Automatic Baseline Recognition for the Correction of Large Sets of Spectra Using Continuous Wavelet Transform and Iterative Fitting

Abstract

A new algorithm for the automatic recognition of peak and baseline regions in spectra is presented. It is part of a study to devise a baseline correction method that is particularly suitable for the simple and fast treatment of large amounts of data of the same type, such as those coming from high-throughput instruments, images, process monitoring, etc. This algorithm is based on the continuous wavelet transform, and its parameters are automatically determined using the criteria of Shannon entropy and the statistical distribution of noise, requiring virtually no user intervention. It was assessed on simulated spectra with different noise levels and baseline amplitudes, successfully recognizing the baseline points in all cases but for a few extremely weak and noisy signals. It can be combined with various fitting methods for baseline estimation and correction. In this work, it was used together with an iterative polynomial fitting to successfully process a real Raman image of 40 000 pixels in about 2.5 h.

Salient space detection algorithm for signal extraction from contaminated and distorted spectrum

“…8 Many methods have been used to extract signals such as the zero-crossing technique 9 (searching for zeros in the first derivative and treating these positions as signal regions), thresholding algorithm 6,7 (where only points three times larger than the standard deviation of the spectrum noise are treated as signal points) or wavelet decomposition and integration. [10][11][12][13] All these methods have significantly contributed to signal extraction. The zero-crossing technique is the simplest method to extract signals, but it is invalid when noises exist.…”

Collaborative Penalized Least Squares for Background Correction of Multiple Raman Spectra

“…Foist et al first noticed this problem and proposed a method to denoise multidimensional spectral data collaboratively [ 27 ]. For background correction, few approaches have been proposed by utilizing the common characteristics shared in a set of related spectra [ 6 , 28 , 29 ]. For instance, the multiple spectra baseline correction (MSBC) algorithm designed in [ 28 ] assumed that the pairwise differences between the background removed spectra are small and inserted a regularization for this prior to the asymmetric least squares.…”

Automatic morphology‐based cubic p‐spline fitting methodology for smoothing and baseline‐removal of Raman spectra

“…There is no single strategy for noise filtering in Raman spectroscopy. Several methods have been proposed to enhance the Raman information . The most frequently used methods are software procedures, which do not require upgrading the existing instrumentation.…”