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Composition Descriptors and Cultivar Transferability in Machine-Learning Models of Ultrasonication-Induced Functional

Hyeonbin Oh1,2, Jung-Hyun Nam1, Bo-Ram Park1

  • 1Department of Food Sciences, National Institute of Crop and Food Science, Rural Development Administration, Wanju-gun 55365, Jeollabuk-do, Republic of Korea.

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Ultrasonication of rice flour affects its properties, with cultivar composition predicting outcomes better than process conditions alone. This finding aids in selecting rice for plant-based foods.

Keywords:
SHAPcultivar variationgelatinized rice slurrymachine learningtransferabilityultrasonication

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Area of Science:

  • Food Science and Technology
  • Materials Science
  • Agricultural Science

Background:

  • Flow-cell ultrasonication impacts pregelatinized rice flour properties like water solubility, viscosity, and retrogradation.
  • These properties are crucial for developing plant-based beverages and convenience foods.
  • Cultivar-specific composition influences rice flour's response to processing.

Purpose of the Study:

  • To determine if rice cultivar composition descriptors (amylose, protein, fiber) can predict ultrasonication responses.
  • To differentiate between process-only predictions and cultivar-associated variations.
  • To assess the transferability of models to unseen rice cultivars.

Main Methods:

  • Six rice cultivars were processed using flow-cell ultrasonication across various amplitude-time settings and slurry concentrations.
  • Water solubility index, apparent viscosity, and setback viscosity were modeled using machine learning algorithms (ElasticNet, PLS, SVR, RF, XGBoost).
  • Model performance was evaluated using nested cross-validation and leave-one-cultivar-out transfer, with input formulations including process variables, composition descriptors, and cultivar identity.

Main Results:

  • Process variables alone provided insufficient predictive power for rice flour properties.
  • Incorporating composition descriptors significantly improved model performance compared to process-only models.
  • Nonlinear algorithms showed comparable performance between composition descriptors and cultivar identity within the tested domain.
  • Leave-one-cultivar-out transfer validation for composition-based models remained uncertain for predicting unseen cultivars.

Conclusions:

  • Cultivar composition descriptors are valuable for predicting ultrasonication effects on rice flour, outperforming process variables alone.
  • While cultivar identity and composition descriptors perform similarly within a known cultivar set, composition offers potential for broader application.
  • Replacing categorical cultivar identifiers with continuous composition descriptors requires rigorous transfer validation for reliable prediction in new cultivars.