摘要
在全球范围内频繁发生芬太尼类物质滥用与致死案件,人体中芬太尼类物质的检测与识别愈发重要。芬太尼类物质在经过人体一段时间后,仍有一部分原体随着尿液排出,因此可通过检测尿液中芬太尼类物质反映其毒品滥用史。表面增强拉曼光谱(SERS)具有快速、高灵敏、易操作等特点,适合尿液中芬太尼类物质的现场检测与分析。但尿液中尿素等物质的背景峰与芬太尼类物质SERS特征峰高度重合,芬太尼类物质特征峰被尿液背景峰所掩盖,这对尿液中芬太尼类的光谱识别造成了很大的干扰。结合Voigt线型建立谱峰解析模型,对尿液与芬太尼类物质重叠部分进行谱峰分析。针对SERS光谱噪声与荧光等因素对谱峰解析模型的干扰,采用无约束Nelder-Mead算法对模型进行优化与计算,利用该算法对迭代参数初始值不敏感的特点,提高谱峰解析模型的准确度。根据SERS光谱半峰宽的特征对解析峰集合进行筛选,对SERS光谱的尿液背景峰进行扣除,以还原芬太尼类SERS光谱1000与1030 cm^(-1)处谱峰特征。实验结果与现象表明,利用Voigt线型建立的谱峰解析模型对尿液中芬太尼类SERS光谱拟合度均达到99%以上,能够通过解峰集合的筛选还原尿液中芬太尼类SERS光谱特征,还原光谱与芬太尼类特征峰的半峰宽与峰比例等特点均高度一致。在空白尿液SERS光谱进行解析时,其解析峰集合中不含有芬太尼类物质的特征谱峰,可以有效区分空白尿液与含芬太尼类物质尿液。利用相似系数(HQI)对还原光谱片段(935~1100 cm^(-1))进行识别,能有效区分尿液中奥芬太尼、呋喃芬太尼、乙酰芬太尼三种芬太尼,并提升光谱之间的区分度。该解析模型有望为尿液中芬太尼类的识别与判断提供解决实际问题的途径。
Fentanyl substance abuse and death cases frequently occur worldwide,and the detection and identification of fentanyl substances in the human body are becoming increasingly important.After some time in the human body,some of the fentanyl substances are still discharged with urine,so the history of drug abuse can be reflected by detecting fentanyl substances in the urine.Surface Enhanced Raman Spectroscopy(SERS)is fast,sensitive and easy to operate,which is suitable for the field detection and analysis of fentanyl in urine.However,the background peaks of urea and other substances in urine are highly coincident with the SERS characteristic peaks of fentanyl,and the characteristic peaks of fentanyl are covered by the background peaks of urine,which causes great interference with the spectral identification of fentanyl in urine.In this paper,the spectral peak analysis model is established based on the Voigt line,and the spectral peak analysis of the overlapping part of urine and fentanyl is carried out.Because of the interference of SERS spectral noise and fluorescence on the peak analytical model,the unconstrained Nelder-Mead algorithm is used to optimize and calculate the model.The algorithm is insensitive to the initial value of iterative parameters to improve the accuracy of the peak analytical model.According to the characteristics of the half-peak width of the SERS spectrum,the analytical peak set was screened,and the urine background peak of the SERS spectrum was deducted to restore the spectral peak characteristics of the fentanyl SERS spectrum at 1000 and 1030 cm^(-1).The experimental results and phenomena show that the spectral peak analysis model established by the Voigt line shape has a fitting degree of more than 99%for the SERS spectrum of fentanyl in urine and can restore the SERS spectrum characteristics of fentanyl in urine through the screening of the peak solution set.The characteristics of the half-peak width and peak ratio of the reduced spectrum and the characteristic peaks of fentanyl are highly consistent.When the SERS spectrum of blank urine is analyzed,the analytical peak set does not contain the characteristic peaks of fentanyl substances,which can effectively distinguish blank urine from urine containing fentanyl substances.The reduced spectral fragment(935~1100 cm^(-1))was identified by the hit quality index(HQI),which can effectively distinguish Ofentanyl,Furanyl and Acetylfentanyl in urine and improve the discrimination between spectra.This analytical model is expected to provide a way to solve practical problems for the identification and judgment of fentanyl in urine.
作者
何遥
李伟
董荣录
祁秋景
李萍
林东岳
孟凡利
杨良保
HE Yao;LI Wei;DONG Rong-lu;QI Qiu-jing;LI Ping;LIN Dong-yue;MENG Fan-li;YANG Liang-bao(Institute of Physical Science and Information Technology,Anhui University,Hefei 230039,China;Institute of Health&Medical Technology,Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei 230031,China;Anhui Provincial Public Security Bureau Physical Evidence Identification Management Office,Hefei 230000,China;College of Information Science and Engineering,Northeastern University,Shenyang 110819,China;Center for Disease Control and Prevention in Dongying,Dongying 257091,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2023年第1期85-92,共8页
Spectroscopy and Spectral Analysis
基金
安徽省重点研究与开发计划项目(202104d07020002)
国家自然科学基金重点项目(62033002)
北京市现场物证检验工程技术研究中心开放课题(2020CSEEKFKT07)资助。
关键词
表面增强拉曼光谱
背景扣除
光谱解析
特征还原
Voigt线型
Surface enhanced Raman spectroscopy
Background correction
Spectrum analysis
Feature restoration
Voigt line shape
