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Plant-based protein extrusion optimization: Comparison between machine learning and conventional experimental design.

Yingfen Jiang1, Noor Irsyad Bin Noor Azlee1, Wing Shan Ko1

  • 1Food, Chemical and Biotechnology Cluster, Singapore Institute of Technology, 1 Punggol Coast Road, Singapore, 828608, Singapore.

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|August 12, 2025
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Summary

Bayesian Optimization (BO) efficiently optimizes high-moisture extrusion (HME) for plant-based meat, achieving better predictions with fewer trials than traditional Response Surface Methodology (RSM). This machine learning approach enhances the development of fibrous meat analogues.

Keywords:
Bayesian optimizationMachine learningPlant-based proteinsResponse surface methodologyTensile strengthTwin-screw extrusion

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Area of Science:

  • Food Science and Technology
  • Process Engineering
  • Machine Learning Applications

Background:

  • High-moisture extrusion (HME) is key for plant-based meat analogues, but process optimization is complex.
  • Traditional methods like Response Surface Methodology (RSM) are trial-intensive and have limited predictive power.
  • Bayesian Optimization (BO), a machine learning technique, offers efficient parameter space exploration for complex processes.

Purpose of the Study:

  • To compare the efficiency and predictive accuracy of BO versus RSM for optimizing HME parameters.
  • To identify optimal conditions for mechanical properties of chicken breast-like meat analogues.
  • To evaluate the impact of tensile strength as a key optimization property.

Main Methods:

  • Compared RSM and BO for optimizing twin-screw HME of plant-based meat analogues.
  • Varied barrel temperature, water content, and cooling die temperature.
  • Constrained BO to RSM's dataset for direct comparison, including tensile strength analysis.

Main Results:

  • BO converged on optimal parameters using fewer trials (10-11) compared to RSM (15 trials).
  • BO demonstrated superior predictive accuracy with lower error (≤24.5%) versus RSM (up to 61.0%).
  • Tensile strength improved model fitting and predictive accuracy for both methods.

Conclusions:

  • BO offers a more efficient and accurate approach for optimizing complex food processing like HME.
  • Machine learning techniques like BO hold significant potential for pilot-scale food system optimization.
  • Reduced experimental trials and enhanced prediction accuracy benefit the development of plant-based meat analogues.