No abstract

A limited number of substances are authorised for the treatment of bees. Maximum residue limits (MRLs) are set for tetracyclines in several matrices, but not for honey. Nevertheless, tetracycline antibiotics may be used in order to prevent bacterial diseases and the loss of honey bee populations. In this study, a sensitive multi-residue LC-MS/MS method was developed and optimised for the quantitative and qualitative determination of tetracycline residues in honey. Homogenisation of samples under acidic conditions was performed and solid-phase extraction was carried out. The eluate was evaporated under nitrogen and dissolved in an aqueous methanol solution prior to filtration. A mobile phase composed of acetic acid-water and acetic acid-acetonitrile was used. Separation of tetracycline, oxytetracycline, chlortetracycline and doxytetracycline was achieved by using gradient elution on a C18 chromatography column. The analytical method was validated according to Commission Decision 2002/657/EC by the analysis of spiked samples around the recommended concentration of 20 μg kg(-1) by EURL Guidance Paper, December 2007. A matrix effect was observed, so quantification was based on an external matrix calibration curve. Calculated decision limits (CCα) were lower than 10 μg kg(-1) for all tetracyclines. Good linearity, repeatability and within-laboratory reproducibility were achieved.

This document is the "Report on SSD2 pilot results" which is the final deliverable of the project OC/EFSA/DATA/2015/02: "Pilot project on the implementation of SSD2 in the frame of the electronic transmission of harmonised data collection of analytical results to EFSA". The report describes the adaptation of the software and databases already developed as part of the project OC/EFSA/DCM/2013/05 for the implementation of SSD2 in the domains of Pesticide residues, Contaminants and Food Additives, to extend their applicability to the domain of Residues of Veterinary Medicinal Products (under Council Directive 96/23/EC). The report also describes the challenges encountered and provides recommendations to EFSA.

Cyprus alongside with another 4 countries has participated successfully in the Grant Agreement GP/EFSA/DATA/2016/01-GA 02, entitled: "Strategic Partnership with Cyprus on Data Quality". The project was co-financed by EFSA, aiming to help both EFSA and data providers from Member States to possess data of high quality in a quantitatively manageable way. The main objective of the grant agreement was the establishment of the data governance, coordination and improvement of the quality of the data submitted to EFSA, in the four domains of: Chemical occurrence, Zoonoses, Pesticide Residues and Veterinary Medicinal Product Residues. The project objectives have been achieved (a) by establishing a national data governance in Cyprus with the assignment of a National Data Coordinator and the definition of related Standard Operating Procedures, (b) by assigning a dedicated person responsible for collecting and transmitting the data to EFSA (Domain Specific Data Stewards) and by developing Key Performance Indicators (KPI) for quantifying the quality of the data transmitted to EFSA and (c) by enhancing the local LIMS software to provide better electronic communication with EFSA (supporting SSD2 transmissions) and making more robust the local automatic data management and validation. Cyprus has been submitting electronically data (for the three domains) to EFSA since 2011, using an in-house LIMS software where all the information concerning the samples and their analysis is recorded, stored and adapted for transmitting the mandatory elements to EFSA. However, no documented procedures were in place for ensuring or improving the data transmitted to EFSA, and no written procedures existed for the data governance at national level either. With the implementation of this project, the procedures were documented and performance indicators were developed, securing the data quality. National data governance was established. The development of ways to monitor, improve and enhance the quality of the data will ensure the long term quality of the data.