التفاصيل البيبلوغرافية
| العنوان: |
Neural Network Method for Diffusion-Ordered NMR Spectroscopy |
| المؤلفون: |
Enping Lin (8114744), Nannan Zou (12034813), Yuqing Huang (153028), Zhong Chen (32985), Yu Yang (83671) |
| سنة النشر: |
2022 |
| المجموعة: |
Smithsonian Institution: Digital Repository |
| مصطلحات موضوعية: |
Biophysics, Immunology, Computational Biology, Space Science, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Physical Sciences not elsewhere classified, Information Systems not elsewhere classified, ordered nmr spectroscopy, nonconvex optimization problem, multivariate fitting methods, lightweight neural network, intrinsically designed algorithms, coordinated multiexponential fitting, art reconstruction algorithms, neural network method, dosy spectrum reconstruction, estimated diffusion coefficients, novel method, diffusion coefficients, sparse constraint, mixed components, laplacian inversion, highly nonlinear, good separation, excellent distinguishment, essential tool, diffusion information, different molecules, compound mixtures |
| الوصف: |
Diffusion-ordered NMR spectroscopy (DOSY) presents an essential tool for the analysis of compound mixtures by revealing intrinsic diffusion behaviors of the mixed components. For the interpretation of the diffusion information, intrinsically designed algorithms for a DOSY spectrum reconstruction are required. The estimated diffusion coefficients are desired to have consistency for all the spectral signals from the same molecule and good separation of signals from different molecules. For this purpose, we propose a novel method that adopts a coordinated multiexponential fitting to ensure the consistency of diffusion coefficients and apply a sparse constraint to enhance the robustness. A lightweight neural network is applied as an optimizer to solve this highly nonlinear and nonconvex optimization problem. The proposed method provides estimated diffusion coefficients with excellent distinguishment between species and outperforms the state-of-the-art reconstruction algorithms, such as the Laplacian inversion and the multivariate fitting methods. |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
unknown |
| Relation: |
https://figshare.com/articles/journal_contribution/Neural_Network_Method_for_Diffusion-Ordered_NMR_Spectroscopy/19108576 |
| DOI: |
10.1021/acs.analchem.1c03883.s001 |
| الاتاحة: |
https://doi.org/10.1021/acs.analchem.1c03883.s001 |
| Rights: |
CC BY-NC 4.0 |
| رقم الانضمام: |
edsbas.9789CDC6 |
| قاعدة البيانات: |
BASE |