Collaborative Penalized Least Squares for Background Correction of Multiple Raman Spectra.

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 a

SUBMITTER: Chen L 

PROVIDER: S-EPMC6136554 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature