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Citation: ZHANG Xun, CHEN Sheng, WU Bo-Shi, YANG Gui-Hua, XU Feng. Adaptive Method for Denoising Raman Spectral Imaging Data and Its Applications to Spectral Analysis in Plant Cell Walls[J]. Chinese Journal of Analytical Chemistry, ;2016, 44(12): 1846-1851. doi: 10.11895/j.issn.0253-3820.160392 shu

Adaptive Method for Denoising Raman Spectral Imaging Data and Its Applications to Spectral Analysis in Plant Cell Walls

ZHANG Xun ^1, ,
CHEN Sheng ¹ ,
WU Bo-Shi ¹ ,
YANG Gui-Hua ² ,
XU Feng ^1,2

1.
Beijing Key Laboratory of Lignocellulosic Chemistry, Beijing Forestry University, Beijing 100083, China;
2.
Key Laboratory of Pulp and Paper Science & Technology, Qilu University of Technology, Jinan 250353, China

Received Date: 24 May 2016
Revised Date: 5 September 2016

Fund Project: This work was supported by the Fundamental Research Funds for the Central Universities (No. BLYJ201620), Chinese Ministry of Education (No. 113014A) and Beijing Municipal Commission of Education (No. 20131002201).

Two inevitable noise signals, baseline drifts and cosmic spikes in Raman spectral imaging data should be eliminated before data analysis. However, current denoising methods for a single spectrum often lead to unstable results with bad reproducible properties. In this study, a novel adaptive method for denoising Raman spectral imaging data was proposed to address this issue. Adaptive iteratively reweighted penalized least-squares (airPLS) and principal component analysis (PCA) based despiking algorithm were applied to correct drifting baselines and cosmic spikes, respectively. The method offers a variety of advantages such as less parameter to be set, no spectral distortion, fast computation speed, and stable results, etc. We utilized the method to eliminate the noise signals in Raman spectral imaging data of Miscanthus sinensis (involving 9010 spectra), and then employed PCA and cluster analysis (CA) to distinguish plant spectra from non-plant spectra. Theoretically, this method could be used to denoise other spectral imaging data and provide reliable foundation for achieving stable analysis results.
- Raman spectral imaging,
- Spectral denoising,
- Penalized least-squares,
- Principal component analysis,
- Cluster analysis
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