A rapid procedure for the determination of 482 pesticide residues in Chinese Materia Medica by GC-Q-TOF/MS and LC-Q-TOF/MS (379 pesticides for LC, 327 pesticides for GC, and 226 pesticides for both) was developed. Radix codonopsis was chosen as the matrix for verification, and a comparative study on the QuEChERS sample preparation was carried out, between a fully automated workstation and manual operation, in terms of limits of quantitation, recovery rate and RSD at 3 spiked levels of 10 μg kg⁻¹, 20 μg kg⁻¹ and 100 μg kg⁻¹. In the linear range of each pesticide in a concentration range of 5–100 μg L⁻¹, the linear correlation coefficients R² of 85% of the pesticides for GC and 88% for LC were equal to or greater than 0.990. Taking recovery 70–120% and RSD ≤ 20% as the satisfactory standard, the automated workstation performed better at 10 μg kg⁻¹ and 20 μg kg⁻¹ than manual operation, and the numbers of satisfactory pesticides of GC & LC were 401 and 418 for the automated approach, and 378 and 400 for manual, while the two approaches were almost even at 100 μg kg⁻¹, 421 vs. 424. Besides, the automated workstation presented lower RSD (more pesticides ≤10%) and better recovery quality (more pesticides within 90–110%). Following the method verification, 50 Radix codonopsis samples purchased from local markets were prepared with the automated workstation and analyzed by GC and LC-Q-TOF/MS. 18 pesticides were detected in 38 samples, one of which was a highly toxic pesticide. The automated QuEChERS workstation can handle 40 samples in one cycle within 6 hours, and realize whole-process automation covering from samples after “weighing” to “injection into vials”. The batch-to-batch, day-to-day, and lab-to-lab consistency and 24 × 7 workability of the automated solution have demonstrated a promising and ideal replacement for manual operation in sample preparation.

Pesticide residue is a prominent factor that leads to food safety problems. For this reason, many countries sample and detect pesticide residues in food every year, which generates a large amount of pesticide residue data. However, the way to deeply analyze and mine these data to quickly identify food safety risks is still an unresolved issue. In this study, we present an intelligent analysis system that supports the collection, processing, and analysis of detection data of pesticide residues. The system is first based on a number of databases such as maximum residue limit standards for the fusion of pesticide residue detection results; then, it applies a series of statistical methods to analyze pesticide residue data from multiple dimensions for quickly identifying potential risks; it uses the Apriori algorithm to mine the implicit association in the data to form pre-warning rules; finally, it applies Word document automatic generation technology to automatically generate pesticide residue analysis and pre-warning reports. The system was applied to analyze the pesticide residue detection results of 42 cities in mainland China from 2012 to 2015. Application results show that the system proposed in this study can greatly improve the depth, accuracy and efficiency of pesticide residue detection data analysis, and it can provide better decision support for food safety supervision.

A high-throughput screening and quantification method for 479 pesticides in green tea was established based on solid-phase extraction combined with liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Pesticides were extracted from samples using an optimized SPE (TPT cartridges) procedure. LC-QTOF-MS in All Ions MS/MS scan mode acquired full MS data for quantification and product ion spectra for identification. LC-QTOF-MS quantification was achieved using matrix-matched standard calibration curves to achieve the optimal method accuracy. The method performance characteristics included the linearity, overall recovery, precision, and measurement uncertainty being evaluated. The validation results exhibited a good sensitivity with the LOQs of 5–55 µg/kg, which was satisfactory for their MRLs in China or the EU. The recoveries of more than 92.7% of the 479 pesticides in green tea were 70–120% at the three spiked levels with a precision of ≤20%. Finally, this method was employed to analyze 479 pesticides in 95 tea samples from markets in China. The test results of the tea samples showed that tolfenpyrad, buprofezin, and pyridaben were found with lower concentrations. The method has effectively improved the determination efficiency of pesticide residue screening by high-resolution mass spectrometry in green tea.