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

OSA Continuum ◽  
2021 ◽  
Author(s):  
Andrew Huzortey ◽  
Benjamin Anderson ◽  
Alfred Owusu
Keyword(s):  
Raman Spectra ◽  
Baseline Correction ◽  
Second Derivative ◽  
Correction Algorithm

scholarly journals Baseline correction algorithm for Raman spectra based on median filtering and un-uniform B-spline

2020 ◽  
Vol 69 (20) ◽  
pp. 200701
Author(s):  
Xin Wang ◽  
Zhe-Ming Kang ◽  
Long Liu ◽  
Xian-Guang Fan
Keyword(s):  
Raman Spectra ◽  
Baseline Correction ◽  
Median Filtering ◽  
Correction Algorithm ◽  
B Spline

A Novel Pre-Processing Algorithm Based on the Wavelet Transform for Raman Spectrum

2018 ◽  
Vol 72 (12) ◽  
pp. 1752-1763 ◽  
Author(s):  
Yang Xi ◽  
Yuee Li ◽  
Zhizhen Duan ◽  
Yang Lu
Keyword(s):  
Wavelet Transform ◽  
Raman Spectra ◽  
Haar Wavelet ◽  
Peak Position ◽  
Wavelet Function ◽  
Baseline Correction ◽  
Second Derivative ◽  
Denoising Method ◽  
Background Removal ◽  
Spectral Peaks

Noise and fluorescent background are two major problems for acquiring Raman spectra from samples, which blur Raman spectra and make Raman detection or imaging difficult. In this paper, a novel algorithm based on wavelet transform that contains denoising and baseline correction is presented to automatically extract Raman signals. For the denoising section, the improved conventional-scale correlation denoising method is proposed. The baseline correction section, which is performed after denoising, basically consists of five aspects: (1) detection of the peak position; (2) approximate second derivative calculation based on continuous wavelet transform is performed using the Haar wavelet function to find peaks and background areas; (3) the threshold is estimated from the peak intensive area for identification of peaks; (4) correction of endpoints, spectral peaks, and peak position; and (5) determine the endpoints of the peak after subtracting the background. We tested this algorithm for simulated and experimental Raman spectra, and a satisfactory denoising effect and a good capability to correct background are observed. It is noteworthy that this algorithm requires few human interventions, which enables automatic denoising and background removal.

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 Iterative morphological and mollifier-based baseline correction for Raman spectra

2016 ◽  
Vol 48 (2) ◽  
pp. 336-342 ◽  
Author(s):  
Matthias Koch ◽  
Christian Suhr ◽  
Bernhard Roth ◽  
Merve Meinhardt-Wollweber
Keyword(s):  
Raman Spectra ◽  
Baseline Correction

scholarly journals Amorphous zinc borate as a simple standard for baseline correction in Raman spectra

2017 ◽  
Vol 48 (11) ◽  
pp. 1644-1653 ◽  
Author(s):  
Aurelio Sanz-Arranz ◽  
Jose A. Manrique-Martinez ◽  
Jesus Medina-Garcia ◽  
Fernando Rull-Perez
Keyword(s):  
Raman Spectra ◽  
Zinc Borate ◽  
Baseline Correction

Joint Baseline-Correction and Denoising for Raman Spectra

2015 ◽  
Vol 69 (9) ◽  
pp. 1013-1022 ◽  
Author(s):  
Hai Liu ◽  
Zhaoli Zhang ◽  
Sanya Liu ◽  
Luxin Yan ◽  
Tingting Liu ◽  
...  
Keyword(s):  
Raman Spectra ◽  
Baseline Correction

Baseline correction for Raman spectra using penalized spline smoothing based on vector transformation

2018 ◽  
Vol 10 (28) ◽  
pp. 3525-3533 ◽  
Author(s):  
Yaoyi Cai ◽  
Chunhua Yang ◽  
Degang Xu ◽  
Weihua Gui
Keyword(s):  
Raman Spectra ◽  
Smoothing Method ◽  
Baseline Correction ◽  
Spline Smoothing ◽  
Penalized Spline ◽  
Penalized Spline Smoothing

A penalized spline smoothing method based on vector transformation (VTPspline) method has been proposed for baseline correction of Raman spectra.

A Novel Baseline Correction Algorithm

2012 ◽  
Vol 220-223 ◽  
pp. 2248-2252
Author(s):  
Er Hui Jia ◽  
Bin Li ◽  
Tao Zhang
Keyword(s):  
Signal Processing ◽  
Performance Evaluation ◽  
Standard Deviation ◽  
Baseline Correction ◽  
Time Varying ◽  
Statistical Features ◽  
Real Word ◽  
Correction Algorithm ◽  
Local Window ◽  
And Performance

This paper studies baseline correction algorithms for subtracting the background of real-word signal. A novel baseline correction algorithm is proposed that can be solved by random signal processing. With respect to generalized statistical features of the raw data, an appropriate threshold of standard deviation is set to extract the true baseline points unfailingly. Under the generalized meaning, the background at one signal point is substituted by the statistical features of its local window. By using this proposed algorithm, we established a time varying signal baseline independently and accurately. And performance evaluation shows that the proposed algorithm is more elaborate and tolerant of real-word data than the previous ones.

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