Optimising Cheese Analogues Formulation using AI Applications

The production of cheese analogues, that are plant-based or hybrid alternatives to dairy cheese, is a field of food product development that has been growing in recent years. This development has been mostly driven by sustainability concerns, rising demand, and the need to deliver the sensory experience consumers expect (for e.g., stretch, melt, and texture close to dairy cheese). Yet achieving these properties while maintaining nutritional value remains a significant formulation challenge, and the field has seen little application of artificial intelligence or machine learning to guide that process.

This project set out to add to this niche. The goal was to build an AI-driven framework that could systematically optimise the formulation of cheese analogues, by significantly reducing the trial-and-error, time consumption aspects and cost that typically characterises food research and development (R&D). Rather than testing every possible ingredient combination by hand, the framework uses two machine learning tools: Computer Vision (CV), which automatically measures functional properties like stretch from video footage in a non-destructive and reproducible way, and Bayesian Optimisation (BO), which intelligently suggests the next formulation to test based on what has already been learned via the results acquired from the CV model.

As a proof of concept, the framework was applied to optimising the stretchability of a protein blend used in cheese analogue formulation. The results demonstrate that meaningful practical insight can be extracted from a small number of experiments. The framework is designed to extend beyond stretch to other properties such as meltability and texture, offering a scalable approach to data-driven food product development.