Data‐Informed Tuning of Texture in Xanthan Gum–Based 3D‐Printed Foods Using ANOVA and Machine Learning

3D food printing enables fabrication of personalized foods with tailored structures and textures; however, reproducible control of post‐print texture remains challenging because formulation‐process interactions are highly coupled and nonlinear. This study investigated the effects of extrusion speed, layer thickness, and nozzle temperature on the texture of xanthan gum–based printed foods using a hybrid framework combining statistical design of experiments and supervised machine learning (ML). A 3 ³ full‐factorial design (27 parameter combinations) was implemented on an extrusion‐based food printer, and hardness, cohesiveness, and chewiness were evaluated using texture profile analysis. Analysis of variance (ANOVA) and Taguchi nominal‐the‐best analysis revealed that extrusion speed mainly influenced hardness and chewiness, whereas layer thickness predominantly affected cohesiveness. Six regression‐based ML models were evaluated, among which Random Forest (RF) showed the most favorable overall predictive performance within the studied dataset. Model‐based evaluation of parameter combinations identified 20 mm/s extrusion speed, 0.3 mm layer thickness, and 90°C nozzle temperature as a balanced operating condition for the target texture ranges. Confirmation experiments showed only small deviations between predicted and measured responses. An additional intermediate “unseen” condition tested on a related screw‐based printer also showed reasonable agreement with RF predictions, indicating local interpolation capability within the examined parameter range. However, because external validation was limited to a single intermediate condition on a related printer configuration, the findings should be interpreted within the studied process window rather than as broad hardware‐independent generalization.

Practical Applications

The proposed ANOVA‐ML workflow provides a systematic strategy for tuning extrusion parameters in XG‐based 3DFP to achieve more consistent texture while reducing trial‐and‐error experimentation within the studied process window.