Abstract
This study aims to develop a mathematical model for optimizing food formulations that incorporate microgreens grown using Internet of Things (IoT) technologies in vertical farming systems. In this research, a factorial experimental design was employed to study the influence of three variable factors on the quality indicators of food products, including protein content (Y1), fat content (Y2), carbohydrate content (Y3), and organoleptic evaluation (Y4). Regression equations were derived to describe the nonlinear relationships between these factors and the quality indicators. The second-degree polynomial regression models demonstrated how the combination of variables affected food formulation optimization. The results showed that the application of IoT in vertical farming provides valuable real-time data, enabling the adjustment of growing conditions to maximize the nutritional and functional value of microgreens. The mathematical models developed in this study provide insights into the optimal composition of multicomponent food products, ensuring higher nutritional value, improved texture, and better overall product quality.