Enhancing Food Safety in Supply Chains: The Potential Role of Large Language Models in Preventing Campylobacter Contamination

Foodborne diseases pose a significant global public health challenge, primarily driven by bacterial infections. Among these, Campylobacter spp. is notable, causing over 95 million cases annually. In response, the Hazard Analysis and Critical Control Points (HACCP) system, a food safety management framework, has been developed and is considered the most effective approach for systematically managing foodborne safety risks, including the prevention of bacterial contaminations, throughout the supply chain. Despite its efficacy, the adoption of HACCP is often incomplete across different sectors of the food industry. This limited implementation can be attributed to factors such as a lack of awareness, complex guidelines, confusing terminology, and insufficient training on the HACCP system's implementation. This study explores the potential of large language models (LLMs), specifically generative pre-trained transformers (GPTs), to mitigate Campylobacter contamination across four typical stages of the supply chain: primary production, food processing, distribution and retail, and preparation and consumption. While the interaction between LLMs and food safety presents a promising potential, it remains largely underexplored. To demonstrate the possible applications of LLMs in this domain, we further configure an open-access customized GPT trained on the FAO's HACCP toolbox and the 12 steps of HACCP implementation, and test it in the context of commercial food preparation. The study also considers critical barriers to implementing GPTs at each step of the supply chain and proposes initial measures to overcome these obstacles.