Multi-residue analysis of pesticides in traditional Chinese medicines using gas chromatography-negative chemical ionisation tandem mass spectrometry

Abstract

In this study, a residue analysis method for the simultaneous determination of 107 pesticides in traditional Chinese medicines (TCMs), Angelica sinensis, A. dahurica, Leonurus heterophyllus Sweet, Pogostemon cablin and Lonicera japonica Thunb., was developed using gas chromatography coupled with tandem mass spectrometry in negative chemical ionisation mode (GC-NCI-MS/MS). NCI has advantages of high sensitivity and selectivity to chemicals with electron-withdrawing groups, and yields low background interference. For sample preparation, QuEChERS (quick, easy, cheap, effective, rugged and safe) was applied. Due to the unique characteristics of TCMs, the clean-up step was optimised by adjusting amounts of primary secondary amine, C18, graphitised carbon black and silica sorbents. Validation was mainly performed by determining analyte recoveries at four different spiking concentrations of 10, 50, 100 and 200 ng g⁻¹, with seven replicates at each concentration. Method trueness, precision, linearity of calibration curves, lowest calibrated levels (LCLs) and matrix effects were determined to demonstrate method and instrument performance. Among the 107 pesticides tested, approximately 80% gave recoveries from 80% to 110% and < 10% relative standard deviation (RSD). The LCLs for nearly all pesticides were 5 ng g⁻¹, and as low as 0.1 ng g⁻¹ for dichlofenthion, endosulfan sulphate, flumetralin, isofenphos-methyl, methyl-pentachlorophenyl sulphide and trifluralin. The results indicate that GC-NCI-MS/MS is an excellent technique for quantitative and qualitative analysis of targeted GC-amenable pesticides at ultra-trace levels, especially in complex matrices such as TCMs.

Keywords:

• GC-MS/MS
• negative chemical ionisation
• pesticide residue analysis
• QuEChERS sample preparation
• traditional Chinese medicines

Acknowledgements

The authors’ sincere thanks go to the entire Shanghai Institute for Food and Drug Control for technical support and excellent cooperation. Moreover, they gratefully acknowledge the support given by Agilent in the use of the instrument and software.

Additional information

Funding

The authors thank the Shanghai Institute for Food and Drug Control for financial support.