Copyright (c) 2025 Pratik Singh, Arvind Maurya, Priyanka Gopi, Prof (Dr) Jasjeet Kaur, Dr. Sanjeev Kanojiya, Dr. Prateek Pandya
This work is licensed under a Creative Commons Attribution 4.0 International License.
Optimization of QuEChERS for High-Sensitivity Pesticide Detection in Simulated Biological Matrices Using UPLC-MS/MS
Corresponding Author(s) : Prateek Pandya
Asian Journal of Chemistry,
Vol. 37 No. 2 (2025): Vol 37 Issue 2, 2025
Abstract
This study presents an analytical method for detection and identification of pesticides in simulated biological matrices, i.e., simulated gastric fluid (SGF) and simulated urine sample (SUS). The approach combines the QuEChERS (quick, easy, cheap, effective, rugged and safe) sample preparation technique with ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) for pesticide analysis. A modified QuEChERS extraction protocol was developed to enhance pesticide recovery, improving both accuracy and reproducibility in pesticide quantification in seven different concentrations ranging from 2.5 ng/mL to 160 ng/mL. This combined methodology offers a robust tool for the precise detection and characterization of pesticide residues in biological fluids, with promising applications in toxicological analysis and forensic investigations. The developed method demonstrates the ability to reliably detect low concentrations of pesticide residues, establishing a strong foundation for comprehensive pesticide detection in complex biological matrices. As a supporting analysis, the molecular docking simulations were employed to explore the interaction dynamics of selected pesticides with key proteins unique to each matrix–pepsin (PEP) in SGF and Tamm-Horsfall (THF) protein in SUS. These simulations revealed the binding affinities and interaction strengths of the pesticides, providing further insight into their stability and persistence within different biological environments. This molecular perspective enhances the interpretation of pesticide residue behaviour, complementing the analytical results and deepening our understanding of pesticide dynamics in biological systems.
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- N. Sarigul, F. Korkmaz and I. Kurultak, Sci. Rep., 9, 20159 (2019); https://doi.org/10.1038/s41598-019-56693-4
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References
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F.P. Carvalho, Food Energy Secur., 6, 48 (2017); https://doi.org/10.1002/fes3.108
W. Boedeker, M. Watts, P. Clausing and E. Marquez, BMC Public Health, 20, 1875 (2020); https://doi.org/10.1186/s12889-020-09939-0
L.G. Sultatos, J. Toxicol. Environ. Health, 43, 271 (1994); https://doi.org/10.1080/15287399409531921
B.N. Okolonkwo, C.F. Amadi and O.E. Chimekagbe, Genet. Mol. Biol., 27 (2022); https://doi.org/10.9734/ajbgmb/2022/v12i230290
A.M. Badr, Environ. Sci. Pollut. Res. Int., 27, 26036 (2020); https://doi.org/10.1007/s11356-020-08937-4
S. Mukherjee and R.D. Gupta, J. Toxicol., 2020, 1 (2020); https://doi.org/10.1155/2020/3007984
M. Patel and P. Patil, IOSR J. Agric. Vet. Sci., 9, 55 (2016); https://doi.org/10.9790/2380-0910015560
M. Galadima, S. Singh, A. Pawar, S. Khasnabis, D.S. Dhanjal, A.G. Anil, P. Rai, P.C. Ramamurthy and J. Singh, Environ. Adv., 5, 100105 (2021); https://doi.org/10.1016/j.envadv.2021.100105
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Y. Hu, J. Wang and Y. Wu, Anal. Methods, 11, 5337 (2019); https://doi.org/10.1039/C9AY01506J
P.M. Dennis, Pathology, 21, 69 (1997); https://doi.org/10.1016/S0031-3025(16)36534-5
H. Musarurwa, L. Chimuka, V.E. Pakade and N.T. Tavengwa, J. Food Compos. Anal., 84, 103314 (2019); https://doi.org/10.1016/j.jfca.2019.103314
M. González-Curbelo, B. Socas-Rodríguez, A.V. Herrera-Herrera, J. González-Sálamo, J. Hernández-Borges and M. Rodríguez-Delgado, Trends Analyt. Chem., 71, 169 (2015); https://doi.org/10.1016/j.trac.2015.04.012
T. Rejczak and T. Tuzimski, Open Chem., 13, 000010151520150109 (2015); https://doi.org/10.1515/chem-2015-0109
R. Perestrelo, P. Silva, P. Porto-Figueira, J.A.M. Pereira, S. Medina, C. Silva and J.S. Câmara, Anal. Chim. Acta, 1070, 1 (2019); https://doi.org/10.1016/j.aca.2019.02.036
S.J. Lehotay, Methods Mol. Biol., 747, 65 (2011); https://doi.org/10.1007/978-1-61779-136-9_4
I.R. Pizzutti, J.V. Dias, A. Kok, C.D. Cardoso and G.M.E. Vela, J. Braz. Chem. Soc., (2016); https://doi.org/10.5935/0103-5053.20160012
A. Lawal and K.H. Low, Chemistry, 8, 693 (2021); https://doi.org/10.18596/jotcsa.845578
J. Wang, V. Yadav, A.L. Smart, S. Tajiri and A.W. Basit, Mol. Pharm., 12, 966 (2015); https://doi.org/10.1021/mp500809f
A. Stolarz, A. Alonso, W. De Bolle, S. Richter, C. Quetel, E. Ponzevera, H. Kühn, A. Verbruggen and R. Wellum, Preparation of Simulated Urine Samples containing Certified Uranium for the NUSIMEP 4 Campaign, Ec.Europa.Eu (2015).
N. Sarigul, F. Korkmaz and I. Kurultak, Sci. Rep., 9, 20159 (2019); https://doi.org/10.1038/s41598-019-56693-4
M.S. Gualdesi, J. Esteve-Romero, M.C. Briñón and M.A. Raviolo, J. Pharm. Biomed. Anal., 78-79, 52 (2013); https://doi.org/10.1016/j.jpba.2013.01.027
J. Eberhardt, D. Santos-Martins, A.F. Tillack and S. Forli, J. Chem. Inf. Model., 61, 3891 (2021); https://doi.org/10.1021/acs.jcim.1c00203
O. Trott and A.J. Olson, J. Comput. Chem., 31, 455 (2010); https://doi.org/10.1002/jcc.21334
P. Singh, P. Gopi, M.S.S. Rani, S. Singh and P. Pandya, J. Mol. Recognit., 37, e3076 (2024); https://doi.org/10.1002/jmr.3076
Dassault Systemes, Biovia Discovery Studio Comprehensive Modeling and Simulations for Life Sciences. In: Dassault Systemes (2023).
BIOVIA, Dassault Systèmes BIOVIA, Discovery Studio Modeling Environment, Release 2017, Dassault Systèmes San Diego (2017).
Scientific Working Group, J. Anal. Toxicol., 37, 452 (2013); https://doi.org/10.1093/jat/bkt054