Department of Biochemistry, Duke University Medical Center, Durham, North Carolina 27710, USA.
Nat Commun. 2016 Jul 27;7:12281. doi: 10.1038/ncomms12281.
The application of sparse-sampling techniques to NMR data acquisition would benefit from reliable quality measurements for reconstructed spectra. We introduce a pair of noise-normalized measurements, and , for differentiating inadequate modelling from overfitting. While and can be used jointly for methods that do not enforce exact agreement between the back-calculated time domain and the original sparse data, the cross-validation measure is applicable to all reconstruction algorithms. We show that the fidelity of reconstruction is sensitive to changes in and that model overfitting results in elevated and reduced spectral quality.
稀疏采样技术在 NMR 数据采集方面的应用将受益于可靠的重建光谱质量测量。我们引入了一对噪声归一化的测量值 和 ，用于区分不充分的建模和过拟合。虽然 和 可以联合用于不强制回溯时域与原始稀疏数据完全一致的方法，但交叉验证测量值适用于所有重建算法。我们表明，重建保真度对 和 的变化敏感，并且模型过拟合会导致光谱质量降低。
