Optimization of a Multi-Residue Screening Method for the Detection of 71 Antimicrobial Residues in Milk Products: the Case of Labneh - Anses - Agence nationale de sécurité sanitaire de l’alimentation, de l’environnement et du travail Accéder directement au contenu
Article Dans Une Revue Food Analytical Methods Année : 2023

Optimization of a Multi-Residue Screening Method for the Detection of 71 Antimicrobial Residues in Milk Products: the Case of Labneh

Résumé

Labneh is a dairy product highly consumed in Lebanon, prepared from straining yogurt. Because there is a lack of surveillance of the antimicrobial residues (AMRs) in milk and dairy products in Lebanon, this project was launched to develop a screening method for the detection of AMRs in labneh. In this study, we optimized a multi-residue screening method to detect 71 AMRs based on liquid–liquid extraction using buffered acetonitrile with 2 M ammonium acetate followed by freezing out the supernatant for 30 min at −23 °C. The extracts were then analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS). This method was then validated against the Guidance (EU) CRLs 2010/01. The performance of the method showed a good specificity with no false negative rate and detection capabilities (CCβ) were below maximum residue levels (MRLs) for all analytes. In addition, this method demonstrated acceptable recoveries estimated in the range 70–130% for 92% of the analytes and the precision was below 30% except for a few macrolides and some β-lactams with an intra-lab reproducibility ranging between 7.30–40.61% and 2.90–47.85%, respectively. Our results showed that the developed method can be applied as a screening method and with acceptable variation in quantitation for the majority of AMRs.
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anses-04205208 , version 1 (12-09-2023)

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Ghinwa Ismail, Khaled El Hawari, Farouk Jaber, Eric Verdon, Mohamad Al Iskandarani. Optimization of a Multi-Residue Screening Method for the Detection of 71 Antimicrobial Residues in Milk Products: the Case of Labneh. Food Analytical Methods, 2023, ⟨10.1007/s12161-023-02513-5⟩. ⟨anses-04205208⟩

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