Generalized maximum entropy deconvolution of spectral segments
作者： A.R Mazzeo , M.A Delsuc , A Kumar , G.C Levy
DOI: 10.1016/0022-2364(89)90087-5
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摘要: Abstract A maximum entropy Fourier spectral deconvolution (MEFSD) program capable of selecting segments larger spectra is used to deconvolute overlapping peaks in experimental and synthetic test cases. Low signal-to-noise features which also have very large (dynamic ranges 100:1, 1000:1, or more) can be selected for processing by performing calculations on a pseudo-FID constructed from the region. Results show efficient powerful with simultaneous noise suppression segments, even when relatively crowded. Quantitative tests MEFSD low spectra, modeled after examples, showed accuracies were at least as good, better, relative peak integrations, over conventional FFT followed Lorentzian curve fitting, several different cases dynamic 1000:1 3:1.
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