Error propagation in pesticide residue measurements estimated by computational simulations and inter-laboratory sample analysis. 2009

Sergio C Nanita, and Melissa Ziegler, and Livio Giammarrusti, and Andreas Huber, and Elena Astor, and Janet C Ruhl
DuPont Crop Protection, Stine-Haskell Research Center, Newark, Delaware, USA. sergio.c.nanita@usa.dupont.com

An assessment of the error associated with conventional pesticide residue analysis has been conducted based on computer simulations and inter-laboratory residue analysis. Computational simulations were conducted based on (i) typical performance and regulatory acceptance criteria of analytical methods, and (ii) field residue distributions. In addition, field samples with incurred residues were sent to different private laboratories and the results compared. The relative difference in pesticide residues obtained when samples from the same field or produce lot are analyzed at separate laboratories was used to quantify the uncertainty associated with residue analyses performed using common analytical technology, and methods that are in compliance with current regulatory requirements. The study showed that differences of > 100% are common and should be expected when samples from the same crop are analyzed at different laboratories. The results also suggest that the error within residue measurements can be particularly detrimental when a result is reported near the maximum residue limit (MRL).

UI MeSH Term Description Entries
D010573 Pesticide Residues Pesticides or their breakdown products remaining in the environment following their normal use or accidental contamination. Pesticide Residue,Residue, Pesticide,Residues, Pesticide
D011786 Quality Control A system for verifying and maintaining a desired level of quality in a product or process by careful planning, use of proper equipment, continued inspection, and corrective action as required. (Random House Unabridged Dictionary, 2d ed) Control, Quality,Controls, Quality,Quality Controls
D002623 Chemistry Techniques, Analytical Methodologies used for the isolation, identification, detection, and quantitation of chemical substances. Analytical Chemistry Techniques,Analytical Chemistry Methods,Analytical Chemistry Method,Analytical Chemistry Technique,Chemistry Method, Analytical,Chemistry Methods, Analytical,Chemistry Technique, Analytical,Method, Analytical Chemistry,Methods, Analytical Chemistry,Technique, Analytical Chemistry,Techniques, Analytical Chemistry
D003198 Computer Simulation Computer-based representation of physical systems and phenomena such as chemical processes. Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D004785 Environmental Pollutants Substances or energies, for example heat or light, which when introduced into the air, water, or land threaten life or health of individuals or ECOSYSTEMS. Environmental Pollutant,Pollutant,Pollutants,Pollutants, Environmental,Pollutant, Environmental
D005506 Food Contamination The presence in food of harmful, unpalatable, or otherwise objectionable foreign substances, e.g. chemicals, microorganisms or diluents, before, during, or after processing or storage. Food Adulteration,Adulteration, Food,Adulterations, Food,Contamination, Food,Contaminations, Food,Food Adulterations,Food Contaminations
D015233 Models, Statistical Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc. Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D015982 Bias Any deviation of results or inferences from the truth, or processes leading to such deviation. Bias can result from several sources: one-sided or systematic variations in measurement from the true value (systematic error); flaws in study design; deviation of inferences, interpretations, or analyses based on flawed data or data collection; etc. There is no sense of prejudice or subjectivity implied in the assessment of bias under these conditions. Aggregation Bias,Bias, Aggregation,Bias, Ecological,Bias, Statistical,Bias, Systematic,Ecological Bias,Outcome Measurement Errors,Statistical Bias,Systematic Bias,Bias, Epidemiologic,Biases,Biases, Ecological,Biases, Statistical,Ecological Biases,Ecological Fallacies,Ecological Fallacy,Epidemiologic Biases,Experimental Bias,Fallacies, Ecological,Fallacy, Ecological,Scientific Bias,Statistical Biases,Truncation Bias,Truncation Biases,Bias, Experimental,Bias, Scientific,Bias, Truncation,Biase, Epidemiologic,Biases, Epidemiologic,Biases, Truncation,Epidemiologic Biase,Error, Outcome Measurement,Errors, Outcome Measurement,Outcome Measurement Error
D018556 Crops, Agricultural Cultivated plants or agricultural produce such as grain, vegetables, or fruit. (From American Heritage Dictionary, 1982) Agricultural Crops,Agricultural Crop
D035501 Uncertainty The condition in which reasonable knowledge regarding risks, benefits, or the future is not available.

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