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动态表面增强拉曼光谱在敌瘟磷快速定量分析中的应用

Dynamic Surface-Enhanced Raman Spectroscopy for Rapid and Quantitative Analysis of Edifenphos

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摘要

动态表面增强拉曼光谱是在干态与湿态表面增强拉曼光谱(SERS)检测的基础上发展而来的, 不仅具有极好的信号增强, 还具有良好的重复性与稳定性。 提出了一种基于动态SERS与多元分析方法的敌瘟磷快速定量分析方法。 实验中, 首先测量100, 50, 10, 5, 1, 05和01 mg·L-1敌瘟磷动态SERS谱图, 并使用多项式校正方法去除光谱基线漂移。 然后, 处理后的全范围(600~1 800 cm-1)与特征范围(674~713, 890~1 195, 1 341~1 399和1 549~1 612 cm-1)光谱分别利用支持向量机回归(SVR)构建定量模型, 实现对敌瘟磷的定量分析。 同时, 实验还评估了主成分分析(PCA)对定量分析结果的影响。 实验结果表明特征范围光谱所建立的模型预测误差较小, 而数据经过PCA处理后预测误差得到进一步下降。 最优回归模型是由特征范围光谱经PCA处理后所构建的模型(RMSECV=0065 7 mg·L-1), 模型能够准确地预测敌瘟磷溶液浓度。 为了测试实际检测中的效果, 该方法被用来对苹果表面的敌瘟磷残留进行检测, 并通过气相色谱法进行验证。 结果表明该方法对于同一样本多次检测值波动较小, 且检测均值与气相色谱检测值相差较小, 相对误差最大仅为513%。 此外, 动态SERS检测可在2 min内完成, 且后续数据处理也可在数秒内完成, 同时整个过程的试剂消耗仅在2 μL左右。 因此, 所提出的方法在敌瘟磷快速准确检测具有极大优势。

Abstract

Dynamic surface-enhanced Raman spectroscopy (SERS) is based on thestate transition of nanostructure from wetstate to dry state to realize spectra measurement, and it not only produces giant Raman enhancement but also provides reproducible and stable SERS signal. In the paper, we proposed a novel method for the rapid and quantitative analysis of edifenphos based on dynamicsurface-enhanced Raman spectroscopy with multivariate analysis method. In experiment, the spectra of 100, 50, 10, 5, 1, 05 and 01 mg·L-1edifenphos were measured using dynamic surface-enhanced Raman spectroscopy, and the baseline shift of spectra was removed by the polynomial correction method. Then, the spectra of full range (600~1 800 cm-1) and characteristic range (674~713, 890~1 195, 1 341~1 399 and 1 549~1 612 cm-1) were used to develop the regression model for the rapid and quantitative analysis ofedifenphos using support vector machine regression (SVR), respectively. Simultaneously, we also evaluated the effect of the principle component analysis (PCA) on the construction of the regression model. The experiments showed the model developed with the spectra of characteristic range had lower prediction error and PCA can improve the prediction accuracy of the corresponding model further. The best regression model (RMSECV=0065 7 mg·L-1) was built with the spectra of characteristic range extracted by PCA, and the regression model can predict the concentration of edifenphos solutions accurately. Finally, to evaluate the effect of the method in practical application, the edifenphos residue on the apple peel were also detected using the proposed method, which was compared with the gas chromatography. The detection results showed the multiple detection value for each sample was in a small range, and the mean value was basically consistent with the detection value of gas chromatography, in which the maximal relative error was about 513 %. Additionally, the detection process of dynamic surface-enhanced Raman spectroscopy only needed 2 min and 2 μL samples volume, and the subsequent data analysis generally consumed several seconds. In a word, the dynamic surface-enhanced Raman spectroscopy can provide the high reproducible and precision detection of edifenphos, and the multivariate analysis method can realize the intelligent and rapid analysis of Raman spectra of edifenphos. Therefore, the dynamic surface-enhanced Raman spectroscopy with the multivariate analysis method is of great advantage for the rapid and accurate detection of edifenphos.

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中图分类号:O65737

DOI:10.3964/j.issn.1000-0593(2018)02-0454-05

基金项目:国家自然科学基金项目(61475163, 31401285), 安徽省科技支撑计划项目(1604a0702016), 安徽省自然科学基金项目(1608085QF127), 国家(863)计划项目(2013AA102302)资助

收稿日期:2016-09-12

修改稿日期:2017-02-05

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作者单位    点击查看

翁士状:安徽大学安徽农业生态大数据工程实验室, 安徽 合肥 230601
袁宝红:安徽三联学院电子电气工程学院, 安徽 合肥 230601
郑守国:中国科学院合肥技术创新工程院, 安徽 合肥 230031
张东彦:安徽大学安徽农业生态大数据工程实验室, 安徽 合肥 230601
赵晋陵:安徽大学安徽农业生态大数据工程实验室, 安徽 合肥 230601
黄林生:安徽大学安徽农业生态大数据工程实验室, 安徽 合肥 230601

联系人作者:翁士状(weng1989@mail.ustc.edu.cn)

备注:翁士状, 1989年生, 安徽大学安徽农业生态大数据工程实验室讲师

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引用该论文

WENG Shi-zhuang,YUAN Bao-hong,ZHENG Shou-guo,ZHANG Dong-yan,ZHAO Jin-ling,HUANG Lin-sheng. Dynamic Surface-Enhanced Raman Spectroscopy for Rapid and Quantitative Analysis of Edifenphos[J]. Spectroscopy and Spectral Analysis, 2018, 38(2): 454-458

翁士状,袁宝红,郑守国,张东彦,赵晋陵,黄林生. 动态表面增强拉曼光谱在敌瘟磷快速定量分析中的应用[J]. 光谱学与光谱分析, 2018, 38(2): 454-458

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