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Bread staling prediction with a multiobjective particle swarm optimization-based bread constitutive modeling method.

Yusheng Zhang1, Hui Yu1, Haiyu Zhang1

  • 1China Agricultural University, College of Engineering, Beijing, China.

Journal of Texture Studies
|June 12, 2023
PubMed
Summary
This summary is machine-generated.

A new food constitutive modeling method using multiobjective particle swarm optimization (MOPSO) quickly detects bread staling. This approach efficiently identifies creep test parameters and predicts viscoelastic parameters for improved bread quality monitoring.

Keywords:
airflow-laserbread stalingcreepgeneralized kelvin modelsmultiple objectivesparticle swarm optimization

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Area of Science:

  • Food Science and Technology
  • Materials Science
  • Computational Intelligence

Background:

  • Current bread staling detection methods are complex and inefficient.
  • Accurate characterization of bread's viscoelastic properties is crucial for monitoring staling.
  • Optimization algorithms are needed to handle complex food models.

Purpose of the Study:

  • To develop a rapid and efficient food constitutive modeling method for bread staling detection.
  • To utilize multiobjective particle swarm optimization (MOPSO) for identifying creep test parameters.
  • To predict bread staling using viscoelastic parameters and moisture content.

Main Methods:

  • Airflow-laser detection technology for non-destructive rheological testing.
  • Multiobjective particle swarm optimization (MOPSO) to identify the generalized Kelvin model.
  • Extreme learning machine regression (ELM) for predicting moisture content and staling.

Main Results:

  • MOPSO demonstrated superior global search ability compared to FEA and NLR, avoiding local optima.
  • The correlation coefficient (R) reached 0.847, with a root mean square error (RMSE) of 0.021 for moisture content prediction.
  • Airflow-laser detection combined with MOPSO effectively identified bread's viscoelastic parameters for staling monitoring.

Conclusions:

  • The proposed MOPSO-based method offers a convenient and efficient approach for detecting bread staling.
  • This technique is suitable for analyzing high-dimensional viscoelastic models of complex foods in industrial settings.
  • The study provides a valuable reference for identifying viscoelastic parameters and monitoring staling in various food products.