Predicting solid fat content (SFC), melting point (MP) and crystallization point (CP) properties of monodiacylglycerols: A linear modeling approach based on chemical composition and acylglycerols profile

Monodiacylglycerols are widely used as emulsifiers in various food products, but their macroscopic properties, such as SFC, melting point (MP), and crystallization point (CP), are difficult to predict, especially if they are derived from various vegetable oils with different fatty acid compositions. Mathematical modelling of fatty acid composition and acylglycerol profiles can quickly predict their properties, avoid costly trials or production problems, and most importantly, maintain product quality consistency. This study used several monodiacylglycerols ( MDAG) samples with different fatty acid and acylglycerol compositions to generate accurate mathematical models. A linear regression model was used as a simple and easy-to-understand model. The results showed that the dominant fatty acid composition and acylglycerol profile could predict the SFC at 40, 50, 60 and 70°C with R 2 (adj) of 0.8229; 0.9212; 0.9182 and 0.7287, respectively. The dominant fatty acid composition and acyl glycerol profile can also predict the melting point and crystallization point of MDAG with R 2 (adj) of 0.8714 and 0.8298, respectively. This information will be very useful for the food industry to design fat products with expected SFC, melting point and crystallization point. • First use of simple linear regression to predict MDAG SFC, MP, and CP • SFC at 40–70 °C predicted with high accuracy (R² adj up to 0.92) • MP and CP modeled from fatty acid and glycerol data (R² adj ≥0.83) • Offers a low-complexity alternative to advanced predictive models and fat product design