Advanced NMR Methods for Mixture Analysis

混合物分析的重要性怎么强调都不为过：它支撑着化学、生物学和药学的进步。巧合的是，我们获得化学结构信息的最好方法，NMR，除了最简单的混合物外，几乎所有的混合物都很难得到。NMR谱相对容易被有经验的化学家解释，但仅适用于纯化合物。然而，核磁共振有很大的潜力，有效的混合物分析;它是非破坏性和非侵入性的，允许研究完全完整的混合物。迄今为止，NMR在混合物分析中所取得的有限成功与其说是源于其固有的局限性，不如说是源于未能将实验和数据分析最佳地结合起来。当组分光谱部分重叠时，可以通过分析来自每种化合物的单个良好分辨的峰（单变量分析）来提供有价值的信息，以提供例如弛豫或扩散数据。为了从几个或所有峰中获得信息（多变量分析），最强大的方法之一是扩散NMR。它的工作原理是根据组分的不同扩散行为分离组分光谱。它适用于具有离散扩散系数的简单混合物，但是需要做更多的工作来允许具有扩散系数分布的更复杂的混合物和系统（例如聚合物）。对于具有广泛信号重叠和/或低信噪比的非常复杂的系统，我们需要扩散可以提供的更多信息来分离光谱。一种显著提高这种化学分辨率的方法是利用多变量数据分析的力量，特别是多路方法。如果我们通过添加正交变异源来扩展数据的维度，我们可以使用特定的多因素方法来提取真实的、未混合的组分光谱，这要归功于多因素模型的固有独特性1，2。这些方法使用所有可用的数据，因此我们不是使用单个信号，而是同时拟合所有光谱。这可以让我们研究浓度非常低的系统3。为了进一步提高光谱分辨率，我们可以使用高分辨率技术，如HSQC，就像在蛋白质原纤维形成动力学研究中所做的那样。最近，我们表明，通过设计NMR脉冲序列来产生合适的数据，可以增加一系列正交尺寸。在这个学生项目中，我们将使用新的NMR实验来研究混合物。这些包括那些产生单变量，多变量和多线性数据，新的互补核磁共振脉冲序列和分解算法将被开发。参考文献（1）Harshman，R. PARAFAC程序的基础。UCLA Working Papers in Phonetics. 1970年，第16页，第1页。(2)兄弟河副空军。和应用程序。化学计量学实验室1997，38（2），149. (3)Khajeh，M.; Botana，A.;伯恩斯坦，M.一、Nilsson，M.;莫里斯，G。a.使用扩散有序光谱和多路化学计量学研究反应动力学。anal. Chem.2010，82（5），2102. (4)詹森，K. S.的; Linse，S.; Nilsson，M.; Akke，M.; Malmendal，A.通过NMR扩散数据的多线性分析揭示淀粉样蛋白纤维形成过程中定义良好的可溶性状态。Journal of the American Chemical Society 2019，141（47），18649. (5)Dal Poggetto，G.; Castanar，L.;亚当斯河W的;莫里斯，G。一、Nilsson，M.解剖和划分：把混合物的核磁共振光谱下的刀。Journal of the American Chemical Society 2019，141（14），5766.

It is hard to overstate the importance of mixture analysis: it underpins progress in much of chemistry, biology and pharmacy. Paradoxically, the best method we have for obtaining information on chemical structure, NMR, struggles with all but the simplest mixtures. NMR spectra are relatively easily interpreted by experienced chemists, but only for pure compounds. However, NMR has great potential for efficient mixture analysis; it is both non-destructive and non-invasive, allowing the study of completely intact mixtures. The limited success enjoyed by NMR in mixture analysis to date stems not so much from its inherent limitations, as from failure to integrate experiment and data analysis optimally. When component spectra are partially overlapped, valuable information can be by analysing a single well resolved peak from each compound (univariate analysis) to provide e.g. relaxation or diffusion data. To obtain information from several, or all, peaks (multivariate analysis) one of the most powerful methods diffusion NMR. It works by separating components spectra by virtue of their different diffusion behaviour. It works well for simple mixtures with discrete diffusing coefficients, but a lot more work needs to be done to allow more complicated mixtures and systems with a distribution of diffusion coefficients (such as polymers). For very complicated systems with extensive signal overlap and/or low signal-to-noise ratio we need more information that diffusion can provide to separate the spectra. One way to substantially improve this chemical resolution is to harness the power of multivariate data analysis, and in particular multiway methods. If we extend the dimensionality of the data, by adding orthogonal sources of variation, we can use specific multiway methods to extract the true, unmixed, component spectra, thanks to the inherent uniqueness of multiway models1,2. These methods use all the available data, so instead of using individual signals, we fit all the spectra at the same time. This can allow us to study systems with very low concentrations3. To further enhance the spectral resolution, we can use high-resolution techniques such as HSQC, as was done in a study of the kinetics of protein fibril formation4. Recently we showed that it is possible to add a range of orthogonal dimensions, by designing NMR pulse sequences to produce suitable data5.In this studentship we will study mixtures using new NMR experiments. These include those that produce univariate, multivariate and multilinear data; novel complementary NMR pulse sequences and decomposition algorithms will be developed. References(1) Harshman, R. Foundations of the PARAFAC procedure. UCLA Working Papers in Phonetics. 1970, 16, 1.(2) Bro, R. PARAFAC. Tutorial and applications. Chemometrics Intell. Lab. Syst. 1997, 38 (2), 149.(3) Khajeh, M.; Botana, A.; Bernstein, M. A.; Nilsson, M.; Morris, G. A. Reaction Kinetics Studied Using Diffusion-Ordered Spectroscopy and Multiway Chemometrics. Anal. Chem. 2010, 82 (5), 2102.(4) Jensen, K. S.; Linse, S.; Nilsson, M.; Akke, M.; Malmendal, A. Revealing Well-Defined Soluble States during Amyloid Fibril Formation by Multilinear Analysis of NMR Diffusion Data. Journal of the American Chemical Society 2019, 141 (47), 18649.(5) Dal Poggetto, G.; Castanar, L.; Adams, R. W.; Morris, G. A.; Nilsson, M. Dissect and Divide: Putting NMR Spectra of Mixtures under the Knife. Journal of the American Chemical Society 2019, 141 (14), 5766.