Baseline correction using adaptive iteratively reweighted penalized least squares

作     者：Zhang, Zhi-Min Chen, Shan Liang, Yi-Zeng 

作者机构：Cent S Univ Coll Chem & Chem Engn Res Ctr Modernizat Chinese Med Changsha 410083 Hunan Peoples R China 

出 版 物：《ANALYST》 (分析化学家)

年 卷 期：2010年第135卷第5期

页      面：1138-1146页

核心收录：

学科分类：081704[工学-应用化学] 07[理学] 08[工学] 0817[工学-化学工程与技术] 070302[理学-分析化学] 0703[理学-化学] 

基　　金：National Nature Foundation Committee of P.R. China [20875104, 10771217] ministry of science and technology of China [2007DFA40680] 

主　　题：swamps environments algorithm baseline correction Environments baseline wander algorithms Swamps Streaming SIMD Extensions peak detection original signal weight sums 

摘      要：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 (TM), which is available as open source software (http://***/p/airpls).