Albahri et al. introduced a machine learning model along with structural group contribution (SGC) approach for predicting the FP of 1471 pure components, including hydrocarbons, oxygenated groups, halogenated groups, and others. Jiao et al. and Torabian et al. used a similar approach to develop models capable of estimating FP values for mixtures containing hydrocarbons and oxygenated groups, combining Quantitative Structure Property Relationship (QSPR) techniques with machine learning. Aljaman et al. also developed a model to predict the FP of similar mixtures, however, instead of using complex QSPR molecular descriptors, the author employed more comprehensive features, such as molecular weight and weight percentage constituent functional groups.