Mestrelab Research, S.L Feliciano Barrera 9B - Baixo, 15706 Santiago de Compostela, Spain.
Anal Chem. 2013 Jun 18;85(12):5778-86. doi: 10.1021/ac400411q. Epub 2013 May 28.
NMR is routinely used to quantitate chemical species. The necessary experimental procedures to acquire quantitative data are well-known, but relatively little attention has been applied to data processing and analysis. We describe here a robust expert system that can be used to automatically choose the best signals in a sample for overall concentration determination and determine analyte concentration using all accepted methods. The algorithm is based on the complete deconvolution of the spectrum which makes it tolerant of cases where signals are very close to one another and includes robust methods for the automatic classification of NMR resonances and molecule-to-spectrum multiplets assignments. With the functionality in place and optimized, it is then a relatively simple matter to apply the same workflow to data in a fully automatic way. The procedure is desirable for both its inherent performance and applicability to NMR data acquired for very large sample sets.
NMR 通常用于定量化学物质。获取定量数据所需的必要实验程序是众所周知的，但相对较少关注数据处理和分析。我们在这里描述了一个强大的专家系统，可用于自动选择样品中最佳信号进行总体浓度测定，并使用所有接受的方法确定分析物浓度。该算法基于光谱的完全解卷积，因此它可以容忍信号非常接近的情况，并包含用于 NMR 共振的自动分类和分子-光谱多重分配的稳健方法。功能就位并进行了优化后，以全自动方式将相同的工作流程应用于数据就相对简单了。该程序因其固有性能和对非常大数据集的 NMR 数据的适用性而受到青睐。
