Comparison of baseline estimation algorithms for chromatographic signals

Ilewicz, Witold; Kowalczuk, Marcin; Niezabitowski, Michał; Buchczik, Dariusz; Gałuszka, Adam

doi:10.1109/mmar.2015.7284001

Cited by 3 publications

(1 citation statement)

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“…Peaks in Raman spectra are the fingerprints of the analyte, and the corresponding peak heights or peak areas have strong correlations with the concentration of the analyte. However, spectral interferences have a strong negative effect on the measurement of peaks, and this in the long run hinders the performance of Raman spectroscopy-based quantitative analysis [ 4 ]. Representative interferences for Raman spectra include backgrounds mainly caused by instrument fluctuations and fluorescent substances.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Penalized Least Squares for Background Correction of Multiple Raman Spectra

Chen

et al. 2018

Journal of Analytical Methods in Chemistry

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Although Raman spectroscopy has been widely used as a noninvasive analytical tool in various applications, backgrounds in Raman spectra impair its performance in quantitative analysis. Many algorithms have been proposed to separately correct the background spectrum by spectrum. However, in real applications, there are commonly multiple spectra collected from the close locations of a sample or from the same analyte with different concentrations. These spectra are strongly correlated and provide valuable information for more robust background correction. Herein, we propose two new strategies to remove background for a set of related spectra collaboratively. Based on weighted penalized least squares, the new approaches will use the fused weights from multiple spectra or the weights from the average spectrum to estimate the background of each spectrum in the set. Background correction results from both simulated and real experimental data demonstrate that the proposed collaborative approaches outperform traditional algorithms which process spectra individually.

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Section: Introductionmentioning

confidence: 99%