Whitaker T.B., Doko M., Maestroni B.M., Slate A.B., Ogunbanwo B.F.
Evaluating the performance of sampling plans to detect fumonisin Bi in maize lots marketed in Nigeria
Journal of AOAC International
U.S. Department of Agriculture, Agricultural Research Service, North Carolina State University, Raleigh, NC 27695-7625; International Atomic Energy Agency (IAEA), Agrochemicals Unit, IAEA/FAO Biotechnology Laboratories, Seibersdorf, Austria; North Carolin
Whitaker, T.B., U.S. Department of Agriculture, Agricultural Research Service, North Carolina State University, Raleigh, NC 27695-7625; Doko, M., International Atomic Energy Agency (IAEA), Agrochemicals Unit, IAEA/FAO Biotechnology Laboratories, Seibersdorf, Austria; Maestroni, B.M., International Atomic Energy Agency (IAEA), Agrochemicals Unit, IAEA/FAO Biotechnology Laboratories, Seibersdorf, Austria; Slate, A.B., North Carolina State University, Biological and Agricultural Engineering Department, Box 7625, Raleigh, NC 27695-7625; Ogunbanwo, B.F., National Agency for Food and Drug Administration and Control, Mycotoxin Unit, Oshodi Central Laboratories, Lagos, Nigeria
Fumonisins are toxic and carcinogenic compounds produced by fungi that can be readily found in maize. The establishment of maximum limits for fumonisins requires the development of scientifically based sampling plans to detect fumonisin in maize. As part of an International Atomic Energy Agency effort to assist developing countries to control mycotoxin contamination, a study was conducted to design sampling plans to detect fumonisin in maize produced and marketed in Nigeria. Eighty-six maize lots were sampled according to an experimental protocol in which an average of 17 test samples, 100 g each, were taken from each lot and analyzed for fumonisin B1 by using liquid chromatography. The total variability associated with the fumonisin test procedure was measured for each lot. Regression equations were developed to predict the total variance as a function of fumonisin concentration. The observed fumonisin distribution among the replicated-sample test results was compared with several theoretical distributions, and the negative binomial distribution was selected to model the fumonisin distribution among test results. A computer model was developed by 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.
Contamination; Fungi; Liquid chromatography; Marketing; Mathematical models; Toxic materials; Binomial distribution; Computer models; Fumonisins; Maize; Theoretical distributions; Drug products; fumonisin; fumonisin B1; article; chemistry; dose response; food analysis; food contamination; high performance liquid chromatography; liquid chromatography; maize; metabolism; methodology; Nigeria; plant; regression analysis; reproducibility; sample size; statistical model; theoretical model; Chromatography, High Pressure Liquid; Chromatography, Liquid; Dose-Response Relationship, Drug; Food Analysis; Food Contamination; Fumonisins; Models, Statistical; Models, Theoretical; Nigeria; Plants; Regression Analysis; Reproducibility of Results; Research Design; Sample Size; Zea mays; Fungi; Zea mays