United States Agency for International Development

The definitions for the purpose of the Codex Alimentarius, as mentioned in the Procedural Manual, are applicable to the General Standard for Contaminants and Toxins in Food and Feed (GSCTFF) and only the most important ones are repeated here. Some new definitions are introduced, where this seems warranted to obtain optimal clarity. When reference is made to foods, this also applies to animal feed, in those cases where this is appropriate. 1.2.2 Contaminant Codex Alimentarius defines a contaminant as follows: “Any substance not intentionally added to food, which is present in such food as a result of the production (including operations carried out in crop husbandry, animal husbandry and veterinary medicine), manufacture, processing, preparation, treatment, packing, packaging, transport or holding of such food or as a result of environmental contamination. The term does not include insect fragments, rodent hairs and other extraneous matter”. This standard applies to any substance that meets the terms of the Codex definition for a contaminant, including contaminants in feed for food-producing animals, except:

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Codex general standard for contaminants and toxins in food and feed

Aflatoxin B1 is a potent hepatacarcinogen that occurs in corn worldwide. The aflatoxin‐producing fungus Aspergillus flavus can grow and produce aflatoxin on corn preharvest and in storage. Within the U.S., aflatoxin contamination of preharvest corn has been reported in 23 states, and contamination is chronic in the southeastern U.S. where hot, drought conditions often favor the fungus and the production of aflatoxin. Management practices have been developed to reduce aflatoxin contamination, but in years when environmental conditions are extremely favorable for the fungus, no control strategy is effective. Resistance to aflatoxin accumulation appears to be heritable, but no commercial hybrids are available with adequate resistance. This review covers the epidemiology of A. flavus in preharvest corn from the infection process to the factors that influence aflatoxin formation. Also discussed are the problems associated with the development of resistant varieties and new strategies that are being de...

Aflatoxin in maize

An overview is presented of the analysis of mycotoxins by rapid methods such as: enzyme linked immunosorbent assay (ELISA); flow through membrane based immunoassay; immunochromatographic assay; fluorometric assay with immunoaffinity clean-up column or with a solid phase extraction clean-up column; and fluorescence polarization method. These methods are currently commercially available and are reliable, rapid methods. This review focuses on the basic principle of each rapid method as well as advantages and limitations of each method. Additionally, we address other emerging technologies of potential application in the analysis of mycotoxins.

A review of rapid methods for the analysis of mycotoxins.

Health and Trade Issues Mycotoxin Contamination and Toxigenic Fungi in Africa and the Mediterranean Basin Mycotoxin Detection Methods Mycotoxin Management Institutional Issues in Mycotoxin Management International Programs on Mycotoxins.

Mycotoxins: Detection Methods, Management, Public Health and Agricultural Trade

Using 12 lb samples, 280 g subsamples, the Waltking method of analysis, and densitometric procedures, the sampling, subsampling, and analytical variances associated with aflatoxin test procedures were estimated. Regression analysis indicated that each of the above variance components is a function of the concentration of aflatoxin in the population being tested. Results, for the test procedures given above, showed that sampling constitutes the greatest single source of error, followed by subsampling and analysis. Functional relationships are presented to determine the sampling, subsampling, and analytical variance for any size sample, subsample, and number of analyses.

Variability of aflatoxin test results.

Suitability of the negative binomial distribution for use in estimating the probabilities associated with sampling lots of shelled peanuts for aflatoxin analysis has been studied. Large samples, called “minilots,” were drawn from 29 lots of shelled peanuts contaminated with aflatoxin. These minilots were subdivided into ca. 12 lb samples which were analyzed for aflatoxin. The mean and variance of these aflatoxin determinations for each minilot were determined. The shape parameterk and the mean aflatoxin concentrationm were estimated for each minilot. A regression analysis indicated the functional relationship betweenk andm to be:k=(2.0866+2.3898m) × 10−6. The observed distribution of sample concentrations from each of the 29 minilots was compared to the negative binomial distribution by means of the Kolmogorov-Smirnov test. The null hypothesis that each of the true unknown distribution functions was negative binomial was not rejected at the 5% significance level for all 29 comparisons.

Comparison of the observed distribution of aflatoxin in shelled peanuts to the negative binomial distribution

The sampling, subsampling (both coarse and fine ground meal), and analytical variances associated with testing shelled corn for aflatoxin were estimated by the use of 500 g samples, 50 g subsamples, and the CB method of analysis. The magnitudes of the variance components increased with an increase in the aflatoxin concentration. Functional relationships were developed to predict the variance for a given aflatoxin concentration and any size sample, subsample, and number of analyses. At 20 ppb total aflatoxin, the coefficient of variantion associated with a 4.54 kg sample, 1 kg subsample of coarsely ground meal (passes a #14 screen), a 50 g subsample of finely ground meal (passes a #20 screen) and one analysis were 21, 8, 11, and 26%, respectively.

Variability associated with testing corn for aflatoxin

A computer model that accounts for sampling, subsampling, and analytical variability was developed to simulate aflatoxin testing programs. Monte Carlo solution techniques were employed to account for conditional probabilities that arise from multiple samples, subsamples, and/or analyses being used in testing programs. The aflatoxin testing program to be used on the 1974 peanut crop was evaluated by use of the described model.

Monte carlo technique to simulate aflatoxin testing programs for peanuts

Summary Eight samples of size 16 and three samples of size 15 consisting of replicate determinations of aflatoxin in peanut subsamples were considered. These data were analyzed to decide whether the samples could have arisen from two-parameter normal or lognormal parents. The method of analysis consisted of first transforming the individual samples in such a way that the transformed values had uniform distributions on the unit interval under the null hypothesis. The transformed values were then pooled and tested for uniformity. Results of the analysis indicated that the normal model fitted the data quite well and definitely better than the lognormal model. Aflatoxin is a toxic material that can be produced in peanuts by the fungus Aspergillus flavus. As a precautionary measure all commercial lots of peanuts in the United States (approximately 20,000 each crop year) are tested for aflatoxin. The concentration of aflatoxin in a lot of shelled peanuts is estimated by measuring the aflatoxin concentration in a random sample of kernels drawn from the lot. To ensure adequate quality control and consumer protection, a sampling plan must provide a high level of protection for the consumer and reasonable assurance to the processor that lots of good peanuts will not be rejected by the testing program. Because aflatoxin is often highly concentrated in a small percentage of the kernels, variation among aflatoxin determinations is large and accurate estimation of the average concentration in a lot is exceedingly difficult (Whitaker et al. [1972, 1974]). In order to predict the risk levels and costs associated with an aflatoxin sampling plan, computer simulation methods have been developed to evaluate sampling programs (Whitaker, Dickens and Wiser [1970]). Estimation of the distribution of the analytically determined subsample concenitration is an important aspect of the computer model. It is assumed that replicated analyses from subsamples with different levels of aflatoxin can all be described by distributions of the same functional form, but, possibly, with different parameters. Two distributions have been used, to our knowledge, for fitting replicated aflatoxin data. Contamination below a certain

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J. W. Dickens

Papers

Variability of aflatoxin test results.

Comparison of the observed distribution of aflatoxin in shelled peanuts to the negative binomial distribution

Variability associated with testing corn for aflatoxin

Monte carlo technique to simulate aflatoxin testing programs for peanuts

On testing normality using several samples: an analysis of peanut aflatoxin data.