Chemical residues in food and water; challenges for a future sustainable agriculture

Transcription

1 1 Inter-Laboratory Sample Analysis and Computational Simulations to Estimate Error Propagation in Pesticide Residue Measurements Livio Giammarrusti, Sergio C. Nanita, Andreas Huber, Melissa Ziegler, Elena Astor, Scott Cairns, Neil Beadle, Steve Cranwell, Janet C. Ruhl, Felipe Ortega, and Andrew Selley E. I. du Pont de Nemours & Company Introduction and Study Objectives Statement of the problem - Analyses conducted at private laboratories are key to evaluate MRL compliance - Inaccurate results may lead to: False positive Destruction of produce in MRL compliance farmers $$$ loss False negative Public exposure safety risk - Results are most important when residues are close to the MRL, for example: Lab A =.1 mg/kg < MRL < Lab B =.3 mg/kg In compliance In violation - What is the expected/acceptable difference in results when representative produce samples are analyzed at two labs? Study goals: 1 - Select a model system (A.I. and crops), preferably with data already available 2 - Define an objective approach to evaluate error in residue measurements 3 - Estimate expected differences in residues reported by two laboratories 4 - Identify factors that contribute the most to variability in reported results 2 MGPR 28 - Piacenza (Italy), 13 and 14 November 1

2 Residue Data Available from DuPont Studies Model System Oxamyl Data available from previous magnitude & decline of residue studies -Tomato -Pepper -Cucumber -Melon -Eggplant -Oranges -Potato -Sugarbeets Duplicate field samples were taken and analyzed in these studies Each sample pair was: -collected under identical conditions (GLP protocol) -analyzed at the same lab, using the same method A total of four (4) laboratories generated the data: -Battelle Labs -Exygen Research -ABC Labs -Morse Labs Analytical method data generated from analysis of samples fortified with known amounts of oxamyl -Percent recoveries available for the four labs and methods used 3 Oxamyl Residue Data Pairs 4 Available data can be grouped in three categories Both results >.3 mg/kg (LOD): 116 residue pairs Both results reported as nd (not detected): 87 residue pairs One result with residues, the other nd : 9 residue pairs MGPR 28 - Piacenza (Italy), 13 and 14 November 2

3 DuPont Analytical Methods at Laboratories 5 LSL <.1 mg/kg %Recovery Distributions Oxamyl Analytical Methods at DuPont Alliance Partners LSL >.1 mg/kg USL Laboratory ABC Battelle Exygen Morse Mean StDev N %Recovery Computational Simulation Analysis at Lab A and Lab B Assuming residue variability in the field = 6 Lab A Sample A identical to Sample B Lab B Create 4, sample pairs that contain residues at exactly.1,.3,.5, and 1. mg/kg 1, sample pairs at each level Send samples to Labs A and B for analysis Evaluate difference between results = Result from Lab A - Result from Lab B Average Result X 1 MGPR 28 - Piacenza (Italy), 13 and 14 November 3

4 Simulated Analysis at Lab A and Lab B Scenario 1 Assuming residue variability in the field = for Results from Lab A and Lab B Scenario 1: Example based on analytical methods at DuPont Alliance Partners Lab A %Recovery = 82 ± 9 Lab B %Recovery = 15 ± 1.5 Loc 4.31 Scale.1867 Thresh N Simulated Analysis at Lab A and Lab B Scenario 2 Assuming residue variability in the field = 8 for Results from Lab A and Lab B Scenario 2: Laboratories with methods that yield low and high average recoveries 2 15 Lab A %Recovery = 75 ± 15 Lab B %Recovery = 115 ± 15 Loc Scale.1625 Thresh N MGPR 28 - Piacenza (Italy), 13 and 14 November 4

5 Simulated Analysis at Lab A and Lab B Scenario 3 Assuming residue variability in the field = 9 for Results from Lab A and Lab B Scenario 3: Worst case possible while still in compliance with OECD regulations mg/kg.1 mg/kg Lab A %Recovery = 6 ± 18 and 7 ± 14 Lab B %Recovery = 12 ± 36 and 12 ± 24 Loc 5.9 Scale.288 Thresh N Simulated Analysis at Lab A and Lab B Summary Probability Distribution Plots Theoretical residues assume field variability = Scenario 1 DuPont Alliance Partners Scenario 2 Lab A %Recovery = 75 ± 15 Lab B %Recovery = 115 ± 15 Loc Scale Thresh Scenario 3 Worst case possible that complies with OECD X = between Lab A and Lab B results 2 MGPR 28 - Piacenza (Italy), 13 and 14 November 5

6 Field & Sampling Variability Let s now add field variability and sampling error to the simulations: Mean residue Sampling error depends on: Consistency (protocol followed?) Sampling time Sample size Topography Sample handling in transit Sample handling at laboratory Homogenization technique Sub-sample size Sample A comes from the sample population as Sample B 11 Lab A A total of 6 were considered 2 examples of field variability & sampling error: Best case from studies under GLP Sampling done without protocol (GLP x 2) Lab B 3 cases of method performance at the labs: Best case (DuPont example) X Medium case with larger diff in % rec Worst case that complies with OECD Evaluate difference between results by calculating % Diff How to Simulate Field & Sampling Variability? 12 Create a residue distribution from decline studies: 116 residue pairs Calculate difference between pairs Histogram of raw difference between residue results Raw diff Difference between each pair of residue values.8.12 Raw difference between residue results StDev.1.2 Best case from GLP studies, after subtracting error contributed by analytical method Raw diff Example for field variability (GLP x 2) MGPR 28 - Piacenza (Italy), 13 and 14 November 6

7 Computational Residue Analysis of Simulated Field Samples 13 Histogram of Residues Create a residue distribution from decline studies 1) Select a residue value (discrete number) from distribution, same value for both samples 2) Add field and sampling error Best case from GLP studies Residue Non-GLP sampling case Raw difference between residue results Probability Distribution Plots - Analytical Methods Scenario 1 Raw diff 3) Send one sample to each lab Scenario 2 Scenario 3 X = between Lab A and Lab B results 4) Repeat 1, times Scenarios Evaluated: 2 x 3 Matrix 14 Error introduced by laboratory and analytical method Field & sampling error Low: DuPont partners Medium: typical example High: worst case that complies w/oecd Best case Ideal & unlikely Good example Good example Representative (yet conservative) case Good example Good example Worst but acceptable MGPR 28 - Piacenza (Italy), 13 and 14 November 7

8 Field Computational Residue Analysis Results Representative example Lab for Results from Lab A and Lab B 3 Lab A reports "nd"; Lab B >.3 mg/kg: 131 residue pairs Both Labs reported "nd": 1271 residue pairs Loc Scale.15 Thresh N Residue detected? Yes or No Labs disagree in 13.1% of results Computational Residue Analysis Results 16 Worst (yet acceptable) case Lab for Results from Lab A and Lab B Field 3 25 Lab A reports "nd"; Lab B >.3 mg/kg: 1811 residue pairs Lab A >.3 mg/kg; Lab B reports "nd": 11 residue pairs Both Labs reported "nd": 145 residue pairs Loc Scale.4615 Thresh N Residue detected? Yes or No Labs disagree in 18.2% of results MGPR 28 - Piacenza (Italy), 13 and 14 November 8

9 Summary: 2 x 3 Matrix 17 Error introduced by laboratory and analytical method Field & sampling error Low: DuPont partners Medium: typical example High: worst case that complies w/oecd Best case P(x 5) =.95 P(5 x 1) =.4 P(x 1) <.1 Y/N 4% P(x 5) =.65 P(5 x 1) =.34 P(x 1) =.1 P(x 5) =.61 P(5 x 1) =.31 P(x 1) =.8 Y/N 1% Y/N 1.9% Typical case P(x 5) =.38 P(5 x 1) =.51 P(x 1) =.11 Y/N 2% P(x 5) =.31 P(5 x 1) =.58 P(x 1) =.11 Y/N 13.1% P(x 5) =.62 P(5 x 1) =.3 P(x 1) =.8 Y/N 18.2% Is oxamyl present above LOD? Y/N Labs DO NOT agree (%) X = between results reported by Lab A and Lab B Residue at MRL of.1 mg/kg TRUE VALUE 18 No field or sampling error added! Error introduced by laboratory and analytical method Decimal places in reported result Low: DuPont partners High: worst case that complies w/oecd 2 e.g..1 Y/N % Y/N 21% 3 e.g..1 Y/N 5% Y/N 67% Is crop in violation of MRL? Y/N Labs DO NOT agree (%) MGPR 28 - Piacenza (Italy), 13 and 14 November 9

10 Private Labs in Spain: Inter-Laboratory Analysis 19 Between Results from Lab A and Lab B (Spain) 2 Residue Y/N? - Disagreement in 28 residue pairs Both Labs reported "nd": 27 residue pairs 15 1 P (x < 5) =.48 P (5 < x < 1) =.36 P (x > 1) = Simulated Analysis vs. Private Laboratories in Spain Probability Distribution Plots Simulated Analysis: Examples of Acceptable Variability Laboratories in Spain X = between reported residues 2 = Result from Lab A - Result from Lab B Average Result X 1 MGPR 28 - Piacenza (Italy), 13 and 14 November 1

11 Conclusions Simulation and experimental sample analysis results are comparable 21 Large differences, i.e. > 1%, should be expected in residue values when duplicate field samples are analyzed at two laboratories Some results will disagree when residues are converted to discrete data Is active present above LOD? Y/N, ~2% disagree Is the crop in violation of MRL? Y/N, for sample with true residue at MRL, AT LEAST -21% of the time, and depends on significant figures reported Error associated with residue determination can be particularly detrimental when a result is reported near the MRL Factors that contribute the most to variability in reported results: Field variability & sampling error lack of protocol Regulatory requirements for analytical methods MGPR 28 - Piacenza (Italy), 13 and 14 November 11