基于灰色相关性分析的调料风险预测研究 - 深度神经网络
Miao Zhang1, Yiran Wan2, Haiyang He2
1Chongqing Yongchuan District Center for Disease Control and Prevention, No. 471, Huilong Avenue, Yongchuan District, Chongqing, China.
Journal of food protection
|November 28, 2024
概括
这项研究使用机器学习开发了一种食品安全风险模型,用于使用大豆和等调味品. 深度神经网络模型准确地预测了调味剂风险水平,帮助监管策略.
科学领域:
- 食品科学 食品科学 食品科学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 食品安全对公共健康至关重要.
- 调味品的安全性对消费者的幸福感产生了重大影响.
- 评估调味品风险需要强大的方法.
研究的目的:
- 为调味品建立一个全面的风险评估模型.
- 利用灰色相关性分析和机器学习进行风险预测.
- 为食品安全控制战略提供数据驱动的基础.
主要方法:
- 灰色相关性分析用于指标权重和风险价值的制定.
- 机器学习模型 (DNN,RF,XGBoost) 用于全面的风险价值预测.
- 模糊合成分析用于风险级别分类.
主要成果:
- 分析了282个大豆和704个样本.
- 深度神经网络 (DNN) 模型展示了最佳的预测性能.
- 基于检测指数,DNN模型准确预测了基于检测指数的综合风险值和风险水平.
结论:
- 开发的模型有效地评估了调料安全风险.
- 机器学习,特别是DNN,为食品安全风险预测提供了强大的工具.
- 这种方法支持监管机构在食品安全管理方面的知情决策.
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