Simultaneous detection of 34 pesticide and metabolite residues in vegetables and fruits using improved QuEChERS-gas chromatography-tandem mass spectrometry with carboxylated multi-walled carbon nanotubes

With the rapid advancement of industrialization， agricultural intensification， and urbanization， issues pertaining to environmental pollution and food safety have become increasingly prominent. The use of pesticides plays a vital role in augmenting agricultural yield and quality， thereby serving as a foundational element of modern agriculture. However， improper or excessive application can lead to pesticide residues， which pose a significant threat to ecological systems and human health. Therefore， it is essential to enhance the surveillance of pesticide residue levels and to pioneer novel， efficient analytical methodologies. Sample pretreatment constitutes a crucial stage in the detection pipeline. Consequently， the development of rapid， green， and efficient sample pretreatment techniques has advanced significantly and gained widespread application in recent years. To effectively monitor pesticide residues in fruits and vegetables while reducing matrix interference， we established a high-throughput method for detecting 34 pesticides and their metabolites. This method utilizes a modified QuEChERS approach with carboxylated multi-walled carbon nanotubes （MWCNTs-COOH） as a clean-up sorbent， followed by analysis with gas chromatography-tandem mass spectrometry （GC-MS/MS）. The detection conditions for 34 pesticide and metabolite residues in fruits and vegetables were established through the optimization of the pretreatment process， chromatographic conditions， and mass spectrometry parameters. Specifically， fruit and vegetable samples were homogenized and extracted using a commercial QuEChERS EN extraction kit. Following shaking and centrifugation， the supernatant was transferred to a purification tube containing 10 mg of MWCNTs-COOH. Subsequently， this supernatant was evaporated to near-dryness under a gentle stream of nitrogen， reconstituted in ethyl acetate containing an internal standard， and filtered through a 0.22 μm nylon membrane filter. The target substances were separated on a HP-5MS UI quartz capillary column （30 m×0.25 mm×0.25 μm） with a programmed temperature gradient， detected by GC-MS/MS in multiple reaction monitoring （MRM） mode， and quantified using a matrix-matched internal standard calibration method. A full scan was performed across an <i>m/z</i> range of 45-500. Retention times and characteristic fragment ions were identified using the NIST mass spectral library. The most intense fragment ion was selected as the precursor ion. Parameters including dwell time and collision energy were then optimized to select the optimal product ion for each transition. Acetonitrile was selected as the optimal extraction solvent， and 10 mg was determined to be the optimal dosage of MWCNTs-COOH for purification. Under the optimized conditions， all 34 pesticides and metabolites demonstrated good linearities within their respective concentration ranges， with correlation coefficients （<i>R</i>²） greater than 0.997 4. The method demonstrated low limits of detection （LODs） and quantification （LOQs）， ranging from 0.023 to 0.817 μg/kg and 0.077 to 2.696 μg/kg， respectively. At three spiked concentration levels （low， medium， and high）， the recoveries for the 34 pesticides and metabolites ranged from 78.9% to 104.5%， with relative standard deviations （RSDs） of 1.0% to 7.8%. Additionally， matrix effects were evaluated for six types of fruits and vegetables at a spiked mass concentration of 0.2 μg/mL. The results indicated significant differences in matrix effects among the sample matrices. Chives exhibited the strongest matrix effect， followed by chili peppers and celery. Bananas， grapes， and peaches were the least affected. Overall， strong matrix effects were observed in over 20% of the analyte-matrix combinations， with vegetables generally exhibiting a stronger influence than fruits. The evaluation confirmed significant differences in matrix effects for the target pesticides and metabolites across the various sample matrices. Consequently， a matrix-matched calibration method was employed for accurate quantification to correct for these effects. The developed method demonstrates high purification efficiency， accuracy， and reproducibility， rendering it highly suitable for the simultaneous determination of multiple pesticide and metabolite residues in fruits and vegetables.