基于三维荧光光谱与平行因子的混合有机农药检测分析  

Detection and Analysis of Mixed Organic Pesticides Based on Three-Dimensional Fluorescence Spectroscopy and PARAFAC

作　　者：王晓燕 蒋喆臻 季仁东 卞海溢 何莹 陈旭 徐春祥[3] WANG Xiao-yan;JIANG Zhe-zhen;JI Ren-dong;BIAN Hai-yi;HE Ying;CHEN Xu;XU Chun-xiang(Faculty of Electronic Information Engineering Department of Communication Engineering,Huaiyin Institute of Technology,Huai'an 223003,China;Jiangsu Engineering Research Center of Lake Environment Remote Sensing Technologies,Huai'an 223003,China;State Key Laboratory of Bioelectronics,Southeast University,Nanjing 210016,China)

机构地区：[1]淮阴工学院电子信息工程学院通讯工程系,江苏淮安223003 [2]江苏省湖泊环境遥感技术工程研究中心,江苏淮安223003 [3]东南大学生物电子学国家重点实验室,江苏南京210016

出　　处：《光谱学与光谱分析》2024年第11期3082-3089,共8页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(62141502);江苏省产学研合作项目(BY20231118);江苏省大学生创新创业训练计划项目(202311049043Z);淮阴工学院研究生科技创新计划项目(HGYK202311)资助。

摘　　要：荧光光谱法作为一种快速、准确和非破坏性的检测方法,已广泛应用于农药残留的检测分析中。应用三维荧光光谱方法结合平行因子算法(PARAFAC)对混合有机农药进行定性定量分析。首先配制不同浓度的多杀菌素-高效氟氯氰菊酯、多杀菌素-宁南霉素混合体系,采用LS55荧光分光光度计扫描三维荧光光谱,发射波长范围设置为200~600 nm,激发波长范围分别为250~322和260~370 nm;应用平行因子算法对预处理后的光谱数据建模分析,通过三线性分解获得混合物中各组分的预测光谱及得分值,将预测光谱与实际光谱进行可视化匹配可识别出农药类别;最后将得分值与对应组分浓度值进行线性拟合分析,并计算均方误差、决定系数和回收率参数。结果表明,两种混合体系对应的各组分预测光谱与真实光谱在荧光特征峰处的重合度较高。多杀菌素-高效氟氯氰菊酯混合体系中各组分对应的预测均方误差分别为1.9856×10^(-8)和4.4800×10^(-7);多杀菌素-宁南霉素对应的预测均方误差分别为2.1552×10^(-7)和5.5722×10^(-5),模型决定系数均超过0.99,平均回收率接近100%,测试结果表明三维荧光光谱结合平行因子可实现对农药混合物的分析,具有较高的分析精度。该研究内容为混合有机农药的定性定量分析提供了一定的方法依据,可推广用于其他种类混合样品的检测分析。Fluorescence spectroscopy is a fast,accurate,and non-destructive detection method widely used to detect and analyze pesticide residues.This article applies the three-dimensional fluorescence spectroscopy method combined with the parallel factor(PARAFAC)algorithm to achieve qualitative and quantitative analysis of mixed organic pesticides.Firstly,in different proportions,two kinds of mixed solutions are prepared,including spinosad-lambda-cyfluthrin and spinosad-ningnanmycin.The three-dimensional fluorescence spectra are obtained by scanning with an LS55 fluorescence spectrophotometer;the emission wavelength range is 200~600 nm,and the excitation wavelength range is 250~322 and 260~370 nm,respectively.Then,the parallel factor algorithm is applied to model and analyze the preprocessed spectral data.The predicted spectra and score values of each component in the mixture are obtained through trilinear decomposition,and the predicted spectra are visually matched with the actual spectra to identify pesticide categories.Finally,a linear fitting analysis is conducted between the score value and the concentration of pesticide component,and the mean squared error,coefficient of determination,and recovery rate parameters are calculated.The results showed that the predicted spectra of the pesticide components corresponding to the two mixtures are consistent with their true spectra,especially with high overlap at the fluorescence characteristic peaks.The prediction means that the squared error of each component in the spinosad-lambda-cyhalothrin mixture is 1.9856×10^(-8)and 4.4800×10^(-7),respectively.The corresponding prediction mean squared error of the spinosad-ningnanmycin mixture is 2.1552×10^(-7)and 5.5722×10^(-5),respectively.The model's coefficient of determination exceeds 0.99,and the average recovery is close to 100%.The test results indicate that three-dimensional fluorescence spectroscopy combined with a parallel factors algorithm can analyze pesticide mixtures with high analytical accuracy.The research content

关 键 词：混合有机农药 三维荧光光谱 平行因子算法 定性定量分析 

分 类 号：O433.4[机械工程—光学工程]