机器学习驱动的食品安全智能转型:从多源风险识别到整个链的预防性控制
Luyao Zhang1, Zhenzhen Qin1, Bufan Xu1
1School of Food Science and Engineering, Shaanxi University of Science and Technology, Xi'an, China.
Critical reviews in food science and nutrition
|December 19, 2025
概括
机器学习 (ML) 通过改进对污染和化学物质迁移等风险的数据分析来提高食品安全. 这项技术为复杂的全球食品供应链提供了高效,准确的解决方案,尽管数据和实施方面的挑战仍然存在.
科学领域:
- 食品科学与技术 食品科学与技术
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 风险管理 风险管理
背景情况:
- 全球化的食品供应链带来了复杂的,不断变化的食品安全风险.
- 传统的风险管理方法难以处理大量数据集,实时分析和多因素相关性.
- 效率和适应性的局限性阻碍了对现代食品安全挑战的有效管理.
研究的目的:
- 审查机器学习 (ML) 在应对食品安全风险方面的应用.
- 探索ML在食品真实性,毒性预测,化学物质迁移和供应链风险控制方面的作用.
- 确定增强基于ML的食品安全框架和未来发展方向的战略.
主要方法:
- 在关键的食品安全领域对机器学习应用程序进行系统审查.
- 整合异质数据 (化学,生物,传感器) 用于多源风险识别.
- 专注于优化数据质量,模型架构和闭环实验验证.
主要成果:
- 机器学习显示出有效和准确的食品安全管理的巨大潜力.
- 机器学习通过整合多种数据源,促进了全面的检测和预测.
- 优化的数据和模型提高了基于ML的风险评估的准确性和可靠性.
结论:
- 机器学习提供了一种数据驱动的方法来克服传统食品安全管理的局限性.
- 挑战包括数据可用性/质量,模型通用性,计算需求和成本.
- 未来的进步需要技术创新,跨学科的合作,以及适应性食品安全系统的标准化框架.
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