Neural Network Model for Prediction Food Nutrient Changes Based on Recipes

During the cooking process, the chemical structure of food ingredients undergoes changes, resulting in nonlinear variations in nutrient composition. This study develops a neural network model to predict nutrient changes in dishes caused by cooking processes. Built upon a feedforward network with a residual connection, the proposed model effectively learns the nonlinear characteristics of the changes by combining logarithmic scaling of the input and residual learning, which helps capture variations in the scale of the input. Experimental results demonstrate improved prediction accuracy for specific nutrients and confirme the model's ability to predict nutrient composition across diverse food ingredients. This research suggests potential applications in personalized diet planning and advancements in the food technology industry.