以红外光谱为数据驱动的聚合物表征的框架
João G Neto1, Douglas A Simon2, Karla Figueiredo3
1Department of Chemical and Materials Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, 22451-900, RJ, Brazil.
概括
自动化红外光谱解释对于微塑料的识别至关重要. 这项研究开发了一个强大的聚合物识别框架,在聚烯检测中达到94.8%的准确性.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 材料科学 材料科学 材料科学
背景情况:
- 红外光谱的手动解释用于微塑料的识别是耗时且不太准确的,特别是对于复杂的样品.
- 经过气候变化和多元组件微塑料由于与参考标签的光谱偏差而存在挑战.
研究的目的:
- 开发一种自动化的参考建模框架,用于使用红外光谱处理识别聚合物.
- 解决目前微塑料识别方法的局限性,特别是对于复杂的环境样本.
主要方法:
- 开发和评估了308个模型,使用各种预处理和参数设置.
- 利用多层感知器和长期短期记忆神经网络架构.
- 专注于聚烯 (PP) 识别作为一个案例研究,使用 579 个光谱的数据库.
主要成果:
- 最好的模型在交叉验证标准偏差内实现了94.8%的测试准确性,用于在交叉验证标准偏差内识别聚烯.
- 证明了框架在处理光谱变化的稳定性.
结论:
- 开发的框架为微塑料识别提供了一个有前途的自动化解决方案.
- 该方法可以扩展到识别微塑料样本中的其他聚合物.
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