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dc.contributor.author王磊
dc.contributor.author郭淑霞
dc.contributor.author戴吟臻
dc.contributor.author杨良保
dc.contributor.author刘国坤
dc.date.accessioned2016-05-17T02:45:43Z
dc.date.available2016-05-17T02:45:43Z
dc.date.issued2015-1-15
dc.identifier.citation分析化学,2015,(1):42-48
dc.identifier.issn0253-3820
dc.identifier.otherFXHX201501009
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/105943
dc.description.abstract通过将自适应平滑滤波器和结合小波变换的支持向量机(SuPPOrT VECTOr MACHInE,SVM)分类器有机组合,建立了低信噪比拉曼光谱的模式识别方法。首先,通过自适应平滑滤波器进行光谱去噪,滤波窗口宽度根据信噪比估计值进行调整,从而在保证特征峰信号强度的同时达到更好的噪声滤波效果;其次,由小波变换实现光谱数据降维,通过小波分解层数优化可以获得训练集的最佳分类准确率;最后,由SVM进行分类,通过交叉验证(CrOSS VAlIdATIOn,CV)实现SVM参数寻优,并根据交叉验证与分类器之间的准确率关系,得出分类器可用参数需满足的条件。基于表面增强拉曼光谱技术,本方法实现了人体尿液中甲基苯丙胺(METHAMPHETAMInE,MAMP)和亚甲基二氧基甲基苯丙胺(3,4-METHylEnEdIO-XyMETHAMPHETAMInE,MdMA)的定性微量分析。实验使用中国科学院合肥智能机械研究所研发的金纳米棒拉曼光谱增强基底,由dElTA nu公司的InSPECTOr型便携拉曼光谱仪采集光谱,激发光波长785 nM,曝光时间为5 S,整体检测准确率高于95.0%。
dc.description.abstractAssembling an adapted smoothing method and a classifier of wavelet transform combined support vector machine( SVM),a Raman spectrum recognition approach was built for low signal noise ratio situation.Firstly,spectra data were denoised by the adapted smoothing method.The smoothing window was adapted to the signal noise ratio,which would effectively remove noise with the intensity of the signal well remained.Secondly,the wavelet transform was used for dimension reduction of the data.The decomposition level of wavelet transform was optimized according to the best classification result of the training set.Lastly,SVM was used for classification.Cross Validation( CV) was applied to obtain the optimized parameters of SVM.Conditions for the effective parameters were searched considering the relation between the cross-validation result and the classification accuracy.Combined with the surface enhanced Raman scattering( SERS)technology,the developed spectrum recognition approach was used for qualitative analysis of methamphetamine( MAMP) and 3,4-methylenedioxymethamphetamine( MDMA) in people' s urine,where the detecting accuracy is above 95.0%.The uniform Au nanorods( NRs) SERS substrate synthetized by the Hefei Institute of Intelligent Machines of Chinese Academy of Sciences was used for the experiment.Raman spectra were acquired using an Inspector Raman( Delta Nu) spectrometer,with the excitation wavelength of 785 nm and the integrate time of 5 seconds.
dc.description.sponsorship国家重大科学仪器设备开发专项(No.2011YQ030124); 国家自然科学基金(No.21373173)资助项目~~
dc.language.isozh_CN
dc.subject拉曼光谱
dc.subject滤波
dc.subject小波变换
dc.subject支持向量机
dc.subjectRaman spectrum
dc.subjectSmoothing
dc.subjectWavelet transform
dc.subjectSupport vector machine
dc.title尿液中常见毒品微量检测的表面增强拉曼光谱识别
dc.title.alternativeSurface Enhanced Raman Scattering Spectrum Recognition for Trace Detection of Common Drugs in Urine
dc.typeArticle


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