High-Throughput Mega-Method for the Analysis of Pesticides, Veterinary Drugs, and Environmental Contaminants by Ultra-High-Performance Liquid Chromatography-Tandem Mass Spectrometry and Robotic Mini-Solid-Phase Extraction Cleanup + Low-Pressure Gas Chromatography-Tandem Mass Spectrometry, Part 1: Beef.

Abstract

In this work, a new mega-method of sample preparation called "QuEChERSER" (more than QuEChERS) is being presented for the first time. Fast, efficient, and cost-effective analysis of chemical contaminants in meat is useful for international trade, domestic monitoring, risk assessment, and other purposes. The goal of this study was to develop and validate a simple high-throughput mega-method for residual analysis of 161 pesticides, 63 veterinary drugs, 24 metabolites, and 14 legacy environmental contaminants (polychlorinated biphenyls) in bovine muscle for implementation in routine laboratory analyses. Sample preparation of 2 g test portions entailed QuEChERS-based extraction with 10 mL of 4:1 (v/v) acetonitrile/water, and then 204 μL was taken, diluted, and ultracentrifuged prior to analysis of veterinary drugs and pesticides by ultra-high-performance liquid chromatography-tandem mass spectrometry. The remaining extract was salted out with 4:1 (w/w) anhydrous MgSO4/NaCl, and 1 mL was transferred to an autosampler vial for automated mini-cartridge solid-phase extraction (Instrument Top Sample Preparation) cleanup with immediate injection using fast low-pressure gas chromatography-tandem mass spectrometry analysis. The automated cleanup and both instruments were all operated in parallel in 13-15 min cycle times per sample. Method validation according to United States Department of Agriculture requirements demonstrated that 221 (85%) of the 259 analytes gave average recovery between 70 and 120% and interday relative standard deviation of ≤25%. Analysis of a certified reference material for veterinary drugs in freeze-dried bovine muscle was also very accurate, further demonstrating that the QuEChERSER mega-method can be implemented to save time, labor, and resources compared to current practices to use multiple methods to cover the same analytical scope.