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 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.

An Improved Iterative Polynomial Fitting Algorithm for Baseline Correction in X-Ray Spectrum

“…We shall give first a brief technical account of the overall situation, even though this is not necessary to understand how to use our algorithm in practice. The principle of the polynomial iterative fitting is to compare and adjust the original spectral data continuously in the process of polynomial iterative fitting, and to compare the adjusted spectral data directly with the points on the fitting curve [10][11][12][13]. The advantage of baseline correction by this method is that the coefficients of the polynomial are gradually adjusted to approach the actual baseline shape, and the calculated baseline function form is closer to the actual baseline.…”

Iterative Regression of Corrective Baselines (IRCB): A New Model for Quantitative Spectroscopy

“…Significant efforts have been made in the development of new preprocessing techniques to improve the capabilities of spectroscopic PAC to model more complex data, such as crude reaction mixtures. As such, new types of data processing are frequently reported, some of which rely on iterative approaches or neural networks for preprocessing optimization. − Although artificial intelligence has previously been applied for preprocessing treatments, few examples for end-to-end automated quantitative model development have been attempted . Automated end-to-end quantitative model development may provide significant advantages for the generalizable accuracy and repeatability of chemometric models.…”