用峰面积判据改善Delsuc法预测的谱

基于Prony算法的Delsuc谱预测是目前NMR LPSVD(Linear Prediction SingularValue Decomposition)法中比较实用的方法。但在高噪声的条件下,时有失峰和假峰出现且要事先知道谱的峰数。本文通过跟踪迭代过程发现Delsuc法的判别因子r_(mis) 很难选定。如果它太小会失峰,而过大又会出现假峰。同时,由于噪声的影响,弱信号的采样点之间的相关性比强信号小,从而在迭代过程中会增加数值不稳定性。文中引入峰面积判据来改善Delsuc法,经模拟与实验均证明结果有效。

SPECTRA WITH DELSUC'S APPROACH IS IMPROVED BY PEAK AREA CRITERION

Based on the prony algorithm, Delsuc's approach for prediction of NMR spectra is more useful in practice. However, missing lines and spurious signals will be und time by time in the very noisy spectra and one must get the periori knowleage of the number of peaks in a spectrum . In this paper, the recursive process has been traced. It is found that the criterion r_(min) in Delsuc's approach is very difficult to be chosen . Some peaks will be missed if r_(min) is too small. Otherwise, the spursious signals will appear if it becomes larger. Meanwhile, the coherency of sampling points in weak signal is less than that in strong signals due to the influence of noise which will increase the instability of data in recursive process. Here an additional criterion of peak area is adopted to remove the drawbacks of Delsuc's approach The success of this suggestion is proved by simulation and experiment.