基于生成对抗网络和贝叶斯网络的全谷物食品风险的因果推断
Zhiyao Zhao1,2, Qian Wang1,2, Zhaoyang Wang1,2
1School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing, China.
Journal of food science
|January 20, 2025
概括
确定全谷物食品 (WGF) 中的风险至关重要. 本研究使用生成对抗网络 (GAN) 和贝叶斯网络 (BNs) 来揭示危险物质如何相互作用,改善WGF的安全性和风险评估.
科学领域:
- 食品安全分析 食品安全分析
- 计算毒理学计算毒理学
- 饮食风险评估 饮食风险评估
背景情况:
- 全谷物食品 (WGF) 是饮食的主食,需要强有力的风险识别.
- 以前的研究往往忽视了不同危险物质和基本食品信息之间的复杂相互作用.
- 在理解影响WGF安全的因果关系方面存在差距.
研究的目的:
- 为WGF风险评估开发一种新的因果推断模型.
- 调查危险物质和WGF中的基本信息之间的相互影响.
- 改进WGF采样数据的归算,并确定WGF风险的根本原因.
主要方法:
- 使用生成对抗网络 (GAN) 来进行数据归算.
- 使用贝叶斯网络 (BN) 进行因果推断和风险分类.
- 将GAN性能与传统数据归算技术进行比较.
主要成果:
- 与传统方法相比,拟议的GAN实现了至少13.65%的根平均平方误差 (RMSE) 的显著降低.
- 贝叶斯网络 (BN) 模型显示了高分类准确度,达到91%.
- 确定了特定的省份,时间段,食品类别和危险物质,这些物质有助于WGF风险,特别是真菌毒素,化合物 (MaC) 和.
结论:
- 在WGF数据中,GAN-BN方法有效地建模了复杂的因果关系.
- 这种方法提高了食品安全分析中缺少数据归算的准确性.
- 这些发现通过确定关键的因果因素,为减轻WGF风险提供了可操作的见解.
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