Large language models in food science: Innovations, applications, and future

Abstract

Background

Large Language Models (LLMs) are increasingly significant in food science, transforming areas such as recipe development, nutritional analysis, food safety, and supply chain management. These models bring sophisticated decision-making, predictive analytics, and natural language processing capabilities to various aspects of food science.

Scope and approach

The review focuses on the application of LLMs in enhancing food science, with a strong emphasis on food safety, especially in contaminant detection and risk assessment. It addresses the roles of AI and LLMs in regulatory compliance and food quality control. Challenges like data biases, misinformation risks, and implementation hurdles, including data limitations and ethical concerns, are discussed. The necessity for interdisciplinary collaboration to overcome these challenges is also highlighted.

Key findings and conclusions

LLMs hold significant potential in automating processes and improving accuracy and efficiency in the global food system. Successful implementation requires continuous updates and ethical considerations. The paper provides insights for academics, industry professionals, and policymakers on the impact of LLMs in food science, emphasizing the importance of interdisciplinary efforts in this domain. Despite potential challenges, the integration of LLMs in food science promises transformative advancements.