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Related Experiment Videos

Machine Learning in Automated Food Processing: A Mini Review.

Lu Zhang1, Remko M Boom1, Yizhou Ma1

  • 1Laboratory of Food Process Engineering, Wageningen University & Research, Wageningen, The Netherlands;

Annual Review of Food Science and Technology
|April 28, 2025
PubMed
Summary
This summary is machine-generated.

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Automated food processing uses machine learning to improve sustainability and productivity. Future research needs multidisciplinary approaches for wider adoption of these advanced food technologies.

Area of Science:

  • Food Science and Technology
  • Artificial Intelligence in Food Processing
  • Sustainable Food Systems

Background:

  • Industrial food processing is increasingly adopting automation and digitalization.
  • Automated systems offer adaptability to raw material variations and quality demands.
  • Current adoption rates for automated food processing systems remain low.

Purpose of the Study:

  • To review the concept of automated food processing.
  • To summarize advances in machine learning applications for food automation.
  • To explore the future potential of automated food processing.

Main Methods:

  • Review of recent literature on machine learning in food processing.
  • Analysis of machine learning applications in formulation, process control, and quality assessment.
Keywords:
Industry 4.0automationdata-driven approachfood personalizationmachine learningsmart food manufacturing

Related Experiment Videos

  • Discussion of future trends including complex raw materials, mass customization, personalized nutrition, and human-machine interaction.

    • Main Results:

    • Machine learning is crucial for enabling automated food processing.
    • Key applications include formulation development, real-time process control, and product quality assessment.
    • Automated systems show potential for enhancing sustainability and productivity.

    Conclusions:

    • Automated food processing, powered by machine learning, can improve food system sustainability.
    • Future advancements require addressing challenges in adapting to complex materials and enabling mass customization and personalized nutrition.
    • Multidisciplinary research is essential for advancing automated food processing technologies.