基于有条件约束的游戏理论和自适应集体学习的安全风险评估方法:适用于小麦粉和大米
Wanbao Sheng1, Huawei Jiang1, Zhen Yang1
1College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001 China.
Food research international (Ottawa, Ont.)
|March 1, 2025
概括
本研究引入了一种新的食品安全风险评估模型,使用游戏理论和自适应集体学习. 该模型准确预测复合风险值,增强食品安全控制和污染物早期预警系统.
科学领域:
- 食品科学 食品科学 食品科学
- 风险评估 风险评估
- 机器学习 机器学习
背景情况:
- 目前的食品安全风险评估方法在因素权重和适应性方面扎.
- 确保全面的食品安全需要强有力的风险控制和评估.
研究的目的:
- 开发一个先进的食品安全风险评估模型.
- 解决现有方法的局限性,例如不合理的重量分配和不良的适应性.
主要方法:
- 开发了一种新型的模型,将条件约束的游戏理论和自适应集体学习结合起来.
- 使用增强的拉格朗奇乘数的游戏理论确定了最佳权重系数.
- 一个具有强大的加权随机森林的自适应集体学习模型预测了复合风险值.
主要成果:
- 该模型显示出高预测准确度,数据集匹配率为0.996 (小麦粉) 和0.991 (大米).
- 该方法在不同食品数据集中显示出强大的概括能力.
- 通过确定的风险值,可以有效识别不合格的产品.
结论:
- 拟议的模型为食品安全风险评估提供了显著的改进.
- 它提供了一个可靠的工具,可以提前警告潜在的食品安全危害.
- 这种方法提高了食品安全评估的准确性和适应性.
更多相关视频
10:26Author Spotlight: Integrating Biochemical Functions of β-Glucanases and Peroxidase Enzymes in Wheat-RWA Interaction
Published on: July 26, 2024
576
08:56Detection of Regulated Ergot Alkaloids in Food Matrices by Liquid Chromatography-Trapped Ion Mobility Spectrometry-Time-of-Flight Mass Spectrometry
Published on: November 22, 2024
535
相关概念视频
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
01:28Methods of Medium Optimization
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...