Machine Learning in Automated Food Processing: A Mini Review

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.
• 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.