一种化学测量方法用于FTIR-Raman-LIBS三模式光谱融合:基于变压器的可回收聚合物的精确识别
Wenxia Xu1, Zhuoqing Fu1, Bo Tang1
1School of Mathematics and Computing Science, Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guilin University of Electronic Technology, Guilin, 541002, PR China; Center for Applied Mathematics of Guangxi (GUET), Guilin, 541002, PR China.
Analytica chimica acta
|October 10, 2025
概括
这项研究引入了一种使用Transformer深度学习和遗传算法进行准确的塑料聚合物分类的新型化学测量方法. 这种方法显著提高了对可持续循环经济的分类准确性.
科学领域:
- 分析化学 分析化学
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
背景情况:
- 塑料废物管理对于循环经济至关重要,但传统的分类方法效率低下,容易出现错误.
- 光谱技术和深度学习为聚合物识别和分类提供了先进的解决方案.
- 为光谱数据分析选择最佳的深度学习模型仍然是一个挑战.
研究的目的:
- 开发一种创新的化学测量方法,用于高精度分类可回收塑料聚合物.
- 首次将Transformer深度学习架构应用于多种光谱模式的融合分析.
- 使用遗传算法优化变压器的超参数,以提高模型的稳定性.
主要方法:
- 使用了富里埃变换红外 (FTIR),拉曼和激光诱导分解光谱 (LIBS) 数据.
- 应用了Transformer深度学习架构用于跨模式分析和光谱数据生成.
- 实现了一个基因算法,用于变压器模型的超参数优化.
- 采用跨模式数据增强来解决样本不平衡.
主要成果:
- 交叉模式数据增强使分类准确度提高了3.84%.
- 多模式融合实现了97.69%的准确率,表现比单模式模型高9.74%.
- 优化的模型在独立的测试组中超过了99.23%的准确性.
结论:
- 拟议的方法为可回收塑料分类中的光谱分析提供了一个实际的范式.
- 这种方法促进了分析化学在可持续技术中的应用.
- 开发的智能工具可扩展到其他领域的光谱分析.
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