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

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...

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Bibliographic Details
Published in分析化学 Vol. 44; no. 12; pp. 1846 - 1851
Main Author 张逊 陈胜 吴博士 杨桂花 许凤
Format Journal Article
LanguageChinese
Published 齐鲁工业大学山东省制浆造纸科学与技术重点实验室，济南250353 2016
北京林业大学林木生物质化学北京市重点实验室,北京,100083%齐鲁工业大学山东省制浆造纸科学与技术重点实验室,济南,250353%北京林业大学林木生物质化学北京市重点实验室，北京100083
Subjects
主成分分析
光谱去噪
惩罚最小二乘
拉曼光谱成像
聚类分析
Cluster analysis
主成分分析
Raman spectral imaging
Spectral denoising
Penalized least-squares
惩罚最小二乘
光谱去噪
聚类分析
拉曼光谱成像
Principal component analysis
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ISSN0253-3820
DOI10.11895/j.issn.0253-3820.160392

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Summary: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。
Bibliography:Raman spectral imaging;Spectral denoising;Penalized least-squares;Principal component analysis;Cluster analysis
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 （
ISSN:0253-3820
DOI:10.11895/j.issn.0253-3820.160392