使用激光诱导分解光谱的泛化光谱对绝缘材料进行高性能识别
Junfei Nie1, Furong Chen1, Ting Luo1
1School of Electrical Engineering, Shaoyang University, Shaoyang, Hunan 422000, China. njf@hnsyu.edu.cn.
Analytical methods : advancing methods and applications
|August 21, 2025
概括
激光诱导分解光谱 (GSM-LIBS) 的新通用光谱方法可以改善绝缘材料的识别. 这种方法提高了回收和减少废物的光谱分析准确性.
科学领域:
- 材料科学
- 分析化学
- 光谱学
背景情况:
- 准确识别绝缘材料对于减少废物,控制污染和回收活动至关重要.
- 传统的方法通常在维度缩小过程中难以保留全面的光谱信息.
研究的目的:
- 引入和评估一种新的激光诱导分解光谱的通用光谱方法 (GSM-LIBS).
- 证明GSM-LIBS在材料识别的光谱分析中优于主要组件分析 (PCA).
主要方法:
- 通过整合多种光谱特征 (峰值强度,积分强度等) 开发的GSM-LIBS 保护全球和地方的光谱信息.
- 应用GSM-LIBS对七种绝缘材料进行分类.
- 使用机器学习模型验证的性能:k-近邻 (KNN),支持矢量机 (SVM) 和神经网络 (NN).
主要成果:
- GSM-LIBS有效地减少了光谱尺寸,同时保留了等离子体状态和元素度等关键信息.
- 分类准确度显著提高:KNN从0. 935升至0. 979,SVM从0. 965升至0. 996,NN从0. 984升至0. 994.
- 在所有测试的机器学习模型中,GSM-LIBS表现优于PCA-LIBS.
结论:
- GSM-LIBS提供了一个更有效的方法来减少光谱维度和特征提取材料识别.
- 该方法显著提高了绝缘材料分类的准确性,支持回收和废物管理.
- 在分析光谱学中GSM-LIBS具有广泛的适用性和有效性.
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