TastePrint: A 3D Food Printing System for Layer-wise Taste Distribution via Airbrushed Liquid Seasoning

2026-03-24

AI Summary

This work proposes a novel 3D food printing approach integrated with a programmable air-spray system to overcome the limitations of conventional methods, which are constrained by material variety and incapable of achieving non-uniform flavor distribution. By dynamically depositing liquid seasonings during the printing process, the method decouples geometric structure from flavor design for the first time. The system features a multi-nozzle spray mechanism and a graphical user interface, enabling layer-by-layer customization of flavor location and intensity. Quantitative models for spray resolution (R² = 0.86) and dosage control (R² = 0.99) were established to ensure precision. User studies demonstrated that personalized designs can be completed in an average of 15 minutes, confirming the system’s high repeatability and usability.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: 3D Computer Vision

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

3D food printing enables the customization of food shapes and textures, but typically produces uniform taste profiles due to the limited diversity of printable materials. We present TastePrint, a 3D food printing system that achieves layer-wise spatial taste distribution by dynamically applying liquid seasonings with a programmable airbrush during fabrication. The system integrates (1) a graphical user interface (GUI) that allows users to import 3D models, slice them into layers, and specify spray positions and intensities for each layer, and (2) a customized 3D food printer equipped with a multi-nozzle spray mechanism. We evaluated the system through technical experiments quantifying spray resolution and deposition accuracy, together with an exploratory usability study involving three home cooks designing personalized taste patterns. The spray-resolution model achieved R2 = 0.86, the spray-amount model achieved R2 = 0.99, and participants completed the design task in approximately 15 min on average. These results indicate that TastePrint can control seasoning placement and quantity with good repeatability while supporting exploratory taste-design workflows. This work establishes a technical foundation for decoupling food geometry from taste design and motivates future sensory studies on personalized, multisensory food fabrication.

Problem

• 3D food printing
• taste distribution
• flavor customization
• spatial seasoning
• multisensory food fabrication

Innovation

• 3D food printing
• spatial taste distribution
• programmable airbrush
• liquid seasoning
• multisensory fabrication