通过尖端机器学习技术预测包装化学物质迁移到食品和水中
Behzad Vaferi1,2, Mohsen Dehbashi3, Reza Yousefzadeh4
1Department of Chemical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran. behzad.vaferi@gmail.com.
Scientific reports
|March 6, 2025
概括
人工智能 (AI) 模型预测化学物质从包装到食品的迁移. 梯度增强回归器 (GBR) 在这个关键的食品安全应用中显示出卓越的准确性.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 计算毒理学计算毒理学
背景情况:
- 从包装中迁移到食品和水中的化学物质对健康构成风险.
- 化学迁移的实验测量是昂贵和耗时的.
- 人工智能 (AI) 为预测化学迁移提供了一个有希望的替代方案.
研究的目的:
- 开发和比较人工智能模型来预测包装材料的化学迁移.
- 确定模拟化学迁移的最有效的人工智能技术.
- 分析温度和化学性质等变量对迁移的影响.
主要方法:
- 使用了五种人工智能技术:长短期记忆 (LSTM),梯度增强回归器 (GBR),多层感知器 (MLP),随机森林 (RF) 和卷积神经网络 (CNN).
- 进行了相关性分析,以了解解释变量的化学迁移影响.
- 优化了每个AI模型的超参数和评估预测准确度.
- 使用杆算法检测异常值以确保模型可靠性.
主要成果:
- 梯度增强回归器 (GBR) 在评估的AI模型中表现出卓越的性能.
- GBR模型准确地预测了1847个实验数据集的高精度.
- GBR模型的关键性能指标包括0.06的MSE,0.15的MAE,0.24的RMSE,6.46%的RAE%和0.9961.1%的R系数.
- 相关性分析确定了影响化学迁移的关键因素.
结论:
- 人工智能,特别是GBR模型,提供了一种可靠和有效的方法来预测包装中的化学迁移.
- 开发的GBR模型可以帮助评估和减轻与化学物质迁移相关的健康风险.
- 这项研究验证了人工智能在食品安全和包装材料评估中的使用.
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