首页 > 论文 > 中国激光 > 47卷 > 10期(pp:1011002--1)

非平滑非负矩阵分解解析土壤多环芳烃三维荧光光谱

Analysis on Three-Dimensional Fluorescence Spectra of PAHs in Soil Using Nonsmooth Non-Negative Matrix Factorization

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

采用荧光分光光度计直接获取土壤中多环芳烃(PAHs)的三维荧光光谱,并利用非平滑非负矩阵分解(nsNMF)对其进行解析,结果表明,非负矩阵分解(NMF)能够从混叠光谱中提取出单一多环芳烃的荧光光谱信号。在随机初始值下,nsNMF优于基于交替式非负最小二乘的标准非负矩阵分解(NMF/ANLS),解析光谱与参考光谱的相似系数均在0.824以上。特别是在农田土壤中,菲和蒽的解析光谱与标准参考光谱的相似系数分别由0.758、0.845(NMF/ANLS)提高到0.907、0.913(nsNMF)。三维荧光光谱结合nsNMF能够实现土壤多环芳烃组分的快速识别。

Abstract

Three-dimensional fluorescence spectra of polycyclic aromatic hydrocarbons (PAHs) in soil are directly recorded using a fluorescence spectrophotometer. To identify the components of PAHs in soil, nonsmooth non-negative matrix factorization (nsNMF) are used. Results show that NMF can effectively extract the fluorescence spectrum signal of a single PAH from the mixture spectrum. The similarity coefficient between the analytical spectra and corresponding reference spectra obtained by nsNMF under random initial values is all above 0.824, which is higher than that of the standard NMF based on alternating non-negative least squares (NMF/ANLS). In farmland soil, the similarity coefficients of phenanthrene and anthracene between the analytical spectra and corresponding reference spectra increased from 0.758 and 0.845 (NMF/ANLS) to 0.907 and 0.913 (nsNMF), respectively. The combination of three-dimensional fluorescence spectra and nsNMF can facilitate rapid identification of components of PAHs in soil.

广告组1 - 空间光调制器+DMD
补充资料

中图分类号:O657.3

DOI:10.3788/CJL202047.1011002

所属栏目:光谱学

基金项目:国家自然科学基金、国家重点研发计划、安徽省重点研究和开发计划;

收稿日期:2020-04-10

修改稿日期:2020-05-25

网络出版日期:2013-10-01

作者单位    点击查看

黄尧:中国科学院安徽光学精密机械研究所环境光学与技术重点实验室, 安徽 合肥 230031中国科学技术大学, 安徽 合肥 230026安徽省环境光学监测技术重点实验室, 安徽 合肥 230031
赵南京:中国科学院安徽光学精密机械研究所环境光学与技术重点实验室, 安徽 合肥 230031安徽省环境光学监测技术重点实验室, 安徽 合肥 230031
孟德硕:中国科学院安徽光学精密机械研究所环境光学与技术重点实验室, 安徽 合肥 230031安徽省环境光学监测技术重点实验室, 安徽 合肥 230031
左兆陆:中国科学院安徽光学精密机械研究所环境光学与技术重点实验室, 安徽 合肥 230031中国科学技术大学, 安徽 合肥 230026安徽省环境光学监测技术重点实验室, 安徽 合肥 230031
程钊:中国科学院安徽光学精密机械研究所环境光学与技术重点实验室, 安徽 合肥 230031中国科学技术大学, 安徽 合肥 230026安徽省环境光学监测技术重点实验室, 安徽 合肥 230031
陈宇男:中国科学院安徽光学精密机械研究所环境光学与技术重点实验室, 安徽 合肥 230031中国科学技术大学, 安徽 合肥 230026安徽省环境光学监测技术重点实验室, 安徽 合肥 230031
陈晓伟:中国科学院安徽光学精密机械研究所环境光学与技术重点实验室, 安徽 合肥 230031中国科学技术大学, 安徽 合肥 230026安徽省环境光学监测技术重点实验室, 安徽 合肥 230031

联系人作者:赵南京(njzhao@aiofm.ac.cn)

备注:国家自然科学基金、国家重点研发计划、安徽省重点研究和开发计划;

【1】Wang S T, Wu X, Zhu W H, et al. Fluorescence detection of polycyclic aromatic hydrocarbons by parallel factor combined with support vector machine [J]. Acta Optica Sinica. 2019, 39(5): 0530002.
王书涛, 吴兴, 朱文浩, 等. 平行因子结合支持向量机对多环芳烃的荧光检测 [J]. 光学学报. 2019, 39(5): 0530002.

【2】Chen Y, Yan X, Zhang X, et al. Surface-enhanced Raman spectroscopy quantitative analysis of polycyclic aromatic hydrocarbons based on support vector machine algorithm [J]. Chinese Journal of Lasers. 2019, 46(3): 0311005.
陈阳, 严霞, 张旭, 等. 基于支持向量机算法的多环芳烃表面增强拉曼光谱的定量分析 [J]. 中国激光. 2019, 46(3): 0311005.

【3】Odabasi M, Falay E O, Tuna G, et al. Biomonitoring the spatial and historical variations of persistent organic pollutants (POPs) in an industrial region [J]. Environmental Science & Technology. 2015, 49(4): 2105-2114.Odabasi M, Falay E O, Tuna G, et al. Biomonitoring the spatial and historical variations of persistent organic pollutants (POPs) in an industrial region [J]. Environmental Science & Technology. 2015, 49(4): 2105-2114.

【4】Wise S A, Sander L C, Schantz M M. Analytical methods for determination of polycyclic aromatic hydrocarbons (PAHs): a historical perspective on the 16 US EPA priority pollutant PAHs [J]. Polycyclic Aromatic Compounds. 2015, 35(2/3/4): 187-247.

【5】Song Y F, Jing X, Fleischmann S, et al. Comparative study of extraction methods for the determination of PAHs from contaminated soils and sediments [J]. Chemosphere. 2002, 48(9): 993-1001.

【6】Zuo Z L, Zhao N J, Meng D S, et al. Identification of petroleum organic matter in soil based on three-dimensional fluorescence spectroscopy [J]. Laser & Optoelectronics Progress. 2019, 56(22): 222601.
左兆陆, 赵南京, 孟德硕, 等. 基于三维荧光光谱的土壤中石油类有机物分类识别 [J]. 激光与光电子学进展. 2019, 56(22): 222601.

【7】Hartline F F. Three-dimensional fluorescence spectroscopy [J]. Science. 1979, 203(4387): 1330-1331.

【8】Levinson J, Sluszny C, Yasman Y, et al. Detector for particulate polycyclic aromatic hydrocarbons in water [J]. Analytical and Bioanalytical Chemistry. 2005, 381(8): 1584-1591.

【9】Kong D M, Zhang C X, Cui Y Y, et al. Detection of oil species in mixed oil based on alternating penalty trilinear decomposition [J]. Acta Optica Sinica. 2018, 38(11): 1130005.
孔德明, 张春祥, 崔耀耀, 等. 基于交替惩罚三线性分解的混合油液油种成分的检测 [J]. 光学学报. 2018, 38(11): 1130005.

【10】Bosco M V, Larrechi M S. PARAFAC and MCR-ALS applied to the quantitative monitoring of the photodegradation process of polycyclic aromatic hydrocarbons using three-dimensional excitation emission fluorescent spectra: comparative results with HPLC [J]. Talanta. 2007, 71(4): 1703-1709.

【11】Wu H L, Shibukawa M, Oguma K. An alternating trilinear decomposition algorithm with application to calibration of HPLC-DAD for simultaneous determination of overlapped chlorinated aromatic hydrocarbons [J]. Journal of Chemometrics. 1998, 12(1): 1-26.

【12】Jin D, Li G G, Zhang Y J, et al. Simultaneous determination of four components of polycyclic aromatic hydrocarbons based on PARAFAC model of three-dimensional fluorescence spectroscopy [J]. Journal of Atmospheric and Environmental Optics. 2010, 5(4): 276-282.
金丹, 李国刚, 张玉钧, 等. 基于PARAFAC算法的三维荧光光谱法测定四种混合芳香烃 [J]. 大气与环境光学学报. 2010, 5(4): 276-282.

【13】Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization [J]. Nature. 1999, 401(6755): 788-791.

【14】Guimet F, Boqué R, Ferré J. Application of non-negative matrix factorization combined with Fisher''''s linear discriminant analysis for classification of olive oil excitation-emission fluorescence spectra [J]. Chemometrics and Intelligent Laboratory Systems. 2006, 81(1): 94-106.

【15】Pauca V P, Piper J, Plemmons R J. Nonnegative matrix factorization for spectral data analysis [J]. Linear Algebra and its Applications. 2006, 416(1): 29-47.

【16】Gao H T, Li T H, Chen K, et al. Overlapping spectra resolution using non-negative matrix factorization [J]. Talanta. 2005, 66(1): 65-73.Gao H T, Li T H, Chen K, et al. Overlapping spectra resolution using non-negative matrix factorization [J]. Talanta. 2005, 66(1): 65-73.

【17】-08-25)[2020-05-24] [EB/OL]. Hoyer P O. Non-negative matrix factorization with sparseness constraints. 2004.-08-25)[2020-05-24] [EB/OL]. Hoyer P O. Non-negative matrix factorization with sparseness constraints. 2004.

【18】Yang Z Y, Zhou G X, Xie S L, et al. Blind spectral unmixing based on sparse nonnegative matrix factorization [J]. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society. 2011, 20(4): 1112-1125.

【19】Kim W, Chen B, Kim J, et al. Sparse nonnegative matrix factorization for protein sequence motif discovery [J]. Expert Systems with Applications. 2011, 38(10): 13198-13207.

【20】Pascual-Montano A, Carazo J M, Kochi K, et al. Nonsmooth nonnegative matrix factorization (nsNMF) [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2006, 28(3): 403-415.

【21】Yang Z Y, Zhang Y, Yan W, et al. A fast non-smooth nonnegative matrix factorization for learning sparse representation [J]. IEEE Access. 2016, 4: 5161-5168.

【22】Li L, Zhang Y J. A survey on algorithms of non-negative matrix factorization [J]. Acta Electronica Sinica. 2008, 36(4): 737-743.
李乐, 章毓晋. 非负矩阵分解算法综述 [J]. 电子学报. 2008, 36(4): 737-743.

【23】Wang B, Yu F Q, Chen Y. Speech enhancement based on nonsmooth nonnegative matrix factorization [J]. Computer Engineering and Applications. 2017, 53(7): 160-164.
王波, 于凤芹, 陈莹. 基于非平滑非负矩阵分解语音增强 [J]. 计算机工程与应用. 2017, 53(7): 160-164.

【24】Kim J, Park H. Fast nonnegative matrix factorization: an active-set-like method and comparisons [J]. SIAM Journal on Scientific Computing. 2011, 33(6): 3261-3281.

【25】Lin C J. On the convergence of multiplicative update algorithms for nonnegative matrix factorization [J]. IEEE Transactions on Neural Networks. 2007, 18(6): 1589-1596.

【26】Lin C J. Projected gradient methods for nonnegative matrix factorization [J]. Neural Computation. 2007, 19(10): 2756-2779.

【27】Koren Y, Carmel L. Robust linear dimensionality reduction [J]. IEEE Transactions on Visualization and Computer Graphics. 1900, 10(4): 459-470.

【28】Boutsidis C, Gallopoulos E. SVD based initialization: a head start for nonnegative matrix factorization [J]. Pattern Recognition. 2008, 41(4): 1350-1362.

引用该论文

Huang Yao,Zhao Nanjing,Meng Deshuo,Zuo Zhaolu,Cheng Zhao,Chen Yunan,Chen Xiaowei. Analysis on Three-Dimensional Fluorescence Spectra of PAHs in Soil Using Nonsmooth Non-Negative Matrix Factorization[J]. Chinese Journal of Lasers, 2020, 47(10): 1011002

黄尧,赵南京,孟德硕,左兆陆,程钊,陈宇男,陈晓伟. 非平滑非负矩阵分解解析土壤多环芳烃三维荧光光谱[J]. 中国激光, 2020, 47(10): 1011002

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF