New Review Maps AI Use Across the Alternative Protein Value Chain

A review published in Trends in Food Science & Technology examines the use of artificial intelligence (AI) in the alternative protein value chain. The review concludes that AI is most effective when used to guide specific decisions rather than automate the entire development process. Alternative proteins are complex food systems with interactions between different components, making their development challenging. AI can help prioritize ingredients, annotate protein structures, infer process states, optimize constraints, generate consumer insights, and more, but progress is uneven across different protein categories.

The review highlights fragmented data, weak standardization, limited transferability, and poor integration with manufacturing and regulatory requirements as barriers to wider deployment of AI in the alternative protein space. Experimental validation is crucial for translating AI outputs into usable products, and the authors call for stronger data infrastructure and human-validated workflows. Future progress in AI application in alternative proteins will depend on closer alignment with food-relevant endpoints such as texture, flavor, safety, nutrition, and consumer acceptance to ensure scalable and reliable use in the food industry.