Non-uniform sampling has been successfully used for solution and solid-state NMR of homogeneous samples. In the solid state, protein samples are often dominated by inhomogeneous contributions to the homogeneous line widths. In spite of different technical strategies for peak reconstruction by different methods, we validate that NUS can generally be used also for such situations where spectra are made up of complex peak shapes rather than Lorentian lines. Using the RMSD between subsampled and reconstructed data and those spectra obtained with uniform sampling for a sample comprising a wide conformational distribution, we quantitatively evaluate the identity of inhomogeneous peak patterns. The evaluation comprises Iterative Soft Thresholding (hmsIST implementation) as a method explicitly not assuming Lorentian lineshapes, as well as Sparse Multidimensional Iterative Lineshape Enhanced (SMILE) algorithm and Signal Separation Algorithm (SSA) reconstruction, which do work on the basis of Lorentian lineshape models, with different sampling densities. Even though individual peculiarities are apparent, all methods turn out principally viable to reconstruct the heterogeneously broadened peak shapes.

中文翻译：

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定量评估非均相固态NMR线形时的非均匀采样。

非均匀采样已成功用于均相样品的溶液和固态NMR。在固态状态下，蛋白质样品通常受均匀线宽的不均匀贡献所支配。尽管采用不同方法进行峰重建的技术策略不同，但我们验证了NUS通常也可用于光谱由复杂峰形而不是洛伦谱线组成的情况。使用二次采样和重构数据之间的RMSD以及通过均匀采样获得的包含宽构象分布的样品的光谱，我们定量评估了不均匀峰模式的身份。评估包含迭代软阈值（hmsIST实现），该方法明确不采用Lorentian线形，以及稀疏多维迭代线形增强（SMILE）算法和信号分离算法（SSA）重构，它们在具有不同采样密度的Lorentian线形模型的基础上进行工作。即使有明显的特殊性，所有方法在重建异质加宽峰形时都基本上可行。