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ARS Home » Southeast Area » Raleigh, North Carolina » Market Quality and Handling Research » Research » Publications at this Location » Publication #202636
Title: Evaluating the performance of sampling plans to detect fumonisn B1 (FB1) in maize lots marketted in Nigeria

Author
item Whitaker, Thomas
item DOKO, M - IAEA, AGROCHEM. UNIT BIO.
item MAESTRONI, B - IAEA, AGROCHEM. UNIT BIOC
item SLATE, ANDREW - NC STATE UNIVERSITY
item OGUNBANWO, B - NAFDAC LAGO

Submitted to: Journal of Association of Official Analytical Chemists International
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/14/2007
Publication Date: 8/29/2007
Citation: Whitaker, T.B., Doko, M.B., Maestroni, B.M., Slate, A.B., Ogunbanwo, B.F. 2007. Evaluating the performance of sampling plans to detect fumonisn B1 (FB1) in maize lots marketted in Nigeria. Journal of Association of Official Analytical Chemists International 90:1050-1059.

Interpretive Summary: Fumonisin is one of several mycotoxins produced by molds that are considered to be a carcinogenic and toxic compound that contaminates grains. Because of limited economic and technical resources, developing countries have a more difficult time of preventing mycotoxin contamination and removing contaminated grains from food channels. As regulatory agencies establish action limits for fumonisin, processors, manufacturers, exporters, and importers inspect grain products to detect and remove contaminated lots from the food and feed chain. It is difficult to determine the exact fumonisin level in large shipments because samples taken from the same contaminated lot will produce a wide range of fumonisin results. A study was developed with the support of the International Atomic Energy Agency to develop a statistical method to evaluate the accuracy with which sampling plans can detect corn shipments contaminated with fumonisin. A method was developed to predict the performance of fumonisin sampling plan designs so that sampling plans can be designed to reduce the number of lots miss-classified. This will reduce both health risks to the consumer and economic loss to the grain industry.

Technical Abstract: Fumonisins are toxic and carcinogenic compounds produced by fungi that can be readily found in maize. Several nations have developed regulatory limits to protect consumers from unreasonably high levels of fumonisins in maize. The establishment of maximum limits for fumonisins in maize requires that grain industries and regulatory agencies develop scientifically based sampling plans for fumonisin in maize that will meet regulatory and/or customer maximum limits for fumonisin. As part of an International Atomic energy Agency effort to assist developing countries to control mycotoxin contamination, a study was carried out to design sampling plans to determine fumonisin in maize produced and marketed in Nigeria, Africa. One hundred maize lots were sampled according to an experimental protocol where 20 test samples, 100 g each, were taken from each lot resulting in a total of 2000 fumonisin analyses. The total variability associated with the combined sampling, sample preparation, and analytical steps of the fumonisin test procedure were measured for each of the 100 maize lots. The total variance was found to be a function of the lot fumonisin concentration and regression equations were developed to predict the total variance as a function of fumonisin concentration. The total variability of the fumonisin test procedure was very similar to the uncertainty associated with a USDA study conducted on maize marketed in the southeastern United States. The observed fumonisin distribution among the 20-fumonisin sample test results was compared to several theoretical distributions. The negative binomial distribution was selected to model fumonisin test results for maize because it gave the best fit across all 100 observed sample distributions. Specific computer software was developed using the variance and distribution information to predict the performance of sampling plan designs to detect fumonisin in maize shipments. The performance of several sampling plan designs was evaluated to demonstrate how to manipulate sample size and accept/reject limits to reduce misclassification of maize lots.