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.