Abstract

There are several smoothing procedures for spectral data which are affected by occasionally occurring outliers. Most of the known methods are based on local averages (or fits) of the spectral data. We introduce here an outlier-insensitive, robust smoothing method which rejects the influence of huge noise spikes. The proposed smoothing algorithm can be tuned by two parameters. The first corresponds to the signal-to-noise ratio, the second to the halfwidths of the spectral bands. We apply this new technique to several spectra and prove the advantages of our method of identifying peaks and baselines in Raman spectroscopy.

Profile analysis with reconstruction robustness for measurement data subject to outliers

Tianqi Gu, Cui Xiong, Dawei Tang, Jianxiong Chen, and Shuwen Lin
Appl. Opt. 61(13) 3777-3785 (2022)

Recovery of Raman spectra with low signal-to-noise ratio using Wiener estimation

Shuo Chen, Xiaoqian Lin, Clement Yuen, Saraswathi Padmanabhan, Roger W. Beuerman, and Quan Liu
Opt. Express 22(10) 12102-12114 (2014)

Outlier modeling for spectral data reduction

Farnaz Agahian and Brian Funt
J. Opt. Soc. Am. A 31(7) 1445-1452 (2014)

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)

Noise robust linear dynamic system for phase unwrapping and smoothing

Julio C. Estrada, Manuel Servin, and Juan A. Quiroga
Opt. Express 19(6) 5126-5133 (2011)