基于机器学习的塑料类型的识别,使用手持式光谱仪
Hedde van Hoorn1, Fahimeh Pourmohammadi1, Arie-Willem de Leeuw2
1Photonics Research Group, The Hague University of Applied Sciences, 2628 AL Delft, The Netherlands.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
手持式光谱设备为塑料识别提供了低成本的解决方案,这对于回收工作至关重要. 虽然SpectraPod的准确性低于基板系统,但SpectraPod的准确性达到了93%,显示出对现场应用的希望.
科学领域:
- 分析化学 分析化学
- 材料科学 材料科学 材料科学
- 环境科学 环境科学
背景情况:
- 全球塑料垃圾需要改进的回收基础设施.
- 高端的光谱成像系统并非在所有地区都是可行的.
- 低成本的手持式光谱设备可以在各种环境中实现塑料识别.
研究的目的:
- 评估两个用于塑料识别的手持红外光谱设备的准确性.
- 将手持设备与高分辨率的基准光谱方法进行比较.
- 确定手持塑料识别技术的局限性并提出改进建议.
主要方法:
- 研究了两个手持设备:塑料扫描仪和SpectraPod.
- 将手持设备与高分辨率的桌面光谱系统进行了比较.
- 在各种塑料样品 (PET,HDPE,PVC,LDPE,PP,PS) 上使用机器学习模型 (SVM,XGBoost,Random Forest,Gaussian Naïve Bayes).
主要成果:
- 高分辨率光谱检测的准确率达到了97%.
- SpectraPod获得了93%的准确性,而塑料扫描仪获得了70%的准确性.
- 手持设备的准确性受到光谱范围和灵敏度的限制.
结论:
- 手持式光谱设备显示出塑料识别的潜力,特别是SpectraPod.
- 结合这两种设备的功能,可以提高手持设备的准确性.
- 需要进一步发展,以克服塑料回收广泛采用的局限性.
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