Detection of chlorpyrifos in apples using gold nanoparticles based on surface enhanced Raman spectroscopy

In this study, gold nanoparticles (AuNPs) were synthesized for rapid and sensitive characterization and quantification of chlorpyrifos in apples. Min-max signal adaptive zooming and second derivative transformation method were adopted to pre-process Raman spectral signal. The min-max signal adaptive zooming method showed a higher correlation coefficient than derivative transformation when developing linear calibration curve between chlorpyrifos pesticide and Raman spectral peak intensity. The present method had a high reproducibility with the relative standard deviation less than 15%. Regression models showed a good linear relationship (R=0.962) between intensity of characteristic spectral peaks (at 677 cm-1) and chlorpyrifos concentration on whole apples ranging from 0.13 mg/kg to 7.59 mg/kg. The application of surface enhancement Raman spectroscopy (SERS) detected chlorpyrifos pesticide to the detection limit of 0.13 mg/kg, which can be applied further for lower concentration in the future. The method presented in this study can provide a way-out for detection of pesticide residue in whole apple to trace amount.