基于集合经验模态分解的拉曼光谱信号特征研究

Research on Raman Spectral Signal Characteristics Based on Ensemble Empirical Mode Decomposition

拉曼光谱信号是一种基于分子振动的散射信号,拉曼光谱仪的激光源波长一般为纳米级,考虑到散射频移,拉曼光谱有效信息主要集中在较高频段。拉曼信号是典型的非平稳信号,并且由于拉曼散射比较弱,信号很容易被高频噪声和荧光背景干扰,想获取较为全面的拉曼信息,需要对信号进行处理,小波变换对拉曼信号的分析结果取决于小波基的选择,不同小波基处理结果有差异;经验模态分解(EMD)方法可以自适应的分析信号,不需要设置参数,但存在模态混叠的问题;集合平均经验模态分解(EEMD),有效的解决了EMD方法中存在的模态混叠问题,能更加清晰的将信号中的不同频率成分划分开来,因此更加适合频率成分丰富的拉曼信号的特征分析和处理。采集了市面上常见的大豆油、花生油、玉米油和葵花籽油样本,通过拉曼光谱仪获得了各自的拉曼光谱信号。使用集合经验模态分解对食用油拉曼光谱信号进行自适应分解和处理,一共获得了10阶固有模态函数(IMF),根据信号的能量分布以及幅值特性, IMF1和IMF2表征为信号中的噪声部分, IMF3-IMF7表征为拉曼特征信号部分,最后一阶IMF10表征为荧光背景成分, IMF8和IMF9为其他物理意义的频率成分。通过对有效信号段的特征增强并重构拉曼信号,使拉曼信号的信噪比获得了2~5倍的提升,其中,难以探测的酯键羰基伸缩振动位于1 745 cm-1的谱峰得到了显著的增强。最后,将原始信号和经过特征增强的信号通过基于连续小波变换的惩罚最小二乘法进行了二次处理,并将获得的信号进行主成分分析后,可知:没有增强的不同类数据样本相互有重叠,不存在明显的类间距,很难完整的区分类型;基于特征增强的数据样本各自聚集,每种类型都可以相互鉴别,可为拉曼光谱信号处理提供一种新的途径。

Raman spectrum signal is a kind of scattering signal based on molecular vibration. The laser source wavelength of Raman spectrometer is generally nanometer. As it is a typical non-stationary signal and considering the scattering frequency shift, the effective information of Raman spectrum is mainly concentrated in the higher frequency band. Because Raman scattering is very weak, and the signal is easily disturbed by high frequency noise and fluorescence background. In order to obtain more comprehensive Raman information, the signal needs to be processed. The results of Raman signal analysis by wavelet transform depend on the choice of wavelet bases, and the results of different wavelet bases are different;Empirical Mode Decomposition(EMD) method can analyze signals adaptively without setting parameters, but it has the problem of mode mixing. The Ensemble Empirical Mode Decomposition(EEMD) effectively solves the problem of mode mixing in EMD method, and can more clearly divide the components of different frequencies in signals, so it is more suitable for the characteristic analysis and processing of Raman signal which has rich frequency components. In this paper, Raman spectrum of soybean oil, peanut oil, corn oil and sunflower seed oil samples are collected by Raman spectrometer. Raman spectrum of edible oil are adaptively decomposed and processed by EEMD, and a total of 10 orders Intrinsic Mode Function(IMF) are obtained. According to the energy distribution and amplitude characteristics of the signal, IMF1 and IMF2 are characterized as the noise components of the signal, IMF3-IMF7 as the Raman characteristic signal components, the last order IMF10 as the fluorescence background component, and IMF8 and IMF9 as the frequency components of other physical meanings. After filtering out the high frequency noise components of IMF1 and IMF2, it obtains the Raman signal after de-noising. In addition, the signal-to-noise ratio of Raman signal is increased by 2~5 times by enhancing and reconstructing the characteristics of the effective signal component. Among them, the dynamic peak at 1 745 cm-1 caused by the ester bond carbonyl stretching vibration is significantly enhanced, which is difficult to detect. Finally, the baseline of original signal and the characteristicenhancing signal are deducted by PLS method based on continuous wavelet transform. After principal component analysis, the different data samples without enhancement overlap with each other, and there is no obvious class spacing, so it is difficult to distinguish the type of samples completely. The data samples based on feature enhancement are gathered separately, and each kind of data samples is clustered obviously. Types can be identified from each other, which provides a new way for Raman spectroscopic signal processing.