Laboratorio Contaminanti Ambientali (Environmental Contaminants Laboratory). Istituto Zooprofilattico Sperimentale dell'Umbria e delle Marche. Via Salvemini 1, 06126, Perugia, Italy

Biograph
Ivan Pecorelli is the head of environmental contaminants laboratory at Istituto Zooprofilattico Sperimentale Umbria e Marche for the Italian National Health System. He supervises analytical activities and development and validation of new SOP under ISO/IEC 17025 quality management system. He is involved in the evaluation of food commodities compliance regarding pesticides and mycotoxins residues in food and feed. He has a Master’s in Pharmaceutical Chemistry and Technology and specialized in Food Chemistry and Technology from the University of Perugia – Italy as well as a Master’s in Environmental Management and Control from the University of Pisa – Italy. He is Principal investigator in several research projects for the Italian Ministry of Health and the European Union – European Social Fund, Italian Ministry of Labour, Health and Welfare in the field of Chemical Food Safety. He participates in the interlaboratory validation of many analytical methods organized by the European Union Reference Laboratory for mycotoxins and FERA (UK). He teaches several training courses both in Italy and abroad in the chemical safety of food and feed and authored more than 50 scientific publications in national and international journals and proceedings in national and international conferences.

Speech Title: Rapid determination of residues of pesticides in honey by GC-µECD and GC-MS/MS: Method validation and estimation of measurement uncertainty according to document No. SANCO/12571/2013

Abstract: Consumers are exposed to pesticides because residues can be found on harvested crops. Such pesticides could have severe undesirable effects if they are not strictly regulated. The amounts of residues found in food must be safe for consumers and must be as low as possible. The European Commission fixes MRLs for all food and animal feed. The Regulation covers pesticides currently or formerly used in agriculture in or outside the EU (around 1100). Farmers, traders and importers are responsible for food safety, which includes compliance with MRLs. Member State authorities are responsible for control and enforcement of the MRLs. To ensure that this is done in an adequate and uniform way, the Commission has three instruments: 1. The co-ordinated EU multi-annual control program sets out for each Member State the main pesticide-crop combinations to monitor and the minimum numbers of samples to take. 2. Community Reference Laboratories co-ordinate, train staff, develop methods of analysis and organize tests to evaluate the skills of the different national control laboratories. 3. Directorate on Health and Food Audits and Analysis of the Commission carries out inspections in the Member States to assess and audit their control activities. In this framework a rapid and straightforward analytical method has been developed and optimized for simultaneous determination of pesticides and veterinary drugs from honey samples by GC-µECD and GC-MS/MS. In accordance with Document No. SANCO/12571/2013 the method was validated by testing the following parameters: linearity, matrix effect, specificity, precision, trueness (bias), measurement uncertainty. The analytical performances were verified by participating in eight multiresidue Proficiency Tests (PT) organized by BIPEA obtaining satisfactory z-scores in all 70 determinations. Measurement uncertainty estimation is a requirement of ISO/IEC 17025 for routine analysis. In particular, the aim of this study is to estimate measurement uncertainty using validation and PTs data instead of a different, more complex and time consuming approach as recommended by Eurachem, Nordtest, Eurolab and Codex CAC/GL 59-2006 guidelines. Measurement uncertainty was estimated, according to the top-down approaches described in Appendix C of SANCO document, using the within-laboratory reproducibility relative standard deviation combined with laboratory bias using PTs data.