Determination of aflatoxin risk components for in-shell Brazil nuts.

A study was conducted on the risk from aflatoxins associated with the kernels and shells of Brazil nuts. Samples were collected from processing plants in Amazonia, Brazil. A total of 54 test samples (40 kg) were taken from 13 in-shell Brazil nut lots ready for market. Each in-shell sample was shelled and the kernels and shells were sorted in five fractions: good kernels, rotten kernels, good shells with kernel residue, good shells without kernel residue, and rotten shells, and analysed for aflatoxins. The kernel:shell ratio mass (w/w) was 50.2/49.8%. The Brazil nut shell was found to be contaminated with aflatoxin. Rotten nuts were found to be a high-risk fraction for aflatoxin in in-shell Brazil nut lots. Rotten nuts contributed only 4.2% of the sample mass (kg), but contributed 76.6% of the total aflatoxin mass (µg) in the in-shell test sample. The highest correlations were found between the aflatoxin concentration in in-shell Brazil nuts samples and the aflatoxin concentration in all defective fractions (R(2)=0.97). The aflatoxin mass of all defective fractions (R(2)=0.90) as well as that of the rotten nut (R(2)=0.88) were also strongly correlated with the aflatoxin concentration of the in-shell test samples. Process factors of 0.17, 0.16 and 0.24 were respectively calculated to estimate the aflatoxin concentration in the good kernels (edible) and good nuts by measuring the aflatoxin concentration in the in-shell test sample and in all kernels, respectively.

Sampling foods for mycotoxins.

It is difficult to obtain precise and accurate estimates of the true mycotoxin concentration of a bulk lot when using a mycotoxin-sampling plan that measures the concentration in only a small portion of the bulk lot. A mycotoxin-sampling plan is defined by a mycotoxin test procedure and a defined accept/reject limit. A mycotoxin test procedure is a complicated process and generally consists of several steps: (1) a sample of a given size is taken from the lot, (2) the sample is ground (comminuted) in a mill to reduce its particle size, (3) a subsample is removed from the comminuted sample, and (4) the mycotoxin is extracted from the comminuted subsample and quantified. Even when using accepted test procedures, there is uncertainty associated with each step of the mycotoxin test procedure. Because of this variability, the true mycotoxin concentration in the lot cannot be determined with 100% certainty by measuring the mycotoxin concentration in a sample taken from the lot. The variability for each step of the mycotoxin test procedure, as measured by the variance statistic, is shown to increase with mycotoxin concentration. Sampling is usually the largest source of variability associated with the mycotoxin test procedure. Sampling variability is large because a small percentage of kernels are contaminated and the level of contamination on a single seed can be very large. Methods to reduce sampling, sample preparation and analytical variability are discussed.

Design of a sampling plan to detect ochratoxin A in green coffee.

The establishment of maximum limits for ochratoxin A (OTA) in coffee by importing countries requires that coffee-producing countries develop scientifically based sampling plans to assess OTA contents in lots of green coffee before coffee enters the market thus reducing consumer exposure to OTA, minimizing the number of lots rejected, and reducing financial loss for producing countries. A study was carried out to design an official sampling plan to determine OTA in green coffee produced in Brazil. Twenty-five lots of green coffee (type 7 - approximately 160 defects) were sampled according to an experimental protocol where 16 test samples were taken from each lot (total of 16 kg) resulting in a total of 800 OTA analyses. The total, sampling, sample preparation, and analytical variances were 10.75 (CV = 65.6%), 7.80 (CV = 55.8%), 2.84 (CV = 33.7%), and 0.11 (CV = 6.6%), respectively, assuming a regulatory limit of 5 microg kg(-1) OTA and using a 1 kg sample, Romer RAS mill, 25 g sub-samples, and high performance liquid chromatography. The observed OTA distribution among the 16 OTA sample results was compared to several theoretical distributions. The 2 parameter-log normal distribution was selected to model OTA test results for green coffee as it gave the best fit across all 25 lot distributions. Specific computer software was developed using the variance and distribution information to predict the probability of accepting or rejecting coffee lots at specific OTA concentrations. The acceptation probability was used to compute an operating characteristic (OC) curve specific to a sampling plan design. The OC curve was used to predict the rejection of good lots (sellers' or exporters' risk) and the acceptance of bad lots (buyers' or importers' risk).

Distribution among sample test results when testing shelled corn lots for fumonisin.

The statistical distribution known as the compound gamma function was studied for suitability in describing the distribution of sample test results associated with testing lots of shelled corn for fumonisin. Thirty-two 1.1 kg test samples were taken from each of 16 contaminated lots of shelled corn. An observed distribution consisted of 32 sample fumonisin test results for each lot. The mean fumonisin concentration, c, and the variance, s2, among the 32 sample fumonisin test results along with the parameters for the compound gamma function were determined for each of the 16 observed distributions. The 16 observed distributions of sample fumonisin test results were compared with the compound gamma function using the Power Divergence test. The null hypothesis that the observed distribution could have resulted from sampling a family of compound gamma distributions was not rejected at the 5% significance level for 15 of the 16 lots studied. Parameters of the compound gamma distribution were calculated from the 32-fumonisin sample test results using the method of moments. Using regression analysis, equations were developed that related the parameters of the compound gamma distribution to fumonisin concentration and the variance associated with a fumonisin test procedure. An operating characteristic curve was developed for a fumonisin sampling plan to demonstrate the use of the compound gamma function.

Sampling grain shipments to detect genetically modified seed.

Using the binomial distribution, the effect of sample size on the variability among sample test results when sampling a lot with 1.0% genetically modified (GM) or biotech seed was evaluated. The coefficient of variation, cv, among 500-seed sample test results taken from a lot with truly 1.0% was computed to be 44.5%. Increasing sample size to 1000 seeds reduced the cv among sample test results to 31.5%. The effects of sample size and accept/reject limits on the buyer's risk (bad lots accepted) and the seller's risk (good lots rejected) was also evaluated assuming a tolerance of 1.0% GM seed. Increasing sample size decreases both the buyer's and seller's risks at the same time. Using an accept/reject limit below the regulatory tolerance decreases the buyer's risk, but increases the seller's risk. Using an accept/reject limit above the regulatory tolerance decreases the seller's risk but increases the buyer's risk.

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 II: modeling the observed distribution of aflatoxin test results.

The suitability of several theoretical distributions to predict the observed distribution of aflatoxin test results in shelled corn was investigated. Fifteen positively skewed theoretical distributions were each fitted to 18 empirical distributions of aflatoxin test results for shelled corn. The compound gamma distribution was selected to model aflatoxin test results for shelled corn. The method of moments technique was chosen to estimate the parameters of the compound gamma distribution. Mathematical expressions were developed to calculate the parameters of the compound gamma distribution for any lot aflatoxin concentration and test procedure. Observed acceptance probabilities were compared to operating characteristic curves predicted from the compound gamma distribution, and all 18 observed acceptance probabilities were found to lie within a 95% confidence band. The parameters of compound gamma were used to calculate the fraction of aflatoxin-contaminated kernels in contaminated lots. At 20 ppb, it was estimated that about 6 in 10,000 kernels are contaminated.

Testing shelled corn for aflatoxin, Part III: evaluating the performance of aflatoxin sampling plans.

The effects of changes in sample size and/or sample acceptance level on the performance of aflatoxin sampling plans for shelled corn were investigated. Six sampling plans were evaluated for a range of sample sizes and sample acceptance levels. For a given sample size, decreasing the sample acceptance level decreases the percentage of lots accepted while increasing the percentage of lots rejected at all aflatoxin concentrations, and decreases the average aflatoxin concentration in lots accepted and lots rejected. For a given sample size where the sample acceptance level decreases relative to a fixed regulatory guideline, the number of false positives increases and the number of false negatives decreases. For a given sample size where the sample acceptance level increases relative to a fixed regulatory guideline, the number of false positives decreases and the number of false negatives increases. For a given sample acceptance level, increasing the sample size increases the percentage of lots accepted at concentrations below the regulatory guideline while increasing the percentage of lots rejected at concentrations above the regulatory guideline, and decreases the average aflatoxin concentration in the lots accepted while increasing the average aflatoxin concentration in the rejected lots. For a given sample acceptance level that equals the regulatory guideline, increasing the sample size decreases misclassification of lots, both false positives and false negatives.

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.

Sampling, sample preparation, and analytical variability associated with testing wheat for deoxynivalenol.

The variability associated with testing wheat for deoxynivalenol (DON) was measured using a 0.454 kg sample, Romer mill, 25 g comminuted subsample, and the Romer Fluoroquant analytical method. The total variability was partitioned into sampling, sample preparation, and analytical variability components. Each variance component was a function of the DON concentration and equations were developed to predict each variance component using regression techniques. The effect of sample size, subsample size, and number of aliquots on reducing the variability of the DON test procedure was also determined. For the test procedure, the coefficient of variation (CV) associated with testing wheat at 5 ppm was 13.4%. The CVs associated with sampling, sample preparation, and analysis were 6.3, 10.0, and 6.3%, respectively. For the sample variation, a 0.454 kg sample was used; for the sample preparation variation, a Romer mill and a 25 g subsample were used; for the analytical variation, the Romer Fluoroquant method was used. The CVs associated with testing wheat are relatively small compared to the CV associated with testing other commodities for other mycotoxins, such as aflatoxin in peanuts. Even when the small sample size of 0.454 kg was used, the sampling variation was not the largest source of error as found in other mycotoxin test procedures.

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.

Variability associated with testing shelled corn for fumonisin.

Variances associated with sampling, sample preparation, and analytical steps of a test procedure that measures fumonisin in shelled corn were estimated. The variance associated with each step of the test procedure increases with fumonisin concentration. Functional relationships between variance and fumonisin concentration were estimated by regression analysis. For each variance component, functional relationships were independent of fumonisin type (total, B1, B2, and B3 fumonisins). At 2 ppm, coefficients of variation associated with sampling (1.1 kg sample), sample preparation (Romer mill and 25 g subsample), and analysis are 16.6, 9.1, and 9.7%, respectively. The coefficient of variation associated with the total fumonisin test procedure was 45% and is about the same order of magnitude as that for measuring aflatoxin in shelled corn with a similar test procedure.

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.

Estimating aflatoxin in farmers' stock peanut lots by measuring aflatoxin in various peanut-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 grade components: sound mature kernels plus sound splits (SMKSS), other kernels (OK), loose shelled kernels (LSK), and damaged kernels (DAM). Kernel mass, aflatoxin mass, and aflatoxin concentration were measured for each of the 2400 component samples. For 120 lots tested, average aflatoxin concentrations in SMKSS, OK, LSK, and DAM components were 235, 2543, 11,775, and 69,775 ng/g, respectively. Aflatoxins in SMKSS, OK, LSK, and DAM components represented 6.9, 7.9, 33.3, and 51.9% of the total aflatoxin mass, respectively. Cumulatively, 3 aflatoxin risk components--OK, LSK, and DAM--accounted for 93.1% of total aflatoxin, but only 18.4% percent of test sample mass. Correlation analysis suggests that the most accurate predictor of aflatoxin concentration in the lot is the cumulative aflatoxin mass in the high 3 risk components OK + LSK + DAM (correlation coefficient, r = 0.996). If the aflatoxin in the combined OK + LSK + DAM components is expressed in concentration units, r decreases to 0.939. Linear regression equations relating aflatoxin in OK + LSK + DAM to aflatoxin concentration in the lot were developed. The cumulative aflatoxin in the OK + LSK + DAM components was not an accurate predictor (r = 0.539) of aflatoxin in the SMKSS component. Statistical analyses of 3 other data sets published previously yielded similar results.

Evaluation of sampling plans used in the United States, United Kingdom, and The Netherlands to test raw shelled peanuts for aflatoxin.

The United States is a large producer and exporter of peanuts. The United Kingdom and The Netherlands are major importers of U.S. peanuts. Each country has a different guideline or legal limit for peanut products containing aflatoxin. Peanuts are tested for aflatoxin in each country by using specifically designed aflatoxin sampling plans to determine if the aflatoxin concentration in a lot of raw shelled peanuts is less than the guideline or legal limit. For raw shelled peanuts, the U.S. plan has the highest sample acceptance limit of 15 ng total aflatoxin/g, the UK plan has a sample acceptance limit of 10 ng total aflatoxin/g, and the Dutch Code of Practice (called the Dutch plan) has the lowest sample acceptance limit at 3 ng aflatoxin B1/g. The U.S. plan uses a maximum of 3 sampling units, each weighing 21.8 kg; the UK plan uses a single sampling unit of 10 kg; and the Dutch plan uses 4 sampling units, each weighing 7.5 kg. The sampling variance is lowest for the U.S. plan and highest for the Dutch plan. The sample preparation variance is lowest for both the Dutch and UK plans and highest for the U.S. plan, primarily because of the mill type used to comminute the kernels in the sample. For a given distribution among lot concentrations, the U.S. plan accepts the greatest number of lots and the Dutch plan rejects the greatest number of lots. The average aflatoxin concentration among accepted lots is highest for the U.S. plan and lowest for the Dutch plan.(ABSTRACT TRUNCATED AT 250 WORDS)

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.

Predicting the distribution of aflatoxin test results from farmers' stock peanuts.

Suitability of the negative binomial function for use in estimating the distribution of sample aflatoxin test results associated with testing farmers' stock peanuts for aflatoxin was studied. A 900 kg portion of peanut pods was removed from each of 40 contaminated farmers' stock lots. The lots averaged about 4100 kg. Each 900 kg portion was divided into fifty 2.26 kg samples, fifty 4.21 kg samples, and fifty 6.91 kg samples. The aflatoxin in each sample was quantified by liquid chromatography. An observed distribution of sample aflatoxin test results consisted of 50 aflatoxin test results for each lot and each sample size. The mean aflatoxin concentration, m; the variance, S2 mean among the 50 sample aflatoxin test results; and the shape parameter, k, for the negative binomial function were determined for each of the 120 observed distributions (40 lots times 3 sample sizes). Regression analysis indicated the functional relationship between k and m to be k = 0.000006425m0.8047. The 120 observed distributions of sample aflatoxin test results were compared to the negative binomial function by using the Kolmogorov-Smirnov (KS) test. The null hypothesis that the true unknown distribution function was negative binomial was not rejected at the 5% significance level for 114 of the 120 distributions. The negative binomial function failed the KS test at a sample concentration of 0 ng/g in all 6 of the distributions where the negative binomial function was rejected. The negative binomial function always predicted a smaller percentage of samples testing 0 ng/g than was actually observed.(ABSTRACT TRUNCATED AT 250 WORDS)