![]() The covariance NMR concept can be generalized to pairs of spectra, which has been referred to as “unsymmetric” covariance NMR, by multiplying the matrices belonging to two spectra along a common dimension resulting in a spectrum that is generally nonsymmetric. 6 In fact, with regularization 10 applied as necessary, the covariance transform followed by a matrix square root leaves the signal-to-noise properties of a spectrum essentially unperturbed. 9 When the covariance spectrum is symmetric, application of the matrix square root strongly attenuates or eliminates artifacts due to relay effects and chemical shift near degeneracy. 3, 5, 8 Conversely, indirect covariance maps an indirect dimension of a spectrum onto the direct dimension. 3 Direct covariance processing endows the indirect (or donor) dimension(s) of a spectrum with the same resolution and spectral width as the corresponding acceptor dimensions, which include the high-resolution detection dimension. Nonlinear methods, on the other hand, may affect the noise lying away from a signal peak different from noise on a signal peak itself and thereby they may improve the apparent but not the actual sensitivity.Ĭovariance NMR is a recently introduced method for spectral resolution enhancement of multidimensional spectra. ![]() This property allows a straightforward assessment of the signal-to-noise (S∕N) ratio by comparing signal intensities to a summary statistics, such as the standard deviation or the median absolute value of the noise floor in a peak-free region. 4, 7ĭue to its linear nature, the FT method converts a free induction decay that includes additive white noise into a spectrum that is superimposed on a homogeneous noise floor. 2, 3, 4, 5, 6 However, many of these methods affect the noise signature resulting in changes in both the apparent and the actual sensitivity. While for standard Fourier transform (FT) the effect of noise on spectra is well understood, 1 more recent processing methods can have advantages, in particular, when the shortening of measurement time of multidimensional spectra is essential. Application to an unsymmetric covariance spectrum, obtained by concatenating two 2D 13C– 1H heteronuclear, single quantum coherence (HSQC) and 13C– 1H heteronuclear, multiple bond correlation (HMBC) spectra of a metabolite mixture along their common proton dimension, reveals that for sufficiently sensitive input spectra the reduction in sensitivity due to covariance processing is modest.īecause NMR measurement time is often limited by the achievable sensitivity, careful consideration must be given to the effect of NMR data processing on spectral noise. In particular, determination of a Z score, which measures the difference in standard deviations of a statistic from its mean, for each spectral point yields a Z matrix, which indicates whether a given peak intensity above a threshold arises from the covariance of signals in the input spectra or whether it is likely to be caused by noise. Different data processing procedures, including the Z-matrix formalism, thresholding, and maxima ratio scaling, are described to assess noise contributions and to reduce noise inhomogeneity. Therefore, methods of noise estimation commonly used in Fourier transform spectroscopy underestimate the amount of uncertainty in unsymmetric covariance spectra. It is shown how the unsymmetric covariance spectrum possesses an inhomogeneous noise distribution across the spectrum with the least amount of noise in regions whose rows and columns do not contain any cross or diagonal peaks and with the largest amount of noise on top of signal peaks. This work presents analytical relationships as well as simulated and experimental results characterizing the propagation of noise by unsymmetric covariance NMR processing, which concatenates two NMR spectra along a common dimension, resulting in a new spectrum showing spin correlations as cross peaks that are not directly measured in either of the two input spectra. ![]() Due to the limited sensitivity of many nuclear magnetic resonance (NMR) applications, careful consideration must be given to the effect of NMR data processing on spectral noise.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |