平行因子结合支持向量机对多环芳烃的荧光检测
|
摘要
基于荧光检测机理,将平行因子与支持向量机(SVM)算法相结合,对多环芳烃中的苊、芴和萘进行检测。将荧光光谱数据预处理后作为训练集,输入到粒子群优化的SVM算法中建立分类模型;利用核一致性分析、残差平方和分析以及迭代次数分析方法确定成分数;采用得到的最佳成分数进行平行因子分解,将得到的发射载荷矩阵作为测试集输入到SVM的分类模型中,分类正确率为100%,最终得到苊、芴和萘的回收率分别为100.45%±6.25%、100.10%±6.39%和95.07%±7.46%。所用算法避免了人为操作增加的时间复杂性及主观因素造成的误差,为多环芳烃的荧光检测提供了一种新方法。
Herein,based on the fluorescence detection mechanism,acenaphthene,fluorene,and naphthalene were detected in polycyclic aromatic hydrocarbons by the parallel factor combined with the support vector machine(SVM)algorithm.The fluorescence spectral data were preprocessed and used as the training set,which was fed into the particle-swarm-optimized SVM algorithm to establish the classification model.The number of components was determined using the methods of core consistency analysis,residual square sum analysis,and iterative frequency analysis,and the optimal component number thus obtained was used to perform parallel factor decomposition.The obtained transmit load matrix was used as the test set and fed into the SVM classification model.The classification accuracy rate was 100%.The recovery rates of 100.45%±6.25%,100.10%±6.39%,and 95.07%±7.46% were achieved for acenaphthene,fluorene,and naphthalene,respectively.The proposed algorithm avoids time complexity caused by human operation and errors caused by subjective factors.Thus,it can be applied for fluorescence detection of polycyclic aromatic hydrocarbons.
Herein,based on the fluorescence detection mechanism,acenaphthene,fluorene,and naphthalene were detected in polycyclic aromatic hydrocarbons by the parallel factor combined with the support vector machine(SVM)algorithm.The fluorescence spectral data were preprocessed and used as the training set,which was fed into the particle-swarm-optimized SVM algorithm to establish the classification model.The number of components was determined using the methods of core consistency analysis,residual square sum analysis,and iterative frequency analysis,and the optimal component number thus obtained was used to perform parallel factor decomposition.The obtained transmit load matrix was used as the test set and fed into the SVM classification model.The classification accuracy rate was 100%.The recovery rates of 100.45%±6.25%,100.10%±6.39%,and 95.07%±7.46% were achieved for acenaphthene,fluorene,and naphthalene,respectively.The proposed algorithm avoids time complexity caused by human operation and errors caused by subjective factors.Thus,it can be applied for fluorescence detection of polycyclic aromatic hydrocarbons.
引文
[1]Kim K H,Jahan S A,Kabir E,et al.A review of airborne polycyclic aromatic hydrocarbons(PAHs)and their human health effects[J].Environment International,2013,60(5):71-80.
[2]Shi X F,Zhang X M,Yan X,et al.Detection of polycyclic aromatic hydrocarbons(PHAs)in water based on three-dimensional surface-enhanced Raman scattering substrates[J].Acta Optica Sinica,2018,38(7):0724001.史晓凤,张心敏,严霞,等.基于三维表面增强拉曼基底的水中多环芳烃检测[J].光学学报,2018,38(7):0724001.
[3]Ncube S,Kunene P,Tavengwa N T,et al.Synthesis and characterization of a molecularly imprinted polymer for the isolation of the 16 US-EPA priority polycyclic aromatic hydrocarbons(PAHs)in solution[J].Journal of Environmental Management,2017,199:192-200.
[4]Yang R J,Dong G M,Yang Y R,et al.Effect of soil particle size on fluorescence characteristics of anthracene and its correction[J].Optics and Precision Engineering,2016,24(11):2665-2671.杨仁杰,董桂梅,杨延荣,等.土壤粒径大小对蒽荧光特性的影响及校正[J].光学精密工程,2016,24(11):2665-2671.
[5]Yin S N,Yao T,Wu T H,et al.Novel metal nanoparticle-enhanced fluorescence for determination of trace amounts of fluoroquinolone in aqueous solutions[J].Talanta,2017,174:14-20.
[6]Yang L L.Studies on the measurement and identification method of petroleum pollutants based on three-dimensional fluorescence spectroscopy[D].Qinhuangdao:Yanshan University,2013.杨丽丽.石油类污染物的三维荧光光谱测量与识别方法研究[D].秦皇岛:燕山大学,2013.
[7]Zhu P,Liu C L,Zhu F.The implementation by PARAFAC decompose components analysis in the three-dimensional fluorescence spectroscopy data[J].Spectroscopy and Spectral Analysis,2015,35(6):1611-1617.祝鹏,刘成林,祝飞.平行因子法分解成分分析在三维荧光光谱数据中的实现[J].光谱学与光谱分析,2015,35(6):1611-1617.
[8]Cabrera-Ba1egil M,Valdés-Sánchez E,Moreno D,et al.Front-face fluorescence excitation-emission matrices in combination with three-way chemometrics for the discrimination and prediction of phenolic response to vineyard agronomic practices[J].Food Chemistry,2019,270:162-172.
[9]Chen Z K,Mi Y,Shen X W,et al.Fluorescence detection of oil pollutants based on PARAFAC and ART algorithms[J].Laser&Optoelectronics Progress,2018,55(1):430-437.陈至坤,弭阳,沈小伟,等.基于PARAFAC和ART算法的油类污染物荧光检测[J].激光与光电子学进展,2018,55(1):430-437.
[10]Zhou Y L,Zhou F F,Jiang C C,et al.Research of identification method for the oil spills species based on fluorescence excitation-emission matrix and parallel factor analysis[J].Spectroscopy and Spectral Analysis,2018,38(2):475-480.周艳蕾,周飞飞,姜聪聪,等.基于三维荧光光谱-平行因子分析的海上溢油识别技术研究[J].光谱学与光谱分析,2018,38(2):475-480.
[11]Li Q C,Zhang L,Wang S R,et al.Influence of light on DON characteristics in overlying water and its environmental implication[J].Environmental Chemistry,2017,36(3):521-531.李秋材,张莉,王圣瑞,等.光照对湖泊上覆水DON影响机制及环境学意义[J].环境化学,2017,36(3):521-531.
[12]Chen Z K.Research for fluorescence spectrum measurement of oil contaminants based on microchannel system[D].Qinhuangdao:Yanshan University,2016.陈至坤.基于微通道系统的石油污染物荧光光谱测量研究[D].秦皇岛:燕山大学,2016.
[13]Cai W L,Xu X Y,Du X,et al.Parallel factor analysis with EEM on dissolved organic matter in Chongqing section of Jialing river[J].Research of Environmental Sciences,2012,25(3):276-281.蔡文良,许晓毅,杜娴,等.嘉陵江重庆段DOM三维荧光光谱的平行因子分析[J].环境科学研究,2012,25(3):276-281.
[14]Maldonado S,Pérez J,Bravo C.Cost-based feature selection for support vector machines:an application in credit scoring[J].European Journal of Operational Research,2017,261(2):656-665.
[15]Wu Y Q,Zhou Y,Long Y L.Small target detection in hyperspectral remote sensing image based on adaptive parameter SVM[J].Acta Optica Sinica,2015,35(9):0928001.吴一全,周杨,龙云淋.基于自适应参数支持向量机的高光谱遥感图像小目标检测[J].光学学报,2015,35(9):0928001.
[16]Chapelle O,Vapnik V,Bousquet O,et al.Choosing multiple parameters for support vector machines[J].Machine Learning,2002,46(1/2/3):131-159.
[17]Wang J F,Zhang L,Chen G X,et al.A parameter optimization method for an SVM based on improved grid search algorithm[J].Applied Science and Technology,2012,39(3):28-31.王健峰,张磊,陈国兴,等.基于改进的网格搜索法的SVM参数优化[J].应用科技,2012,39(3):28-31.
[18]Cheng K,Lu Z Z,Wei Y H,et al.Mixed kernel function support vector regression for global sensitivity analysis[J].Mechanical Systems and Signal Processing,2017,96:201-214.
[19]Huang X.The study on kernels in support vector machine[D].Suzhou:Soochow University,2008.黄啸.支持向量机核函数的研究[D].苏州:苏州大学,2008.
[20]Wang D F,Meng L.Performance analysis and parameter selection of PSO algorithms[J].Acta Automatica Sinica,2016,42(10):1552-1561.王东风,孟丽.粒子群优化算法的性能分析和参数选择[J].自动化学报,2016,42(10):1552-1561.
[21]Zhu C H,Chen G Q,Zhu C,et al.Studies of the fluorescence properties of methanol and ethanol based on the density functional theory[J].Spectroscopy and Spectral Analysis,2018,38(4):1133-1138.朱从海,陈国庆,朱纯,等.基于密度泛函的甲醇和乙醇荧光特性研究[J].光谱学与光谱分析,2018,38(4):1133-1138.
[2]Shi X F,Zhang X M,Yan X,et al.Detection of polycyclic aromatic hydrocarbons(PHAs)in water based on three-dimensional surface-enhanced Raman scattering substrates[J].Acta Optica Sinica,2018,38(7):0724001.史晓凤,张心敏,严霞,等.基于三维表面增强拉曼基底的水中多环芳烃检测[J].光学学报,2018,38(7):0724001.
[3]Ncube S,Kunene P,Tavengwa N T,et al.Synthesis and characterization of a molecularly imprinted polymer for the isolation of the 16 US-EPA priority polycyclic aromatic hydrocarbons(PAHs)in solution[J].Journal of Environmental Management,2017,199:192-200.
[4]Yang R J,Dong G M,Yang Y R,et al.Effect of soil particle size on fluorescence characteristics of anthracene and its correction[J].Optics and Precision Engineering,2016,24(11):2665-2671.杨仁杰,董桂梅,杨延荣,等.土壤粒径大小对蒽荧光特性的影响及校正[J].光学精密工程,2016,24(11):2665-2671.
[5]Yin S N,Yao T,Wu T H,et al.Novel metal nanoparticle-enhanced fluorescence for determination of trace amounts of fluoroquinolone in aqueous solutions[J].Talanta,2017,174:14-20.
[6]Yang L L.Studies on the measurement and identification method of petroleum pollutants based on three-dimensional fluorescence spectroscopy[D].Qinhuangdao:Yanshan University,2013.杨丽丽.石油类污染物的三维荧光光谱测量与识别方法研究[D].秦皇岛:燕山大学,2013.
[7]Zhu P,Liu C L,Zhu F.The implementation by PARAFAC decompose components analysis in the three-dimensional fluorescence spectroscopy data[J].Spectroscopy and Spectral Analysis,2015,35(6):1611-1617.祝鹏,刘成林,祝飞.平行因子法分解成分分析在三维荧光光谱数据中的实现[J].光谱学与光谱分析,2015,35(6):1611-1617.
[8]Cabrera-Ba1egil M,Valdés-Sánchez E,Moreno D,et al.Front-face fluorescence excitation-emission matrices in combination with three-way chemometrics for the discrimination and prediction of phenolic response to vineyard agronomic practices[J].Food Chemistry,2019,270:162-172.
[9]Chen Z K,Mi Y,Shen X W,et al.Fluorescence detection of oil pollutants based on PARAFAC and ART algorithms[J].Laser&Optoelectronics Progress,2018,55(1):430-437.陈至坤,弭阳,沈小伟,等.基于PARAFAC和ART算法的油类污染物荧光检测[J].激光与光电子学进展,2018,55(1):430-437.
[10]Zhou Y L,Zhou F F,Jiang C C,et al.Research of identification method for the oil spills species based on fluorescence excitation-emission matrix and parallel factor analysis[J].Spectroscopy and Spectral Analysis,2018,38(2):475-480.周艳蕾,周飞飞,姜聪聪,等.基于三维荧光光谱-平行因子分析的海上溢油识别技术研究[J].光谱学与光谱分析,2018,38(2):475-480.
[11]Li Q C,Zhang L,Wang S R,et al.Influence of light on DON characteristics in overlying water and its environmental implication[J].Environmental Chemistry,2017,36(3):521-531.李秋材,张莉,王圣瑞,等.光照对湖泊上覆水DON影响机制及环境学意义[J].环境化学,2017,36(3):521-531.
[12]Chen Z K.Research for fluorescence spectrum measurement of oil contaminants based on microchannel system[D].Qinhuangdao:Yanshan University,2016.陈至坤.基于微通道系统的石油污染物荧光光谱测量研究[D].秦皇岛:燕山大学,2016.
[13]Cai W L,Xu X Y,Du X,et al.Parallel factor analysis with EEM on dissolved organic matter in Chongqing section of Jialing river[J].Research of Environmental Sciences,2012,25(3):276-281.蔡文良,许晓毅,杜娴,等.嘉陵江重庆段DOM三维荧光光谱的平行因子分析[J].环境科学研究,2012,25(3):276-281.
[14]Maldonado S,Pérez J,Bravo C.Cost-based feature selection for support vector machines:an application in credit scoring[J].European Journal of Operational Research,2017,261(2):656-665.
[15]Wu Y Q,Zhou Y,Long Y L.Small target detection in hyperspectral remote sensing image based on adaptive parameter SVM[J].Acta Optica Sinica,2015,35(9):0928001.吴一全,周杨,龙云淋.基于自适应参数支持向量机的高光谱遥感图像小目标检测[J].光学学报,2015,35(9):0928001.
[16]Chapelle O,Vapnik V,Bousquet O,et al.Choosing multiple parameters for support vector machines[J].Machine Learning,2002,46(1/2/3):131-159.
[17]Wang J F,Zhang L,Chen G X,et al.A parameter optimization method for an SVM based on improved grid search algorithm[J].Applied Science and Technology,2012,39(3):28-31.王健峰,张磊,陈国兴,等.基于改进的网格搜索法的SVM参数优化[J].应用科技,2012,39(3):28-31.
[18]Cheng K,Lu Z Z,Wei Y H,et al.Mixed kernel function support vector regression for global sensitivity analysis[J].Mechanical Systems and Signal Processing,2017,96:201-214.
[19]Huang X.The study on kernels in support vector machine[D].Suzhou:Soochow University,2008.黄啸.支持向量机核函数的研究[D].苏州:苏州大学,2008.
[20]Wang D F,Meng L.Performance analysis and parameter selection of PSO algorithms[J].Acta Automatica Sinica,2016,42(10):1552-1561.王东风,孟丽.粒子群优化算法的性能分析和参数选择[J].自动化学报,2016,42(10):1552-1561.
[21]Zhu C H,Chen G Q,Zhu C,et al.Studies of the fluorescence properties of methanol and ethanol based on the density functional theory[J].Spectroscopy and Spectral Analysis,2018,38(4):1133-1138.朱从海,陈国庆,朱纯,等.基于密度泛函的甲醇和乙醇荧光特性研究[J].光谱学与光谱分析,2018,38(4):1133-1138.