ArticleJournal of analytical methods in chemistry2018
Collaborative Penalized Least Squares for Background Correction of Multiple Raman Spectra.
Article in Journal of analytical methods in chemistry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Iterative Regression of Corrective Baselines (IRCB): A New Model for Quantitative Spectroscopy.Journal of chemical information and modeling · 2024Article
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Authors and funding
4 authors.
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Abstract
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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Registered trials
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