Adaptive noise model based iteratively reweighted penalized least squares for fluorescence background subtraction from Raman spectra

2021 ◽  
Author(s):  
Anatoly A. Saveliev ◽  
Ekaterina V. Galeeva ◽  
Dmitry A. Semanov ◽  
Roman R. Galeev ◽  
Ilshat R. Aryslanov ◽  
...  
Keyword(s):  
Least Squares ◽  
Raman Spectra ◽  
Background Subtraction ◽  
Noise Model ◽  
Penalized Least Squares ◽  
Model Based ◽  
Adaptive Noise

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 Collaborative Penalized Least Squares for Background Correction of Multiple Raman Spectra

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Long Chen ◽  
Yingwen Wu ◽  
Tianjun Li ◽  
Zhuo Chen
Keyword(s):  
Least Squares ◽  
Raman Spectra ◽  
Analytical Tool ◽  
Background Correction ◽  
Strongly Correlated ◽  
Penalized Least Squares ◽  
Background Spectrum ◽  
Average Spectrum ◽  
Real Experimental Data ◽  
New Strategies

Although Raman spectroscopy has been widely used as a noninvasive analytical tool in various applications, backgrounds in Raman spectra impair its performance in quantitative analysis. Many algorithms have been proposed to separately correct the background spectrum by spectrum. However, in real applications, there are commonly multiple spectra collected from the close locations of a sample or from the same analyte with different concentrations. These spectra are strongly correlated and provide valuable information for more robust background correction. Herein, we propose two new strategies to remove background for a set of related spectra collaboratively. Based on weighted penalized least squares, the new approaches will use the fused weights from multiple spectra or the weights from the average spectrum to estimate the background of each spectrum in the set. Background correction results from both simulated and real experimental data demonstrate that the proposed collaborative approaches outperform traditional algorithms which process spectra individually.

Spatial Correlation Interpretation of Effects of As+ Implantation on the Raman Spectra of GaAs

1983 ◽  
Vol 27 ◽  
Author(s):  
D.E. Aspnes ◽  
K.K. Tiong ◽  
P.M. Amirtharaj ◽  
F.H. Pollak
Keyword(s):  
Spatial Correlation ◽  
Raman Spectra ◽  
Selection Rule ◽  
Phonon Mode ◽  
Red Shift ◽  
Correlation Model ◽  
Model Based ◽  
Ion Implanted

ABSTRACTThe red shift and asymmetric broadening of the LO phonon mode of ion-implanted GaAs are both described quantitatively by a spatial correlation model based on a damage-induced relaxation of the momentum selection rule previously used by Richter, Wang, and Ley to describe similar effects in microcrystalline Si. The success of the model for a qualitatively different disorder microstructure suggests it may be possible to evaluate average sizes of crystallographically perfect regions in semiconductors from the phonon lineshapes of their Raman spectra.

Functional Linear Regression

2018 ◽  
Author(s):  
Hervé Cardot ◽  
Pascal Sarda
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Linear Models ◽  
Estimation Error ◽  
Asymptotic Properties ◽  
Principal Component Regression ◽  
Principal Component ◽  
Penalized Least Squares ◽  
Open Problems ◽  
Functional Linear Regression

This article presents a selected bibliography on functional linear regression (FLR) and highlights the key contributions from both applied and theoretical points of view. It first defines FLR in the case of a scalar response and shows how its modelization can also be extended to the case of a functional response. It then considers two kinds of estimation procedures for this slope parameter: projection-based estimators in which regularization is performed through dimension reduction, such as functional principal component regression, and penalized least squares estimators that take into account a penalized least squares minimization problem. The article proceeds by discussing the main asymptotic properties separating results on mean square prediction error and results on L2 estimation error. It also describes some related models, including generalized functional linear models and FLR on quantiles, and concludes with a complementary bibliography and some open problems.

Introduction to Penalized Least-Squares

2020 ◽  
pp. 55-120
Author(s):  
Jianqing Fan ◽  
Runze Li ◽  
Cun-Hui Zhang ◽  
Hui Zou
Keyword(s):  
Least Squares ◽  
Penalized Least Squares
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