Afrough Armin, Pérez-Mendigorri Maria, Vosegaard Thomas
Interdisciplinary Nanoscience Center, Aarhus University, Aarhus, Denmark.
Department of Chemistry, Aarhus University, Aarhus, Denmark.
Magn Reson Chem. 2025 Jul;63(8):604-612. doi: 10.1002/mrc.5540. Epub 2025 May 30.
The cost and complexity of modern NMR spectrometers have led to the establishment of centralized, ultrahigh-field facilities with multiple instruments that benefit from shared infrastructure and expertise. Many users have no NMR background, as they come from diverse scientific fields. This requires either heavy involvement of NMR experts in the data treatment or that data processing workflows are made user-friendly, robust, and amenable to automation. This paper discusses how at the Danish Center for Ultrahigh Field NMR Spectroscopy at Aarhus University we develop automated-or guided-data processing workflows to serve the broad community of users of the Center. By providing consistency checks in the algorithms and reporting intermediate results, our data analysis tools raise flags if they are-or are likely-failing. We illustrate this approach with two examples: an automated quantitative lipidomics workflow and a semi-automated multi-exponential relaxation analysis in food matrices. The lipidomics workflow uses H-P TOCSY spectra, database matching, and quantitative P measurements, while color-coded reliability labels highlight potential pitfalls. The multi-exponential relaxation analysis automatically determines an appropriate value for the regularization parameter via the L-curve. Both examples show how guided automation reduces expert supervision and accelerates data processing. We plan to further refine these automated workflows, share our software openly, and explore additional application areas to foster a semi-automated NMR facility.
现代核磁共振光谱仪的成本和复杂性促使人们建立了集中式的超高场设施,配备多台仪器,这些仪器受益于共享的基础设施和专业知识。许多用户没有核磁共振背景,因为他们来自不同的科学领域。这就要求核磁共振专家大量参与数据处理,或者使数据处理工作流程变得用户友好、稳健且易于自动化。本文讨论了在奥胡斯大学的丹麦超高场核磁共振光谱中心,我们如何开发自动化或引导式数据处理工作流程,以服务该中心广泛的用户群体。通过在算法中提供一致性检查并报告中间结果,我们的数据分析工具在出现故障或可能出现故障时会发出警示。我们用两个例子来说明这种方法:一个自动化的定量脂质组学工作流程和食品基质中的半自动多指数弛豫分析。脂质组学工作流程使用H-P TOCSY光谱、数据库匹配和定量P测量,同时用颜色编码的可靠性标签突出潜在问题。多指数弛豫分析通过L曲线自动确定正则化参数的合适值。这两个例子都展示了引导式自动化如何减少专家监督并加速数据处理。我们计划进一步完善这些自动化工作流程,公开分享我们的软件,并探索更多应用领域,以打造一个半自动核磁共振设施。