基于表面增强拉曼光谱的苹果毒死蜱残留无损检测方法
Nondestructive Detection of Chlorpyrifos in Apples Based on Surface Enhanced Raman Scattering
Received date: 2015-05-11
Online published: 2015-07-23
Supported by
Project supported by the National Science and Technology Support Program (No. 2014BAD04B05).
对表面增强拉曼光谱(Surface Enhanced Raman Spectroscopy, SERS)技术在苹果毒死蜱残留量无损快速准确检测中的应用进行了研究. 以银溶胶作为表面增强剂, 采用实验室搭建的拉曼光谱系统, 无损地采集苹果样品拉曼光谱. 分别采用二阶导数法(Secondary Derivative Transformation, SD)和极小极大值自适应缩放法(Min-max Signal Adaptive Zooming, MSAZ)扣除所得拉曼光谱的荧光背景以消除样品和环境的干扰, 从而提高检测的准确度. 对获取光谱后的苹果样品采用标准理化方法检测其毒死蜱含量, 在最优条件下, 建立拉曼光谱与15.52～0.064 mg/kg范围内毒死蜱含量的线性关系, 结果表明毒死蜱的两个主要特征峰强度和其浓度呈良好的线性关系, 预测集的相关系数为0.969, 预测均方根误差为1.24 mg/kg. 本研究所开发的方法为果蔬安全品质的无损快速检测提供了一种新思路.
翟晨 , 彭彦昆 , 李永玉 , 徐田锋 . 基于表面增强拉曼光谱的苹果毒死蜱残留无损检测方法[J]. 化学学报, 2015 , 73(11) : 1167 -1172 . DOI: 10.6023/A15050326
A new non-destructive, rapid and accurate surface-enhanced Raman spectroscopy (SERS) assay method using silver colloidal nanoparticles for the detection of chlorpyrifos pesticides in apples is developed and optimized. Silver colloidal nanoparticles are paperad by reduction of silver nitrate with hydroxylamine hydrochloride and investigated using scanning electron microscope and visible-ultraviolet spectroscopy. Trace amounts of acetone, silver colloid, and nitric acid are dropped onto apple samples and air exposed for 20 s. The SERS spectra are collected non-destructively from the apple sample with a self-developed Raman system. The relative standard deviation (RSD) is calculated to evaluate the stability of the present method, including the stability of silver colloid and the stability of the Raman system, which indicates that the proposed strategy has good stability. Secondary derivative transformation and min-max signal adaptive zooming method are used to remove the fluorescence background for eliminating the environmental impact and improving the accuracy of SERS results. Raman spectral signals are collected from 30 points on each sample with 450 mW laser power and 3 s exposure time. Chlorpyrifos concentrations in 41 samples are determined with gas chromatography after SERS spectra taken. The characteristic peaks of chlorpyrifos at 621 cm-1 and 680 cm-1 are visible even at the concentration of 0.064 mg/kg. Under optimal conditions, linear regression models are established between the SERS signal and the chlorpyrifos concentrations in the range of 15.52～0.064 mg/kg. There are good linear relationships between the concentrations of chlorpyrifos pesticides in apples and the Raman intensities of its two major characteristic peaks, for the best of which the correlation coefficient of prediction (Rp) is 0.969 and the root mean square error of prediction (RMSEP) is 1.24 mg/kg. The present study provides a potential method for the rapid detection of pesticides which can help to strengthen the safety and quality of our food supplies.
[1] Li, R.; He, L.; Wei, W.; Hao, L.; Ji, X.; Zhou, Y.; Wang, Q. Food Control 2015, 51, 212.
[2] Kim, Y. A.; Lee, E. H.; Kim, K. O.; Lee, Y. T.; Hammock, B. D.; Lee, H. S. Anal. Chim. Acta 2011, 693, 106.
[3] Boeris, V.; Arancibia, J. A.; Olivieri, A. C. Anal. Chim. Acta 2014, 814, 23.
[4] Garcia-Rodriguez, D.; Cela-Torrijos, R.; Lorenzo-Ferreira, R. A.; Carro-Diaz, A. M. Food Chem. 2012, 135, 259.
[5] Zhang, Z.; Liu, R.; Xu, D.; Liu, J. Acta Chim. Sinica 2012, 70, 1686 (in Chinese). (张宗绵, 刘睿, 徐敦明, 刘景富, 化学学报, 2012, 70, 1686.)
[6] Wang, X.; Du, Y.; Zhang, H.; Xu, Y.; Pan, Y.; Wu, T.; Hu, H. Food Control 2014, 46, 108.
[7] Li, X.; Gao, G.; Niu, L.; Lin, M.; Qin, Z.; Liu, J.; Yao, H. Chinese J. Anal. Chem. 2012, 40, 1494 (in Chinese). (李雪, 高国明, 牛丽媛, 林漫漫, 覃宗定, 刘军贤, 姚辉璐, 分析化学, 2012, 40, 1494.)
[8] Ren, B.; Tian, Z. Modern Instruments 2004, 10, 1 (in Chinese). (任斌, 田中群, 现代仪器, 2004, 10, 1.)
[9] Li, C.; Lai, K.; Zhang, Y.; Pei, L.; Huang, Y. Acta Chim. Sinica 2013, 71, 221 (in Chinese). (李春颖, 赖克强, 张源园, 裴鹭, 黄轶群, 化学学报, 2013, 71, 221.)
[10] Chen, X.; Zhao, A.; Gao, Q.; Gan, Z.; Tao, W. Acta Chim. Sinica 2014, 72, 467 (in Chinese). (陈旭成, 赵爱武, 高倩, 甘自保, 陶文玉, 化学学报, 2014, 72, 467.)
[11] Stiufiuc, R.; Iacovita, C.; Lucaciu, C. M.; Stiufiuc, G.; Dutu, A. G.; Braescu, C.; Leopold, N. Nanoscale Res. Lett. 2013, 8, 47.
[12] Muller, C.; David, L.; Chis, V.; Pinzaru, S. C. Food Chem. 2014, 145, 814.
[13] He, L.; Chen, T.; Labuza, T. P. Food Chem. 2014, 148, 42.
[14] Xie, Y.; Mukamurezi, G.; Sun, Y.; Wang, H.; Qian, H.; Yao, W. Eur. Food Res. Technol. 2012, 234, 1091.
[15] Dhakal, S.; Li, Y.; Peng, Y.; Chao, K.; Qin, J.; Guo, L. J. Food Eng. 2014, 123, 94.
[16] Gao, G.; Li, X.; Qin, Z.; Wei, K.; Huang, H.; Liu, J.; Yao, H. Acta Optica Sinica 2013, 33, 0230002-1 (in Chinese). (高国明, 李雪, 覃宗定, 魏坤莲, 黄汉明, 刘军贤, 姚辉璐, 光学学报, 2013, 33, 0230002-1.)
[17] Canamares, M. V.; Garcia-Ramos, J. V.; Gomez-Varga, J. D.; Domingo, C.; Sanchez-Cortes, S. Langmuir 2005, 21, 8546.
[18] Kang, Y.; Wu, T.; Liu, B.; Wang, X.; Du, Y. Microchim. Acta 2014, 181, 1333.
[19] Jana, N. R.; Pal, T. Adv. Mater. 2007, 19, 1761.
[20] Si, M.; Zhang, P. Spectroscopy and Spectral Analysis 2001, 21, 501 (in Chinese). (司民真, 张鹏翔, 光谱学与光谱分析, 2001, 21, 501.)
[21] Li, D.; Cao, B.; Yang, G.; Li, Z.; Gao, S.; Zhou, M.; Men, Z.; Zhang, X. The Journal of Light Scattering 2009, 21, 132 (in Chinese). (李东飞, 曹彪, 杨光, 里佐威, 高淑琴, 周密, 门志伟, 张学亚, 光散射学报, 2009, 21, 132.)
[22] Shende, C.; Inscore, F.; Sengupta, A.; Stuart, J.; Farquharson, S. Sens. Instrum. Food Qual. Saf. 2010, 4, 101.
[23] Fan, Y. X.; Lai, K. Q.; Rasco, B. A.; Huang, Y. Q. Food Control 2014, 37, 153.
[24] Chen, Z.; Chen, J.; Han, M.; Wang, W.; Cao, W. J. Agro-Environ. Sci. 2011, 30, 2197 (in Chinese). (陈振德, 陈建美, 韩明三, 王文娇, 曹委, 农业环境科学学报, 2011, 30, 2197.)
[25] Zhang, X. M.S. Thesis, North West Agriculture and Forestry University, Yangling, 2007 (in Chinese). (张晓荣, 硕士论文, 西北农林科技大学, 杨凌, 2007.)
[26] Leopold, N.; Lendl, B. J. Phys. Chem. B 2003, 107, 5723.
/
| 〈 |
|
〉 |
