Employing an Artificial Neural Network Model to Predict Thermal Properties of a Drink Made from Buttermilk Sweetened with Date Syrup

Predictive modeling using artificial neural networks (ANN) has drawn a lot of interest as a quick, dependable, and non-destructive method for assessing food qualities. In order to forecast the thermal conductivity and thermal diffusivity of a beverage made from buttermilk sweetened with date syrup from the Khlass type, this study developed an ANN model. We looked into the effects of date syrup concentration (5, 10, and 15%), storage cooling temperature (0, 5, 10, 15 °C), and storage duration (0, 3, 6, 9, 12, and 15 days). The findings of the experiment showed that while higher date syrup concentrations and storage temperatures led to higher values of these attributes, longer storage times decreased both thermal conductivity and thermal diffusivity. Thermal diffusivity was between 1.317 × 10−7 and 2.247 × 10−7 m2/s, while thermal conductivity was between 0.533 and 0.632 W/m K. Using a trial-and-error method, the best ANN architecture was found to include three input neurons, one hidden layer with twenty neurons, and two output neurons. With mean absolute errors of 1.80 × 10−3 W/m K and 1.7 × 10−9 m2/s for thermal conductivity and thermal diffusivity, respectively, using the testing points, the model shows excellent forecast accuracy. According to sensitivity analysis, the most significant factor influencing both thermal properties was storage duration.