LINEAR PREDICTION CHOLESKY DECOMPOSITION VS FOURIER-TRANSFORM SPECTRAL-ANALYSIS FOR ION-CYCLOTRON RESONANCE MASS-SPECTROMETRY

The fast Fourier transform (FFT) method of spectral analysis converts a time domain signal to a more easily visualized frequency domain spec trum but does not distinguish between signal and noise and produces sp ectral artifacts (e.g., ''Gibb's oscillations'') for a truncated and/o r improperly sampled time domain signal, For example, FFT cannot resol ve 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 r esonance mass spectrometry, The algorithm is robust and capable of ext racting spectral parameters (frequency, time domain exponential dampin g 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 oscill ations, and increased spectral resolving power for a time domain signa l 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.