Display options
Cite
Chen H, Xu W, Broderick NGR. An Adaptive and Fully Automated Baseline Correction Method for Raman Spectroscopy Based on Morphological Operations and Mollification. Appl Spectrosc. 2018;73(3):284-293doi: 10.1177/0003702818811688.
Chen, H., Xu, W., & Broderick, N. G. R. (2019). An Adaptive and Fully Automated Baseline Correction Method for Raman Spectroscopy Based on Morphological Operations and Mollification. Applied spectroscopy, 73(3), 284-293. https://doi.org/10.1177/0003702818811688
Chen, Hao, et al. "An Adaptive and Fully Automated Baseline Correction Method for Raman Spectroscopy Based on Morphological Operations and Mollification." Applied spectroscopy vol. 73,3 (2019): 284-293. doi: https://doi.org/10.1177/0003702818811688
Chen H, Xu W, Broderick NGR. An Adaptive and Fully Automated Baseline Correction Method for Raman Spectroscopy Based on Morphological Operations and Mollification. Appl Spectrosc. 2019 Mar;73(3):284-293. doi: 10.1177/0003702818811688. Epub 2018 Nov 14. PMID: 30334459.
Copy Download .nbib Format: NLM
Share it on

Appl Spectrosc. 2019 Mar;73(3):284-293. doi: 10.1177/0003702818811688. Epub 2018 Nov 14.

An Adaptive and Fully Automated Baseline Correction Method for Raman Spectroscopy Based on Morphological Operations and Mollification.

Applied spectroscopy

Hao Chen, Weiliang Xu, Neil G R Broderick

Affiliations

  1. 1 Department of Mechanical Engineering, the University of Auckland, Auckland, New Zealand.
  2. 2 The Dodd-Walls Centre for Photonic and Quantum Technologies, Dunedin, New Zealand.
  3. 3 Department of Physics, the University of Auckland, Auckland, New Zealand.

PMID: 30334459 DOI: 10.1177/0003702818811688

Abstract

Baseline drift is a commonly identified and severe problem in Raman spectra, especially for biological samples. The main cause of baseline drift in Raman spectroscopy is fluorescence generated within the sample. If left untreated, it will affect the following qualitative or quantitative analysis. In this paper, an adaptive and fully automated baseline estimation algorithm based on iteratively averaging morphological opening and closing operations is presented. The proposed method is able to deal with different shapes and amplitudes of baselines. It is tested on both simulated and experimental Raman spectra. Comparison of the proposed method with other morphology-based methods and a well-developed penalized least squares-based method is made. The results demonstrate the superior performance of the proposed method and its advantages-in terms of accuracy, adaptivity, and computing speed-over other algorithms. In general, this method can also be applied to other spectroscopic data or other types of one-dimensional data.

Keywords: Baseline correction; Raman spectroscopy; adaptive; morphological operations

Publication Types