Rapid detection of five pesticide residues using complexes of gold nanoparticle and porphyrin combined with ultraviolet visible spectrum

Abstract
BACKGROUD Pesticides are widely used to control insect infestation and weeds in agriculture. However, concerns about the pesticide residues in agricultural products have been raised in recent years because of public interest in health and food quality and safety. Thus, rapid, convenient, and accurate analytical methods for the detection and quantification of pesticides are urgently required. RESULTS A nanohybrid system composed of gold nanoparticles (AuNPs) and tetrakis(N-methyl-4-pyridiniumyl) porphyrin (TMPyP) was used as an optical probe for the detection and quantification of five pesticides (Paraquat, Dipterex, Dursban, methyl thiophanate and Cartap). The method is based on the aggregation effect of pesticides on the carboxyl group modified by AuNPs. Subsequently, with the help of particle swarm optimization-optimized sample weighted least squares-support vector machine (PSO-OSWLS-SVM), all the pesticides could be successfully quantified. In addition, partial least squares discriminant analysis (PLS-DA) was applied and the five pesticides were satisfactorily recognized based on data array obtained from the ultraviolet visible (UV-visible) spectra of AuNP-TMPyP complex. Furthermore, the quantitative and qualitative analysis of the five pesticides could be also achieved in the complex real samples, in which all the relative standard deviations (RSDs) were less than 0.3 parts per thousand and all the linear absolute correlation coefficients were more than 0.9990. Furthermore, recognition rate of the training set and the prediction set based on multiplicative scatter correction (MSC), or second-order derivative (2nd derivative) UV-visible spectra in PLS-DA model could reach 100%. CONCLUSION This method was successfully applied for the rapid and accurate determination of multicomponent pesticide residues in real food samples. (c) 2020 Society of Chemical Industry
Funding Information
  • National Natural Science Foundation of China (21665022, 21706233, 21776321, 31972164)

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