Abstract
Accurate antibiotic residue detection in honey is crucial for food safety and veterinary drug regulation in apiculture. Honey is a particularly challenging matrix. The high sugar content， strong viscosity， and pronounced matrix effects of the sample make it difficult to extract and detect target analytes effectively. Moreover， multiple antibiotics are used in beekeeping， while existing residue standards often provide incomplete coverage. These limitations highlight the urgent need for a robust and environmentally friendly multi-residue analytical method. In this study， a green and sensitive method was developed for the simultaneous determination of 38 antibiotic residues， including chloramphenicol， quinolones， and sulfonamides， in honey. Sample preparation was based on magnetic dispersive solid-phase extraction （MDSPE） using a mixed-mode hydrophilic-lipophilic balance （mHLB） magnetic sorbent. This sorbent combines hydrophilic and hydrophobic interactions， enabling efficient extraction of chemically diverse antibiotics. Method optimization was performed systematically. A Plackett-Burman design was used to screen seven factors influencing extraction. The sorbent dosage， sample volume， and extraction time were identified as the most significant. These variables were further optimized by response surface methodology， which minimized the number of experimental runs while clarifying interactions among factors. The final protocol required only 10 mg of sorbent， 5 mL of sample extract， and 1 mL of methanol for elution. This miniaturized approach represents an environmentally sustainable method. Chromatographic separation was achieved on a Poroshell 120 SB-C18 column （100 mm×2.1 mm，2.7 μm） using a gradient of acidified methanol and water. Detection was carried out by high performance liquid chromatography-tandem mass spectrometry （HPLC-MS/MS） with electrospray ionization operated in both positive and negative modes， and multiple reaction monitoring ensured high sensitivity and selectivity. The method was fully validated. Excellent linearity was obtained for all analytes in the range of 0.02-200 μg/L， with correlation coefficients above 0.99. Limits of detection ranged from 0.01 to 0.12 μg/kg， while limits of quantification ranged from 0.02 to 0.39 μg/kg. Recoveries at three spiked levels ranged from 70.5% to 109.3%， with relative standard deviations （RSDs） below 10.8%. Matrix effects ranged from 0.82 to 1.19， indicating effective suppression of interference and reliable quantification in complex honey samples. The method was successfully applied to 42 commercial honey batches from different geographical regions. Several antibiotics were detected， including chloramphenicol， as well as other compounds without established maximum residue limits. These findings indicate potential misuse of antibiotics in beekeeping and reveal important gaps in current regulatory frameworks， underscoring the need for continuous monitoring and timely updates to residue standards. In addition to high sensitivity and wide applicability， the method demonstrated strong environmental advantages. Green performance was assessed using the AGREEprep tool， which evaluates twelve principles of sustainable sample preparation. The method achieved a high score of 0.73， reflecting minimal solvent consumption， low sorbent use， low energy demand， and negligible hazardous waste. These features align with the principles of green analytical chemistry and significantly reduce the environmental impact of residue analysis. In conclusion， this work establishes an MDSPE-HPLC-MS/MS for multi-class antibiotic residue detection in honey. The method combines simplicity， sensitivity， robustness， and environmental compatibility. It provides reliable technical support for risk assessment and regulatory improvement of antibiotic residues in honey， and it offers a valuable reference for the development of green analytical methods for trace contaminants in other complex food and environmental matrices.