负强度NMR谱的盲源分离
Blind Source Separation for NMR Spectra with Negative Intensity
摘要：
NMR光谱数据集，特别是在样品有限的系统中，如果包含多种化学成分（相、多晶型物、分子、晶体、玻璃等），则很难解释。以及重叠共振的可能性。在本文中，我们基准几个盲源分离技术分析的NMR光谱数据包含负强度。出于基准测试的目的，我们生成了一个大的合成四极固态NMR类光谱的合成基，模型自旋晶格T1弛豫或章动尖端/翻转角实验。我们的基准测试方法完全集中在盲源分离技术的能力，以重现光谱的基础纯成分。总的来说，我们发现FastICA（快速独立分量分析），SIMPLISMA（SIMPLE使用交互式自建模混合分析）和NNMF（非负矩阵分解）是性能最好的技术。我们证明，数据集归一化方法之前，盲源分离并没有显着改善的结果。在研究的噪音水平范围内，我们没有发现技术排名的剧烈变化。FastICA和SIMPLISMA的准确性迅速下降，如果多余的（不真实的）纯成分的预测。我们的研究结果表明SVD（奇异值分解）方法的性能不佳，我们提出了矩阵初始化的替代技术。基准技术也适用于真正的固态NMR数据集。总体而言，来自合成数据集的建议与来自真实数据分析的建议和结果一致。讨论提供了一些额外的建议，光谱学家应用盲源分离的NMR数据集，并为未来的基准研究。
英文摘要：
NMR spectral datasets, especially in systems with limited samples, can be difficult to interpret if they contain multiple chemical components (phases, polymorphs, molecules, crystals, glasses, etc...) and the possibility of overlapping resonances. In this paper, we benchmark several blind source separation techniques for analysis of NMR spectral datasets containing negative intensity. For benchmarking purposes, we generated a large synthetic datasbase of quadrupolar solid-state NMR-like spectra that model spin-lattice T1 relaxation or nutation tip/flip angle experiments. Our benchmarking approach focused exclusively on the ability of blind source separation techniques to reproduce the spectra of the underlying pure components. In general, we find that FastICA (Fast Independent Component Analysis), SIMPLISMA (SIMPLe-to-use-Interactive Self-modeling Mixture Analysis), and NNMF (Non-Negative Matrix Factorization) are top-performing techniques. We demonstrate that dataset normalization approaches prior to blind source separation do not considerably improve outcomes. Within the range of noise levels studied, we did not find drastic changes to the ranking of techniques. The accuracy of FastICA and SIMPLISMA degrades quickly if excess (unreal) pure components are predicted. Our results indicate poor performance of SVD (Singular Value Decomposition) methods, and we propose alternative techniques for matrix initialization. The benchmarked techniques are also applied to real solid state NMR datasets. In general, the recommendations from the synthetic datasets agree with the recommendations and results from the real data analysis. The discussion provides some additional recommendations for spectroscopists applying blind source separation to NMR datasets, and for future benchmark studies.
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