Department of Food Science and Agricultural Chemistry, McGill University, Montreal, QC, Canada.
Department of Food Science and Agricultural Chemistry, McGill University, Montreal, QC, Canada.
Adv Food Nutr Res. 2024;111:35-70. doi: 10.1016/bs.afnr.2024.06.009. Epub 2024 Jun 22.
Integration of machine learning (ML) technologies into the realm of smart food safety represents a rapidly evolving field with significant potential to transform the management and assurance of food quality and safety. This chapter will discuss the capabilities of ML across different segments of the food supply chain, encompassing pre-harvest agricultural activities to post-harvest processes and delivery to the consumers. Three specific examples of applying cutting-edge ML to advance food science are detailed in this chapter, including its use to improve beer flavor, using natural language processing to predict food safety incidents, and leveraging social media to detect foodborne disease outbreaks. Despite advances in both theory and practice, application of ML to smart food safety still suffers from issues such as data availability, model reliability, and transparency. Solving these problems can help realize the full potential of ML in food safety. Development of ML in smart food safety is also driven by social and industry impacts. The improvement and implementation of legal policies brings both opportunities and challenges. The future of smart food safety lies in the strategic implementation of ML technologies, navigating social and industry impacts, and adapting to regulatory changes in the AI era.
将机器学习 (ML) 技术融入智能食品安全领域代表了一个快速发展的领域，具有极大的潜力来改变食品质量和安全的管理和保障。本章将讨论 ML 在食品供应链不同环节的应用能力，涵盖从收获前的农业活动到收获后的处理以及到消费者的交付。本章详细介绍了将最先进的 ML 应用于推进食品科学的三个具体示例，包括利用它来改善啤酒风味、使用自然语言处理预测食品安全事件以及利用社交媒体检测食源性疾病爆发。尽管在理论和实践方面都取得了进展，但 ML 在智能食品安全中的应用仍然存在数据可用性、模型可靠性和透明度等问题。解决这些问题有助于充分发挥 ML 在食品安全中的潜力。智能食品安全中 ML 的发展也受到社会和行业影响的推动。改进和实施法律政策既带来了机遇，也带来了挑战。智能食品安全的未来在于战略性地实施 ML 技术，应对社会和行业的影响，并适应人工智能时代的监管变化。
