改进的3D异步相关数据预处理方法用于中国手工造纸的拉曼光谱
Chunsheng Yan1, Zhongyi Cheng2, Linquan Cao3
1Zhejiang University Library, Hangzhou, 310058, China; State Key Laboratory of Extreme Photonics and Instrumentation, Hangzhou 310058, China.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|January 14, 2024
概括
一种新的3D异步关联方法 (3D-ACM) 增强了拉曼光谱分析,用于分类中国手工造纸. 这种技术显著提高了光谱分辨率和机器学习模型的性能,用于材料识别.
科学领域:
- 材料科学 材料科学 材料科学
- 频谱学是一种光谱学.
- 数据科学数据科学数据科学
背景情况:
- 对像中国手工纸这样的材料进行准确的分类至关重要.
- 传统的光谱分析方法可能缺乏足够的分辨率和隐藏信息提取能力.
研究的目的:
- 引入和评估一种新的3D异步相关方法 (3D-ACM),用于增强材料分类.
- 评估3D-ACM在与各种用于光谱数据分析的机器学习模型相结合时的性能.
主要方法:
- 在拉曼光谱数据上开发了一个涉及张量积和希尔伯特变换运算的3D-ACM.
- 在预处理的数据中应用了六种机器学习模型 (PCA-LR,SVM-LR,KNN,RF,CNN).
- 使用R平方值评估模型性能.
主要成果:
- 3D-ACM显著增加了光谱分辨率,并揭示了隐藏的信息.
- 机器学习模型 (PLS-LR,KN,RF,CNN) 在使用3D-ACM时实现了接近或等于1的R平方值.
- 性能与PCA等无监督方法相提并论.
结论:
- 3D-ACM是一种用于光谱数据预处理的多功能数学技术.
- 它为材料分类和识别提供了卓越的性能,无需额外的实验设置.
- 3D-ACM对材料科学和数据分析的未来应用有着显著的前景.
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