Use of Combined Uncertainty of Pesticide Residue Results for Testing

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Use of Combined Uncertainty of Pesticide Residue Results for Testing Compliance with MRLs Zsuzsa Farkas, Andrew Slate, Thomas B. Whitaker, Gabriella Kötelesné Suszter, and Árpád Ambrus J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/jf505512h • Publication Date (Web): 06 Feb 2015 Downloaded from http://pubs.acs.org on February 18, 2015

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Journal of Agricultural and Food Chemistry

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Use of Combined Uncertainty of Pesticide Residue Results for Testing Compliance

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with MRLs.

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Zsuzsa Farkas1, Andrew Slate2, Thomas B. Whitaker2, Gabriella Suszter3, Árpád Ambrus1a

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1. National Food Chain Safety Office, Budapest Tábornok u 2, 1143 Hungary

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2. North Carolina State University 124 Weaver Labs, Raleigh, NC 27695-7625, USA

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3. Wessling Hungary Ltd, Budapest Fóti út 56, 1047 Hungary

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1a Retired Scientific Adviser, ,

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1 ACS Paragon Plus Environment


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ABSTRACT

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The uncertainty of pesticide residue levels in crops due to sampling, estimated for 106 individual crops

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and 24 crop groups from residue data obtained from supervised trials was adjusted with a factor of 1.3 to

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accommodate the larger variability of residues under normal field conditions. Further adjustment may be

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necessary in case of mixed lots. The combined uncertainty of residue data including the contribution of

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sampling is used for calculation of an action limit, which should not be exceeded when compliance with

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maximum residue limits is certified as part of pre-marketing self control programs. On the contrary, for

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testing compliance of marketed commodities the residues measured in composite samples should be ≥

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the decision limit calculated only from the combined uncertainty of the laboratory phase of the residue

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determination. The options of minimizing the combined uncertainty of measured residues are discussed.

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The principles described are also applicable to other chemical contaminants.

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KEY WORDS: sampling, uncertainty of measurement results, compliance with legal limits, pesticide

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residues, self control of production, certification of compliance

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INTRODUCTION

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Distribution of pesticide residues.

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The distribution of pesticide residues in/on treated crops is affected by many factors including, , for

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instance, the type and spatial form of plants, cultivation and application method, and weather conditions

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during application. The field to field variation of the magnitude of pesticide residues and their within

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field concentration ranges can be expected to vary up to 10000 and 100 fold, respectively.1 Horváth and

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co-workers have recently reviewed the characteristics of distribution of pesticide residues in crop units

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or primary samples.2 Their statistical analyses of over 19000 residue data representing combinations of

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20 crops and 46 pesticides confirmed previous assumptions3,4 that the residue sets obtained from the

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analyses of crop units can be best described with lognormal distribution having a relative standard

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deviation, CV, around 0.8. Even if 100-300 natural crop units are collected from one field, the CV of the

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measured residues would provide only one estimate of the variability of residues. If the random

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sampling operations are repeated many times the average of CV values approaches the true CV of the

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parent populations. Consequently, the average of CV values obtained from the analyses of crop units

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taken from different fields would provide the best estimate for the typical CV of residue values in the

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given crop units.5 Due to the large variation of pesticide residues in crop units, the average residues in

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composite samples containing 5-10 or larger number of primary samples6 will also substantially vary.

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Uncertainty of measurement results.

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The uncertainty of sampling, as one of the components of the combined uncertainty of the results of

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pesticide residue analysis, has been estimated based on three independent databases: residues in 21000

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primary samples5,7, residues in composite samples taken from commercially treated crops8, and residues

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derived from 25876 samples taken from 12087 independent supervised residue trials.9

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The contribution of sub-sampling, sample processing and homogenization to the variability of analytical

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results has been studied by several authors.10-14 It was shown that the efficient comminuting of the

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samples, especially those with hard peel and soft pulp, is a difficult task, and the result largely depends 3 ACS Paragon Plus Environment


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on the crop material, equipment used, as well as the temperature and the length of processing. On the

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other hand, it is worth to note that, if the residues are stable under the sample processing conditions, the

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efficiency of sample processing does not depend on the nature of the chemical substances. In addition to

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homogenization of the sample material, the sample size reduction15 may also contribute to the combined

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uncertainty with a sub-sampling (CVSS) of 8-12%.

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Maximum residue limits for chemical contaminants.

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For assuring the safety of marketed food, regulatory agencies define maximum residue limits (MRL) for

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pesticides16-19 and maximum limits (ML) for chemical contaminants.17,20,21 In addition, the Codex

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Alimentarius Commission (CAC) establishes MRLs or MLs to facilitate international trade.22-24 The

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MRLs and MLs are defined as the maximum legally permissible average concentration of chemical

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contaminants in a composite sample with specified minimum mass and size in terms of the number of

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primary samples (single sample increments). The sample sizes defined by the national authorities are

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generally the same or very similar to those given in the relevant Codex Standards, such as for instance

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for pesticide residues.5

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The objective of this paper is to show that available data on the uncertainty (variability) of pesticide

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residue measurement results may be used in the process of certification of compliance with MRLs. The

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principles described are also applicable for other chemical contaminants.

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METHODS

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Estimation of combined uncertainty of the measured residues.

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The combined relative standard uncertainty (expressed as relative standard deviation or coefficient of

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variation) of the residues measured in composite samples (CVR) can be expressed as25

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= + +

+


Equ. 1

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It incorporates the uncertainties of sampling (CVS), sub-sampling (CVSS), sample processing (CVSP)

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(chopping, mincing and homogenization of analytical sample) and analysis (CVA). The methods for

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estimation of sampling uncertainty have been described in detail in previous publications,7,8,26 therefore

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they are not repeated here. Where the contribution of uncertainty of sub-sampling and sample

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processing is included in the estimated uncertainty of the laboratory phase of measurement (CVL), the

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two phases of the process can be distinguished as:

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CV = CV + CV

Equ. 2

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CV = CV + CV + CV

Equ. 3

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It is to be noted that the CVL should always be determined either from retained test portions of the

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laboratory samples which contain incurred residues,10 or after treatment of the surface of the individual

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crop units with the test compound.14 Furthermore, the results of collaborative studies or proficiency tests

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can only provide information on the uncertainty of the analytical procedure (CVA) from the point of

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extraction and not for the combined uncertainty of the analytical results (CVL), as the appropriate

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homogeneity of those samples had been carefully checked before distribution to the participants.

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Principles of control of compliance of food commodities with the legal limits.

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As the consequence of the definition of MRLs, there are two distinctly different situations in the control

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of compliance of food commodities with the legal limits. Different sampling plans are required for

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testing compliance with MRLs of pesticide residues in commodities before and after marketing..

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Control of commodities on the market (enforcement or monitoring program).

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As the permissible maximum concentrations of residues and chemical contaminants apply to the average

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concentration of the substances in the bulk/laboratory sample, the combined uncertainty of the

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laboratory phase of the determination of the residue concentration (CVL) shall be taken into account in

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deciding on the compliance or non-compliance of the sampled product. If the samples taken satisfy the

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minimum requirements for the number of primary samples and the mass of the laboratory/bulk sample,6

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the average residue determined in the sample provides the basis for deciding on the acceptability of the 5 ACS Paragon Plus Environment


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sampled lot. In order to make regulatory action in accordance with the relevant ISO and Codex

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guidelines,27,28 among other regulatory agencies, the European Commission (EC) regulations clearly

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indicate that a lot is considered non-compliant if the measured analyte concentration corrected for

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recovery, where it should specifically be considered, minus two times the expanded uncertainty of the

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results are above the legal limit. For pesticide residues the default combined relative uncertainty

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reflecting the CVL value (equ. 3) is defined as 25%.29 This means that a lot could be rejected if the

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residue measured in the composite sample taken from the lot would be >2×MRL. We call this

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concentration the Decision Limit (DL). Figure 1 illustrates the situation where the MRL is 0.2 mg/kg

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and the apple lot (mean of 0.1 mg/kg and CVR of 0.35) would be tested on the market applying the

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decision limit of 0.4 mg/kg (0.4-2×0.25×0.4=0.2 mg/kg) as defined by the EC. Under this condition a

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testing laboratory would reject a lot containing residue ≤0.2 mg/kg in 2.3% of the cases, because the

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dispersion of analytical measurement results can be considered normal.30

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Premarketing self-control.

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When the product is tested before placing it on the market, it should be certified that at least a specified

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proportion of the product in terms of the minimum size and mass of bulk/laboratory sample complies

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with the legal limit. In this case the combined uncertainty (CVR) including sampling uncertainty shall be

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taken into account, and the measured value should not be directly compared to the MRL as it may allow

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placing a product on the market with a substantial proportion containing the contaminant above the

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permitted limit. Let’s assume that the MRL is 0.1 mg/kg, and the average concentration of a pesticide

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residue is also 0.1 mg/kg in the sampled lot. If the measured residue in the sample taken from the given

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lot is < 0.1 mg/kg the sampled lot would be declared to be compliant. However the decision would only

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be correct in about 56% and would be wrong in 44% of the cases as indicated by the cumulative

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frequency distribution curve in Figure 2. Therefore a lower concentration must be selected as Action

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Limit (AL) which should not be exceeded by the residue measured in the sample. The AL can be

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calculated by taking into account the combined relative uncertainty of the measured value (CVR) (equ. 1)

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and the targeted level of compliance. 6 ACS Paragon Plus Environment

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The relationship of AL and MRL can be described with the following equations: AL + k × CV × AL = MRL AL =

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Equ. 4 Equ. 5

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The value of k depends on the agreed acceptable violation rate (Pv) or the compliant proportion of the

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lot, Pc, relative to the minimum sample size specified in the corresponding regulation (1-Pv=Pc). For

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normal distribution, which may approximately be assumed with samples of size ≥25 for fruits and

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vegetables, the k is equal to the corresponding standard normal variate, Z. A normal distribution may

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also be assumed for liquid materials and processed products, such as for instance tomato puree. It is

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pointed out that in other cases the relative frequency distribution of pesticide residues can be best

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described with a lognormal distribution.1

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Calculation of AL for given acceptance probability at MRL.

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If the distribution of xi can be described by the lognormal function, then the transformed variable yi =

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Ln(xi) is normally distributed. If the distribution of xi has a mean (m) and standard deviation (s), then the

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distribution of the transformed variable yi has a mean (M) and standard deviation (S). M and S can be

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calculated from m and s using the equations: #$

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M = Ln "

(

Equ. 6

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) = *+ ,.$ + 10

Equ. 7

%&$ #$ ' -$

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Since yi is normally distributed, the probability Pi (0.0 to 1.0) for any value yi occurring from a

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distribution with mean M and standard deviation S can be calculated from normal distribution tables

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using the standardized equation:

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1=

%23 45'

67 = 8 + 1 × )

Equ. 8 Equ. 9

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In the sampling domain, m takes on the value of the legal limit MRL and xi takes on the value of the AL.

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Typically xi ≤ m (AL≤MRL), therefore Z will vary from zero to a negative value usually between 0.0

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and -3.5 based upon the specified acceptance probability (acceptable violation rate), Pv. Once m and s 7 ACS Paragon Plus Environment


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are transformed into M and S and the acceptance probability (that will define Z) is specified, yi can be

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determined from Equation 9. Then xi can be calculated by transforming yi back to xi. 97 = exp %67 '

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Equ. 10

where xi is the AL in the linear scale.

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RESULTS AND DISCUSSION

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Comparison of sampling uncertainties estimated from supervised trials and commercial field

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samples.

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Based on the limited data available, the average sampling uncertainty for primary samples, CVSprim of

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0.78, 0.81, 0.74 and 0.45, were estimated for residues in crop units of a small, medium and large size

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crops and Brassica leafy vegetables, respectively.7 Additional residue data measured in carrot and

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parsley enabled the estimation of CVSprim values of 0.59 and 0.60, respectively.8 Duplicate samples taken

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from 12087 supervised trials carried out in 106 different crops treated with 66 different pesticides

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provided the best available database for estimation of uncertainty of sampling.31 The supervised trials

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are carried out usually on small scale with strictly controlled conditions for the application of pesticides.

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They aim to provide information on the maximum residues which can occur on treated crops under the

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critical use conditions [shortest interval between last application and harvest (pre-harvest interval),

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highest permitted dose and application frequency] specified in the registration or use permit

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documents.32 Consequently, these trials may not reflect the within field residue distribution which occurs

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where the pesticides are applied according to the general agricultural practice. Table 1 summarizes the

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estimated sampling uncertainties obtained from the databases consisting of residues in/on primary

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samples, which were taken from fields treated according to normal farming practice and from supervised

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trials. The CVS values were calculated for the minimum size of composite samples (12) specified by the

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Codex Standard for sampling plant products.5 In table 2 the sampling uncertainties calculated from

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residues in samples taken from commercial fields and from supervised trials are compared. The relative 8 ACS Paragon Plus Environment

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sampling uncertainty of residues in field composite samples (CVcs) was calculated from 4 replicate

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samples and for supervised trials mostly from duplicate samples with range statistics.8 The same

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tendency was observed in both cases. As the number of datasets from which the CV values were

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calculated greatly varies and the sample sizes in supervised trials had only sometimes been reported,

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precise comparison of the variability of residues in supervised trial and field samples could not be done.

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Therefore the averages of CV ratios (1.15 and 1.19) are considered to be the best indicator of the

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difference in the variability of residues. Exceptions are papaya, strawberry, cabbage, carrot and parsley

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for which the ratios of relative sampling uncertainty of composite samples taken from supervised trials

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and relative sampling uncertainty of residues in field composite samples (CVcs/CVStcs) are within the

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range of 0.44 and 0.70. The low variability in field trials cannot be explained based on the available

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information. As it is quite unlikely that supervised trials lead to higher variability of residues than field

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trials, these values were disregarded.

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Taking into account the uncertainties of the estimations a rounded correction factor of 1.2 is used to

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account for the larger variability of residues in field samples.

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Effect of number of residue values below the LOQ on the estimated sampling uncertainty.

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The range of residues in primary samples taken from a single field is typically hundred fold. Depending

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on the average residue and the limit of quantification (LOQ) of the analytical method various

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proportions of the primary samples may not contain detectable residues. The proportion of residues

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below LOQ is not known. The effect of the LOQ values in relation to the average of residue data

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population was studied with experimental supervised trial data1 and synthetic lognormal distributions, as

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it was found that the within field distribution of residues in crop units (primary samples) can be best

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described with lognormal distribution.2 Of the supervised trial residue datasets, compiled by the FAO

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WHO Joint Meeting on Pesticide Residues for estimation of MRLs, those were selected which contained

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relatively large number of data points. The descriptive statistical parameters of the selected datasets are

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summarized in Table 3.The selected datasets contained no residue below the LOQ value, except dataset

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8 where the 27 values below the varying LOQs were replaced with the maximum likelihood estimation 9 ACS Paragon Plus Environment


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(MLE) procedure assuming lognormal distribution provided by Villanueva.33 The residue values were

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also normalized by dividing them with the corresponding average residues of their dataset in order to

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assist comparison of the spread of residues in the datasets with different mean residues.

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For studying the effect of LOQ [mg/kg] values on the CV of the data population, the selected data sets

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were arranged in increasing order. The lowest number of residue values corresponding to the selected

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percentage of the original data population (10%-90%) were replaced with various LOQ values ranging

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from 2× reported LOQ to 0.7 average residues in the dataset. These datasets are called adjusted datasets.

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For instance, in case of dataset 1 the lowest 11 values (0.03, 0.06, 0.07, 0.09, 0.09, 0.1, 0.1, 0.11,

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0.12,0.12, 0.12) were replaced with 0.04, 0.2, ...1.16) and then the descriptive statistical parameters were

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recalculated for each adjusted data set. The process was repeated with 20% of the residue values in the

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original data set, and so on.

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The percentage of residue values below the LOQ and the value of LOQ affect the average and the

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relative standard deviation of the original residue population (CVo). The relationships of the

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corresponding CV and average residue of the adjusted datasets are complex, and they are best illustrated

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graphically. Figure 3 shows the ratios of the CV-s of adjusted dataset (CVAdj) and the original dataset

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(CVo) as the function of the ratio of LOQ and the average residue (=>' of adjusted dataset (LOQ/=> '.

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Figure 4 illustrates the CVAdj/CVo relationship as the function of the LOQ/=> residues and the percentage

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of residues at or below the LOQ obtained from the dataset 8 (Table 3. MLE fitted prochloraz residues).

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As similar LOQ/=> residues could be obtained from various combinations of LOQ values (Figure 4) and

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their percentage proportion in the adjusted datasets, the CVAdj values corresponding to LOQ/=> ranges

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from ≤0.03 to ≤0.7 were grouped and the averages of LOQ/=> and the CVAdj/CVo values being in the

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given ranges were calculated. The results obtained from the supervised trials and synthetic datasets are

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shown in Table 4. The results indicate certain variation among datasets which is attributed to the

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distribution of residues within the individual datasets and sampling uncertainty. As the typical sampling

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uncertainties were obtained from datasets with detectable residues, they do not represent the situations 10 ACS Paragon Plus Environment

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where the various proportions of the treated crops may contain residues below the LOQ. The probability

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of finding primary samples with non-detectable residues increases as the LOQ/=> increases. However,

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no general tendency in the relationship between the LOQ/=> and CVAdj/CVo could be established. The

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grand averages of CVAdj/CVo up to LOQ/=>

Use of Combined Uncertainty of Pesticide Residue Results for Testing

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