Automated Methods for Identification and Quantification of Structural Groups from Nuclear Magnetic Resonance Spectra Using Support Vector Classification

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for elucidating the structure of unknown components and the composition of liquid mixtures. However, these tasks are often tedious and challenging, especially if complex samples are considered. In this work, we introduce automated methods for the identification and quantification of structural groups in pure components and mixtures from NMR spectra using support vector classification. As input, a 1H NMR spectrum and a 13C NMR spectrum of the liquid sample (pure component or mixture) that is to be analyzed is needed. The first method, called group-identification method, yields qualitative information on the structural groups in the sample. The second method, called group-assignment method, provides the basis for a quantitative analysis of the sample by identifying the structural groups and assigning them to signals in the 13C NMR spectrum of the sample; quantitative information can then be obtained with readily available tools by simple integration. We demonstrate that both methods, after being trained to NMR spectra of nearly 1000 pure components, yield excellent predictions for pure components that were not part of the training set as well as mixtures. The structural group-specific information obtained with the presented methods can, e.g., be used in combination with thermodynamic group-contribution methods to predict fluid properties of unknown samples.

核磁共振波谱法（NMR）是解析未知组分结构与液态混合物组成的强有力工具。然而此类任务通常繁琐且极具挑战性，尤其在涉及复杂样品时更为突出。本研究提出了基于支持向量分类（Support Vector Classification）的自动化分析方法，可从核磁共振波谱中对纯组分及混合物内的结构基团实现识别与定量分析。该方法的输入为待分析液态样品（纯组分或混合物）的氢谱（¹H NMR）与碳谱（¹³C NMR）数据。第一种方法被称为基团识别法，可输出样品中结构基团的定性信息。第二种方法被称为基团归属法，通过识别结构基团并将其归属至样品碳谱（¹³C NMR）中的信号峰，为样品的定量分析提供基础；随后可借助现有简易工具通过简单积分操作获取定量信息。我们通过实验证明，在基于近千种纯组分的核磁共振波谱数据完成训练后，这两种方法均可对未参与训练的纯组分以及混合物取得优异的预测性能。通过本研究提出的方法获得的结构基团特异性信息，可与热力学基团贡献法相结合，用于预测未知样品的流体物性。