摘要
针对增强多环芳烃定量分析速率较低,导致多环芳烃探测即时性较差,提出一种基于极限学习机的多环芳烃表面增强拉曼光谱定量分析方法。探究多环芳烃的污染来源及其对大气、水和土壤的具体影响,采用极限学习机对通过划分方式得到的粒层空间学习,明确最佳划分和粒层,构建出基于粒计算的极限学习机模型,通过该模型增强多环芳烃探测速率;将硫氰化钾当作内标物,利用极限学习机构建定量分析模型,剖析p H值对多环芳烃增强效果的影响,运用SERS基底对菲、芘、苯并(k)荧蒽进行光谱定量分析。研究结果证实,所提方法运行简便、灵敏度高,可有效完成多环芳烃成分精准定量分析任务。
Aiming at the low quantitative analysis rate of enhanced polycyclic aromatic hydrocarbons and the poor instantaneous detection of polycyclic aromatic hydrocarbons,a quantitative analysis method of surface enhanced Raman spectroscopy of polycyclic aromatic hydrocarbons based on extreme learning machine is proposed.To explore the sources of pollution of polycyclic aromatic hydrocarbons and the specific impact on atmosphere,water and soil,the extreme learning machine is used to learn the granular layer space obtained through the division method.Clarifying the optimal division and granular layer,and constructing the extreme learning based on granular calculation machine model to enhance the detection rate of polycyclic aromatic hydrocarbons.The potassium thiocyanide is set as an internal standard,a quantitative analysis model is constructed by using extreme learning machines to analyze the effect of pH on the enhancement effect of polycyclic aromatic hydrocarbons.The uses of SERS substrates,pyrene,benzo(k)fluoranthene quantitative analysises are carried out.The results of the study show that the proposed method is easy to operate and has high sensitivity,and can effectively complete the accurate quantitative analysis of PAHs.
作者
李红双
李贞玉
王润雪
LI Hongshuang;LI Zhenyu;WANG Runxue(Jilin University of Architecture and Technology,Changchun 130000,China;School of Chemical Engineering,Changchun University of Technology,Changchun 130000,China)
出处
《激光杂志》
北大核心
2020年第12期45-49,共5页
Laser Journal
基金
吉林省教育厅“十三五”科学研究规划项目(No.GH19172)
吉林省教育厅“十三五”产业化项目(No.JJKH20200683KJ)。
关键词
极限学习机
多环芳烃
表面增强拉曼光谱
定量分析
limit learning machine
polycyclic aromatic hydrocarbons
surface enhanced Raman spectroscopy
quantitative analysis
