一种基于关联规则采矿和GNN的食品安全向抽样决策方法
Jiabin Yu1, Xinyue Ma1, Xin Zhang1
1School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing, China.
NPJ science of food
|July 9, 2025
概括
本研究介绍了一种针对食品安全采样方法,使用关联分析和图形神经网络来改善决策. 该方法提高了采样频率和危险物质测序,以更好地监督食品安全.
科学领域:
- 食品科学 食品科学 食品科学
- 数据科学数据科学数据科学
- 风险管理 风险管理
背景情况:
- 传统的食品安全采样方法往往缺乏客观性和精确的目标.
- 抽样中的主观性可能导致资源分配效率低下,风险识别效率降低.
研究的目的:
- 为食品安全制定一个有针对性的抽样决策方法.
- 解决当前抽样实践中主观性和低目标性的局限性.
- 提高食品安全监测的效率和准确性.
主要方法:
- 使用关联分析构建了一个食品决策因素推理模块.
- 采用了改进的频繁模式增长算法来挖掘食物因子关联规则.
- 开发了一个决策支持模块,使用图形神经网络进行采样频率.
- 应用了CRITIC-TOPSIS方法来确定危险物质的采样顺序.
主要成果:
- 为采样频率和危险物质顺序生成决策结果.
- 验证了使用全国和省级数据对加工谷物产品 (2020-2022) 的方法.
- 证明了拟议的有针对性的抽样方法的广泛适用性和有效性.
结论:
- 拟议的有针对性的抽样方法显著提高了食品安全的客观性和向性.
- 关联分析,图形神经网络和CRITIC-TOPSIS的整合为抽样决策提供了一个强大的框架.
- 这种方法为加强食品安全监督和风险管理提供了有价值的工具.
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