REP11/PR JOINT FAO/WHO FOOD STANDARDS PROGRAMME ...

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REP11/PR Appendix IX 99 Table 1. Selection and Examples of Representative Commodities (continued) Codex Group / Subgroup Subgroup 006C, Assorted tropical and subtropical, Inedible, Rough or Hairy Peel - Large Subgroup 006D, Assorted tropical and subtropical, Inedible Peel - Cactus Subgroup 006E, Assorted tropical and subtropical, Inedible Peel - Vines Subgroup 006F, Assorted tropical and subtropical, Inedible Peel - Palms Examples of Representative Commodities 1,2 Atemoya and Pineapple Pitahaya and Prickly pear Kiwifruit or Passionfruit Muriti or Palmyra Palm Extrapolation to the following commodities Inedible rough or hairy peel - Large (FI 2023): Atemoya; Biriba; Breadfruit; Champedak; Cherimoya; Custard apple; Durian; Elephant apple; Ilama; Jackfruit; Mammy apple; Marmalade-box; Marang; Monkey-bread tree; Pineapple; Poshte; Pulasan; Rambutan; Sapodilla; Sapote, Mammey; Screwpine; Soncoya; Soursop; Sugar apple; Sun sapote Inedible Peel - Cactus (FI 2024): Pitahaya; Prickly pear; Saguaro Inedible Peel - Vines (FI 2025): Granadilla; Granadilla, Giant; Kiwifruit; Monstera; Passionflower, Winged-stem; Passionfruit; Passionfruit, banana Inedible Peel - Palms (FI 2026): Guriri; Muriti; Palmyra Palm; Salak
REP11/PR Appendix X 100 APPENDIX X DRAFT REVISION OF THE GUIDELINES ON THE ESTIMATION OF MEASUREMENT UNCERTAINTY OF RESULTS FOR THE DETERMINATION OF PESTICIDE RESIDUES (ANNEX TO CAC/GL 59-2006) (At Step 5/8) Introductory notes 1. As noted in the Guideline document CAC/GL 59-2006, the estimation of measurement uncertainty (MU) associated with analytical data is a requirement for laboratories accredited under ISO/IEC 17025 and an expectation for all laboratories operating under Good Laboratory Practice (GLP) in pesticide residue analysis. Decisions in regard to compliance of food, whether for domestic or international standards for chemical residues and contaminants, need to take into consideration the uncertainty associated with the test results reported by laboratories for analysis of specific lots or consignments. 2. It is not uncommon for laboratories to report widely different estimates of MU in Proficiency Tests (PT) despite the fact that they employ very similar test methods for analysis. This evidence suggests that the estimation of MU still appears to be a developing science for a number of food laboratories. This annex is intended to describe some of the options laboratories might employ in estimating measurement uncertainty, particularly the use of in-house method validation, quality control and long-term precision data for multi-residue pesticide methods. It is also anticipated that a more harmonised approach to the estimation of MU for pesticide residue results will minimise possible disputes in compliance decisions for residue levels near MRLs. 3. There are broadly two approaches commonly employed for the determination of MU; the so-called GUM (Guide to the Expression of Uncertainty in Measurement) or ‘bottom-up’ approach and the ‘top-down’ procedures based around application of analytical precision and bias. 4. The GUM approach is based on a rigorous analysis of all the individual components of an analytical process and the estimation of random and systematic errors assigned to these steps. This process, whilst initially very laborious, requires the analyst to have or develop a detailed understanding of the analytical steps on the process and identify the critical control points in the method. Unless all steps are considered in the process, it is possible to underestimate the MU. On the other hand, some operational errors may cancel out which, if ignored, could provide an overestimate of the uncertainty. It is generally acknowledged that the bottom-up approach is more suited to physical metrology than to analytical chemistry activities and, in particular, to the more complex multi-pesticide residue methods. 5. Proponents of the top-down approach note that laboratory data collected from in-house validation, long-term precision and analytical quality control (QC) is likely to provide more reliable information on MU. Where available, PT data can also be used to estimate MU, either as the sole basis for estimates or more often in combination with in-house data. The inter-laboratory reproducibility data from PT studies can also provide a useful ‘benchmark’ for single laboratory estimates. 6. All options should be considered in the estimation of MU. The initial aim should be to obtain the best possible estimate using the information available. Initial laboratory estimates should be verified by comparison with alternative methods, literature reports and comparisons from PT studies. Furthermore professional judgement has an important role when estimating and verifying measurement uncertainty. Estimates should be reviewed as more precision data becomes available, for example, within-batch QC data routinely generated during the course of an analytical program. 7. This Annex focuses on the estimation of MU using the top-down approach, based on data obtained from different sources. Applying a default value for MU for pesticide residues in foods 8. EU member states have adopted a MU ‘default’ value of +/- 50% for pesticide residues in food consignments entering the EU. The default value is based around the statistical results of a number of EU-based PT studies involving competent residue laboratories participating in a number of multi-residue studies on fruit and vegetables. The mean relative standard deviations reported from a number of these studies have ranged between 20 to 25% providing a MU approximating to 50%. 9. In the absence of other statistical data, a laboratory testing food commodities for compliance with EU pesticide MRL regulations can adopt a default MU of 50% provided it could establish its analytical proficiency through participation in EU or similar PT studies and/or it can demonstrate acceptable long-term precision and bias associated with its test results. In the longer term however, it should be incumbent on the laboratory to verify its adoption of the default MU by independently estimating MU based on in-house precision and validation data. Precision data derived from the use of the Horwitz relationship 10. In the absence of data from inter-laboratory studies on a particular method, the reproducibility standard deviation, and hence MU, may be determined from an equation reported by Horwitz which correlates reproducibility standard deviation with analyte concentration. The Horwitz relationship between coefficient of variation (CV) and analyte concentration is based on the results from a large number of food-based collaborative studies reported in the literature. The Horwitz equation is also a helpful tool to compare in-house MU estimates against the expected value derived from published inter-laboratory studies.
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E REP11/PR JOINT FAO/WHO FOOD STAND
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CL 2011/9-PR ii PART C: REQUEST FOR
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CL 2011/9-PR iv • agreed that Mem
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CL 2011/9-PR vi Fluopicolide (235)
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CL 2011/9-PR viii LIST OF ABBREVIAT
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REP11/PR 2 10. In reply to the requ
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REP11/PR 4 REPORT ON 2010 JMPR RESP
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REP11/PR 6 52. The Committee decide
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REP11/PR 8 FLUBENDIAMIDE (242) 77.
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REP11/PR 10 Assorted tropical and s
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REP11/PR 12 STATUS OF THE DRAFT PRI
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REP11/PR 14 128. Some delegations p
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REP11/PR 16 148. The Committee note
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REP11/PR 18 SUMMARY STATUS OF WORK
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REP11/PR Appendix I 20 LIST OF PART
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REP11/PR Appendix I 22 Mrs Lidia GO
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REP11/PR Appendix I 24 Dr PAN Canpi
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REP11/PR Appendix I 26 ECUADOR/ÉQU
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REP11/PR Appendix I 28 Mr. ISTVAN B
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REP11/PR Appendix I 30 MALAYSIA/MAL
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REP11/PR Appendix I 32 REPUBLIC OF
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REP11/PR Appendix I 34 Mr Poh Leong
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REP11/PR Appendix I 36 UNITED STATE
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REP11/PR Appendix I 38 Mr Mika Chib
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REP11/PR Appendix I 40 INTER-AMERIC
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REP11/PR Appendix I 42 Mr ZHENG Zun
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REP11/PR Appendix II 44 DRAFT MAXIM
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REP11/PR Appendix III 46 Commodity
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Page 125 and 126: REP11/PR Appendix XI 116 2015 JMPR
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REP11/PR JOINT FAO/WHO FOOD STANDARDS PROGRAMME ...

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