title: [Adaptive EEMD residue related baseline correction algorithm].
abstract: Baseline correction is an important part of spectral analysis; the existing algorithms usually need to set the key parameters and does not have adaptability. The spectral baseline is fitted by the residue according to the feature of ensemble empirical mode decomposition (EEMD for short). The correlation between residual and original signal, the self-correlation and the cross-correlation of residual form the residual related rule. The residual related rule is proposed to judge whether the residual is a component of baseline, based on which adaptive EEMD residual related base line algorithm is proposed. With experiment on the simulated spectrum data of superimposing curve background and the linear background, the results showed that in the case of known baseline mathematical assumption: EEMD residual related method is not so good for polynomial fitting, it is almost no difference from linear fitting, but is better than the wavelet decomposition. In the absence of spectral background knowledge, the real Raman spectrum data are tested. The model is established between Raman spectra treated by the procedure above and chlorophyll, and the model corrected by EEMD residual related baseline method has the biggest correlation coefficient and prediction coefficient, but the smallest root mean square error of cross validation and relative prediction deviation. The effect of EEMD residual related baseline method effects on the peak position, peak intensity and peak width is the smallest in all kinds of baseline correction methods. EEMD residual method has the best baseline correction effect. Experiments show that this algorithm can be used for Raman spectra baseline correction, without prior knowledge of the sample composition analysis, and there is no need to select appropriate fitting function, fitting data points, fitting order as well as basis function and decomposition levels, also there is no need of mathematical hypothesis of baseline signal distribution, so the adaptability is very strong.
authors.forename: Xiao-yu
authors.initials: XY
authors.lastname: Zhan
authors.forename: Yi-ming
authors.initials: YM
authors.lastname: Fang
authors.forename: Yong
authors.initials: Y
authors.lastname: Guan
authors.forename: Zhi-gang
authors.initials: ZG
authors.lastname: Wang
authors.forename: Liang
authors.initials: L
authors.lastname: Tong
authors.forename: Tan
authors.initials: T
authors.lastname: Feng