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Pseudo 2D NMR data

Although all 2D NMR experiments are acquired as a collection of ID spectra, there are still fundamental differences in the resulting data and the way in which they are traditionally pre-processed and analysed. In this paper, we therefore choose to divide 2D NMR data into two different groups which we call real 2D and pseudo 2D NMR data in order to be able in a simple way to highlight and keep track of this difference. [Pg.217]

Examples of traditional analysis of pseudo 2D NMR data are the calculation of longitudinal relaxation times (7)), transverse relaxation times (T2) and diffusion coefficients (D). For longitudinal and transverse relaxation times, the intensity of a selected peak is plotted as a function of a... [Pg.217]

Fig. 3. (A) An example of real 2D NMR data being a part of a H- N] HSQC spectrum of a peptide and (B) pseudo 2D NMR data in the form of diffusion-weighted data. Fig. 3. (A) An example of real 2D NMR data being a part of a H- N] HSQC spectrum of a peptide and (B) pseudo 2D NMR data in the form of diffusion-weighted data.
With pseudo 2D NMR data consisting of a series of ID profiles, analysis by multivariate techniques is obvious, since the large number of potentially overlapping variables makes visual analysis very difficult and improved methods of analysis are already called for. Analysis of real 2D NMR data by multivariate techniques is less obvious, since a lot of information can already be extracted from the 2D Fourier-transformed data. However, if real 2D NMR data from a series of samples needs to be compared, the application of multivariate techniques is an obvious possibility. [Pg.219]

Calculate the T, values for the carbonyl carbons of the oligosaccharide. Load the raw data of the pseudo 2D C T, inversion recovery experiment D NMRDATA OLIGOSAC 1D C OCT1 2D 001001.SER. Decompose the 2D data matrix into a series of 1D FIDs, process and plot them according to the recommendations given in previous chapters. Exploit the options for automatic and serial processing. Determine the T, values for the individual carbon nuclei using the interactive fit routine of ID WIN-NMR. Use the Help information if necessary. [Pg.238]

Figure 4.1 Pseudo-2D plot of continuous-flow 1 H LC-NMR data obtained on human urine after dosing with paracetamol (1). The resonances from the glucuronide (2) and sulfate (3) conjugate metabolites of paracetamol and their ID slices are shown... Figure 4.1 Pseudo-2D plot of continuous-flow 1 H LC-NMR data obtained on human urine after dosing with paracetamol (1). The resonances from the glucuronide (2) and sulfate (3) conjugate metabolites of paracetamol and their ID slices are shown...
Figure 4.3 Pseudo-2D contour plot of the continuous-flow 19F NMR data obtained on a 50 jl1 injection of human urine collected from 0-4 h after a 1000 mg dose of BW935U83... Figure 4.3 Pseudo-2D contour plot of the continuous-flow 19F NMR data obtained on a 50 jl1 injection of human urine collected from 0-4 h after a 1000 mg dose of BW935U83...
Figure 4.10 Continuous-flow 19F LC-NMR-MS data displayed as a pseudo-2D plot with 19F chemical shift on the horizontal axis and retention time on the vertical axis. The peaks of interest are labelled with the relevant molecular weights... Figure 4.10 Continuous-flow 19F LC-NMR-MS data displayed as a pseudo-2D plot with 19F chemical shift on the horizontal axis and retention time on the vertical axis. The peaks of interest are labelled with the relevant molecular weights...
Fig. 4.7 LC/NMR impurity profile of a batch of drug substance, shown as a pseudo-2D plot (chemical shift vs time) A, a projection of the total NMR data on the... Fig. 4.7 LC/NMR impurity profile of a batch of drug substance, shown as a pseudo-2D plot (chemical shift vs time) A, a projection of the total NMR data on the...
In this section, we shall consider the operation of more widely used diffusion sequences and their variants, and consider methods for processing the acquired data. This essentially falls into two possible approaches, either direct regression analysis to provide values of the diffusion coefficients or treatments that present the pseudo-2D DOSY spectra mentioned above. To understand the process of measuring diffusion coefficients by NMR, we shall first consider the simplest gradient spin-echo experiment. [Pg.304]

Figure 5 Pseudo-2D plot of on-flow H HPLC-NMR-MS data obtained on rat urine after dosing with efavirenz. The data supply the F chemical shift, retention time, and mass of the metabolites. Figure 5 Pseudo-2D plot of on-flow H HPLC-NMR-MS data obtained on rat urine after dosing with efavirenz. The data supply the F chemical shift, retention time, and mass of the metabolites.
The initial experiment, an on-flow F detected HPLC-NMR-MS run, takes advantage of the CF3 group present in the parent drug. Since there are no endogenous fluorinated compounds in rat urine, responses in the spectrum must arise from metabolites of efavirenz. This experiment provided the retention times of the metabolites that were of sufficient concentration to be detected. The F chemical shift and the mass of the metabolites were also obtained from this single experiment. Figure 5 shows the data as a pseudo-2D plot. The peaks of interest are labelled with their molecular mass. [Pg.308]


See other pages where Pseudo 2D NMR data is mentioned: [Pg.217]    [Pg.222]    [Pg.225]    [Pg.231]    [Pg.217]    [Pg.222]    [Pg.225]    [Pg.231]    [Pg.365]    [Pg.41]    [Pg.42]    [Pg.213]    [Pg.101]    [Pg.192]    [Pg.261]    [Pg.46]    [Pg.192]    [Pg.303]    [Pg.310]    [Pg.331]    [Pg.555]    [Pg.174]    [Pg.356]    [Pg.303]    [Pg.90]    [Pg.920]    [Pg.327]    [Pg.134]    [Pg.389]    [Pg.99]   
See also in sourсe #XX -- [ Pg.217 ]




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2D-NMR

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