Rapid, accurate, and automatic quantitation of two‐dimensional nuclear magnetic resonance(2D‐NMR) data is a challenging problem. Recently, a Bayesian information criterion based subband Steiglitz–McBride algorithm has been shown to exhibit superior performance on all three fronts when applied to the quantitation of one‐dimensional NMR free induction decay data. In this paper, we demonstrate that the 2D Steiglitz–McBride algorithm, in conjunction with 2D subband decomposition and the 2D Bayesian information criterion, also achieves excellent results for 2D‐NMR data in terms of speed, accuracy, and automation—especially when compared in these respects to the previously published analysis techniques for 2D‐NMR data.

中文翻译：

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用于自动分析 2D-NMR 数据的 2D 子带 Steiglitz-McBride 算法

二维核磁共振 (2D-NMR) 数据的快速、准确和自动定量是一个具有挑战性的问题。最近，基于贝叶斯信息准则的子带 Steiglitz-McBride 算法在应用于一维 NMR 自由感应衰减数据的定量时，已被证明在所有三个方面都表现出优异的性能。在本文中，我们证明了 2D Steiglitz-McBride 算法与 2D 子带分解和 2D 贝叶斯信息准则相结合，在 2D-NMR 数据的速度、准确性和自动化方面也取得了出色的结果——尤其是在与这些方面与之前发表的 2D-NMR 数据分析技术相比。