Influence of Ingredients on the Self-Diffusion of Aroma Compounds in a Model Fruit Preparation:  An Nuclear Magnetic Resonance−Diffusion-Ordered Spectroscopy Investigation

2006 ◽  
Vol 54 (3) ◽  
pp. 665-671 ◽  
Author(s):  
Geraldine Savary ◽  
Elisabeth Guichard ◽  
Jean-Louis Doublier ◽  
Nathalie Cayot ◽  
Celine Moreau
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Aroma Compounds ◽  
The Self ◽  
Self Diffusion ◽  
Nuclear Magnetic

Diffusionskonstanten einiger organischer Flüssigkeiten

1970 ◽  
Vol 25 (5) ◽  
pp. 777-780 ◽  
Author(s):  
Gottfried J. Krüger ◽  
Rene Weiss
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Acetic Acid ◽  
Magnetic Resonance ◽  
Diffusion Coefficients ◽  
Spin Echo ◽  
The Self ◽  
Self Diffusion ◽  
Resonance Spin ◽  
Nuclear Magnetic

The self diffusion coefficients of acetone, ethanol, diethoxymethane, diethylether, diglycole-dimethylether, 1,2-dimethoxyethane, acetic acid, methyliodide, tetrahydrofuran, toluene, trimethylphosphite, and 1,3,5-trifluorobenzene have been measured in dependence on temperature using nuclear magnetic resonance spin echo techniques.

High-temperature pulsed field gradient nuclear magnetic resonance self-diffusion measurements of n-alkanes in MFl-type zeolites

1992 ◽  
Vol 88 (23) ◽  
pp. 3505-3509 ◽  
Author(s):  
Wilfried Heink ◽  
Jörg Kärger ◽  
Harry Pfeifer ◽  
Klaas P. Datema ◽  
Andreas K. Nowak
Keyword(s):  
Nuclear Magnetic Resonance ◽  
High Temperature ◽  
Magnetic Resonance ◽  
Field Gradient ◽  
Pulsed Field ◽  
Self Diffusion ◽  
Nuclear Magnetic

scholarly journals A Fast Self-Learning Subspace Reconstruction Method for Non-Uniformly Sampled Nuclear Magnetic Resonance Spectroscopy

2020 ◽  
Vol 10 (11) ◽  
pp. 3939 ◽  
Author(s):  
Zhangren Tu ◽  
Huiting Liu ◽  
Jiaying Zhan ◽  
Di Guo
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Nmr Spectroscopy ◽  
Magnetic Resonance ◽  
The Self ◽  
Resonance Spectroscopy ◽  
Reconstruction Method ◽  
Subspace Method ◽  
High Quality ◽  
Self Learning ◽  
Nuclear Magnetic

Multidimensional nuclear magnetic resonance (NMR) spectroscopy is one of the most crucial detection tools for molecular structure analysis and has been widely used in biomedicine and chemistry. However, the development of NMR spectroscopy is hampered by long data collection time. Non-uniform sampling empowers rapid signal acquisition by collecting a small subset of data. Since the sampling rate is lower than that of the Nyquist sampling ratio, undersampling artifacts arise in reconstructed spectra. To obtain a high-quality spectrum, it is necessary to apply reasonable prior constraints in spectrum reconstruction models. The self-learning subspace method has been shown to possess superior advantages than that of the state-of-the-art low-rank Hankel matrix method when adopting high acceleration in data sampling. However, the self-learning subspace method is time-consuming due to the singular value decomposition in iterations. In this paper, we propose a fast self-learning subspace method to enable fast and high-quality reconstructions. Aided by parallel computing, the experiment results show that the proposed method can reconstruct high-fidelity spectra but spend less than 10% of the time required by the non-parallel self-learning subspace method.

Sign in / Sign up

Export Citation Format

Share Document