Brinks Sørensen Mathies, Riis Andersen Michael, Siewertsen Mette-Maya, Bro Rasmus, Strube Mikael Lenz, Gotfredsen Charlotte Held
Department of Chemistry, Technical University of Denmark, Kgs Lyngby, DK-2800, Denmark.
Department of Applied Mathematics and Computer Science, Kgs Lyngby, DK-2800, Denmark.
Heliyon. 2024 Aug 30;10(17):e36998. doi: 10.1016/j.heliyon.2024.e36998. eCollection 2024 Sep 15.
We introduce NMR-Onion, an open-source, computationally efficient algorithm based on Python and PyTorch, designed to facilitate the automatic deconvolution of 1D NMR spectra. NMR-Onion features two innovative time-domain models capable of handling asymmetric non-Lorentzian line shapes. Its core components for resolution-enhanced peak detection and digital filtering of user-specified key regions ensure precise peak prediction and efficient computation. The NMR-Onion framework includes three built-in statistical models, with automatic selection via the BIC criterion. Additionally, NMR-Onion assesses the repeatability of results by evaluating post-modeling uncertainty. Using the NMR-Onion algorithm helps to minimize excessive peak detection.
我们介绍了NMR-Onion,这是一种基于Python和PyTorch的开源且计算高效的算法,旨在促进一维核磁共振(NMR)谱的自动去卷积。NMR-Onion具有两个创新的时域模型,能够处理不对称的非洛伦兹线形。其用于分辨率增强的峰检测和用户指定关键区域数字滤波的核心组件确保了精确的峰预测和高效的计算。NMR-Onion框架包括三个内置统计模型,并通过贝叶斯信息准则(BIC)进行自动选择。此外,NMR-Onion通过评估建模后的不确定性来评估结果的可重复性。使用NMR-Onion算法有助于将过多的峰检测降至最低。