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

Rahul Soni¹, Vivek V Bhandarkar¹, Ponappa K¹

¹deLOGIC Laboratory, Mechanical Engineering Discipline, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India.

Journal of Food Science

|June 26, 2026

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Controlling texture in 3D food printing is complex. This study used design of experiments and machine learning to optimize extrusion parameters for consistent xanthan gum food textures, reducing trial-and-error.

Area of Science:

Food Science and Technology
Materials Science
Engineering

Background:

3D food printing offers personalized nutrition but struggles with reproducible texture control due to complex formulation-process interactions.
Achieving consistent texture in 3D printed foods is crucial for consumer acceptance and product development.

Purpose of the Study:

To investigate the impact of extrusion speed, layer thickness, and nozzle temperature on the texture of xanthan gum-based 3D printed foods.
To develop a hybrid framework combining statistical design of experiments and machine learning for optimizing 3D food printing parameters.
To identify optimal printing conditions for achieving desired food textures.

Main Methods:

A 3^3 full-factorial design was used to test 27 parameter combinations on an extrusion-based food printer.

Keywords:

3D printing design of experiments food texture machine learning process optimization xanthan gum

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Texture Profile Analysis (TPA) was employed to measure hardness, cohesiveness, and chewiness.

Analysis of Variance (ANOVA) and six supervised machine learning models, including Random Forest (RF), were utilized for data analysis and prediction.

Main Results:

Extrusion speed significantly influenced hardness and chewiness, while layer thickness primarily affected cohesiveness.
Random Forest (RF) demonstrated the best predictive performance among the evaluated machine learning models.
Optimal parameters identified were 20 mm/s extrusion speed, 0.3 mm layer thickness, and 90°C nozzle temperature, with confirmation experiments showing good agreement.

Conclusions:

The developed ANOVA-ML workflow provides a systematic approach to tune extrusion parameters for consistent texture in xanthan gum 3D food printing.
This method reduces experimental effort and improves control over food texture within the studied process window.
The findings offer practical applications for optimizing 3D food printing processes for tailored food products.