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Supplementary Data - Simple, Sensitive, and Reliable Method for the Simultaneous Determination of Multiple Antibiotics in vegetables.pdf (262.82 kB)

A simple, sensitive, and reliable method for the simultaneous determination of multiple antibiotics in vegetables through SPE-HPLC-MS/MS - Supplementary Data

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posted on 2019-08-19, 04:20 authored by Yao Feng, Wen-Juan Zhang, Jian-Ming Xue, Shuo-Qing Zhang, Zhao-Jun Li

Antibiotics, widely used in livestock breeding, enter the environment through animal manure because of incomplete absorption in animals, especially the farmland ecosystem. Therefore, antibiotics may be adsorbed by plants and even become hazardous to human health through the food chain. In this study, a simple, sensitive, and reliable method was developed for the simultaneous determination of eleven antibiotics, including four sulfonamides, two tetracyclines, three fluoroquinolones, tylosin, and chloramphenicol in different vegetable samples using SPE-HPLC-MS/MS. Vegetable samples were extracted by acetonitrile added with hydrochloric acid (125:4, v/v). The extracts were enriched by circumrotating evaporation, and then cleaned through SPE on a hydrophilic-lipophilic balance (HLB) cartridge. All compounds were determined on a C18 reverse phase column through HPLC-MS/MS. The mean recoveries of 11 antibiotics from spiked samples of vegetables ranged from 71.4% to 104.0%. The limits of detection and quantification were 0.06–1.88 μg/kg and 0.20–6.25 μg/kg, respectively. The applicability of this technique demonstrated its good selectivity, high efficiency, and convenience by the analysis of 35 vegetable samples available from a vegetable greenhouse. Antibiotic residues in vegetables have aroused wide concern from the public. Therefore, standards should be established for antibiotic residues in vegetables to ensure food safety and human health.

Funding

National Key Technology R&D Program of China (No. 2018YFD0500206)

National Natural Science Foundation of China (No. 31572209)

Chinese Academy of Agricultural Sciences (Project No. 2017-13)

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