Goldindec: A Novel Algorithm for Raman Spectrum Baseline Correction

Abstract

Raman spectra have been widely used in biology, physics, and chemistry and have become an essential tool for the studies of macromolecules. Nevertheless, the raw Raman signal is often obscured by a broad background curve (or baseline) due to the intrinsic fluorescence of the organic molecules, which leads to unpredictable negative effects in quantitative analysis of Raman spectra. Therefore, it is essential to correct this baseline before analyzing raw Raman spectra. Polynomial fitting has proven to be the most convenient and simplest method and has high accuracy. In polynomial fitting, the cost function used and its parameters are crucial. This article proposes a novel iterative algorithm named Goldindec, freely available for noncommercial use as noted in text, with a new cost function that not only conquers the influence of great peaks but also solves the problem of low correction accuracy when there is a high peak number. Goldindec automatically generates parameters from the raw data rather than by empirical choice, as in previous methods. Comparisons with other algorithms on the benchmark data show that Goldindec has a higher accuracy and computational efficiency, and is hardly affected by great peaks, peak number, and wavenumber.

Label‐Free Molecular Imaging of Biological Cells and Tissues by Linear and Nonlinear Raman Spectroscopic Approaches

“…One example of this development is given in Ref. [34],ap olynomial fitting is carried out with an ew cost function, and the algorithm parameters were estimated based on the data at hand. Background region with no Raman information are identified by agenetic algorithm, and these positions are used to fit the baseline based on cubic splines.…”

“…[33] In Ref. [34],ap olynomial fitting is carried out with an ew cost function, and the algorithm parameters were estimated based on the data at hand. Thea uthors made their program called "Goldindec" freely available.T ests were only done using artificial data.…”

Characterizing glucose, illumination, and nitrogen-deprivation phenotypes ofSynechocystisPCC6803 with Raman spectroscopy

“…The Rametrix TM LITE Toolbox was used to process all spectra, perform principal component analysis (PCA), and discriminant analysis of principal components (DAPC). Processing Raman spectra consisted of: (i) averaging the 20 spectra replicates; (ii) truncating spectra to the Raman shift biological range (400-1,800 cm −1 ); (iii) baselining using the Goldindec algorithm (Liu et al, 2015); and (iv) vector normalization. Goldingdec algorithm parameters used were: (i) baseline polynomial order = 3; (ii) estimated peak ratio = 0.5; and (iii) smoothing window size = 5.…”

A practical convolutional neural network model for discriminating Raman spectra of human and animal blood

“…Chen et al used Wiener estimation to recover Raman spectra from measurements with extremely low SNR. Liu et al proposed an iterative baseline correction algorithm named Goldindec, which not only suppress the influence of peaks but also solves the problem of low‐correction accuracy when there is a high‐peak number. Liu et al presented a model to estimate a smooth spectrum and remove the baseline simultaneously.…”