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[Research of the Raman Signal De-Noising Method Based on Feature Extraction].
Guang Pu Xue Yu Guang Pu Fen Xi ; 36(12): 4082-7, 2016 Dec.
Artigo em Chinês | MEDLINE | ID: mdl-30256578
To improve time resolution of the Raman measurement system, we need to adopt short scanning time. In this case, the weak Raman signal with vibrational spectrum of the molecular structure is easily to be buried by the high background noise, which influences the further analysis seriously. So it is necessary to de-noise the raw Raman signals. Conventional methods manage to de-noise signal by means of smoothing or averaging based on the difference between signal and noise in frequency characteristic or statistical features. They are commonly applied in the situation where the background noise is not so strong, and cannot give satisfactory results to the Raman signals with low signal-to-noise ratio. In this paper, the algorithm proposed detects peak positions and get peak half-width based on wavelet transform, and then reconstructs the Raman signals by least square fitting algorithm with characteristic parameters obtained, which extracts the useful signal from high background noise efficiently. In the simulation, the Raman curve fitted by the proposed algorithm was smooth, and the peak positions obtained were accurate, so the signal-to-noise ratio improved significantly. In the experiment, we adopted this algorithm to de-noise the tested Raman signal of Cefuroxime Axetil Tablets and Roxithromycin, respectively. The peak positions, peak half-width and amplitude were obtained and proved to be accurate. Therefore, the useful pure Raman signal could be recovered from the high background noise efficiently by the proposed algorithm, which improved the time resolution of Raman system. Both the simulation and the experiment showed that the proposed method could be easily performed with only a few parameters. Comparing with conventional methods, it could achieve satisfactory results under high background noise and provide accurate and reliable information for further analysis.

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Coleções: Bases de dados internacionais Base de dados: MEDLINE Idioma: Chinês Revista: Guang Pu Xue Yu Guang Pu Fen Xi Ano de publicação: 2016 Tipo de documento: Artigo