This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Scientists from Leeds' School of Food Science and Nutrition have developed an advanced AI process that can rapidly identify plant proteins capable of acting as emulsifiers from tens of millions of initial candidates. To date, it has identified nearly 800.
The discovery will cut years of costly trial-and-error research and bring the next generation of plant-based foods and cosmetics closer. Emulsifiers are essential for binding oil and water into stable, homogeneous mixtures in food, cosmetics, pharmaceuticals and industrial applications. Common uses include lotions, medicinal creams, sauces, ice cream, mayonnaise and paints.
There is increasing interest in creating natural, sustainable alternatives to high-carbon-footprint synthetic or animal-derived emulsifiers. The research, published in Communications Chemistry, was led by postdoctoral researcher Dr. Simha Sridharan and supervised by Professor Anwesha Sarkar, both of the university's Sarkar Lab.
They worked alongside AI researchers at Leeds' School of Food Science and Nutrition and in close collaboration with Dr. Rik Sarkar, a machine learning expert at the University of Edinburgh. Sridharan said, "As we want to shift toward more sustainable, plant-based ingredients, scientists face a major challenge: There are millions of potential plant proteins, but testing them all to identify the right emulsifier is expensive and involves a time-consuming trial-and-error approach.
Until now, there has been no reliable way to predict which plant proteins are likely to behave as emulsifiers like animal proteins." There is growing consumer interest in natural emulsifiers in place of commonly used animal-based emulsifiers, such as milk proteins like caseins or whey. This new tool could help identify new possibilities, reduce years of testing and accelerate the transition toward sustainable, plant-based food systems. Sridharan and Sarkar used a simulation model to understand how proteins attach between oil and water mixtures, which is crucial for them to act as emulsifiers.
In collaboration with Rik Sarkar, they applied machine learning to fingerprint specific segments of the protein that dictate their attachment behavior. By combining machine learning and statistical physics, the team was able to screen plant proteins to identify those that would resemble the emulsification performance of animal proteins in a fraction of the time needed for conventional experiments. Rik Sarkar said, "Emulsifiers often have a characteristic chemical structure called diblocks.
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