Baseline correction using adaptive iteratively reweighted penalized least squares

The Analyst ◽  
2010 ◽  
Vol 135 (5) ◽  
pp. 1138 ◽  
Author(s):  
Zhi-Min Zhang ◽  
Shan Chen ◽  
Yi-Zeng Liang
Keyword(s):  
Least Squares ◽  
Baseline Correction ◽  
Penalized Least Squares

Baseline Correction for Raman Spectra Based on Locally Symmetric Reweighted Penalized Least Squares

2018 ◽  
Vol 45 (12) ◽  
pp. 1211001 ◽  
Author(s):  
赵恒 Zhao Heng ◽  
陈娱欣 Chen Yuxin ◽  
续小丁 Xu Xiaoding ◽  
胡波 Hu Bo
Keyword(s):  
Least Squares ◽  
Raman Spectra ◽  
Baseline Correction ◽  
Penalized Least Squares

scholarly journals An Automatic Baseline Correction Method Based on the Penalized Least Squares Method

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2015
Author(s):  
Feng Zhang ◽  
Xiaojun Tang ◽  
Angxin Tong ◽  
Bin Wang ◽  
Jingwei Wang
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Correction Method ◽  
Baseline Correction ◽  
Penalized Least Squares ◽  
Gaussian Peak ◽  
Baseline Drift ◽  
Extended Range ◽  
Different Types ◽  
Smoothness Parameter

Baseline drift spectra are used for quantitative and qualitative analysis, which can easily lead to inaccurate or even wrong results. Although there are several baseline correction methods based on penalized least squares, they all have one or more parameters that must be optimized by users. For this purpose, an automatic baseline correction method based on penalized least squares is proposed in this paper. The algorithm first linearly expands the ends of the spectrum signal, and a Gaussian peak is added to the expanded range. Then, the whole spectrum is corrected by the adaptive smoothness parameter penalized least squares (asPLS) method, that is, by turning the smoothing parameter λ of asPLS to obtain a different root-mean-square error (RMSE) in the extended range, the optimal λ is selected with minimal RMSE. Finally, the baseline of the original signal is well estimated by asPLS with the optimal λ. The paper concludes with the experimental results on the simulated spectra and measured infrared spectra, demonstrating that the proposed method can automatically deal with different types of baseline drift.

Baseline correction for infrared spectra using adaptive smoothness parameter penalized least squares method

2020 ◽  
Vol 53 (3) ◽  
pp. 222-233 ◽  
Author(s):  
Feng Zhang ◽  
Xiaojun Tang ◽  
Angxin Tong ◽  
Bin Wang ◽  
Jingwei Wang ◽  
...  
Keyword(s):  
Least Squares ◽  
Infrared Spectra ◽  
Least Squares Method ◽  
Baseline Correction ◽  
Penalized Least Squares ◽  
Smoothness Parameter

Baseline correction method based on doubly reweighted penalized least squares

2019 ◽  
Vol 58 (14) ◽  
pp. 3913 ◽  
Author(s):  
Degang Xu ◽  
Song Liu ◽  
Yaoyi Cai ◽  
Chunhua Yang
Keyword(s):  
Least Squares ◽  
Correction Method ◽  
Baseline Correction ◽  
Penalized Least Squares

An improved PD-AsLS method of baseline estimated in EDXRF analysis

2021 ◽  
Author(s):  
Qingxian Zhang ◽  
Hui Li ◽  
Hongfei Xiao ◽  
Jian Zhang ◽  
Xiaozhe Li ◽  
...  
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Fluorescence Analysis ◽  
Energy Dispersive ◽  
Baseline Correction ◽  
Penalized Least Squares ◽  
X Ray ◽  
Edxrf Analysis ◽  
Asymmetric Least Squares

Baseline correction is an important step in energy-dispersive X-ray fluorescence analysis. The asymmetric least squares method (AsLS), adaptive iteratively reweighted penalized least squares method (airPLS), and asymmetrically reweighted penalized least...

Baseline correction using asymmetrically reweighted penalized least squares smoothing

The Analyst ◽  
2015 ◽  
Vol 140 (1) ◽  
pp. 250-257 ◽  
Author(s):  
Sung-June Baek ◽  
Aaron Park ◽  
Young-Jin Ahn ◽  
Jaebum Choo
Keyword(s):  
Least Squares ◽  
Baseline Correction ◽  
Penalized Least Squares ◽  
Spectral Analyses

Baseline correction methods based on penalized least squares are successfully applied to various spectral analyses.

Multiple Constrained Reweighted Penalized Least Squares for Spectral Baseline Correction

2020 ◽  
Vol 74 (12) ◽  
pp. 1443-1451
Author(s):  
Guofeng Yang ◽  
Jiacai Dai ◽  
Xiangjun Liu ◽  
Meng Chen ◽  
Xiaolong Wu
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Baseline Correction ◽  
Penalized Least Squares ◽  
Baseline Drift ◽  
Background Elimination ◽  
Asymmetric Least Squares ◽  
Quantitative Analyses ◽  
Left And Right ◽  
Additional Constraints

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.

Sign in / Sign up

Export Citation Format

Share Document