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Food Safety and Toxicology
Statistical Techniques to Analyze Pesticide Data Program Food Residue Observations Arpad Z. Szarka, Carol G. Hayworth, Tharacad S. Ramanarayanan, and Robert S. I. Joseph J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b00863 • Publication Date (Web): 14 Jun 2018 Downloaded from http://pubs.acs.org on June 19, 2018
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Journal of Agricultural and Food Chemistry
Statistical Techniques to Analyze Pesticide Data Program Food Residue Observations
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Arpad Z. Szarka*, Carol G. Hayworth, Tharacad S. Ramanarayanan, and Robert S. I. Joseph
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Operator and Consumer Safety, Syngenta Crop Protection, LLC, Greensboro, NC, 27419.
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(*) Corresponding author: A. Z. Szarka, e-mail:
[email protected]
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ABSTRACT
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The U.S. EPA conducts dietary risk assessments to ensure that levels of pesticides on food are
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safe in the U.S. food supply. Often these assessments utilize conservative residue estimates,
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Maximum Residue Levels (MRLs), and a high-end estimate derived from the registrant-
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generated field trial data sets. A more realistic estimate of consumers’ pesticide exposure from
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food may be obtained by utilizing residues from food monitoring programs such as the Pesticide
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Data Program (PDP) of the US Department of Agriculture. A substantial portion of food residue
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concentrations in PDP monitoring programs is below the limits of detection (left-censored) which
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makes the comparison of regulatory field trial and PDP residue levels difficult. In this paper we
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present a novel adaption of established statistical techniques, Kaplan-Meier Estimator (K-M),
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Robust Regression on Ordered Statistic (ROS) and Maximum Likelihood Estimator (MLE), to
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quantify the pesticide residue concentrations in the presence of heavily censored data sets.
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The examined statistical approaches include the most commonly used parametric and non-
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parametric methods for handling left-censored data that have been used in the field of medical
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and environmental sciences. This work presents a case study in which thiamethoxam bell
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pepper residue data generated from the registrant field trials were compared with PDP
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monitoring residue values.
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compared with commonly used simple substitution methods for determination of summary
The results from the statistical techniques were evaluated and
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statistics. It was found that the maximum likelihood estimator (MLE) is the most appropriate
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statistical method to analyze this residue data set. Using the MLE technique, the data analyses
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showed that the median and mean PDP bell pepper residue levels are approximately 19 times
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and 7 times lower, respectively than the corresponding statistic of field trial residues.
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KEYWORDS:
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maximum likelihood estimator, regression on ordered statistic, dietary risk assessment
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Journal of Agricultural and Food Chemistry
1. INTRODUCTION
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The U.S. EPA conducts dietary risk assessments to ensure that the levels of pesticides
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in food are safe in the U.S. food supply. Dietary risk assessment outcomes depend upon the
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toxicity of pesticide, food intake, and the magnitude of pesticide residues in food, including the
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raw agricultural commodities (RACs). The magnitude of residues in RACs is an estimate of the
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pesticide concentration present in consumers’ food intake.
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estimation of residue concentrations in RACs will afford a more accurate and reliable
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assessment of dietary risk. In recognizing the importance of quantifying potential pesticide
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concentrations in food, current regulations require registrants to generate pesticide field trial
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residue data during the development of a pesticide in order to register the product with the EPA.
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These data are also used to establish maximum legal limits i.e. Maximum Residue Levels
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(MRLs) or tolerances for crops. The generated field trial data become part of the body of
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knowledge associated with the use of the product. In addition to evaluation of dietary risk, a
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wider variety of scientific tests are required by law to ensure the safe use of the pesticide,
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including efficacy, product chemistry, potential human health, environmental effects, and the
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impact on non-target organisms.
Consequently, improving the
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To conserve resources, the U.S. EPA has implemented a tiered approach in dietary risk
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assessment. 1 The lowest tier assessments utilize conservative residue estimates, MRLs, and a
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high-end estimate derived from the registrant generated field trial data sets. At the next tier, the
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MRLs are replaced by the average of the field residue values for chronic assessments and the
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entire distribution of residues for the acute assessments. The field trials are conducted under
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worst-case scenarios i.e. at the maximum application rates, maximum number of applications
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and minimum pre-harvest intervals (PHIs), specified in the product label.
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residues generated under these conditions represent conservative, worst-case estimates. A
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more realistic estimate of consumers’ pesticide exposure from food may be obtained utilizing
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Therefore, food
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residues from food monitoring programs. Monitoring programs sample, test, and report on
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pesticide residues in agricultural commodities. In the USA, monitoring data are available from
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the USDA’s Pesticide Data Program (PDP) and registrant-supported market basket studies.
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PDP is a national program that collects residue data on selected agricultural commodities in the
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food supply based on a rigorous statistical design to ensure that sample collections provide
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reliable estimates of pesticide residues in the U.S. food supply. The PDP data can provide
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more realistic estimates of residues in RACs as opposed to the worst case field trial data. The
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PDP data are reflective of the actual pesticide use and usage scenarios, including the percent of
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crop treated (%CT). PDP samples are taken close to the point of consumption and the data
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take into account the reduction of residues that may occur between pesticide treatment and
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consumption; therefore, it provides a more realistic measure of the pesticide residues. While
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food monitoring data have been frequently used as refinement in residue estimates, a
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quantitative assessment of residue reduction has not been available for the PDP data.
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In general, the PDP data are reflective of multiple residue reduction factors.
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Consequently, the observed pesticide residues are significantly lower compared to registrant
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generated field trial residues. In addition, the PDP data frequently include values reported as
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“less than detection limit”. These values are known to be less than some value (e.g. 15%) of residue data set contains
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observations
