Baseline correction using local smoothing optimization penalized least squares
Yuqiang Li, Tianhong Pan, Haoran Li, Shan Chen · 2022
Baseline shift, caused by interference factors, seriously contaminates the essential information of the analysis object. The current baseline correction methods maintain a constant smoothing parameter throughout the process, while the real analyzed signal is composed of multiple intervals with different smoothness. To address this problem, a local smoothing optimization penalized least squares (lsoPLS) method is presented in this work. The local smoothing optimization strategy using the derivative difference between the previously fitted baseline and the original signal is utilized to identify the interval location, and smoothing parameter is optimized by the interval characteristics. Two simulated cases and one real experiment confirm that the proposed method has better performance in the multi-interval baseline correction and can be served as an effective pre-processing method for the complex spectral signal.