Abstract

Baseline drift occurs in various measured spectra, and the existence of a baseline signal will influence qualitative and quantitative analyses. Therefore, it is necessary to perform baseline correction or background elimination before spectral analysis. In this paper, a multiple constrained asymmetric least squares method based on the penalized least squares principle is proposed for baseline correction. The method takes both baseline and peak characteristics into account. Based on the prior knowledge that the left and right boundaries of characteristic peaks should be symmetrical, additional constraints of penalized least squares are added, which ensure the symmetry of spectra. The experimental results of the proposed method on simulated spectra are compared with existing baseline correction methods to verify the accuracy and adaptability of the proposed method. The method is also successfully applied to the baseline correction of real spectra. The results show that it can be effective for estimating the baseline. In addition, this method can also be applied to the baseline correction of other similar spectral signals.

© 2020 The Author(s)

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)

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)

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)

Baseline correction method based on improved asymmetrically reweighted penalized least squares for the Raman spectrum

Jianfeng Ye, Ziyang Tian, Haoyun Wei, and Yan Li
Appl. Opt. 59(34) 10933-10943 (2020)

Penalized least-squares for imaging with hypertelescopes

Adam J. Webb and Michael C. Roggemann
Appl. Opt. 61(18) 5350-5357 (2022)