We present a methodology for assessing similarity between the 13C Nuclear Magnetic Resonance (NMR) spectrum of a novel experimental fuel of interest, and sets of spectra of reference hydrocarbon fuels where their 13C NMR spectra have been used to create empirical models for prediction of properties. Using data from a set of complex biofuels, our results show the methodology is effective in finding relevant differences among fuels that are sometimes difficult to determine by visual inspection of the 13C NMR spectra. An important consideration when applying empirical models for predicting property values of an experimental fuel is to determine how closely the 13C NMR spectrum of the new fuel resembles those used to fit the model. Empirical models usually produce more accurate predictions when used for interpolation than for extrapolation. However, calculating and visualizing the closeness between a new observation and the dataset used to fit a predictive model is not always straightforward, and no single metric can capture the similarities and differences present in a high-dimensional space. This problem is made more difficult because the 27 dimensions comprising different integration regions, each a descriptor of a particular carbon type represented in the 13C NMR spectra, define a space too large to fully explore. This is exacerbated by the relatively small number of samples of the datasets used to create empirical models. Our approach searches for outliers in spaces with few dimensions, and provides an overall view in a low-dimensional space of the position of the experimental fuel with respect to other sets. The most and least similar fuels from a particular set are determined based on the linear correlation with the spectrum of the experimental fuel. Our results show that fuels of similar end use tend to cluster together in this type of low dimensional space representation.

Similarity, reduced dimensionality representation, and outlier detection in 13C nuclear magnetic resonance spectra of fuels used for building empirical models

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

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