Wideline 2H -NMR Spectroscopy and Imaging of Solids

1994 ◽  
Vol 49 (1-2) ◽  
pp. 19-26 ◽  
Author(s):  
B. Blümich
Keyword(s):  
Nmr Spectroscopy ◽  
Excitation Power ◽  
Magic Angle Spinning ◽  
Magic Angle ◽  
2H Nmr ◽  
Stochastic Excitation ◽  
2H Nmr Spectroscopy ◽  
Spatially Inhomogeneous ◽  
Recent Developments ◽  
Angle Spinning

Abstract Recent developments, focussing on reduction of the rf excitation power by stochastic excitation, on improvements in sensitivity and excitation bandwidth by magic angle spinning, and on combining wideline spectroscopy with spatial resolution for investigations o f spatially inhomogeneous objects are reviewed.

scholarly journals Sampling Method Affects HR-MAS NMR Spectra of Healthy Caprine Brain Biopsies

Metabolites ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 38
Author(s):  
Annakatrin Häni ◽  
Gaëlle Diserens ◽  
Anna Oevermann ◽  
Peter Vermathen ◽  
Christina Precht
Keyword(s):  
Nmr Spectroscopy ◽  
Sampling Method ◽  
Magic Angle Spinning ◽  
Mas Nmr ◽  
Magic Angle ◽  
1H Nuclear Magnetic Resonance ◽  
Rapid Changes ◽  
Tissue Degradation ◽  
Angle Spinning

The metabolic profiling of tissue biopsies using high-resolution–magic angle spinning (HR-MAS) 1H nuclear magnetic resonance (NMR) spectroscopy may be influenced by experimental factors such as the sampling method. Therefore, we compared the effects of two different sampling methods on the metabolome of brain tissue obtained from the brainstem and thalamus of healthy goats by 1H HR-MAS NMR spectroscopy—in vivo-harvested biopsy by a minimally invasive stereotactic approach compared with postmortem-harvested sample by dissection with a scalpel. Lactate and creatine were elevated, and choline-containing compounds were altered in the postmortem compared to the in vivo-harvested samples, demonstrating rapid changes most likely due to sample ischemia. In addition, in the brainstem samples acetate and inositols, and in the thalamus samples ƴ-aminobutyric acid, were relatively increased postmortem, demonstrating regional differences in tissue degradation. In conclusion, in vivo-harvested brain biopsies show different metabolic alterations compared to postmortem-harvested samples, reflecting less tissue degradation. Sampling method and brain region should be taken into account in the analysis of metabolic profiles. To be as close as possible to the actual situation in the living individual, it is desirable to use brain samples obtained by stereotactic biopsy whenever possible.

Pulsed Field Gradient Selection in Two-Dimensional Magic Angle Spinning NMR Spectroscopy of Dipolar Solids

1998 ◽  
Vol 134 (2) ◽  
pp. 355-359 ◽  
Author(s):  
Tilo Fritzhanns ◽  
Siegfried Hafner ◽  
Dan E. Demco ◽  
Hans W. Spiess ◽  
Frank H. Laukien
Keyword(s):  
Nmr Spectroscopy ◽  
Field Gradient ◽  
Magic Angle Spinning ◽  
Magic Angle ◽  
Two Dimensional ◽  
Pulsed Field ◽  
Angle Spinning

scholarly journals Machine learning for a rapid discrimination of ginseng cultivation age using 1H-NMR spectra

2020 ◽  
Vol 63 (1) ◽  
Author(s):  
Wonho Lee ◽  
Dahye Yoon ◽  
Seohee Ma ◽  
Dae Young Lee ◽  
Jae Won Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Nmr Spectroscopy ◽  
Magic Angle Spinning ◽  
Magic Angle ◽  
Systematic Classification ◽  
New Class ◽  
Angle Spinning ◽  
Rapid Discrimination ◽  
Ginseng Cultivation

Abstract The scientific and systematic classification of cultivation age is important for preventing age falsification and ensuring the quality of ginseng. Therefore, we applied deep learning to classify the cultivation age of ginseng. Deep learning, which is based on an artificial neural network, is one of the new class of models for machine learning, and is state-of-the-art. It is a powerful tool and has been used to solve complex problems in many fields. In the present study, powdered samples of 4-, 5-, and 6-year-old ginseng were measured using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy. NMR data were analyzed with deep learning and partial least-squares discriminant analysis (PLS-DA) to improve accuracy. The accuracy of the PLS-DA was 87.1% and the accuracy of the deep learning model was 93.9%. NMR spectroscopy with deep learning can be a useful tool for discrimination of ginseng cultivation age.

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