Analytical and Bioanalytical Chemistry

, Volume 411, Issue 27, pp 7187–7196 | Cite as

Detection of systemic pesticide residues in tea products at trace level based on SERS and verified by GC–MS

  • De Zhang
  • Pei LiangEmail author
  • Jiaming Ye
  • Jing Xia
  • Yongfeng Zhou
  • Jie Huang
  • Dejiang Ni
  • Lisha Tang
  • Shangzhong Jin
  • Zhi YuEmail author
Research Paper


Surface-enhanced Raman spectroscopy (SERS) has the potential to detect pesticide residues in agricultural products. However, some systemic pesticides, such as chlorpyrifos, can enter the plant tissue, and not just stay on the surface. Consequently, many SERS studies halted at practical application because of its complexity. In this work, SERS technology was used to detect chlorpyrifos residues in tea products at the semiquantitative level. A simple pretreatment method effectively avoided interference of other fluorescent substances, and all major peaks could be distinguished on the basis of a novel substrate. A principal component analysis algorithm was applied to form a regression model, and a nanogram detection limit was obtained. Furthermore, chlorpyrifos residues in the same tea products were also measured by gas chromatography–mass spectrometry, and the results show a small range of errors. From the comparative study of the two detection methods, the results suggest the great promise of SERS technology for rapid inspection of agricultural products.


Chlorpyrifos Systemic pesticides Tea products Surface-enhanced Raman spectroscopy 



The project was financially supported by the Fundamental Research Funds for the Central Universities (program no. 2662017JC035), and the National Science Foundation for Young Scholars of China (grant no. 31000316), the Application Research Program of Commonweal Technology of Zhejiang Province (no. 2014C37042), the Zhejiang Province University Students in Scientific and Technological Innovation Activities (no. 2016R409011), the Science and Technology project of Zhejiang Province (no. 2016C33026), and the Science and Technology Project of Three Gorges Emigration. PL also thanks the National Demonstration Base for Micro/nano-fabrication & Optoelectronic Detection and the International Science and Technology Cooperation for the support.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Supplementary material

216_2019_2103_MOESM1_ESM.pdf (347 kb)
ESM 1 (PDF 346 kb)


  1. 1.
    Zhang W, Li X, Hua F, Chen W, Wang W, Chu GX, et al. Interaction between ester-type tea catechins and neutrophil gelatinase-associated lipocalin: inhibitory mechanism. J. Agric. Food Chem. 2018;66(5):1147–56. Scholar
  2. 2.
    Yang X, Xu DC, Qiu JW, Ma Y, Wang J. Simultaneous determination of 118 pesticide residues in Chinese teas by gas chromatography-mass spectrometry. In: Central theme, technology for all: sharing the knowledge for development. Proceedings of the International Conference of Agricultural Engineering, Xxxvii Brazilian Congress of Agricultural Engineering, International Livestock Environment Symposium - Iles Viii, Iguassu Falls City, Brazil, August To September, 2008. pp 39-46Google Scholar
  3. 3.
    Hou R, Zhang Z, Pang S, Yang T, Clark JM, He L. Alteration of the nonsystemic behavior of the pesticide ferbam on tea leaves by engineered gold nanoparticles. Environ. Sci. Technol. 2016;50(12):6216–23. Scholar
  4. 4.
    Lee WJ, Hoppin JA, Rusiecki JA, Kamel F, Blair A, Sandler DP. Mortality among pesticide applicators exposed to chlorpyrifos in the Agricultural Health Study. Environ. Health Perspect. 2007;115(4):528–34.CrossRefGoogle Scholar
  5. 5.
    Flaskos J. The developmental neurotoxicity of organophosphorus insecticides: a direct role for the oxon metabolites. Toxicol. Lett. 2012;209(1):86–93.CrossRefGoogle Scholar
  6. 6.
    Galloway T, Handy R. Immunotoxicity of organophosphorous pesticides. Ecotoxicology. 2003;12(1-4):345–63.CrossRefGoogle Scholar
  7. 7.
    Farag AT, Radwan AH, Sorour F, Okazy AE, El-Agamy ES, El-Sebae EK. Chlorpyrifos induced reproductive toxicity in male mice. Reprod. Toxicol. 2010;29(1):80–5.CrossRefGoogle Scholar
  8. 8.
    Salic A, Mitchison TJ. A chemical method for fast and sensitive detection of DNA synthesis in vivo. Proc. Natl. Acad. Sci. U. S. A. 2008;105(7):2415–20.CrossRefGoogle Scholar
  9. 9.
    Ghalwa NA, Abushawish HM, Hamada M, Hartani K, Basheer AAH. Studies on degradation of diquat pesticide in aqueous solutions using electrochemical method. Am. J. Anal. Chem. 2012;3(2):99–105.CrossRefGoogle Scholar
  10. 10.
    Wang J, Cheung W, Leung D. Determination of pesticide residue transfer rates (percent) from dried tea leaves to brewed tea. J. Agric. Food Chem. 2014;62(4):966–83. Scholar
  11. 11.
    Zhu P, Miao H, Du J, Zou JH, Zhang GW, Zhao YF, et al. Organochlorine pesticides and pyrethroids in Chinese tea by screening and confirmatory detection using GC-NCI-MS and GC-MS/MS. J. Agric. Food Chem. 2014;62(29):7092–100. Scholar
  12. 12.
    Cao Yalin, Tang Hang, Chen Dazhou,Li Lie. A novel method based on MSPD for simultaneous determination of 16 pesticide residues in tea by tea by LC–MS/MS. Journal of Chromatography B. 2015;998–999:72–79.CrossRefGoogle Scholar
  13. 13.
    Rahim AA, Nofrizal S, Saad B. Rapid tea catechins and caffeine determination by HPLC using microwave-assisted extraction and silica monolithic column. Food Chem. 2014;147:262–8. Scholar
  14. 14.
    Cialla-May D, Zheng XS, Weber K, Popp J. Recent progress in surface-enhanced Raman spectroscopy for biological and biomedical applications: from cells to clinics. Chem. Soc. Rev. 2017;46(13):3945–61. Scholar
  15. 15.
    Ruditskiy A, Xia Y. The science and art of carving metal nanocrystals. ACS Nano. 2017;11(1):23–7. Scholar
  16. 16.
    Tian Z-H, Ren B, Wu D-Y. Surface-Enhanced Raman Scattering: From Noble to Transition Metals and from Rough Surfaces to Ordered Nanostructures. The Journal of Physical Chemistry B. 2002;106(37):9463–9483.CrossRefGoogle Scholar
  17. 17.
    Zhou Q, Meng G, Liu J, Huang Z, Han F, Zhu C, et al. A hierarchical nanostructure-based surface-enhanced Raman scattering sensor for preconcentration and detection of antibiotic pollutants. Adv. Mater. Technol. 2017;2(6):1700028. Scholar
  18. 18.
    Panneerselvam R, Liu G-K, Wang Y-H, Liu J-Y, Ding S-Y, Li J-F, et al. Surface-enhanced Raman spectroscopy: bottlenecks and future directions. Chem. Commun. 2018. Scholar
  19. 19.
    Zhang D, Liang P, Yu Z, Huang J, Ni D, Shu H, et al. The effect of solvent environment toward optimization of SERS sensors for pesticides detection from chemical enhancement aspects. Sens Actuators B. 2018;256:721–8. Scholar
  20. 20.
    Zhang J, Huang F, Lin Z. Progress of nanocrystalline growth kinetics based on oriented attachment. Nanoscale. 2010;2(1):18–34. Scholar
  21. 21.
    Li C, Cheng Y, Xu S, Chao Z, Zhen L, Liu X, et al. Ag2O@Ag core-shell structure on PMMA as low-cost and ultra-sensitive flexible surface-enhanced Raman scattering substrate. J. Alloys Compd. 2017;695:1677–84.CrossRefGoogle Scholar
  22. 22.
    Cabrera C, Artacho R, Giménez R. Beneficial effects of green tea—a review. J. Am. Coll. Nutr. 2006;25(2):79–99. Scholar
  23. 23.
    Feng S, Hu Y, Ma L, Lu X. Development of molecularly imprinted polymers-surface-enhanced Raman spectroscopy/colorimetric dual sensor for determination of chlorpyrifos in apple juice. Sens. Actuators B. 2017;241:750–7. Scholar
  24. 24.
    Huang S, Hu J, Guo P, Liu M, Wu R. Rapid detection of chlorpyriphos residue in rice by surface-enhanced Raman scattering. Anal. Methods. 2015;7(10):4334–9. Scholar
  25. 25.
    Xu Q, Guo X, Xu L, Ying Y, Wu Y, Wen Y, et al. Template-free synthesis of SERS-active gold nanopopcorn for rapid detection of chlorpyrifos residues. Sens. Actuators B. 2017;241:1008–13. Scholar
  26. 26.
    Chen J, Huang Y, Kannan P, Zhang L, Lin Z, Zhang J, et al. Flexible and adhesive surface enhance Raman scattering active tape for rapid detection of pesticide residues in fruits and vegetables. Anal. Chem. 2016;88(4):2149–55. Scholar
  27. 27.
    Shende C, Inscore F, Sengupta A, Stuart J, Farquharson S. Rapid extraction and detection of trace Chlorpyrifos-methyl in orange juice by surface-enhanced Raman spectroscopy. Sensing and Instrumentation for Food Quality and Safety. 2010;4(3-4):101–7. Scholar
  28. 28.
    He Y, Xiao S, Dong T, Nie P. Gold nanoparticles with different particle sizes for the quantitative determination of chlorpyrifos residues in soil by SERS. Int J Mol Sci 2019;20(11). doi: CrossRefGoogle Scholar
  29. 29.
    Hui-Min MA, Wang YQ, Qian H. International comparative analysis of standards for tea pesticide residue limits. China Tea Processing 2012.Google Scholar
  30. 30.
    You T, Jiang L, Yin P, Shang Y, Zhang D, Guo L, et al. Direct observation of p,p '-dimercaptoazobenzene produced from p-aminothiophenol and p-nitrothiophenol on Cu2O nanoparticles by surface-enhanced Raman spectroscopy. J. Raman Spectrosc. 2014;45(1):7–14. Scholar
  31. 31.
    Yang C, Liang P, Tang L, Zhou Y, Cao Y, Wu Y, et al. Synergistic effects of semiconductor substrate and noble metal nano-particles on SERS effect both theoretical and experimental aspects. Appl. Surf. Sci. 2018;436:367–72. Scholar
  32. 32.
    Manisekaran R. Literature survey on magnetic, gold, and core-shell nanoparticles. Design and Evaluation of Plasmonic/Magnetic Au-MFe2O4(M-Fe/Co/Mn). 2018. Scholar
  33. 33.
    Chen Y, Bai X, Su L, Du Z, Shen A, Materny A, et al. combined labelled and label-free SERS probes for triplex three-dimensional cellular imaging. Sci. Rep. 2016;6:19173.CrossRefGoogle Scholar
  34. 34.
    Wu YX, Liang P, Dong QM, Bai Y, Yu Z, Huang J, et al. Design of a silver nanoparticle for sensitive surface enhanced Raman spectroscopy detection of carmine dye. Food Chem. 2017;237:974–80. Scholar
  35. 35.
    Ratkaj M, Biljan T, Miljanić S. Quantitative analysis of entacapone isomers using surface-enhanced Raman spectroscopy and partial least squares regression. Appl. Spectrosc. 2012;66(12):1468.CrossRefGoogle Scholar
  36. 36.
    Janči VT, Kljusurić D, Gajdoš MJ, Ivanda L, Vidaček M. Sanja Artificial neural network models for determination of histamine in fish by surface enhanced Raman spectroscopy. In: Wefta Conference, 2017.Google Scholar
  37. 37.
    Li SX, Zeng QY, Li LF, Zhang YJ, Wan MM, Liu ZM, et al. Study of support vector machine and serum surface-enhanced Raman spectroscopy for noninvasive esophageal cancer detection. J. Biomed. Opt. 2013;18(2):27008.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • De Zhang
    • 1
  • Pei Liang
    • 2
    Email author
  • Jiaming Ye
    • 3
  • Jing Xia
    • 1
  • Yongfeng Zhou
    • 2
  • Jie Huang
    • 2
  • Dejiang Ni
    • 1
  • Lisha Tang
    • 2
  • Shangzhong Jin
    • 2
  • Zhi Yu
    • 1
    Email author
  1. 1.Key Laboratory of Horticultural Plant Biology, Ministry of Education, College of Horticulture & Forestry SciencesHuazhong Agricultural UniversityWuhanChina
  2. 2.College of Optical and Electronic TechnologyChina Jiliang UniversityHangzhouChina
  3. 3.Analysis and Testing CenterYangtze Delta Region Institute of Tsinghua UniversityJiaxingChina

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