Multivariate Curve Resolution for 2D Solid-State NMR spectra
Author(s) -
F. Bruno,
Roberto Francischello,
G. Bellomo,
Lucia Gigli,
Alessandra Flori,
Luca Menichetti,
Leonardo Tenori,
Claudio Luchinat,
Enrico Ravera
Publication year - 2020
Publication title -
analytical chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.117
H-Index - 332
eISSN - 1520-6882
pISSN - 0003-2700
DOI - 10.1021/acs.analchem.9b05420
Subject(s) - chemistry , spectral line , analytical chemistry (journal) , resolution (logic) , multivariate statistics , noise reduction , signal (programming language) , signal to noise ratio (imaging) , mixing (physics) , data processing , time domain , nmr spectra database , biological system , computational physics , artificial intelligence , chromatography , optics , statistics , physics , computer science , mathematics , quantum mechanics , astronomy , biology , programming language , operating system , computer vision
We present a processing method, based on the multivariate curve resolution approach (MCR), to denoise 2D solid-state NMR spectra, yielding a substantial S/N ratio increase while preserving the lineshapes and relative signal intensities. These spectral features are particularly important in the quantification of silicon species, where sensitivity is limited by the low natural abundance of the 29 Si nuclei and by the dilution of the intrinsic protons of silica, but can be of interest also when dealing with other intermediate-to-low receptivity nuclei. This method also offers the possibility of coprocessing multiple 2D spectra that have the signals at the same frequencies but with different intensities (e.g.: as a result of a variation in the mixing time). The processing can be carried out on the time-domain data, thus preserving the possibility of applying further processing to the data. As a demonstration, we have applied Cadzow denoising on the MCR-processed FIDs, achieving a further increase in the S/N ratio and more effective denoising also on the transients at longer indirect evolution times. We have applied the combined denoising on a set of experimental data from a lysozyme-silica composite.
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