Baseline correction using adaptive iteratively reweighted penalized least squares

TLDR

Baseline drift blurs signals and degrades analytical results, and although polynomial fitting can partially correct it, it requires user intervention and is unreliable in low signal‑to‑noise settings. The authors propose a novel adaptive iteratively reweighted penalized least squares (airPLS) algorithm that requires no user intervention or prior information. airPLS iteratively adjusts weights of the sum‑of‑squares errors between the fitted baseline and the original signal, with the weights updated adaptively based on the difference between the previous baseline and the signal. The algorithm is fast, flexible, and demonstrated on simulated and real datasets, with implementations available in open‑source R and MATLAB code at http://code.google.com/p/airpls.

Abstract

Baseline drift always blurs or even swamps signals and deteriorates analytical results, particularly in multivariate analysis. It is necessary to correct baseline drift to perform further data analysis. Simple or modified polynomial fitting has been found to be effective to some extent. However, this method requires user intervention and is prone to variability especially in low signal-to-noise ratio environments. A novel algorithm named adaptive iteratively reweighted Penalized Least Squares (airPLS) that does not require any user intervention and prior information, such as peak detection etc., is proposed in this work. The method works by iteratively changing weights of sum squares errors (SSE) between the fitted baseline and original signals, and the weights of the SSE are obtained adaptively using the difference between the previously fitted baseline and the original signals. The baseline estimator is fast and flexible. Theory, implementation, and applications in simulated and real datasets are presented. The algorithm is implemented in R language and MATLAB™, which is available as open source software (http://code.google.com/p/airpls).