Recently, development of the QSPR models for mixtures has received much
attention. The QSPR modeling of mixtures requires the use of appropriate
mixture descriptors. In this study, 12 mathematical equations were
considered to compute mixture descriptors from the individual components for
the prediction of normal boiling points of 78 ternary azeotropic mixtures.
Multiple linear regression (MLR) was employed to build all QSPR models.
Memorized_ACO algorithm was employed for subset variable selection. An
ensemble model was also constructed using averaging strategy to improve the
predictability of the final QSAR model. The models have been validated by a
test set comprised of 24 ternary azeotropes and by different statistical
tests. The resulted ensemble QSPR model had R2training, R2test, and q2 of
0.97, 0.95, and 0.96, respectively. Mean absolute error (MAE) as a good
indicator of model performance were found to be 3.06 and 3.52 for training
and testing sets, respectively.