使用遗传算法优化的 1H-13C 重聚焦恒定时 INEPT 进行快速准确的定量。
Fast and accurate quantification using Genetic Algorithm optimized 1H-13C refocused constant-time INEPT.
机构信息
Department of Physics and NMR Research Centre, Indian Institute of Science, Bangalore 560 012, India.
出版信息
J Magn Reson. 2013 Sep;234:106-11. doi: 10.1016/j.jmr.2013.06.013. Epub 2013 Jun 27.
Using Genetic Algorithm, a global optimization method inspired by nature's evolutionary process, we have improved the quantitative refocused constant-time INEPT experiment (Q-INEPT-CT) of Mäkelä et al. (JMR 204 (2010) 124-130) with various optimization constraints. The improved 'average polarization transfer' and 'min-max difference' of new delay sets effectively reduces the experimental time by a factor of two (compared with Q-INEPT-CT, Mäkelä et al.) without compromising on accuracy. We also discuss a quantitative spectral editing technique based on average polarization transfer.
利用遗传算法（一种受自然进化过程启发的全局优化方法），我们改进了 Mäkelä 等人提出的定量重聚焦等时 INEPT 实验（Q-INEPT-CT）（JMR 204 (2010) 124-130），并添加了各种优化约束。新延迟集的改进的“平均极化转移”和“最小-最大差值”有效地将实验时间缩短了一半（与 Mäkelä 等人的 Q-INEPT-CT 相比），同时不影响准确性。我们还讨论了一种基于平均极化转移的定量光谱编辑技术。
Zotero 插件