No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Use of Combined Uncertainty of Pesticide Residue Results for Testing Compliance with Maximum Residue Limits (MRLs)
Abstract
The sampling uncertainty estimated for 106 individual crops and 24 crop groups from residue data obtained from supervised trials was adjusted with a factor of 1.3 to accommodate the larger variability of residues under normal field conditions. Further adjustment may be necessary in case of mixed lots. The combined uncertainty of residue data including the contribution of sampling is used for calculation of action limit, which should not be exceeded when compliance with maximum residue limits is certified as part of pre-marketing self control programs. In contrary, for testing compliance of marketed commodities the residues measured in composite samples should be ≥ the decision limit calculated only from the combined uncertainty of the laboratory phase of the residue determination. The options of minimizing the combined uncertainty of measured residues are discussed. The principles described are also applicable for other chemical contaminants.
... In the studies of Farkas and co-workers, supervised residue trial data was used for the estimation of sampling uncertainty, where replicate composite samples were available. Modelling was used for the calculation of confidence intervals of estimated sampling uncertainty values (Farkas et al., 2015a(Farkas et al., , 2015b. ...
... Based on the results they recommended a factor of 1.2 to account for the larger variability that can occur in case of residues measured in field samples, which must be incorporated in the estimated sampling uncertainty values for practical use. It was also declared that the sampling uncertainty cannot be defined if the decision unit includes crops of different origin (Farkas et al., 2015b). ...
... In other cases, lognormal distribution describes best the relative frequency distribution of pesticide residues (Horváth et al., 2013) and the residue values after log-transformation follow normal distribution. Details of the calculation are described by Farkas et al. (2015b). Figure 10.13 depicts, for example, the operation characteristic curves obtained following different sampling plans. ...
... The test procedure employed in analytical chemistry generally consists of 3 steps including sampling, sample preparation, and chemical analysis. The relative contribution of these steps to the variation in analytical results have been extensively studied by the authors [1,7,9,29,32]. The total variation: σ total = σ sampling + σ sampleprep + σ analytical . ...
... The total variation: σ total = σ sampling + σ sampleprep + σ analytical . The variability associated with different steps makes it hard to determine the true concentration of a bulk lot and accurately classify lots for risk management and regulatory decisions [7]. To reduce misclassification of sample lots, it is important to properly design sampling plans and evaluate the variability characteristics associated with the test procedure [32]. ...
The animal feed regulatory community utilizes analytical variations (AVs) that account for the normal variability that occurs within and between laboratories. The AV was created using the Association of American Feed Control Officials (AAFCO) proficiency testing (PT) program results and represent the coefficient of variation times 2 (2 CV). This study was performed to assess the accuracy of the published AVs within the association’s Official Publication (OP) using AAFCO PT data from 2014 to 2020. This study also reevaluated the current AV values by applying the concept of the measurement uncertainties including the expanded uncertainty (U), Horwitz value, and 2 CV for the assessment of the label guarantees. Although greater variations were observed at lower concentrations, the majority of data points from the PT rounds showed an acceptable reproducibility for the select analytical methods except for cobalt, fat, fiber, moisture, and selenium where 2 CV was higher than the current AAFCO AV. Regression models were developed based on the 95% tolerance interval of expanded uncertainty and 2 CV and were validated using the 2019-2020 AAFCO PT data. The validation data fell within the expanded uncertainty and 2 CV model bounds indicating the new models were fit-for-purpose and accurately characterized current AAFCO PT data. The study results imply the AAFCO AV and its issues need to be better understood to improve an accuracy and traceability of the laboratory’s measurement and thus regulatory decisions to help animal feed producers and processors lower economic losses.
... As the errors due to sampling for pesticide residues can be large, estimated to range from 20 to over 100% (Ambrus & Soboleva 2004;Farkas, et al 2015a), they can contribute significantly to the total error, even given errors as high as 25% for pesticide residue analytical test measurements. The combined uncertainty of measured pesticide residues, expressed as a relative standard deviation, (CVR) is equal to the square root of the sum of the squares of the primary sampling (CVS), sample processing (CVSP) and analytical testing uncertainties (CVA) as seen in Eq. 9.8 (Farkas et al., 2015b). Methods for estimating this error will be discussed in Chapter 10. ...
... As mentioned previously, it is very important to design a sampling plan to fit the purpose. Farkas et al. (2015b) explained that different sampling plans are needed for testing compliance with MRLs of pesticide residues on commodities before (include sampling uncertainty) and after marketing (based on measurand + laboratory uncertainty). ...
... However, the widespread application of pyrethroids has caused contamination of environmental compartments and has led to the ongoing possibility of food-chain contamination, leading to the bioaccumulation of these insecticides in food products of animal origin, which will pose a threat to environmental and food quality [2,5]. Because of this risk to public health, many countries and organizations have set maximum-residue limits (MRLs) for pyrethroid pesticides in vegetables [6]. ...
A novel rapid and cost-effective pre-processing method for the simultaneous determination of pyrethroid pesticides in vegetables has been developed and validated. The process of pesticide extraction was carried out by the QuEChERS (quick, easy, cheap, effective, rugged and safe) method combined with filtration by filter paper, and cleanup was carried out by the multi-plug-filtration-cleanup (m-PFC) method with no centrifuge program during the whole process. The pre-processing method is optimized for gas chromatography (GC). The process is convenient and time saving, requiring just a few seconds per sample. The recovery rate (70–120%), limit of detection (0.0001–0.007 mg/kg), precision (0.2–9.3%) and accuracy for each analyte were determined in 10 representative vegetables with good results. Finally, the feasibility of the developed method was further confirmed by the successful determination of pyrethroid-pesticide residues in pyrethroid-containing practical samples within the processing method coupled with thin-layer chromatography and a colloidal-gold test strip.
Pesticide residues in crops are widely monitored, and the residue reduction techniques at the post-harvest stage are important to maintain food safety. In dried crops, pesticide residues can be concentrated after dehydration, which increases concerns regarding residue risk. Therefore, the residue reduction effects of ultraviolet (UV), ozone, and photochemical advanced oxidative process (pAOP) were investigated for dried peppers at the post-harvest stage. UV254 treatment reduced 59.7% of the residue concentration on average, while UV360 showed a reduction of only 13.3% under 9.6 W m⁻² of UV exposure for 24 h. Gaseous ozone treatments reduced the residue concentrations up to 57.9% on average. In contrast, the pAOP treatment reduced the concentration up to 97% and was superior to UV or ozone treatment alone. Increased drying temperature under pAOP condition resulted in higher reduction ratios at 40–80 °C. The pAOP conditions with 12 and 24 µmol/mol of ozone and UV254 irradiation for 24–48 h reduced the residue concentrations to 39–67%. Particularly, difenoconazole, fludioxonil, imidacloprid, and thiamethoxam residue concentrations were drastically reduced by over 50% under 12 µmol/mol ozone of the pAOP condition, while carbendazim, fluquinconazole, and pyrimethanil were relatively stable and their concentrations reduced below 50% under 24 µmol/mol ozone of the pAOP treatment. Various drying-related quality parameters of drying peppers such as water-soluble color, capsanthin, capsaicinoids, acid value, peroxide value, and thiobarbituric acid value were slightly altered, but not significantly, under 12 µmol/mol ozone of the pAOP condition, while the peroxide value was significantly altered under the higher ozone conditions. Therefore, pAOP treatment combined with gaseous ozone can be used for reducing residual pesticides in peppers without greatly reducing quality.
An automated multi-filtration cleanup (Auto m-FC) method with nitrogen-enriched activated carbon material based on modified QuEChERS (quick, easy, cheap, effective, rugged, and safe) extracts was developed. It was applied to pesticide multi-residue analysis in six representative crop commodities. The automatic device was aimed to improve the cleanup efficiency and reduce manual operation workload in cleanup step. By controlling extracts volume, flow rate and Auto m-FC cycles, the device could finish cleanup process accurately. In this work, nitrogen-enriched activated carbon mixed with alternative sorbents and anhydrous magnesium sulfate (MgSO4) was packed in a column for Auto m-FC and followed by liquid chromatography with tandem mass spectrometric (LC-MS/MS) detection. This newly developed carbon material showed excellent cleanup performance. It was validated by analyzing 23 pesticides in six representative matrices spiked at two concentration levels of 10 and 100 μg/kg. Water addition volume, salts, sorbents, Auto m-FC procedure including the flow rate and the Auto m-FC cycles for each matrix were optimized. Then, three general Auto m-FC methods were introduced to high water content, high oil and starch content, difficult commodities. Spike recoveries were within 82 and 106% and 1–14% RSD for all analytes in the tested matrices. Matrix-matched calibrations were performed with the coefficients of determination over 0.997 between concentration levels of 10 and 1000 μg/kg. The developed method was successfully applied to the determination of pesticide residues in market samples.
The frequent use of various veterinary drugs could lead to residue bioaccumulation in animal tissues, which could cause dietary risks to human health. In order to quickly analyze the residues, a liquid chromatography tandem mass spectrometry (LC–MS/MS) method was developed for detecting Sulfonamides, Tilmicosin and Avermectins (AVMs) residues in animal samples. For sample preparation, modified QuEChERS (quick, easy, cheap, effective, rugged and safe) and ultrasound-assisted extraction (UAE) methods were used. For sample cleanup, n-Hexane delipidation and multi-plug filtration cleanup (m-PFC) method based on primary-secondary amine (PSA) and octadecyl-silica (C18) were used, followed by LC–MS/MS analysis. It was validated on 7 animal matrices (bovine, caprine, swine meat and their kidneys, milk) at two fortified concentration levels of 5 and 100 μg/kg. The recoveries ranged from 82 to 107% for all analytes with relative standard deviations (RSDs) less than 15%. Matrix-matched calibrations were performed with coefficients of determination above 0.998 for all analytes within concentration levels of 5–500 μg/kg. The developed method was successfully used to analysis veterinary drugs of real animal samples from local markets.
An automated multi-plug filtration cleanup (m-PFC) method on modified QuEChERS (quick, easy, cheap, effective, rugged, and safe) extracts was developed. The automatic device was aimed to reduce labor-consuming manual operation workload in the cleanup steps. It could control the volume and the speed of pulling and pushing cycles accurately. In this work, m-PFC was based on multi-walled carbon nanotubes (MWCNTs) mixed with other sorbents and anhydrous magnesium sulfate (MgSO4) in a packed tip for analysis of pesticide multi-residues in crop commodities followed by liquid chromatography with tandem mass spectrometric (LC-MS/MS) detection. It was validated by analyzing 25 pesticides in six representative matrices spiked at two concentration levels of 10 and 100 μg/kg. Salts, sorbents, m-PFC procedure, automated pulling and pushing volume, automated pulling speed, and pushing speed for each matrix were optimized. After optimization, two general automated m-PFC methods were introduced to relatively simple (apple, citrus fruit, peanut) and relatively complex (spinach, leek, green tea) matrices. Spike recoveries were within 83 and 108% and 1 to 14% RSD for most analytes in the tested matrices. Matrix-matched calibrations were performed with the coefficients of determination >0.997 between concentration levels of 10 and 1000 μg/kg. The developed method was successfully applied to the determination of pesticide residues in market samples.
The sampling uncertainty for pesticide residues in carrots, parsley leaves and selected medium size crops was estimated with simple random sampling by applying range statistics. The primary samples taken from treated fields consisted of individual carrots or a handful of parsley leaves. The samples were analysed with QUEChERs extraction method and LCMS/MS detection with practical LOQ of 0.001 mg/kg. The results indicate that the average sampling uncertainties estimated with simple random sampling and range statistics were practically the same. The confidence interval for the estimated sampling uncertainty decreased with the number of replicate samples taken from one lot and the number of lots sampled. The estimated relative ranges of sampling uncertainty are independent from the relative standard deviation of the primary samples. Consequently the conclusions drawn from these experiments are generally applicable. There is no optimum for sample size and number of lots to be tested for estimation of sampling uncertainty. Taking a minimum of 6 replicate samples from at least 8-12 lots is recommended to obtain a relative 95% range of sampling uncertainty within 50%. The cost of sampling/analyses, the consequences of wrong decision should also be taken into account when a sampling plan is prepared.
The widely held view that the frequency distribution of analytical error is log-normal at concentrations of analyte near the detection limit is examined in detail. It is argued that (a) there is no theoretical reason why such distributions should be log-normal and there is abundant evidence that they are not; (b) quasi-log-normal distributions can be produced as artifacts by data recording practices; and (c) inordinately large numbers of analytical results would be needed to distinguish a log-normal distribution from a normal distribution.
When food manufacturers specify a maximum limit for the amount of foreign material (FM) in the lot, handlers estimate the true percent FM in a commercial lot by measuring FM in a small sample taken from the lot before shipment to a food manufacturer. Because of the uncertainty (variability) in FM among samples taken from the same lot, it is difficult to obtain a precise estimate of the true FM in the lot. The objectives of this study were to (1) measure the variability and FM distribution among sample test results when estimating the true lot proportion of FM in a lot of shelled peanuts, (2) compare the measured variability and FM distribution among sample test results to that predicted by the binomial distribution, (3) develop a computer model, based upon the binomial distribution, to evaluate the performance (buyer's risk and seller's risk) of sampling plan designs used to estimate FM in a bulk lot of shelled peanuts, and (4) demonstrate with the model the effect of increasing sample size to reduce misclassification of lots.
Eighty-eight samples, 9 kg (20 lb) each, were selected at random from each of six commercial lots of shelled medium runner peanuts. The percent FM (PFM), based upon number of kernels was determined for each sample. The mean, variance, and distribution among the 88 sample test results were calculated for each of the six lots. Results indicated that the variance and distribution among the 88 sample test results are very similar to that predicted by the binomial distribution. The performance of various sampling plan designs was demonstrated using the binomial distribution.
To estimate the uncertainty of the sample size reduction step, each unit in laboratory samples of papaya and cucumber was cut into four segments in longitudinal directions and two opposite segments were selected for further homogenisation while the other two were discarded. Jackfruit was cut into six segments in longitudinal directions, and all segments were kept for further analysis. To determine the pesticide residue concentrations in each segment, they were individually homogenised and analysed by chromatographic methods. One segment from each unit of the laboratory sample was drawn randomly to obtain 50 theoretical sub-samples with an MS Office Excel macro. The residue concentrations in a sub-sample were calculated from the weight of segments and the corresponding residue concentration. The coefficient of variation calculated from the residue concentrations of 50 sub-samples gave the relative uncertainty resulting from the sample size reduction step. The sample size reduction step, which is performed by selecting one longitudinal segment from each unit of the laboratory sample, resulted in relative uncertainties of 17% and 21% for field-treated jackfruits and cucumber, respectively, and 7% for post-harvest treated papaya. The results demonstrated that sample size reduction is an inevitable source of uncertainty in pesticide residue analysis of large-sized crops. The post-harvest treatment resulted in a lower variability because the dipping process leads to a more uniform residue concentration on the surface of the crops than does the foliar application of pesticides.
Sample processing is a very important component of uncertainty in analytical results. In order to have reliable results, the
laboratory sample should be properly processed to obtain statistically homogenous matrix—before the representative test portions
are withdrawn for analysis. The use of 14C-labeled compound is preferable because the analyte can be quantified without cleanup. The method is based on surface treatment
of cucumber with 14C-chlorpyrifos, determination of 14C-chlorpyrifos activity in the replicate test portions of different size, and determination of the uncertainty of sample processing.
A rapid and sensitive liquid chromatography-tandem mass spectrometry method, in electrospray ionization positive mode, has been developed for the determination of 160 selected multi-class pesticides over a 33-min run time. Extracts were obtained using the acetonitrile-based QuEChERS (quick, easy, cheap, effective, rugged and safe) sample preparation technique. The validation study was carried out on tomato, pear and orange matrices following DG SANCO/2007/3131 of the European Quality Control Guidelines. These matrices represent high water, high sugar and high acidic content commodities, respectively. Matrix influence on recoveries and its effects on ionization were evaluated for the three matrices. Ten out of the 160 pesticides showed very low intensity, linearity and/or sensitivity problems. Linearity was studied in the 5-500 microg kg(-1) concentration range. Soft (<20%), medium (20-50%), and strong (>50%) matrix effects were obtained for 69%, 20%, and 11% of the studied compounds, respectively. Recoveries were investigated at the 10 and 100 microg kg(-1) levels, and depending on the commodity, 97%, 98% and 97% of the compounds in tomato, pear and orange, respectively, were in the 70-120% range. More than 90% of the investigated compounds had less or equal to a 5 microg kg(-1) limit of detection in the studied matrices. The relative standard deviations obtained exceeded 20% in only very few cases. The overall standard deviation obtained in the survey study (0.1551) was used for the method's uncertainty estimation. The expanded uncertainty resulted as being 0.3002 (coverage factor K=2, confidence level 95%). The method was applied on 59 real samples from 14 different kinds of fruits and vegetables. Thirty-three compounds were detected in 50 positive samples.
A 'bottom-up' approach for the expression of results obtained from analytical methods that include analytical steps with recovery inherently different from 100% [mass transfer steps (MTS): extraction, evaporation, clean-up procedures, digestion, etc.] is presented. The estimation of the combination of all MTS uncertainty involves the comparison of the experimental dispersion of replicated analyses of spiked samples with the estimation of the uncertainty obtained for the combination of all uncertainty sources except MTS ones ('incomplete' estimation). The estimation of MTS uncertainty by difference is performed after evaluating the statistical difference between the 'incomplete' estimation and the experimental dispersion (F-test). When the two estimations are statistically equivalent, the MTS uncertainty is considered to be negligible in relation to the other sources budget. The assumption of constancy of MTS performance within the analytical range is tested through single analyses at several concentration levels and is evaluated by the inclusion of the expected values at the intervals resulting from the combination of the MTS uncertainty estimation performed at one concentration level and the 'incomplete' estimation. The developed methodology can also be useful for method optimisation and validation and for the detection of small trends in results. The determination of pesticides in sweet peppers by GC-NPD was used to explore the above concepts.
The results of 89 new field trials and 11 supervised trials were considered, together with 91 sets of residue data evaluated earlier. The datasets consisted of 22,643 valid residue data. As all variability factors calculated from individual sample sets are affected by the uncertainties of sampling and analysis, the average of the P(0.975)/R(ave) (97.5th percentile of the residue population divided by the average residues in the lot) values gives the best estimate for the variability factor. The Harrell-Davis (H-D) method gave an average value of 2.89 for the variability factor for all samples, while the average variability factors obtained from samples derived from the new field and supervised trials were 2.8 and 2.7 with the IUPAC and H-D methods respectively. The number of residue values below the LOQ in a sample set significantly affects the observed variability factors. It was found that datasets containing over 20% non-quantifiable residues might not reflect the true variability of the residues. Mixing of treated and non-treated commodities may significantly increase the apparent variability. Consequently, only datasets of known origin and consisting of well-quantifiable residues should be used for estimation of the variability factor. Samples taken from marketed lots may not represent a single lot, and thus they have limited value in estimating the variability factor. The large number of residue data confirms the applicability of the default variability factor of 3 adopted by the FAO/WHO for deterministic estimation of the acute intake of pesticide residues.
Principles and Practices of Method Validation is an overview of the most recent approaches used for method validation in cases when a large number of analytes are determined from a single aliquot and where a large number of samples are to be analysed. Much of the content relates to the validation of new methods for pesticide residue analysis in foodstuffs and water but the principles can be applied to other similar fields of analysis. Different chromatographic methods are discussed, including estimation of various effects, eg. matrix-induced effects and the influence of the equipment set-up. The methods used for routine purposes and the validation of analytical data in the research and development environment are documented. The legislation covering the EU-Guidance on residue analytical methods, an extensive review of the existing in-house method validation documentation and guidelines for single-laboratory validation of analytical methods for trace-level concentrations of organic chemicals are also included. With contributions from experts in the field, any practising analyst dealing with method validation will find the examples presented in this book a useful source of technical information.
The typical sampling uncertainties were calculated as the average of relative standard deviations (CV) of residues measured in individual crops tested in supervised residue trials and from their pooled variance for crop groups. The relative confidence intervals of the sampling uncertainty for different crops were estimated from the random duplicate composite samples generated with computer modeling from residues in 182 independent primary sample sets each consisting of 100-320 residue data. The relative 95% confidence intervals found to be independent from the CV of primary residue data populations, therefore the calculated values are generally applicable. In view of the potentially serious consequences of underestimated sampling uncertainties, their upper confidence limits are recommended for practical use to verify the compliance of products and for planning statistically based sampling programs. Sampling uncertainties are reported for 24 crop groups and 106 individual crops.
The supervised trial datasets (1950), consisting of a minimum of five residue values and selected by the experts of FAO/WHO Joint Meeting on Pesticide Residues for recommending maximum residue levels between 1997 and 2011, were evaluated to obtain information on the typical spread of residue values in individual datasets. The typical relative standard deviation, CV, of field-to-field variation of pesticide residues was about 80%. The spread of residues in datasets is independent from the chemical structure of pesticides, residue level, pre-harvest interval and number of values in the datasets. The CV ranges within the Codex commodity groups and between groups overlapped and their difference were not statistically significant. The number of residues below the limit of quantification (LOQ) affects the CV at various extents depending on the ratio of LOQ/R mean. The combined uncertainty of the highest residue in a dataset significantly affects the CV of the dataset. The lowest and intermediate ones have less influence. The residues in different fields receiving the same treatment vary within large range: 55%, 72%, 78%, 86% and 89% of the 25,766 residues values were, respectively, within 3, 4, 5, 6 and 7 times the median value of the corresponding dataset.
The sampling constant and testing method proposed by Ingamells and Switzer, and Wallace and Kratochvil were used for estimating the uncertainty of sample preparation of apples and head‐cabbages. The sampling constants of about 1.6 kg and 10–20 kg were determined for cabbage and apple, respectively. Consequently about 64 g cabbage and 440–880 g apples should be extracted for holding the uncertainty of sample preparation
The characteristic features of distribution of pesticide residues in crop units and single sample increments were studied based on more than 19,000 residue concentrations measured in root vegetables, leafy vegetables, small-, medium- and large-size fruits representing 20 different crops and 46 pesticides. Log-normal, gamma and Weibull distributions were found to provide the best fit for the relative frequency distributions of individual residue data sets. The overall best fit was provided by lognormal distribution. The relative standard deviation of residues (CV) in various crops ranged from 15-170%. The 100-120 residue values being in one data set was too small to identify potential effects of various factors such as the chemical and physical properties of pesticides and the nature of crops. Therefore, the average of CV values, obtained from individual data sets, were calculated and considered to be the best estimate for the likely variability of unit crop residues for treated field (CV = 0.8) and market samples (CV = 1.1), respectively. The larger variation of residues in market samples was attributed to the potential mixing of lots and varying proportion of non-detects. The expectable average variability of residues in composited samples can be calculated from the typical values taking into account the sample size.
This paper accounts for the major sources of errors associated with pesticide residue analysis and illustrates their magnitude based on the currently available information. The sampling, sample processing and analysis may significantly influence the uncertainty and accuracy of analytical data. Their combined effects should be considered in deciding on the reliability of the results. In the case of plant material, the average random sampling (coefficient of variation, CV=28–40%) and sample processing (CV up to 100%) errors are significant components of the combined uncertainty of the results. The average relative uncertainty of the analytical phase alone is about 17–25% in the usual 0.01–10mg/kg concentration range. The major contributor to this error can be the gas-liquid chromatography (GLC) or high-performance liquid chromatography (HPLC) analysis especially close to the lowest calibrated level. The expectable minimum of the combined relative standard uncertainty of the pesticide residue analytical results is in the range of 33–49% depending on the sample size.The gross and systematic errors may be much larger than the random error. Special attention is required to obtain representative random samples and to eliminate the loss of residues during sample preparation and processing.
The estimation of the uncertainty associated to analytical methods is necessary in order to establish the comparability of results. Multiresidue analytical methods lack very often of information about uncertainty of results with likely implications when results are compared with maximum residue levels (MRL) established by regulations. An adequate identification and estimation of each uncertainty source allows to laboratories to establish the accuracy of results and to balance with time-consuming and costs.
In order to provide residue data for refining the estimated sampling uncertainty, a coordinated research program was initiated for performing field studies on residues in individual items of leafy vegetables, small and large crops. The trials were carried out in 13 countries with 3 small fruits, 5 large crops, 2 medium/large crops and 3 leafy vegetables. The 25 pesticide active ingredients applied represented the dicarboximide (3), organophosphorus (8), synthetic pyrethroids (5), phthalimides (2), organochlorine (1) and other types of pesticides (6). In addition, 11 supervised field trials were performed in grapes and lettuce by the pesticide manufacturers, and their results were provided for evaluation. The studies represented actual agriculture practice around the world, and provide reliable data for estimation of sampling uncertainty. Based on the 12346 residue data, the best estimate for the relative sampling uncertainty for composite samples, assuming sample size of 10 for small crops and leafy vegetables and 5 for large crops, with 95% confidence limits in brackets are: small commodities: 0.25 (0.20-0.29); Brassica leafy vegetables: 0.20 (0.16-0.24); large commodities: 0.33 (0.29-0.38).
In a batch of food items previously treated with a pesticide, the residue of the pesticide
remaining on/in single food items at the time of consumption varies between items,
due to a variety of factors. So there is a distribution of residues, with some items
containing more pesticide than others. It is important to take account of this variation
when assessing the risk to consumers from acute dietary exposure to pesticides in
medium and large-sized food items (e.g. apples or melons). Therefore, international
assessment procedures are based on the 97.5th percentile of the distribution of
residues; i.e. the level that is exceeded by 2.5% of residues in food items (i.e. 1 in 40).
This residue level is not measured directly, but estimated by measuring the
concentration in a small batch of items and multiplying it by a 'variability factor' to
estimate the 97.5th percentile residue.
Recently, it has been proposed that a default value of 3 should generally be used for
the variability factor, replacing a range of default values for different commodities. The
Commission has asked the PPR Panel to advise on the scientific basis for choosing a
single default value for the variability factor.
The PPR Panel examined the range of variability factors from existing studies where
residues were measured separately in individual food items. The PPR Panel excluded
from this analysis studies for which the variability factor could not be estimated
reliably, i.e. studies with less than 50 items, or where the result was strongly affected
by pesticide residues below the level that could be quantified.
On average, variability factors estimated from samples collected in the marketplace
were higher than those from samples obtained in experimental studies (supervised
trials). The PPR Panel therefore recommends that consideration be given to using
different variability factors when doing exposure assessments with data from these two
types of study.
The average variability factor for supervised trials was 2.8. However, the variability
factor is itself variable, and the Commission may wish to consider this when choosing
an appropriate default value for use in dietary exposure assessments. To assist in this,
the PPR Panel provides tables presenting a range of statistics. For example, it is
estimated that the variability factors for supervised trials will exceed the proposed
default value of 3 in 34% of cases, whereas the previous default value of 7 for mediumsized
food items will be exceeded in 0.2% of cases. Similarly, the variability factors for
market surveys averaged 3.6, and will exceed 3 in about 65% of cases and 7 in about
1% of cases.
The data analysed by the PPR Panel related mostly to medium-sized commodities
(between 25 and 250 g, e.g. apples). The PPR Panel concluded there was insufficient
evidence to support a real difference between variability factors for medium and largesized
commodities, and therefore considered its results applicable to both types.
However, the PPR Panel recommends that this should be re-examined when further
data on large-sized commodities become available.
The results are affected by a number of uncertainties. The PPR Panel calculated
confidence intervals to indicate the degree of uncertainty due to limitations in the
amounts of data available for the analysis. Other uncertainties, e.g. in extrapolating
variability factors between pesticides and between crops, were considered qualitatively.
Finally, the PPR Panel noted that the assessment of acute risks from dietary exposure
uses conservative assumptions for portion size and the mean residue concentration as
well as the variability factor. The combined effect of these conservative assumptions on
the overall level of consumer protection may warrant further consideration.
The uneven distribution of pesticide residues among the treated objects leads to an inevitable variability of pesticide residue levels measured in the samples, which may significantly contribute to the combined uncertainty of the analytical results. A total of 8844 unit-crop residue data derived from 57 lots and 19 field trials were evaluated to determine the characteristic features of residue distribution in unit crops and composite samples. The average residue levels and the corresponding coefficient of variation (CV) values obtained for individual units taken from a given lot showed wide variation from lot to lot. There was no significant difference between the CVs of residue levels in sample sets of various unit crops or composite sample populations of different sizes taken from various crops. The CV values for levels of residues taken from individual lots followed normal distribution. Very good correlation was found between the CVs of the parent and sample populations. The experimentally obtained values were very close to those expected on the basis of the central limit theorem. The estimated typical relative standard uncertainties of sampling medium-size crops for pesticide residue analysis in the cases of sample sizes of 5, 10, and 25 were 37, 25, and 16%, respectively.
The homogeneity of analytical samples and the stability of pesticides during the sample processing of oranges and tomatoes were evaluated. The mean concentrations of 14C-labeled chlorpyrifos in analytical portions (subsamples) after processing show that homogeneity is dependent on sample type as well as the processing procedure. The homogeneity of analytical samples of tomatoes processed cryogenically was much better than those processed at ambient temperature. For tomatoes, the minimum analytical portion masses required for between-analytical portion variation of < 0.3 Ho were 110 and 5 g for processing at ambient and cryogenic temperatures, respectively. Results for orange showed that analytical portion sizes of 5 g provided sufficient homogeneity from both sample processing procedures. Assessments of pesticide stability demonstrated that most were relatively stable during processing at either ambient or cryogenic temperatures. However, some pesticides, including dichlofluanid, chlorothalonil, tolylfluanid, and dicloran, appeared to suffer much greater losses (>20%) during processing at ambient temperature. For these analytes, loss is interpreted as chemical degradation.
This paper presents methods for calculating confidence intervals for estimates of sampling uncertainty (s(samp)) and analytical uncertainty (s(anal)) using the chi-squared distribution. These uncertainty estimates are derived from application of the duplicate method, which recommends a minimum of eight duplicate samples. The methods are applied to two case studies--moisture in butter and nitrate in lettuce. Use of the recommended minimum of eight duplicate samples is justified for both case studies as the confidence intervals calculated using greater than eight duplicates did not show any appreciable reduction in width. It is considered that eight duplicates provide estimates of uncertainty that are both acceptably accurate and cost effective.
Contribution of sampling to the variability of pesticide residue data 1368−1379. (8) Farkas, Zs.; HorváthHorváth, Zs.; Kerekes, K.; Ambrus, A ́ .; HámosHámos, A.; SzeitznéSzabó,SzeitznéSzabóSzeitznéSzabó, M. Estimation of sampling uncertainty for pesticide residues in root vegetable crops
- A Ambrus
- E Soboleva
Ambrus, A ́.; Soboleva, E. Contribution of sampling to the
variability of pesticide residue data. J. AOAC Int. 2004, 87, 1368−1379.
(8) Farkas, Zs.; HorváthHorváth, Zs.; Kerekes, K.; Ambrus, A ́.; HámosHámos, A.;
SzeitznéSzabó,SzeitznéSzabóSzeitznéSzabó, M. Estimation of sampling uncertainty for pesticide
residues in root vegetable crops. J. Environ. Sci. Health, Part B 2014, 49,
1−14.
setting maximum levels for certain contaminants in foodstuffs setting maximum levels for certain contaminants in foodstuffs as regards dioxins and dioxin-like PCBs. Off
(20) Commission Regulation (EU) No. 420/2011 of 29 April 2011
amending Regulation (EC) No. 1881/2006 setting maximum levels
for certain contaminants in foodstuffs. Off. J. Eur. Union 2011, L111,
3−6.
(21) Commission Regulation (EC) No. 199/2006 of 3 February
2006 amending Regulation (EC) No. 466/2001 setting maximum
levels for certain contaminants in foodstuffs as regards dioxins and
dioxin-like PCBs. Off. J. Eur. Union. 2006, L32, 34−38.
- B Maestroni
- A Ghods
J. Environ. Sci. Health, Part B 1996, 31, 443−450.
(11) Maestroni, B.; Ghods, A.; El-Bidaoui, M.; Rathor, N.; Jarju, O.
Testing the efficiency and uncertainty of sample processing using 14 C labelled chlorpyrifos. Part I12) Maestroni, B.; Ghods, A.; El-Bidaoui, M.; Rathor, N.; Ton, T.; Ambrus, A. Testing the efficiency and uncertainty of sample processing using 14 C labelled chlorpyrifos. Part II
- A Ambrus
P.; Ton, T.; Ambrus, A ́. Testing the efficiency and uncertainty of
sample processing using 14 C labelled chlorpyrifos. Part I. In Principles
of Method Validation; Fajgelj, A., Ambrus, A., Eds.; Royal Society of
Chemistry: Cambridge, UK, 2000; pp 49−58.
(12) Maestroni, B.; Ghods, A.; El-Bidaoui, M.; Rathor, N.; Ton, T.;
Ambrus, A. Testing the efficiency and uncertainty of sample processing
using 14 C labelled chlorpyrifos. Part II. In Principles of Method
Validation; Fajgelj, A., Ambrus, A., Eds.; Royal Society of Chemistry:
Cambridge UK, 2000; pp 59−74.
HorváthHorváth, Zs.; Farkas, Zs.; Szabó,Szabó, I. J.; DorogháziDorogházi, E.; Szeitzné-Szeitzné-Szabó,Szabó, M. Nature of the field-to-field distribution of pesticide residues
- A Ambrus
Ambrus, A ́.; HorváthHorváth, Zs.; Farkas, Zs.; Szabó,Szabó, I. J.; DorogháziDorogházi, E.;
Szeitzné-Szeitzné-Szabó,Szabó, M. Nature of the field-to-field distribution of pesticide
residues. J. Environ. Sci. Health, Part B 2014, 49, 229−244.
Recommended method of sampling for thedetermination of pesticide residues for compliance with MRLs
- Codex Secretariat
Codex Secretariat. Recommended method of sampling for the
determination of pesticide residues for compliance with MRLs, 2002;
- Mycotoxin Sampling
- User Tool
- Guide
Mycotoxin Sampling Tool, User Guide; 2013; http://www.fstools.org/
mycotoxins/Documents/UserGuide.pdf (accessed Oct 30, 2014).
Validation of targeted sampling with the new data and development of quantitative indices on reliability and precision, accounting for on food risk factor information
- Zs Farkas
- Á Ambrus
- I J Szabó
Farkas, Zs.; Ambrus, A.; Szabo, I. J. Validation of targeted
sampling with the new data and development of quantitative indices
on reliability and precision, accounting for on food risk factor
information. Deliverable 7.2 of BASELINE Project 2013; https://
secure.baselineeurope.eu/gest/documentspublic/docup/D7.2_
Testing the efficiency and uncertainty of sample processing using 14 C labelled chlorpyrifos. Part II. In Principles of Method Validation
- B Maestroni
- A Ghods
- M El-Bidaoui
- N Rathor
- T Ton
- A Ambrus
Maestroni, B.; Ghods, A.; El-Bidaoui, M.; Rathor, N.; Ton, T.;
Ambrus, A. Testing the efficiency and uncertainty of sample processing
using 14 C labelled chlorpyrifos. Part II. In Principles of Method
Validation; Fajgelj, A., Ambrus, A., Eds.; Royal Society of Chemistry:
Cambridge UK, 2000; pp 59−74.
amending Regulation (EC) No. 396/ 2005 on maximum residue levels of pesticides in or on food and feed of plant and animal origin, as regards the implementing powers conferred on the Commission
Regulation (EC) No. 299/2008 of the European parliament and
of the council of 11 March 2008 amending Regulation (EC) No. 396/
2005 on maximum residue levels of pesticides in or on food and feed
of plant and animal origin, as regards the implementing powers
conferred on the Commission. Off. J. Eur. Union 2008, L97, 67−71.
amending Regulation (EC) No. 1881/2006 setting maximum levels for certain contaminants in foodstuffs
Commission Regulation (EU) No. 420/2011 of 29 April 2011
amending Regulation (EC) No. 1881/2006 setting maximum levels
for certain contaminants in foodstuffs. Off. J. Eur. Union 2011, L111,
3−6.
amending Regulation (EC) No. 466/2001 setting maximum levels for certain contaminants in foodstuffs as regards dioxins and dioxin-like PCBs
Commission Regulation (EC) No. 199/2006 of 3 February
2006 amending Regulation (EC) No. 466/2001 setting maximum
levels for certain contaminants in foodstuffs as regards dioxins and
dioxin-like PCBs. Off. J. Eur. Union. 2006, L32, 34−38.
(32) Food and Agriculture Organization. Submission and evaluation of pesticide residues data for the estimation of maximum residue levels in food and feed. FAO Plant Production and Protection Paper 197
- Zs Farkas
- Zs Horvath
- I J Szabo
- A Ambrus
Farkas, Zs.; Horvath, Zs.; Szabo, I. J.; Ambrus, A. Estimation of
sampling uncertainty of pesticide residues based on supervised residue
trial data. J. Agric. Food Chem. 2014, DOI: 10.1021/jf5055112.
(32) Food and Agriculture Organization. Submission and evaluation
of pesticide residues data for the estimation of maximum residue levels
in food and feed. FAO Plant Production and Protection Paper 197; FAO:
Rome, Italy, 2009 http://www.fao.org/fileadmin/templates/
agphome/documents/Pests_Pesticides/JMPR/FAO_manual2nded_
Pesticide Residues in Food, Codex Classification of Foods and Animal Feeds
Codex Alimentarius Commission. Codex Alimentarius Vol. 2.
Pesticide Residues in Food, Codex Classification of Foods and Animal
Feeds; FAO: Rome, Italy, 1993; http://www.codexalimentarius.org/
search-results/?cx=018170620143701104933%3Ai-zresgmxec&cof=
accessed Oct 30, 2014).
EPA’s Office of Pesticide Programs, Washingon DC
- P U S Villanueva