Bayesian Methods for Quantitative Analysis of NMR Signals in Reaction and Process Monitoring

反应和过程监测中 NMR 信号定量分析的贝叶斯方法

• 批准号: 310714510
• 负责人: Dr. Kerstin Münnemann, since 1/2018
• 金额: --
• 依托单位: Lehrstuhl für Thermodynamik (LTD)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/310714510?language=en
• 关键词: Bayesian; Methods; Quantitative; Analysis; NMR

Online NMR spectroscopy is a promising analysis method for reaction and process monitoring. This method facilitates non-invasive investigations of complex liquid multicomponent mixtures without prior calibration. In the present project, new analysis methods based on Bayesian statistics will be developed that enable the automatic and robust quantitative analysis of large series of NMR data. The asset of the Bayesian method is that it incorporates prior knowledge of the investigated NMR spectra in the analysis. Therefore, also difficult spectra (e.g. low signal-to-noise ratio, overlapping peaks, flowing samples) can be analysed quantitatively with this method where the existing approaches fail. The new analysis method will be optimised for applications in reaction and process monitoring by exploiting prior knowledge of the investigated sample, e.g. the stoichiometry of the occurring reactions or the flow velocity of the sample. Robust quantitative analysis of spectra of complex mixtures acquired with benchtop NMR spectrometers will be facilitated by taking prior knowledge into account that is gained from spectra of these samples acquired with high field NMR spectrometers. The new analysis methods developed in this project will contribute to extend the possible fields of applications of NMR spectroscopy for reaction and process monitoring.

在线核磁共振谱是一种很有前途的反应和过程监测分析方法。这种方法有利于非侵入性调查的复杂液体多组分混合物，而无需事先校准。在本项目中，将开发基于贝叶斯统计的新分析方法，使大系列NMR数据的自动和可靠的定量分析成为可能。贝叶斯方法的优点是它在分析中结合了所研究的NMR谱的先验知识。因此，在现有方法失败的情况下，也可以用该方法定量分析困难的光谱（例如，低信噪比、重叠峰、流动样品）。新的分析方法将通过利用所研究样品的先验知识，例如发生反应的化学计量或样品的流速，针对反应和过程监测中的应用进行优化。通过考虑从高场NMR光谱仪获得的这些样品的光谱中获得的先验知识，将有助于对台式NMR光谱仪获得的复杂混合物的光谱进行稳健的定量分析。本项目开发的新分析方法将有助于扩展NMR光谱在反应和过程监测中的可能应用领域。

项目成果

Improving the accuracy of model based quantitative NMR

提高基于模型的定量 NMR 的准确性

• DOI: 10.5194/mr-2019-4
• 发表时间: 2020
• 作者: Y. Matviychuk;E. Steimers;E. v. Harbou;D. J. Holland
• 通讯作者: D. J. Holland

Multi-objective optimization for an automated and simultaneous phase and baseline correction of NMR spectral data.

• DOI: 10.1016/j.jmr.2018.02.012
• 发表时间: 2018-04
• 期刊: Journal of magnetic resonance
• 影响因子: 2.2
• 作者: M. Sawall;E. von Harbou;Annekathrin Moog;R. Behrens;Henning Schröder;J. Simoneau;E. Steimers;K. Neymeyr

A comparison of non-uniform sampling and model-based analysis of NMR spectra for reaction monitoring.

用于反应监测的核磁共振谱的非均匀采样和基于模型的分析的比较

• DOI: 10.1002/mrc.5095
• 发表时间: 2020
• 期刊: Magnetic resonance in chemistry : MRC
• 作者: E. Steimers;Y. Matviychuk;A. Friebel;K. Münnemann;E. von Harbou;D. J. Holland
• 通讯作者: D. J. Holland

Application of a new method for simultaneous phase and baseline correction of NMR signals (SINC)

应用新方法对 NMR 信号进行同步相位和基线校正 (SINC)

• DOI: 10.1002/mrc.4964
• 发表时间: 2020
• 期刊: Magnetic Resonance in Chemistry
• 影响因子: 2
• 作者: E. Steimers;M. Sawall;R. Behrens;D. Meinhardt;J. Simoneau;K. Münnemann;K. Neymeyr;E. von Harbou
• 通讯作者: E. von Harbou

Bayesian approach for automated quantitative analysis of benchtop NMR data.

用于台式 NMR 数据自动定量分析的贝叶斯方法

• DOI: 10.1016/j.jmr.2020.106814
• 发表时间: 2020
• 期刊: Journal of magnetic resonance
• 影响因子: 2.2
• 作者: Y. Matviychuk;E. Steimers;E. von Harbou;D. J. Holland
• 通讯作者: D. J. Holland