Machine Learning in Automated Food Processing

This project reviews recent advances in machine learning for enabling automated and intelligent food processing systems. Automated processing aims to adapt operations to variability in raw materials, changing product specifications, and sustainability targets, while maintaining consistent quality. The review summarizes applications of machine learning across formulation development, process monitoring and control, and product quality assessment. Examples include vision-based inspection, predictive process models, and data-driven optimization of processing conditions. Beyond summarizing current technologies, the work outlines future opportunities for adaptive processing, mass customization, personalized nutrition, and human–machine interaction. A key message is that successful automated food processing requires multidisciplinary integration of food science, control engineering, data science, and materials science. The project identifies open research questions and provides a conceptual roadmap toward more resilient and flexible processing systems.