通过塑料分类的插曲来融合NIR和LIBS光谱
Lei Yang1, Ao Liu1, Junjie Wang1
1Anhui Provincial Key Laboratory of Measuring Theory and Precision Instrument, Anhui Provincial Engineering Research Center of Semiconductor Inspection Technology and Instrument, School of Instrument Science and Optoelectronics Engineering, Hefei University of Technology, Hefei, 230009, Anhui, China.
Talanta
|October 30, 2025
概括
这项研究通过融合激光诱导分解光谱 (LIBS) 和近红外光谱 (NIR) 数据来增强塑料回收. 一种新的插值方法在分类塑料类型和颜色方面达到99.2%的准确性.
科学领域:
- 频谱学是一种光谱学.
- 材料科学 材料科学 材料科学
- 环境科学 环境科学
背景情况:
- 塑料污染需要通过精确的材料分类来有效回收.
- 激光诱导分解光谱 (LIBS) 和近红外光谱 (NIR) 的结合为塑料识别提供了补充数据.
- LIBS和NIR之间的数据尺度不匹配限制了多式联络融合的有效性.
研究的目的:
- 为准确的塑料分类制定有效的多式联运数据融合战略.
- 为了解决LIBS和NIR之间的数据尺度不匹配,使用立方线线插值.
- 实施塑料类型和颜色的双重分类策略,包括黑色塑料.
主要方法:
- 使用立方线插值重新采样NIR光谱,将其数据尺度与LIBS对齐.
- 中级数据融合纳入预测中的变量重要性 (VIP) 和特征选择的变量重要性 (VI).
- 在融合特征上使用长短期记忆 (LSTM) 和卷积神经网络 (CNN) 模型进行分类.
主要成果:
- 改进的中级数据融合策略实现了99.2%的预测准确度.
- 这种融合方法显著优于单模LIBS (19.29%的改进) 和NIR (4.76%的改进) 模型.
- 该战略成功地区分了塑料类型和颜色,包括具有挑战性的黑色塑料.
结论:
- 立方线间波有效地解决了多模式光谱分析中的数据尺度不匹配问题.
- 拟议的中级聚变方法为塑料分类提供了强大的,高度准确的方法.
- 这种先进的光谱聚变技术在提高塑料回收效率方面具有重大前景.
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