  Liquid transportation fuels require costly and time-consuming tests to
characterize metrics, such as Research Octane Number (RON) for gasoline. If
fuel sale restrictions requiring use of standard Cooperative Fuel Research
testing procedures do not apply, these tests may be avoided by using
multivariate statistical models to predict RON and other quantities. Here we
show that an accurate statistical model for the RON of gasoline and
gasoline-like fuels can be constructed by ensuring the representation of key
functional groups in the spectroscopic data set are used to train the model. We
found that a principal component regression model for RON based on IR
absorbance and informed using neat and 134 mixtures of n-heptane, isooctane,
toluene, ethanol, methylcyclohexane, and 1-hexene could predict RON for the 10
Coordinating Research Council Fuels for Advanced Combustion Engine (FACE)
gasolines and 12 FACE gasoline blends with ethanol within 34.8+/-36.1 on
average and 51.2 in the worst case. We next studied the effect of adding 28
additional minor components found in the FACE gasolines to the statistical
model, and determined that it was necessary to add additional representatives
of the branched alkane and aromatics classes to reduce model error. For
example, adding 2,3-dimethylpentane and xylene to the previous model allowed it
to predict RON for the 22 target fuels within 0.3+/-4.4 on average and 7.9 in
the worst case. However, we determined that the specific choice of fuel in
those classes mattered less than ensuring the representation of the relevant
functional group. This work builds upon previous efforts by creating models
informed by neat and surrogate fuels---rather than complex real fuels---that
could predict the performance of complex unknown fuels.
