分析化学2016，Vol.44Issue(12)：1846-1851,6.DOI:10.11895/j.issn.0253-3820.160392
自适应拉曼光谱成像数据去噪及其在植物细胞壁光谱分析中的应用
Adaptive Method for Denoising Raman Spectral Imaging Data and Its Applications to Spectral Analysis in Plant Cell Walls
摘要
Abstract
Two inevitable noise signals, baseline drifts and cosmic spikes in Raman spectral imaging data should be eliminated before data analysis. However, current denoising methods for a single spectrum often lead to unstable results with bad reproducible properties. In this study, a novel adaptive method for denoising Raman spectral imaging data was proposed to address this issue. Adaptive iteratively reweighted penalized least-squares (airPLS) and principal component analysis (PCA) based despiking algorithm were applied to correct drifting baselines and cosmic spikes, respectively. The method offers a variety of advantages such as less parameter to be set, no spectral distortion, fast computation speed, and stable results, etc. We utilized the method to eliminate the noise signals in Raman spectral imaging data of Miscanthus sinensis ( involving 9010 spectra) , and then employed PCA and cluster analysis ( CA) to distinguish plant spectra from non-plant spectra. Theoretically, this method could be used to denoise other spectral imaging data and provide reliable foundation for achieving stable analysis results.关键词
拉曼光谱成像/光谱去噪/惩罚最小二乘/主成分分析/聚类分析Key words
Raman spectral imaging/Spectral denoising/Penalized least-squares/Principal component analysis/Cluster analysis引用本文复制引用
张逊,陈胜,吴博士,杨桂花,许凤..自适应拉曼光谱成像数据去噪及其在植物细胞壁光谱分析中的应用[J].分析化学,2016,44(12):1846-1851,6.基金项目
本文系北京林业大学科技创新计划项目(No. BLYJ201620),教育部重点科研项目(No.113014A)和北京市优秀博士论文导师资助项目(No.20131002201)资助 （No. BLYJ201620）
