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.

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.

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 parameter k and the mean aflatoxin concentration m were estimated for each minilot. A regression analysis indicated the functional relationship between k and rn to be: k = (2.0866 + 2.3898m) x 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. .2 aTest results are given in ppb aflatoxin and are ordered according to aflatoxin concentration.

Abstract

Samples (200-lb) from 40 commercial lots of shelled peanuts which contained an average concentration of 48 parts-per-billion aflatoxin were sorted with an electronic color sorter 3 to 5 times and then hand picked in an attempt to remove discolored kernels which usually contain higher concentrations of aflatoxin than other kernels. Prediction equations indicated that cumulative removal of 2, 4, 6, 8 and 10 % of the kernels from each sample by electronic sorting would remove an average of 16, 28, 37, 45 and 51 % of the aflatoxin, respectively. Electronic sorting became less selective for aflatoxin-contaminated kernels during each additional sorting operation. Careful hand picking for discoloration was far more selective for aflatoxin-contaminated kernels than electronic color sorting. An average 72 % of the aflatoxin was in kernels that were removed by electronic sorting and subsequent hand picking. The efficacy of aflatoxin removal with electronic sorting was highly variable among lots. This variability indicates that each lot should be pretested to determine if aflatoxin can be effectively removed before the expense of electronic color sorting is incurred.