Determination of 11 neonicotinoid insecticides in pet food using LC–MS/MS - Liqiang Guo; Yajing Li; Le Wang; Xiaojie Xu; Kai Li (Journal of Analytical Science and Technology)
Abstract Pets have become indispensable family members in many households, and the pet food market is entering a period of rapid development. The safety of pet food has also garnered significant attention. Neonicotinoid insecticides, a class of compounds structurally similar to nicotine, account for over 25% of the global insecticide market and are widely present throughout nearly the entire ecosystem, including the raw materials used in pet food production. However, current detection, monitoring, and regulation of neonicotinoid insecticides focus on human food, neglecting pet food. Therefore, establishing detection methods for neonicotinoid insecticides in pet food is of great importance. The aim of this study was to develop an accurate, reproducible, and robust LC–MS/MS method for the detection of 11 neonicotinoids in pet food. QuEChERS was employed as a pretreatment method, and the extraction solvents, extraction methods, and dispersive solid-phase extraction sorbents were optimized. Matrix effects were compensated by using matrix-matched calibration curves prepared from blank pet food extracts. Regression lines with coefficients of determination (r 2) above 0.9990 were obtained for all analytes. The LODs in three types of pet food (solid complete nutrition rabbit food, semisolid cat food, and liquid cat food) ranged from 0.07 to 0.50 μg/kg, and LOQs ranged from 2.82 to 9.85 μg/kg, respectively. The average recoveries of the 11 neonicotinoids in four types of pet food (general adult dog food, solid complete nutrition rabbit food, semisolid cat food, and liquid cat food) ranged from 67.0 to 110.6%, with relative standard deviations (RSDs) between 3.07 and 14.6%. Furthermore, the method's robustness and reliability were confirmed through successful inter-laboratory validation, achieving excellent recoveries (87.1–111.7%) and relative standard deviations (RSDs < 11.4%).