摘要
拉曼光谱成像数据存在基线漂移与宇宙射线干扰峰两类噪声信号,无法直接用于光谱分析研究,必须去除。现有单光谱去噪方法处理结果不稳定、可重复性差。针对这一问题,本研究提出了一种自适应拉曼光谱成像数据新型去噪法,采用优化的自适应迭代惩罚最小二乘法(Adaptive iteratively reweighted penalized least-squares,air PLS)和基于主成分分析(PCA)的干扰峰消除算法修正光谱基线漂移和宇宙射线干扰峰,具有输入参数少、光谱失真小、处理速度快、去噪结果稳定等优点。利用本方法去除了芒草(Miscanthus sinensis)细胞壁拉曼光谱成像数据(9010条光谱)中的噪声信号,并对去噪后数据进行PCA和聚类分析(CA),成功区分非植物光谱与植物光谱,分类结果优于未去噪数据。预期本方法可应用于其它光谱成像数据去噪,为光谱的解译和定量分析提供可靠的研究基础。
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.
作者
张逊
陈胜
吴博士
杨桂花
许凤
ZHANG Xun CHEN Sheng WU Bo-Shi YANG Gui-Hua XU Feng(Beijing Key Laboratory of Lignoeellulosic Chemistry, Beijing Forestry University, Beijing 100083, China Key Laboratory of Pulp and Paper Science ＆ Technology, Qilu University of Technology, Jinan 250353, China)
出处
《分析化学》
SCIE
EI
CAS
CSCD
北大核心
2016年第12期1846-1851,共6页
Chinese Journal of Analytical Chemistry
基金
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)
北京市优秀博士论文导师资助项目(No.20131002201)资助~~
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
Raman spectral imaging
Spectral denoising
Penalized least-squares
Principal component analysis
Cluster analysis
