|Johansson, Anders - AJ AND ASSOCIATES|
Submitted to: Journal of Food Protection
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: September 1, 2004
Publication Date: January 12, 2005
Citation: Whitaker, T.B., Johansson, A.S. 2005. Sampling uncertainties for the detection of chemical agents in complex food matrices. Journal of Food Protection. 68:1306-1313. Interpretive Summary: Government regulatory agencies are concerned about food security and the deliberate contamination of the U.S. food supply by terrorists. Part of the overall food security effort to ensure a safe food supply for the American consumer calls for additional inspection of foods for intentional contamination by various chemical agents. Because of the random errors associated with test procedures to detect chemical agents, it is difficult to determine with 100% certainty the true concentration of a chemical agent in food lots. Studies were conducted to measure the uncertainty associated with sampling, sample preparation, and analytical steps in a typical inspection program and determine which steps contribute the most error to the overall uncertainty of the testing program. When inspecting foods where a small percentage of the items in the lot are contaminated, the sampling step constitutes the largest source of error and can only be reduced by increasing sample size. When inspecting foods where a large percentage of the items in the lot are contaminated, it is prudent to reduce the errors associated with all three steps of the test procedure. Reducing variability of the chemical agent test procedure will reduce the number of lots misclassified by the sampling plan. Methods have been developed for several chemical agents and several commodities to evaluate and design sampling plans that minimize the consumer's risks associated with sampling plans to detect chemical agents in foods.
Technical Abstract: Using uncertainty associated with detection of aflatoxin in shelled corn as a model, the uncertainty associated with detecting chemical agents intentionally added to food products was investigated. Sources of variability that affect precision are the primary focus of the paper. Test procedures used to detection chemical agents generally include sampling, sample preparation, and analytical steps. The uncertainty of each step contributes to the total uncertainty of the detection method or test procedure. The variance (or standard deviation) increases with the level of contamination while the coefficient of variation (CV) decreases with the level of contamination. Using variance as a statistical measure of uncertainty, the variance associated with each step of the test procedure used to detect aflatoxin in shelled corn was determined for both low and high contamination levels. For example, when using a 1-kg sample, Romer mill, 50-g subsample, and high performance liquid chromatography to test a lot of shelled corn at 10 ng/g aflatoxin, the total variance associated with the test procedure is 149.2 (CV=122.1%). The sampling, sample preparation, and analytical steps account for 83.0, 15.6, and 1.4% of the total uncertainty (variance of 149.2), respectively. A variance of 149.2 suggests that repeated test results will vary from 0 to 33.9 ng/g. Using the same test procedure to detect aflatoxin in a lot at 10,000 ng/g, the total variance is 264,719 (CV=5.1%). The sampling, sample preparation, and analytical steps account for 41, 57, and 2% of the total uncertainty (variance of 264,719), respectively. A variance of 264,79 suggests that repeated test results will vary from 8,992 to 11,008 ng/g. Foods contaminated at low levels reflect a situation where a small percentage of particles are contaminated and sampling becomes the largest source of uncertainty. Large samples are required to overcome the "needle-in-the-haystack" problem. Foods intentionally contaminated at high levels are much easier to identify or detect than foods with low levels of contamination because the relative standard deviation (CV) decreases and the percentage of contaminated kernels increases with an increase in concentration.