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基于张量产品的二维相关数据预处理方法,用于中国手工造纸的拉曼光谱

Chunsheng Yan¹, Si Luo², Linquan Cao³

¹Zhejiang University Library, Hangzhou 310058, China; State Key Laboratory of Modern Optical Instrumentation, Hangzhou 310058, China.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|June 25, 2023

概括

两种新方法,交叉相关性 (CCM) 和二维相关性 (TDCM),显著提高了中国手工造纸的拉曼光谱分析. TDCM预处理,特别是TDACM,大大提高了机器学习模型的准确性,在KNN和RF模型中接近100%.

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
化学测量化学测量化学测量

背景情况:

拉曼光谱对于分析中国手工造纸等材料至关重要.
预处理原始光谱数据对于准确的分析和机器学习模型性能至关重要.
现有的方法可能无法完全捕捉复杂样本分析的微妙光谱变化.

研究的目的:

引入新的预处理方法:用于拉曼光谱的交叉相关性 (CCM) 和二维相关性 (TDCM).
评估CCM,TDCM (包括TDSCM和TDACM) 的有效性,以及对机器学习模型准确性的基线删除.
使用不同的预处理技术,比较无监督 (PCA) 和监督 (SVM-LR,KNN,RF) 模型的性能.

主要方法:

开发并应用交叉关联方法 (CCM) 来扩大光谱维度.
实施二维相关联方法 (TDCM),包括同步 (TDSCM) 和异步 (TDACM),以创建N×N光谱矩阵.
利用四种机器学习模型 (PCA-LR,SVM-LR,KNN,RF) 来分析中国手工造纸的预处理拉曼光谱.

主要成果:

主要组件分析 (PCA) 模型在所有数据类型中显示了近乎完美的R平方值.
对于监督模型 (SVM-LR,KNN,RF),R平方值连续改进:原始数据<基线删除
两维异步关联方法 (TDACM) 的预处理导致KNN和射频模型的R平方值接近1 ,几乎提高了100%的准确性.

结论:

CCM和TDCM是拉曼光谱数据分析的有效预处理技术.
TDACM预处理显著提高了监督机器学习模型 (KNN,RF) 对中国手工纸张分析的准确性.
监督模型的增强精度接近无监督PCA的精度,为光谱数据解释提供了强大的方法.

关键词:

数据预处理数据的预处理.机器学习机器学习拉曼光谱法拉曼光谱法张量器的产物是张量器的产物.两个维的相关性对应.

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