[Research on the Method of Eliminating Noise and Background in the Meantime in Detecting Ethanol Contention Based on Raman Spectra].

In the process of detecting ethanol content by Raman spectra, the precision of correction model prediction is affected by noise and baseline drift, which is caused by the spectral fluorescence and sample pool's background. Use ensemble empirical mode decomposition to decompose spectrum into several intrinsic mode functions, which are without aliasing. The permutation entropy is employed to judge the intrinsic mode functions. Set the intrinsic mode functions which are on behalf of noise and background to zero, and then the signal is without noise and background. In this paper combine ensemble empirical mode decomposition and permutation entropy, and apply to the Raman spectrum, which are used to detect ethanol content. At the same time compare with wavelet transform and average smoothing filter. The experimental result shows that the application of empirical mode decomposition and permutation entropy can effectively eliminate the noise and background. The precision of correction model prediction is improved. This method simply employs and doesn't need to set parameters, which has great value of application in the process of detecting ethanol content by Raman spectra.