Biomedical subjects
T B Whitaker
Publications and source records attributed to T B Whitaker.
Investigations of the problems of assessing aflatoxin levels in peanuts.
In this study, a number of probability distributions that have been used to model the occurrence of aflatoxin in peanuts are compared. Two distributions, the compound gamma and the negative binomial, are shown to have special appeal in that both can be justified by reasoning from the fundamental biological and stochastic processes that generate the aflatoxin. Since method of moments and maximum likelihood give consistent estimates of parameters in both models, practical considerations suggest using the former. One hundred twenty data sets, each consisting of fifty observations, were not sufficient to provide goodness-of-fit tests to establish either as superior to the other as a model. Both models fit the data well, appreciably better than other models examined. An attractive aspect of the compound gamma and the negative binomial distributions is that, as a consequence of their theoretical underpinnings, both involve parameters that have meaningful interpretations. In the compound gamma, the alpha parameter reflects the shape of the kernel-to-kernel aflatoxin content distribution, the lambda parameter reflects the number (or frequency) of contaminated kernels in the sample, and the beta parameter is a scale parameter. In the negative binomial, the two parameters can be used as measures of mean or location and shape.
Design of sampling plans for mycotoxins in foods and feeds.
The control of the occurrence of mycotoxins in foods and feeds requires effective surveillance and quality control procedures which facilitate the identification and control of the mycotoxin problem respectively. Surveillance and quality control procedures involve a sequence of sampling, sample preparation, and analysis steps; and the integrity of the data produced by these procedures will be determined by the effectiveness of these steps. It is imperative that the sampling step is performed as accurately as possible so that the sample collected is representative of the batch of food or feed under investigation. Needless to say, the collection of a biased sample will completely invalidate the resultant analytical data. Most attempts to develop effective sampling protocols have focused upon the aflatoxins, since the majority of current regulations are concerned specifically with this group of mycotoxins. However, the design of effective sampling protocols has been severely hindered by the highly skewed distribution of the aflatoxins in foods and feeds. Studies already performed indicate that representative samples of commodities, composed of large particles (e.g., corn and oilseed kernels) should be 10 kg in weight, at least, and composed of approximately one hundred incremental samples. Similar studies have indicated that samples of oilseed cakes and meal, however, should be composed of fifty incremental samples which afford a composite sample of approximately 5 kg in weight.
Evaluation of a testing program for aflatoxin in corn.
A computer model that accounts for sampling and analytical variability was developed to simulate the aflatoxin testing program administered by the North Carolina Department of Agriculture (NCDA) to regulate aflatoxin in corn meal. Monte Carlo solution techniques were employed to account for conditional probabilities that rise from multiple samples being used in the testing program. The NCDA testing program was then evaluated by applying the computer model to a hypothetical group of 1000 corn meal lots with the same distribution of aflatoxin concentrations as was observed among aflatoxin assays made by NCDA on commercial lots of corn meal from 1977 to 1980. The average of the 1000 lots assayed was 17.7 parts per billion (ppb). The model predicted that 79.5% of the lots would be accepted and 20.5% of the lots would be rejected by the NCDA testing program. The accepted and rejected lots contained an average of 5.7 and 64.2 ppb aflatoxin, respectively. The testing program accepted 7.3% of the lots with more than 20 ppb aflatoxin (consumers' risk) and rejected 1.0% of the lots with 20 ppb or less (processors' risk). A correct decision was made 94% of the time.
Dilution errors in aflatoxin determinations caused by compounds extracted from peanuts.
Several methods have been developed to analyze peanuts for aflatoxin by using thin layer chromatography (TLC). These methods depend on solvent extraction of aflatoxin from a sample of the product. Unfortunately, solvent solutions used to extract aflatoxin from peanuts also extract measurable quantities of other compounds such as oils, fats, sugars, and protein. The volume of these extracted compounds causes error in measuring the proportion of the solvent solution analyzed for aflatoxin. Also, because the cleanup procedures for some methods are inadequate, the volume of some of these extracted compounds also causes error in measuring the proportion of the extracted aflatoxin placed on TLC plates. These 2 errors cause underestimation of aflatoxin concentrations by approximately 11, 14, and 5% for the CB method, the modified version of the BF method generally used for raw peanuts, and a water slurry method, respectively. The correction specified by the CB method for fats in the extraction solvent reduces the approximate error for the CB method from 11 to 1%.
Evaluation of cottonseed aflatoxin testing programs.
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Monte Carlo technique to simulate aflatoxin testing programs for peanuts.
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Variability associated with testing cottonseed for aflatoxin.
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On testing normality using several samples: an analysis of peanut aflatoxin data.
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.
Sampling cottonseed lots for aflatoxin contamination.
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Variability of aflatoxin test results.
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Comparison of the observed distribution of aflatoxin in shelled peanuts to the negative binomial distribution.
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Design and analysis of sampling plans to estimate aflatoxin concentrations in shelled peanuts.
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Theoretical investigations into the accuracy of sampling shelled peanuts for aflatoxin.
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Performance of sampling plans to determine aflatoxin in farmers' stock peanut lots by measuring aflatoxin in high-risk-grade components.
Five 2 kg test samples were taken from each of 120 farmers' stock peanut lots contaminated with aflatoxin. Kernels from each 2 kg sample were divided into the following U.S. Department of Agriculture grade components: sound mature kernels plus sound splits (SMKSS), other kernels (OK), loose shelled kernels (LSK), and damaged kernels (DAM). The kernel mass (g), aflatoxin mass (ng), and aflatoxin concentration (ng of aflatoxin/g of peanuts) were measured for each of the 2400 component samples. The variabilities associated with measuring aflatoxin mass (ng) in OK + LSK + DAM, or A(OLD)ng, and in LSK + DAM, or A(LD)ng, and aflatoxin concentration (ng/g) in OK + LSK + DAM, or A(OLD)ng/g, and in LSK + DAM, or A(LD)ng/g, were determined. The variance associated with measuring aflatoxin in each of the 4 combinations of components increased with aflatoxin, and functional relationships were developed from regression analysis. The variability associated with estimating the lot concentration from each of the 4 combinations of components was also determined. The coefficients of variation (CV) associated with estimating the aflatoxin for a lot with aflatoxin at 100 ng/g were 90, 86, 94 and 96% for aflatoxin masses A(OLD)ng and A(LD)ng and aflatoxin concentrations A(OLD)ng/g and A(LD)ng/g, respectively. The performance of aflatoxin sampling plans using the combination of aflatoxin masses in OK + LD + DAM and LD + DAM components was evaluated with a 2 kg test sample and a 50 ng/g accept/reject limit.
Sampling agricultural commodities for mycotoxins.
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Performance of three pneumatic probe samplers and four analytical methods used to estimate aflatoxins in bulk cottonseed.
The requirement by the U.S. Food and Drug Administration that agricultural products susceptible to aflatoxin contamination contain aflatoxin at levels < or =20 parts per billion for consumer-ready products has led to the establishment of inspection programs by various industries. In Arizona, cottonseed samples from 100 ton piles are collected by an accumulation of 3 or more probings with a pneumatic probe. When sampling compacted cottonseed piles, the large official pneumatic probe (7.6 x 127 cm) decreases in efficiency. Two smaller probes (1.9 x 127 cm and 1.9 x 254 cm ) were therefore developed and tested for their suitability for sampling cottonseed piles. Three rapid analytical methods (one thin-layer chromatographic and 2 immunochemical) were tested for suitability as on-site assay systems. An analysis of variance of the analytical test results showed no differences between the various probes tested. Of the rapid methods, however, only the AflaTest-P immunoaffinity column gave results similar to those of the official AOAC thin-layer chromatography method. In terms of safety, however, all methods prevent material contaminated above regulatory limits from reaching the consumer.
Testing shelled corn for aflatoxin, Part I: estimation of variance components.
The variability associated with testing lots of shelled corn for aflatoxin was investigated. Eighteen lots of shelled corn were tested for aflatoxin contamination. The total variance associated with testing shelled corn was estimated and partitioned into sampling, sample preparation, and analytical variances. All variances increased as aflatoxin concentration increased. With the use of regression analysis, mathematical expressions were developed to model the relationship between aflatoxin concentration and the total, sampling, sample preparation, and analytical variances. The expressions for these relationships were used to estimate the variance for any sample size, subsample size, and number of analyses for a specific aflatoxin concentration. Test results on a lot with 20 parts per billion aflatoxin using a 1.13 kg sample, a Romer mill, 50 g subsamples, and liquid chromatographic analysis showed that the total, sampling, sample preparation, and analytical variances were 274.9 (CV = 82.9%), 214.0 (CV = 73.1 %), 56.3 (CV = 37.5%), and 4.6 (CV = 10.7%), respectively. The percentage of the total variance for sampling, sample preparation, and analytical was 77.8, 20.5, and 1.7, respectively.