Akademik Veri Yönetim Sistemi
The 07th International Conference on Food, Nutrition, Health and Lifestyle 2025 (NUTRICON 2025), Bangkok, Tayland, 17 - 18 Kasım 2025, ss.82, (Özet Bildiri)
Personalized nutrition offers tailored dietary recommendations based on an individual’s genetic, metabolic, and lifestyle characteristics. This approach is gaining increasing attention due to the recognized role of diet in disease prevention and management. Three-dimensional (3D) food printing is an emerging technology capable of creating complex, customized food structures with precise control over composition and texture. It holds significant potential for personalized nutrition, enabling the production of foods suited to specific dietary needs such as allergies, intolerances, or chronic conditions like diabetes and obesity. Moreover, it supports the development of personalized supplements and medical foods with precise nutritional formulations. However, high costs and limited system integration have hindered widespread adoption. This study aims to design and develop a cost-effective, intelligent 3D food printing system integrated with a robotic pre-processing unit and automated control software. The proposed system allows users to remotely upload personalized recipes and produce food products tailored to individual dietary requirements. The system developed using a modular architecture that integrates a robotic pre-processing unit, a multi-material extrusion-based 3D food printer, and software based control interface. The robotic unit handles ingredient sorting, portioning, and temperaturecontrolled pre-treatment before printing. The software layer, developed in C# and ROS (Robot Operating System), automate recipe processing, convert nutritional data into printable parameters, and allow remote uploads via a secure web platform. Evaluation includes mechanical calibration tests (e.g., layer resolution, printing speed accuracy), and nutritional analyses of printed samples (protein, carbohydrate, and fat retention). System reliability and automation performance were validated through repeated batch trials. Initial prototyping demonstrated the system’s capability to reproduce personalized recipes with nutrient deviations of less than 5% from target values while maintaining consistent texture. Nutrient preservation efficiency and printing precision were assessed through comparative tests against manually prepared samples. Iterative tuning and calibration of printing parameters led to a 20% reduction in material waste compared to baseline printing. These outcomes confirm the system’s feasibility and usability. This study introduces a novel, software controlled approach to 3D food printing with integrated robotic automation and remote personalization capabilities. With no comparable system currently available on the market, it represents a significant step toward affordable, data-informed precision nutrition. As the global 3D food printing market is projected to grow at a 57.5% CAGR by 2027, this innovation aligns with the increasing demand for sustainable, personalized food technologies, offering broad societal, health, and economic benefits.
Keywords: personalized nutrition, 3D food printing, precision nutrition, smart food technology