseline correction techniques, although more convenient to use, tend to be less accurate than semi-automated baseline correction. A fully automated baseline correction algorithm, the automated iterative moving averaging algorithm (AIMA), is presented and compared with three recently introduced semi-automated algorithms, namely the adaptive iteratively reweighted penalized least squares (airPLS), Asymmetric Least Squares baseline correction (ALS) and a parametric method, using NMR, Raman and HPLC chromatograms. AIMA's potential in increasing the accuracy of multivariate analysis via SELTI-TOF and LCMS chromatograms was also assessed. The results show that the AIMA's accuracy is comparable to these semi-automated algorithms and has the advantage of ease of use. An AIMA plug-in for an open source metabolomics analysis tool, MZmine, was also developed. The AIMA plug-in is available at http://padel.nus.edu.sg/software/padelaima.

Raman Imaging of Lignocellulosic Feedstock

“…For removing background coming from the measured material (fluorescence) or signal from the substrate different methods have been developed that are capable of handling irregularly shaped baselines [125][126][127][128]. Baseline correction of Raman spectra is especially important prior to multivariate methods and different solutions to improve baseline correction methods have been developed [125,129,130].…”

Improved Savitzky–Golay-method-based fluorescence subtraction algorithm for rapid recovery of Raman spectra

“…Therefore, developing a powerful background subtraction method is essential for the application of in vivo Raman spectroscopy. Various techniques have been developed to isolate the Raman features based on both instrumental [7][8][9][10][11][12][13][14][15][16] and computational methods [17][18][19][20][21][22][23][24][25][26][27][28][29]. On one hand, the instrumental-method-based technique includes shifted excitation [7][8][9][10][11][12] and time gating [15,16].…”

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

“…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.…”