基于FTIR光谱学结合机器学习的初始和回收聚烯的识别
Zi-Wen Dong1, Xue-Chao Song2, Qin-Bao Lin1
1College of Packaging Engineering, Jinan University, Zhuhai, China.
概括
这项研究引入了一种使用富里埃变换红外光谱法 (FTIR) 和机器学习来识别食品包装中的回收聚烯 (PP) 的新方法. 这有助于通过检测退化的材料来确保消费者的安全.
科学领域:
- 聚合物科学 聚合物科学
- 分析化学是一种分析化学.
- 食品安全科学 食品安全科学
背景情况:
- 聚烯 (PP) 的回收对于循环经济至关重要,特别是食品包装.
- 在回收过程中降解可能导致回收PP的化学泄露,造成健康风险.
- 区分回收和原始PP对于确保食品包装安全至关重要.
研究的目的:
- 开发一种新的方法来区分原始和回收聚烯 (PP).
- 评估与食品包装中的回收 PP 化学液相关的潜在人类健康风险.
- 确保食品包装材料的安全性和完整性.
主要方法:
- 使用富里埃变换红外光谱法 (FTIR) 进行材料分析.
- 应用数据处理技术,包括第一阶导数,主要组件分析 (PCA) 和正交部分最小平方差分分析 (OPLS-DA).
- 开发和评估机器学习模型,特别是随机森林 (RF) 模型,用于分类.
主要成果:
- 在使用经过处理的FTIR数据 (一级衍生品,PCA,OPLS-DA) 实现了对原始和回收 PP 的最佳区分.
- 随机森林 (RF) 模型表现出高性能,在训练组中准确率为98.6%,在测试组中准确率为86.1%.
- 曲线下的面积 (AUC) 达到0.963,表明可靠的歧视能力.
结论:
- 结合FTIR和机器学习的方法可靠地区分原始和回收 PP.
- 这种方法对于确保食品包装应用中回收 PP 的安全性至关重要.
- 该研究为回收塑料行业的质量控制提供了强有力的工具.
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