Abstract

A baseline correction algorithm using a least-squares procedure is developed. Linear or quadratic types of baselines are obtained through successive fitting and rejection of data points on a statistical basis. After the entire spectrum or a subsection is fitted to a least-squares line, the standard error of estimate is utilized as a criterion to determine if the fluctuation of each data point about the line can be thought of as the baseline fluctuation. Comments on various baseline correction procedures are also made.

Baseline correction for Raman spectra using a spectral estimation-based asymmetrically reweighted penalized least squares method

Yixin Guo, Weiqi Jin, Weilin Wang, Yuqing He, and Su Qiu
Appl. Opt. 62(18) 4766-4776 (2023)

Raman spectra recovery using a second derivative technique and range independent baseline correction algorithm

Andrew Atiogbe Huzortey, Benjamin Anderson, and Alfred Owusu
OSA Continuum 4(9) 2468-2480 (2021)

Baseline correction method based on improved adaptive iteratively reweighted penalized least squares for the x-ray fluorescence spectrum

Xiaoyu Jiang, Fusheng Li, Qingya Wang, Jie Luo, Jun Hao, and Muqiang Xu
Appl. Opt. 60(19) 5707-5715 (2021)

Automatic baseline correction method for the open-path Fourier transform infrared spectra by using simple iterative averaging

Xianchun Shen, Liang Xu, Shubin Ye, Rong Hu, Ling Jin, Hanyang Xu, and Wenqing Liu
Opt. Express 26(10) A609-A614 (2018)

Baseline correction method based on doubly reweighted penalized least squares

Degang Xu, Song Liu, Yaoyi Cai, and Chunhua Yang
Appl. Opt. 58(14) 3913-3920 (2019)