Linear prediction Cholesky decomposition vs Fourier transform spectral analysis for ion cyclotron resonance mass spectrometry
Linear prediction Cholesky decomposition vs Fourier transform spectral analysis for ion cyclotron resonance mass spectrometry
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DOI：
10.1021/ac960755h
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发表时间：
1997-03-15
影响因子：
7.4
通讯作者：
Marshall, AG
中科院分区：
文献类型：
--
作者：
Guan, SH;Marshall, AG
频谱分析的快速傅里叶变换 (FFT) 方法将时域信号转换为更容易可视化的频域频谱，但不区分信号和噪声，并且会为截断和/或采样不当的时域信号产生频谱伪影（例如，“吉布斯振荡”）。例如，如果采样持续时间小于两个信号之间频率差的一个周期，则 FFT 无法解析两个信号。 两个信号，这里，线性预测Cholesky分解谱分析应用于离子回旋共振质谱，该算法具有鲁棒性，能够从由多个指数阻尼噪声正弦波组成的信号中提取谱参数（频率、时域指数阻尼常数、幅度和相位），与FFT数据缩减相比，线性预测可以提供显着增加的谱参数。 灵敏度（对于等于或低于 rms 噪声水平的信号）、消除吉布斯振荡以及提高在采集周期结束之前被截断或衰减到 rms 噪声水平的时域信号的频谱分辨能力，本分析可以使用 2.5 小时 PC 计算时间处理多达 8K 时域数据集。
The fast Fourier transform (FFT) method of spectral analysis converts a time domain signal to a more easily visualized frequency domain spectrum but does not distinguish between signal and noise and produces spectral artifacts (e.g., ''Gibb's oscillations'') for a truncated and/or improperly sampled time domain signal, For example, FFT cannot resolve two signals if the sampling duration is less than one cycle of the frequency difference between the two signals, Here, linear prediction Cholesky decomposition spectral analysis is applied to ion cyclotron resonance mass spectrometry, The algorithm is robust and capable of extracting spectral parameters (frequency, time domain exponential damping constant, magnitude, and phase) from a signal consisting of multiple exponentially damped noisy sinusoids, Compared to FFT data reduction, linear prediction can offer significantly increased sensitivity (for signals at or below the rms noise level), elimination of Gibb's oscillations, and increased spectral resolving power for a time domain signal that either is truncated or has damped to the rms noise level before the end of the acquisition period, The present analysis can handle up to 8K time domain data sets with 2.5 h PC computation time.