Machine learning for food colloids: Novel tools to advance fundamental understanding, stability, texture, and processability
Kelvin Wong, Társila Rodrigues Arruda, Keith T. Butler, Stefan Guldin, Stephen Schrettl
Abstract
Evolving demands for healthier and more sustainable foods require reformulating ingredients and innovating production processes while maintaining sensory quality and shelf-life. Because traditional physics-based models struggle with the multi-scale complexity of food colloids, machine learning (ML) has emerged as a powerful alternative for predicting the behavior of these systems, in which dispersed components critically shape texture and functionality. This article highlights recent ML applications to enhance colloidal stability and rheological properties, demonstrating how supervised and unsupervised algorithms can capture complex, nonlinear relationships. Key examples include neural networks and chemometric models that predict emulsion stability, monitor microstructures, and forecast gel strength. We further discuss how ML-driven approaches reduce time-consuming experimental work and accelerate product innovation. Looking ahead, future opportunities lie in leveraging larger datasets, adopting inverse design strategies, and implementing insights from adjacent fields to deliver the next generation of data-informed, functional food colloids.