Abstract：
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.
摘要：
将机器学习（ML）技术集成到智能食品安全领域代表了一个快速发展的领域，具有改变食品质量和安全管理和保证的巨大潜力。本章将讨论机器学习在食品供应链不同部分的能力，包括收获前的农业活动，收获后的过程和交付给消费者。本章详细介绍了应用尖端ML推进食品科学的三个具体示例，包括它用来改善啤酒的味道，使用自然语言处理来预测食品安全事件，并利用社交媒体来检测食源性疾病的爆发。尽管理论和实践都取得了进展，机器学习在智能食品安全中的应用仍然存在数据可用性等问题，模型可靠性，和透明度。解决这些问题有助于充分发挥ML在食品安全方面的潜力。ML在智能食品安全中的发展也受到社会和行业影响的推动。法律政策的完善和实施带来了机遇和挑战。智能食品安全的未来在于ML技术的战略实施，导航社会和行业影响，适应人工智能时代的监管变化。
