Machine Learning in Automated Food Processing: A Mini Review
Lu Zhang, Remko M. Boom, Yizhou Ma
Abstract
Industrial food processing is rapidly transforming into automation and digitalization. Automated food processing systems adapt to variations in raw materials and product quality requirements. Implementing automated processing systems can potentially improve the sustainability of our food systems by improving productivity while reducing environmental impacts. Nevertheless, the adoption of automated food processing systems is still relatively low. In this review, we discuss the concept of automated food processing and summarize the recent advances in applications of machine learning technologies to enable automated food processing. Machine learning can find its applications in formulation development, process control, and product quality assessment. We share our vision on the potential of automated food processing systems to adapt to complex raw materials, mass customization, personalized nutrition, and human-machine interaction. Finally, we pinpoint relevant research questions and stress that future research on automated food processing requires multidisciplinary approaches.