Baseline subtraction using robust local regression estimation
By A. Ruckstuhl, Matthew P. Jacobson, R. Field, J. Dodd. 2001
157 citations recorded by the source corpus.
Abstract A technique entitled robust baseline estimation is introduced, which uses techniques of robust local regression to estimate baselines in spectra that consist of sharp features superimposed upon a continuous, slowly varying baseline. The technique is applied to synthetic spectra, to evaluate its capabilities, and to laser-induced fluorescence spectra of OH (produced from the reaction of ozone with hydrogen atoms). The latter example is a particularly challenging case for baseline estimation because the experimental noise varies as a function of frequency.
Subfields: Analytical Chemistry, Biomedical Engineering, Mechanics of Materials
View Paper: https://www.semanticscholar.org/paper/07d0140906f452a223a9e0c504e6be0a0636bacb
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In Analytical Chemistry
In Biomedical Engineering
Citations in the corpus
- Chromatogram baseline estimation and denoising using sparsity (BEADS) (2014, methodology): “Methods based on linear and non-linear [36, 26, 41] filtering, or multiscale forms of filtering with wavelet transforms [9, 24, 7, 31] have been proposed.”
- Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution (2017, methodology): “…database which contains raw (uncorrected) spectra for 512 minerals and six widely-used baseline correction meth-ods: modified polynomial fitting[1], rubber band[3], robust local regression estimation[33], iterative restricted least squares, asymmetric least square smoothing[6], rolling ball[2] .”