Abstract
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 29Si 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.
Highlights
Sensitivity is one of the largest limitations in solid-state nuclear magnetic resonance and becomes more severe as the gyromagnetic ratio and the natural abundance of the investigated nucleus decrease
According to the standard nomenclature used in chemometrics, multivariate curve resolution approach (MCR) decomposes the data matrix D into a “concentration” matrix C and a “spectra” matrix S, leaving behind a residuals matrix E: D = CST + E
We have presented the use of MCR for denoising of low sensitivity solid-state NMR two-dimensional spectra
Summary
Sensitivity is one of the largest limitations in solid-state nuclear magnetic resonance (ssNMR) and becomes more severe as the gyromagnetic ratio and the natural abundance of the investigated nucleus decrease Under these conditions, the signal intensity is low and, quite often, the low relaxation mechanisms efficiency requires experiments with long recovery delays. Consistent efforts are devoted to the development of processing methods that allow for signal extraction from noisy spectra, in this and in different areas of NMR These efforts led to several options to reduce noise: wavelet transform,[22] Savitzky-Golay,[23] random QR denoising,[24] singular spectrum analysis,[25] and Cadzow filtering.[26,27] Each of these methodologies, has its own benefits and drawbacks. As a representative noise region, we selected a slice of the spectrum where no signal is present
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