基于小波去噪理论的拉曼光谱数据处理

Raman Spectrum Signal Processing Based on Wavelet Denoising Methods

  • 摘要: 利用光谱仪采集得到四氯化碳的拉曼光谱信号,针对光谱数据量大,干扰信息与有效信息并存,不利于对光谱数据进行定性定量分析的问题,采用小波阈值去噪的方法对原始拉曼光谱进行去噪处理,结果表明:小波硬阈值法可获得最优去噪质量,当小波基函数为db2,尺度分解为4,阈值量化为‘Heursure’,硬阈值处理,重构光谱的信噪比最大,均方根误差最小.研究表明:采用小波硬阈值法能有效去除四氯化碳拉曼光谱信号的噪声,最大程度保留其拉曼光谱特征信息.

     

    Abstract: Raman signal of carbon tetrachloride was collected by laser Raman spectrometer. As there is a large amount of spectral data, interference information and effective information coexist. These are not conducive to the spectral data for qualitative and quantitative analysis. Therefore, wavelet thresholding method was used to the original Raman spectroscopy denoising was used. The results show that wavelet hard-threshold can obtain the optimal denoising quality. When wavelet base function being db2, scale decomposition being 4, the threshold quantization being ‘Heursure’, hard-threshold value being processed, signal to noise ratio(SNR) of the reconstructed spectral is the maximum, and root mean square error(RMSE) is the mini-mum. It showed that wavelet hard-threshold could effectively remove the noise information of the raw Raman signals of carbon tetrachloride, and saved the spectra details to the maximum extent.

     

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