Determination of Dicofol in Tea Using Surface-Enhanced Raman Spectroscopy Coupled Chemometrics

Dicofol is a highly toxic residual pesticide in tea, which seriously endangers human health. A method for detecting dicofol in tea by combining stoichiometry with surface-enhanced Raman spectroscopy (SERS) technology was proposed in this study. AuNPs were prepared, and silver shells were grown on the surface of AuNPs to obtain core-shell Au@AgNPs. Then, the core-shell Au@AgNPs were attached to the surface of a PDMS membrane by physical deposition to obtain a Au@AgNPs/PDMS substrate. The limit of detection (LOD) of this substrate for 4-ATP is as low as 0.28 x 10(-11) mol/L, and the LOD of dicofol in tea is 0.32 ng/kg, showing high sensitivity. By comparing the modeling effects of preprocessing and variable selection algorithms, it is concluded that the modeling effect of Savitzky-Golay combined with competitive adaptive reweighted sampling-partial least squares regression is the best (Rp = 0.9964, RPD = 10.6145). SERS technology combined with stoichiometry is expected to rapidly detect dicofol in tea without labels.

Automated summary

A method combining stoichiometry with surface-enhanced Raman spectroscopy (SERS) technology was proposed for detecting dicofol, a highly toxic residual pesticide in tea. Core-shell Au@AgNPs were prepared and attached to a PDMS membrane to obtain a sensitive Au@AgNPs/PDMS substrate. The LOD for dicofol in tea using this substrate was found to be 0.32 ng/kg, showing high sensitivity. The best modeling effect for detection was achieved using Savitzky-Golay combined with competitive adaptive reweighted sampling-partial least squares regression (Rp = 0.9964, RPD = 10.6145). SERS technology combined with stoichiometry has the potential to rapidly detect dicofol in tea without labels.