基于LCEEMD的低信噪比拉曼光谱自适应去噪方法研究

LCEEMD Adaptive Denosing Method for Raman Spectra with Low SNR

在生物体拉曼光谱快速采集或低功率采集过程中,往往会获得低信噪比拉曼光谱。针对低信噪比光谱数据,提出应用补充总体经验模态方法(CEEMD)分解拉曼光谱,并且依据特征模态分量的归一化排列熵值(NPE)按比例扣除噪声成分的方法,称为局部补充总体均值经验模分解方法(LCEEMD)。LCEEMD方法不仅解决了经验模态(EMD)分解中高频信号与噪声的模态混叠问题,还有效降低了总体经验模态分解法(EEMD)中的残留噪声。仿真数据实验显示,LCEEMD方法在处理10db信噪比模拟光谱时获得了39.615 0db信噪比,0.001 17标准差和0.999 9相关系数。在人体皮肤拉曼光谱试验中,LCEEMD方法滤波后数据准确呈现出角质层脂质酰胺I带激发拉曼强谱峰以及甘油三酸酯中(C—O)酯微弱谱峰。在水稻叶片可溶性糖定量预测模型中,LCEEMD方法取得了0.871 7预测相关系数和0.912 0预测标准误差,优于EMD和EEMD软阈值去噪(0.511 4,1.647 8和0.638 2,1.508 8)。LCEEMD方法实施过程中,根据去噪性能指标反馈调整归一化排列熵阈值,直至获得最佳去噪效果,滤波过程无需参数设置,可以自适应实现。

In the process of rapid scanning or low power excitation,low SNR Raman usually spectra of biological samples can be acquired.In order to remove the noise in the low SNR spectra,we decomposed the spectra by the CEEMD method and separated the noise from spectra according to the Normalization Permutation Entropy in this paper.The method proposed was named as Complementary Ensemble Empirical Mode Decomposition(CEEMD).LCEEMD method can be used to denoise the Raman spectra,which effectively overcame the modal aliasing between high frequency Raman signals and noise components in EMD.Furthermore,CEEMD reduced residual noise,which were presented in EEMD.Simulation experiments showed that LCEEMD method can improve the SNR of data from 10 dB to 39.615 0 db with a standard deviation of 0.001 17 and correlation coefficient 0.999 9.The denoising experiments indicated that the skin Raman spectrum denosied by LCEEMD showed Raman strongcharacteristic peaks excited by the amide I-belt of cuticle lipid and weak peakof triglycerides(C O),and most peak intensities were consistent with the references.What’s more,the measurement for water-soluble sugar(rice leaf)was modeled with the removal noise data processedby LCEEMD.The prediction coefficient was 0.871 7 and standard error of prediction was 0.912 0,however they were 0.511 4,1.647 8 and 0.638 2,1.5088 in models denosied by EMD and EEMD.In the process of noise removal by LCEEMD,the threshold of the Normalization Permutation Entropy was adjusted accordingto denoising performance indexes automatically where parameters needn’t to beset and the LCEEMD method is an adaptive noise filtering.