Background and Objectives: Slip melting point (SMP) is one of the most important qualitative and functional properties of fats. However, the common melting point determination methods are generally time-consuming. Therefore, in this research, regression models were developed for description of the SMP of interesterified fats as a function of fatty acid composition.

Materials and Methods: Blends of soybean oil and palm stearin were subjected to chemical interesterification, and the fatty acid composition and SMP of the products were determined. SMP of the interesterified fats was modeled as a function of fatty acid composition using regression analysis. Goodness of fit of different models was evaluated, and finally, the best model for SMP prediction was determined. 

Results: The SMP of blends decreased after interesterification significantly (p<0.05). A good Pearson’s correlation coefficient (r > 0.99) was observed between SMP and all fatty acids (p < 0.01). Accordingly, the following strategies were used for SMP modeling: as a multiple function of palmitic, stearic, oleic, linoleic and linolenic acids; as a multiple function of total saturated and unsaturated fatty acids; as a function of saturated fatty acids. The proposed models for prediction of the SMP of interesterified soybean oil and palm stearin blends showed a correlation coefficient (between the experimental and predicted data) of greater than 0.99, and a mean absolute error lower than 0.52°C, indicating the high prediction power of the models.

Conclusions: The results of model validation showed that the model describes SMP as a multiple function of total saturated and unsaturated fatty acids and the Gompertz model describes SMP as a function of total saturated fatty acids could predict the SMP of different interesterified fats in the best way. The models may be useful in the design and development of new interesterified fat formulations.

Keywords: Chemical interesterification, Modeling, Slip melting point, Fatty acid composition