EMD时频分析拉曼光谱和近红外光谱

用时频方法分析拉曼光谱和近红外光谱。经验模态分解光谱成为特征模态分量，模态分量比重计算显示拉曼光谱能量均匀分布于各个分量，而近红外光谱的低阶特征模态分量只承载了较少的原光谱有效信息。真实光谱和数值实验均显示，经验模态分解视拉曼光谱为调幅信号，具有高频能量吸附特性; 视近红外光谱为调频信号，在一阶特征模态分量中可以较好实现高频窄带解调。一阶特征模态分量希尔伯特变换显示，经验模态分解拉曼光谱时易出现模态混叠现象。进一步在时频域分析玉米叶片近红外光谱，经验模态分解后截掉低能量的一、二阶分量，用剩余特征模态分量重构光谱信号，均方根误差为1.001 1，相关系数为0.981 3，两个指标反映出重构精度较高; 分解趋势项表明在近红外光波段，吸光度随着波长的减小呈现递增趋势; 特征模态分量的希尔伯特变换显示，657 cm-1是碱胁迫光谱特有频率，可作为碱胁迫光谱特征频率来辨识。

EMD Time-Frequency Analysis of Raman Spectrum and NIR

This paper analyzes the Raman spectrum and Near Infrared Spectrum (NIR) with time-frequency method .The empiri-cal mode decomposition spectrum becomes intrinsic mode functions ,which the proportion calculation reveals the Raman spectral energy is uniform distributed in each component ,while the NIR's low order intrinsic mode functions only undertakes fewer pri-mary spectroscopic effective information .Both the real spectrum and numerical experiments show that the empirical mode decom-position (EMD) regard Raman spectrum as the amplitude-modulated signal ,which possessed with high frequency adsorption property ;and EMD regards NIR as the frequency-modulated signal ,which could be preferably realized high frequency narrow-band demodulation during first-order intrinsic mode functions .The first-order intrinsic mode functions Hilbert transform reveals that during the period of empirical mode decomposes Raman spectrum ,modal aliasing happened .Through further analysis of corn leaf's NIR in time-frequency domain ,after EMD ,the first and second orders components of low energy are cut off ,and re-construct spectral signal by using the remaining intrinsic mode functions ,the root-mean-square error is 1.001 1 ,and the correla-tion coefficient is 0.981 3 ,both of these two indexes indicated higher accuracy in re-construction ;the decomposition trend term indicates the absorbency is ascending along with the decreasing to wave length in the near-infrared light wave band ;and the Hil-bert transform of characteristic modal component displays ,657 cm-1 is the specific frequency by the corn leaf stress spectrum , which could be regarded as characteristic frequency for identification .