Location: Market Quality and Handling Research
Title: Sampling hazelnuts for aflatoxin: Effects of sample size and accetp/reject limit on reducing risk of misclassifying lots Authors
|Guner, Ozay - MARMARA RES. CENTER|
|Seyhan, Ferda - MARMARA RES. CENTER|
|Yilmaz, Aysun - MARMARA RES. CENTER|
|Slate, Andrew - NC STATE UNIVERSITY|
|Giesbrecht, F - NC STATE UNIVERSITY|
Submitted to: Official Methods of Analysis of AOAC International
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 26, 2007
Publication Date: August 29, 2007
Citation: Guner, O., Seyhan, F., Yilmaz, A., Whitaker, T.B., Slate, A.B., Giesbrecht, F.G. 2007. Sampling hazelnuts for aflatoxin: Effects of sample size and accetp/reject limit on reducing risk of misclassifying lots. Official Methods of Analysis of AOAC International. Interpretive Summary: Aflatoxin is a carcinogenic and toxic compound produced by molds found in several agricultural commodities such as peanuts, grains, and treenuts. Regulatory agencies worldwide have established maximum limits for aflatoxin in foods as a method to reduce aflatoxin contaminated foods. In the United States, the FDA has established a limit of 20 parts per billion total aflatoxins. As a result, hazelnuts are inspected by exporters, importers, processors, and food manufacturers to detect and remove contaminated lots from the food chain. It is difficult to determine aflatoxin levels of large shipments or lots because of the errors associated with sampling, sample preparation, and analysis, collectively called the aflatoxin test procedure. Errors associated with the aflatoxin test procedure results in some lots being misclassified. Some of the good lots test bad and some of the bad lots test good. A method was developed to evaluate the number of lots misclassified by a specific aflatoxin-sampling plan. Examples of sampling plan designs were shown to demonstrate how to reduce the number of lots misclassified. The evaluation method will help processors, importers, and exporters reduce the number of lots misclassified. Designing sampling plans that reduce misclassification of lots will reduce both health risks to the consumer and economic loss to processors, importers, exporters, and food manufacturers.
Technical Abstract: About 100 countries have established regulatory limits for aflatoxin in food and feeds. Because these limits vary widely among regulating countries, the Codex Committee on Food Additives and Contaminants (CCFAC) began work in 2004 to harmonize aflatoxin limits and sampling plans for aflatoxin in almonds, pistachios, hazelnuts, and Brazil nuts. Studies were developed to measure the uncertainty and distribution among replicated sample aflatoxin test results taken from aflatoxin-contaminated treenut lots. The uncertainty and distribution information is used to develop a model that can evaluate the performance (risk of misclassifying lots) of aflatoxin sampling plan designs for treenuts. Once the performance of aflatoxin-sampling plans can be predicted, aflatoxin-sampling plans can be designed to reduce the risks of misclassifying lots traded in either the domestic or export markets. This paper describes a study to evaluate the performance of sampling plans designed to detect aflatoxin in hazelnuts lots. Twenty hazelnut lots with varying levels of contamination were sampled according to an experimental protocol where 16 test samples were taken from each lot. The observed aflatoxin distribution among the 16-aflatoxin sample test results was compared to lognormal, compound gamma, and negative binomial distributions. The negative binomial distribution was selected to model aflatoxin distribution among sample test results because it gave acceptable fits to observed distributions among sample test results taken from a wide range of lot concentrations. Using the negative binomial distribution, computer models were developed to calculate operating characteristic curves for specific aflatoxin sampling plan designs. The effect of sample size and accept/reject limits on the chances of rejecting good lots (sellers’ risk) and accepting bad lots (buyers’ risk) was demonstrated for various sampling plan designs.