Bayesian versus Fourier spectral analysis of ion cyclotron resonance time-domain signals.
Bayesian versus Fourier spectral analysis of ion cyclotron resonance time-domain signals.
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DOI：
10.1021/ac00201a021
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发表时间：
1990-01
影响因子：
7.4
通讯作者：
J. E. Meier;A. Marshall
中科院分区：
文献类型：
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作者：
J. E. Meier;A. Marshall
通过时域信号的傅里叶变换（FT）获得的频域频谱仅对于无限持续时间的连续无噪声时域信号是准确的。对于离散噪声截断时域信号，非 FT（例如贝叶斯分析）方法可以提供时域信号频率、弛豫时间和相对丰度的更准确的频谱估计。在本文中，我们表明，对模拟和实验离子回旋共振 (ICR) 时域噪声信号进行贝叶斯分析可以产生质量精度比从幅度模式离散快速傅里叶变换 (FFT) 谱获得的质量精度提高 10 倍或更多的谱。此外，贝叶斯分析还提供了一个有用的优点，即它可以自动估计其迭代确定的光谱参数的精度。贝叶斯分析的主要缺点是与 FFT 相比其计算时间较长（在相同硬件上处理大约 4K 时域数据点需要数小时与数秒）；贝叶斯计算时间随着谱峰数量的增加而快速增加，并且随着时域数据点的数量的增加（速度较慢）。因此，贝叶斯分析对于那些涉及相对较少数据点和/或需要高质量精度的 FT/ICR 应用应该是有用的。
The frequency-domain spectrum obtained by Fourier transformation (FT) of a time-domain signal is accurate only for a continuous noiseless time-domain signal of infinite duration. For discrete noisy truncated time-domain signals, non-FT (e.g., Bayesian analysis) methods may provide more accurate spectral estimates of time-domain signal frequencies, relaxation time(s), and relative abundances. In this paper, we show that Bayesian analysis of simulated and experimental ion cyclotron resonance (ICR) time-domain noisy signals can produce a spectrum with mass accuracy improved by a factor of 10 or more over that obtained from a magnitude-mode discrete fast Fourier transform (FFT) spectrum. Moreover, Bayesian analysis offers the useful advantage that it automatically estimates the precision of its iteratively determined spectral parameters. The main disadvantage of Bayesian analysis is its lengthy computation time compared to that of FFT (hours vs seconds on the same hardware for approximately 4K time-domain data points); the Bayesian computation time increases rapidly with the number of spectral peaks and (less rapidly) with the number of time-domain data points. Bayesian analysis should thus prove useful for those FT/ICR applications involving relatively few data points and/or requiring high mass accuracy.