Use of Combined Uncertainty of Pesticide Residue Results for Testing Compliance with Maximum Residue Limits (MRLs).

The uncertainty of pesticide residue levels in crops due to sampling, estimated for 106 individual crops and 24 crop groups from residue data obtained from supervised trials, was adjusted with a factor of 1.3 to accommodate the larger variability of residues under normal field conditions. Further adjustment may be necessary in the case of mixed lots. The combined uncertainty of residue data including the contribution of sampling is used for calculation of an action limit, which should not be exceeded when compliance with maximum residue limits is certified as part of premarketing self-control programs. On the contrary, for testing compliance of marketed commodities the residues measured in composite samples should be greater than or equal to the decision limit calculated only from the combined uncertainty of the laboratory phase of the residue determination. The options of minimizing the combined uncertainty of measured residues are discussed. The principles described are also applicable to other chemical contaminants.

Development of an incurred cornbread model for gluten detection by immunoassays.

Gluten that is present in food as a result of cross-contact or misbranding can cause severe health concerns to wheat-allergic and celiac patients. Immunoassays, such as enzyme-linked immunosorbent assay (ELISA) and lateral flow device (LFD), are commonly used to detect gluten traces in foods. However, the performance of immunoassays can be affected by non-assay-related factors, such as food matrix and processing conditions. Gluten (0-500 ppm) and wheat flour (20-1000 ppm) incurred cornbread was prepared at different incurred levels and baking conditions (204.4 °C for 20, 27, and 34 min) to study the accuracy and precision of gluten measurement by seven immunoassay kits (three LFD and four ELISA kits). The stability and immunoreactivity of gluten proteins, as measured by western blot using three different antibodies, were not adversely affected by the baking conditions. However, the gluten recovery varied depending upon the ELISA kit and the gluten source used to make the incurred cornbread, affecting the accuracy of gluten quantification (BioKits, 9-77%; Morinaga, 91-137%; R-Biopharm, 61-108%; and Romer Labs, 113-190%). Gluten recovery was reduced with increased baking time for most ELISA kits analyzed. Both the sampling and analytical variance increased with an increase in the gluten incurred level. The predicted analytical coefficient of variation associated with all ELISA kits was below 12% for all incurred levels, indicative of good analytical precision.

Correlation between aflatoxin contamination and various USDA grade categories of shelled almonds.

The California almond industry is interested in determining if there is a correlation between aflatoxin contamination and almonds classified into various U.S. Department of Agriculture (USDA) grades. A 12 000 g sample was taken from each of 50 lots of shelled almonds. The almonds in each sample were then partitioned into five USDA grades: high quality (HQ), insect damage (ID), mold damage (MOD), mechanical damage (MED), and other defects (OD). Across all 50 samples, kernels in the HQ grade accounted for 83.7% of the kernel mass and 3.2% of the aflatoxin mass. Conversely, kernels in the remaining four damage grades (ID, MOD, MED, and OD) accounted for 16.3% of the kernel mass and 96.8% of the aflatoxin mass. ID kernels had the highest risk for aflatoxin contamination. Almonds in the ID grade accounted for 76.3% of the total aflatoxin mass and 7.2% of the kernel mass. Regression equations were developed to predict the aflatoxin concentration in each 12 000 g sample by measuring the aflatoxin mass in one or more of the four damage grades. Regression equations demonstrated that aflatoxin mass only in the insect damaged kernels was also an effective way to predict the aflatoxin concentration in each 12 000 g sample.

Effect of sample size in the evaluation of "in-field" sampling plans for aflatoxin B(1) determination in corn.

Use of proper sampling methods throughout the agri-food chain is crucial when it comes to effectively detecting contaminants in foods and feeds. The objective of the study was to estimate the performance of sampling plan designs to determine aflatoxin B(1) (AFB(1)) contamination in corn fields. A total of 840 ears were selected from a corn field suspected of being contaminated with aflatoxin. The mean and variance among the aflatoxin values for each ear were 10.6 mug/kg and 2233.3, respectively. The variability and confidence intervals associated with sample means of a given size could be predicted using an equation associated with the normal distribution. Sample sizes of 248 and 674 ears would be required to estimate the true field concentration of 10.6 mug/kg within +/-50 and +/-30%, respectively. Using the distribution information from the study, operating characteristic curves were developed to show the performance of various sampling plan designs.

Sampling and analytical variability associated with the determination of aflatoxins and ochratoxin A in bulk lots of powdered ginger marketed in 1-lb bags.

Ginger has been used as a food, dietary supplement, and condiment for centuries. Mycotoxins such as the aflatoxins (AF) and ochratoxin A (OTA) have been reported in ginger roots in several studies. It is important to design effective sampling methods that will accurately and precisely predict the true mycotoxin level in a bulk lot. The objective of this study was to measure the sampling and analytical variability associated with the test procedure used to measure AF and OTA in a bulk lot of powdered ginger using a 5-g laboratory sample and HPLC analytical methods. Twelve 5-g laboratory samples were taken from each of two lots. Duplicate aliquots were removed from each 5-g laboratory sample/solvent blend, and each aliquot was simultaneously analyzed for AF and OTA by HPLC analytical methods. Using a balanced nested design, the total variance associated with the above AF and OTA test procedures was partitioned into sampling and analytical variance components for each lot. Averaged across both lots, the sampling and analytical variances accounted for 87% and 13% of the total variance, respectively, for AF and 97% and 3%, respectively, for OTA. The sampling and analytical coefficients of variation were 9.5% and 3.6%, respectively, for AF, and 16.6% and 2.9%, respectively, for OTA when using a single 5-g laboratory sample and HPLC analytical methods. Equations are derived to show the effect of increasing laboratory sample size and/or number of aliquots on reducing the variability of the test procedures used to estimate OTA and AF in powdered ginger.

Sampling and analytical variability associated with the determination of total aflatoxins and ochratoxin A in powdered ginger sold as a dietary supplement in capsules.

The U.S. Food and Drug Administration is studying the need to monitor dietary supplements for mycotoxins such as total aflatoxins and ochratoxin A. An effective mycotoxin-monitoring program requires knowledge of the sampling and analytical variability associated with the determination of total aflatoxins (AF) and ochratoxin A (OTA) in dietary supplements. Three lots of ginger sold as a powder in capsule form and packaged in individual bottles were analyzed for both AF and OTA. The total variability associated with measuring AF and OTA in powdered ginger was partitioned into bottle-to-bottle, within bottle, and analytical variances. The variances were estimated using a nested design. For AF and OTA, the within-bottle variance associated with the 5 g laboratory sample size was the largest component of variability accounting for about 43% and 85% of the total variance, respectively; the analytical variance accounted for about 34% and 9% of the total variability, respectively; and the bottle-to-bottle variance accounted for about 23% and 7% of the total variance, respectively. When the total variance is converted into the coefficient of variation (CV or standard deviation relative to the mean concentration), the CV is lower for AF (16.9%) than OTA (24.7%).

Sampling hazelnuts for aflatoxin: effect of sample size and accept/reject limit on reducing the risk of misclassifying lots.

About 100 countries have established regulatory limits for aflatoxin in food and feeds. Because these limits vary widely among regulating countries, the Codex Committee on Food Additives and Contaminants began work in 2004 to harmonize aflatoxin limits and sampling plans for aflatoxin in almonds, pistachios, hazelnuts, and Brazil nuts. Studies were developed to measure the uncertainty and distribution among replicated sample aflatoxin test results taken from aflatoxin-contaminated treenut lots. The uncertainty and distribution information is used to develop a model that can evaluate the performance (risk of misclassifying lots) of aflatoxin sampling plan designs for treenuts. Once the performance of aflatoxin sampling plans can be predicted, they can be designed to reduce the risks of misclassifying lots traded in either the domestic or export markets. A method was developed to evaluate the performance of sampling plans designed to detect aflatoxin in hazelnuts lots. Twenty hazelnut lots with varying levels of contamination were sampled according to an experimental protocol where 16 test samples were taken from each lot. The observed aflatoxin distribution among the 16 aflatoxin sample test results was compared to lognormal, compound gamma, and negative binomial distributions. The negative binomial distribution was selected to model aflatoxin distribution among sample test results because it gave acceptable fits to observed distributions among sample test results taken from a wide range of lot concentrations. Using the negative binomial distribution, computer models were developed to calculate operating characteristic curves for specific aflatoxin sampling plan designs. The effect of sample size and accept/reject limits on the chances of rejecting good lots (sellers' risk) and accepting bad lots (buyers' risk) was demonstrated for various sampling plan designs.

Determining the variability associated with testing shelled corn for aflatoxin using different analytical procedures in Louisiana in 1998.

The number of elevator facilities with laboratories to test shelled corn for aflatoxin on site is increasing. The inherent difficulty in accurately determining the true aflatoxin concentration of a lot of corn may have serious implications. Deviations from the true value are of even greater significance at busy locations where a high throughput is desired. This study was instituted to measure (1) the differences in aflatoxin test results between elevator laboratories and the Louisiana Agricultural Chemistry (LAC) laboratory and (2) the variability in aflatoxin test results associated with sampling, sample preparation, and analysis of shelled corn at such locations. One hundred lots of shelled corn from 10 elevators in Louisiana were analyzed for aflatoxin using the Aflatest method (at elevators and at the LAC laboratory) and high-performance column liquid chromatography (HPLC; LAC laboratory only). Mean aflatoxin levels determined at elevator laboratories were significantly (P < 0.05) lower from those obtained in the LAC laboratory using the Aflatest method. Overall, Aflatest method results were lower than those obtained by HPLC. This difference may be attributed to analyst technical dexterity, difficulty in providing careful attention to detail in a high throughput environment, and/or substandard facilities found at elevators. The total variance was partitioned into the combined sampling plus subsampling variance and analytical variance. The sampling and sample preparation steps accounted for about 91.5% of the total variability. When using the HPLC analytical method, the analytical step contributed only 8.5% to the total variance.

Evaluating the performance of sampling plans to detect hypoglycin A in ackee fruit shipments imported into the United States.

Hypoglycin A (HGA) is a toxic amino acid that is naturally produced in unripe ackee fruit. In 1973, the U.S. Food and Drug Administration (FDA) placed a worldwide import alert on ackee fruit, which banned the product from entering the United States. The FDA has considered establishing a regulatory limit for HGA and lifting the ban, which will require development of a monitoring program. The establishment of a regulatory limit for HGA requires the development of a scientifically based sampling plan to detect HGA in ackee fruit imported into the United States. Thirty-three lots of ackee fruit were sampled according to an experimental protocol in which 10 samples, i.e., ten 19 oz cans, were randomly taken from each lot and analyzed for HGA by using liquid chromatography. The total variance was partitioned into sampling and analytical variance components, which were found to be a function of the HGA concentration. Regression equations were developed to predict the total, sampling, and analytical variances as a function of HGA concentration. The observed HGA distribution among the test results for the 10 HGA samples was compared with the normal and lognormal distributions. A computer model based on the lognormal distribution was developed to predict the performance of sampling plan designs to detect HGA in ackee fruit 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 ackee fruit lots.

Evaluating the performance of sampling plans to detect fumonisin B1 in maize lots marketed in 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.

Sampling almonds for aflatoxin, part II: estimating risks associated with various sampling plan designs.

About 100 nations have established regulatory limits for aflatoxin in food and feeds. Because these limits vary widely from one country to another, the Codex Alimentarius Commission, working through the Codex Committee on Food Additives and Contaminants, has initiated work to harmonize aflatoxin limits and sampling plans for almonds, pistachios, hazelnuts, and Brazil nuts. Studies were developed to measure the uncertainty and distribution among test results for replicate samples taken from aflatoxin-contaminated almond shipments. The uncertainty and distribution information was used to develop a model to evaluate the performance of aflatoxin sampling plans so that harmonized sampling plans can be developed for almonds that reduce the misclassifying of lots in the export trade. Twenty lots of shelled almonds were sampled according to an experimental protocol in which sixteen 10 kg samples were taken from each lot. The observed aflatoxin distribution among the 16 sample test results was compared with 3 theoretical distributions. The negative binomial distribution was selected to model aflatoxin distribution among sample test results because it gave acceptable fits across all 20 observed sample distributions. By using the variance and distribution information, operating characteristics curves were developed to predict the effect of sample size and accept/reject limits on the probability of rejecting good lots and accepting bad lots.

Sampling hazelnuts for aflatoxin: uncertainty associated with sampling, sample preparation, and analysis.

The variability associated with the aflatoxin test procedure used to estimate aflatoxin levels in bulk shipments of hazelnuts was investigated. Sixteen 10 kg samples of shelled hazelnuts were taken from each of 20 lots that were suspected of aflatoxin contamination. The total variance associated with testing shelled hazelnuts was estimated and partitioned into sampling, sample preparation, and analytical variance components. Each variance component increased as aflatoxin concentration (either B1 or total) 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. The sampling, sample preparation, and analytical variances associated with estimating aflatoxin in a hazelnut lot at a total aflatoxin level of 10 ng/g and using a 10 kg sample, a 50 g subsample, dry comminution with a Robot Coupe mill, and a high-performance liquid chromatographic analytical method are 174.40, 0.74, and 0.27, respectively. The sampling, sample preparation, and analytical steps of the aflatoxin test procedure accounted for 99.4, 0.4, and 0.2% of the total variability, respectively.

Testing green coffee for ochratoxin A, part III: performance of ochratoxin A sampling plan.

Green coffee shipments are often inspected for ochratoxin A (OTA) and classified into good or bad categories depending on whether the OTA estimates are above or below a defined regulatory limit. Because of the uncertainty associated with the sampling, sample preparation, and analytical steps of an OTA test procedure, some shipments of green coffee will be misclassified. The misclassification of lots leads to some good lots being rejected (sellers' risk) and some bad lots being accepted (buyers' risk) by an OTA sampling plan. Reducing the uncertainty of an OTA test procedure and using an accept/reject limit less than the regulatory limit can reduce the magnitude of one or both risks. The uncertainty of the OTA test procedure is most effectively reduced by increasing sample size (or increasing the number of samples analyzed), because the sampling step is the largest source of uncertainty in the OTA test procedure. The effects of increasing sample size and changing the sample accept/reject limit relative to the regulatory limit on the performance of OTA sampling plans for green coffee were investigated. For a given accept/reject limit of 5 microg/kg, increasing sample size increased the percentage of lots accepted at concentrations below the regulatory limit and increased the percentage of lots rejected at concentrations above the regulatory limit. As a result, increasing sample size reduced both the number of good lots rejected (sellers' risk) and the number of bad lots accepted (buyers' risk). For a given sample size (1 kg), decreasing the sample accept/reject limit from 5 to 2 microg/kg relative to a fixed regulatory limit of 5 microg/kg decreased the percentage of lots accepted and increased the percentage of lots rejected at all OTA concentrations. As a result, decreasing the accept/reject limit below the regulatory limit increased the number of good lots rejected (sellers' risk), but decreased the number of bad lots accepted (buyers' risk).

Sampling almonds for aflatoxin, part I: estimation of uncertainty associated with sampling, sample preparation, and analysis.

Domestic and international regulatory limits have been established for aflatoxin in almonds and other tree nuts. It is difficult to obtain an accurate and precise estimate of the true aflatoxin concentration in a bulk lot because of the uncertainty associated with the sampling, sample preparation, and analytical steps of the aflatoxin test procedure. To evaluate the performance of aflatoxin sampling plans, the uncertainty associated with sampling lots of shelled almonds for aflatoxin was investigated. Twenty lots of shelled almonds were sampled for aflatoxin contamination. The total variance associated with measuring B1 and total aflatoxins in bulk almond lots was estimated and partitioned into sampling, sample preparation, and analytical variance components. All variances were found to increase with an increase in aflatoxin concentration (both B1 and total). By using regression analysis, mathematical expressions were developed to predict the relationship between each variance component (total, sampling, sample preparation, and analysis variances) and aflatoxin concentration. Variance estimates were the same for B1 and total aflatoxins. The mathematical relationships can be used to estimate each variance for a given sample size, subsample size, and number of analyses other than that measured in the study. When a lot with total aflatoxins at 15 ng/g was tested by using a 10 kg sample, a vertical cutter mixer type of mill, a 100 g subsample, and high-performance liquid chromatography analysis, the sampling, sample preparation, analytical, and total variances (coefficient of variation, CV) were 394.7 (CV, 132.4%), 14.7 (CV, 25.5%), 0.8 (CV, 6.1%), and 410.2 (CV, 135.0%), respectively. The percentages of the total variance associated with sampling, sample preparation, and analytical steps were 96.2, 3.6, and 0.2, respectively.

Predicting aflatoxin and fumonisin in shelled corn lots using poor-quality grade components.

A study was conducted to determine if aflatoxin and fumonisin are concentrated in the poor-quality grade components of shelled corn. Four 1.0 kg test samples were each taken from 23 lots of shelled corn marketed in North Carolina. Inspectors from the Federal Grain Inspection Service divided each test sample into 3 grade components: (1) damaged kernels (DM), (2) broken corn and foreign material (BCFM), and )3) whole kernels (WH). The aflatoxin and fumonisin concentration was measured in each component and a mass balance equation was used to calculate the total concentration of each mycotoxin in each test sample. Averaged across all test samples, the aflatoxin concentrations in the DM, BCFM, and WH components were 1300.3, 455.2, and 37.3 ppb, respectively. Averaged across all test samples, the fumonisin concentrations in the DM, BCFM, and WH components were 148.3, 51.3, and 1.8 ppm, respectively. The DM and BCFM components combined accounted for only 5.0% of the test sample mass, but accounted for 59.8 and 77.5% of the total aflatoxin and fumonisin mass in the test sample, respectively. Both aflatoxin mass (ng) and aflatoxin concentration (ng/g) in the combined DM and BCFM components had high correlations with aflatoxin concentration in the lot. The highest correlation occurred when aflatoxin mass (ng) in the combined DM and BCFM components was related to aflatoxin concentration in the lot (0.964). Similar results were obtained for fumonisin. This study indicated that measuring either aflatoxin or fumonisin in the combined DM and BCFM grade components could be used as a screening method to predict either aflatoxin or fumonisin in a bulk lot of shelled corn.

Testing green coffee for ochratoxin A, part II: Observed distribution of ochratoxin A test results.

The suitability of 4 theoretical distributions (normal, lognormal, negative binomial, and gamma) to predict the observed distribution of ochratoxin A (OTA) in green coffee was investigated. One symmetrical and 3 positively skewed theoretical distributions were each fitted to 25 empirical distributions of OTA test results for green coffee beans. Parameters of each theoretical distribution were calculated by using Methods of Moments. The 3 skewed theoretical distributions provided acceptable fits to each of the 25 observed distributions. Because of its simplicity, the lognormal distribution was selected to model OTA test results for green coffee. Using variance equations determined in previous studies, mathematical expressions were developed to calculate the parameters of the log normal distribution for a given OTA lot concentration and test procedure. Observed acceptance probabilities were compared to an operating characteristic curve predicted from the lognormal distribution, and all 25 observed acceptance probabilities were found to lie within the 95% confidence band associated with the predicted operating characteristic curve. The parameters of compound gamma distribution were used to calculate the fraction of OTA contamination beans within a contaminated lot. The percent-contaminated beans were a function of the lot concentration and increased with lot concentration. At a lot concentration of 5 microg/kg, approximately 6 beans per 10,000 beans are contaminated.

Sampling uncertainties for the detection of chemical agents in complex food matrices.

Using uncertainty associated with detection of aflatoxin in shelled corn as a model, the uncertainty associated with detecting chemical agents intentionally added to food products was evaluated. Accuracy and precision are two types of uncertainties generally associated with sampling plans. Sources of variability that affect precision were the primary focus of this investigation. Test procedures used to detect chemical agents generally include sampling, sample preparation, and analytical steps. The uncertainty of each step contributes to the total uncertainty of the test procedure. Using variance as a statistical measure of uncertainty, the variance associated with each step of the test procedure used to detect aflatoxin in shelled corn was determined for both low and high levels of contamination. For example, when using a 1-kg sample, Romer mill, 50-g subsample, and high-performance liquid chromatography to test a lot of shelled corn contaminated with aflatoxin at 10 ng/g, the total variance associated with the test procedure was 149.2 (coefficient of variation of 122.1%). The sampling, sample preparation, and analytical steps accounted for 83.0, 15.6, and 1.4% of the total variance, respectively. A variance of 149.2 suggests that repeated test results will vary from 0 to 33.9 ng/g. Using the same test procedure to detect aflatoxin at 10,000 ng/g, the total variance was 264,719 (coefficient of variation of 5.1%). The sampling, sample preparation, and analytical steps accounted for 41, 57, and 2% of the total variance, respectively. A variance of 264,719 suggests that repeated test results will vary from 8,992 to 11,008 ng/g. Foods contaminated at low levels reflect a situation in which a small percentage of particles is contaminated and sampling becomes the largest source of uncertainty. Large samples are required to overcome the "needle-in-the-haystack" problem. Aflatoxin is easier to detect and identify in foods intentionally contaminated at high levels than in foods with low levels of contamination because the relative standard deviation (coefficient of variation) decreases and the percentage of contaminated kernels increases with an increase in concentration.

Immunochemical analytical methods for the determination of peanut proteins in foods.

Peanut proteins can cause allergenic reactions that can result in respiratory and circulatory effects in the body sometimes leading to shock and death. The determination of peanut proteins in foods by analytical methods can reduce the risk of serious reactions in the highly sensitized individual by allowing for the detection of these proteins in a food at various stages of the manufacturing process. The method performance of 4 commercially available enzyme-linked immunosorbent assay (ELISA) kits was evaluated for the detection of peanut proteins in milk chocolate, ice cream, cookies, and breakfast cereals: ELISA-TEK Peanut Protein Assay, now known as "Bio-Kit" for peanut proteins, from ELISA Technologies Inc.; Veratox for Peanut Allergens from Neogen Corp.; RIDASCREEN Peanut Kit from R-Biopharm GmbH; and ProLisa from Canadian Food Technology Ltd. The 4 test kits were evaluated for accuracy (recovery) and precision using known concentrations of peanut or peanut proteins in the 4 food matrixes. Two different techniques, incurred and spiked, were used to prepare samples with 4 known concentrations of peanut protein. Defatted peanut flour was added in the incurred samples, and water-soluble peanut proteins were added in the spiked samples. The incurred levels were 0.0, 10, 20, and 100 microg whole peanut per g food; the spiked levels were 0.0, 5, 10, and 20 microg peanut protein per g food. Performance varied by test kit, protein concentration, and food matrix. The Veratox kit had the best accuracy or lowest percent difference between measured and incurred levels of 15.7% when averaged across all incurred levels and food matrixes. Recoveries associated with the Veratox kit varied from 93 to 115% for all food matrixes except cookies. Recoveries for all kits were about 50% for cookies. The analytical precision, as measured by the variance, increased with an increase in protein concentration. However, the coefficient of variation (CV) was stable across the 4 incurred protein levels and was 7.0% when averaged across the 4 food matrixes and analytical kits. The R-Biopharm test kit had the best precision or a CV of 4.2% when averaged across all incurred levels and food matrixes. Because measured protein values varied by test kit and food matrix, a method was developed to normalize or transform measured protein concentrations to an adjusted protein value that was equal to the known protein concentration. The normalization method adjusts measured protein values to equal the true protein value regardless of the type test kit or type food matrix.

Testing green coffee for ochratoxin A, part I: estimation of variance components.

The variability associated with testing lots of green coffee beans for ochratoxin A (OTA) was investigated. Twenty-five lots of green coffee were tested for OTA contamination. The total variance associated with testing green coffee was estimated and partitioned into sampling, sample preparation, and analytical variances. All variances increased with an increase in OTA concentration. Using regression analysis, mathematical expressions were developed to model the relationship between OTA 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 OTA concentration. Testing a lot with 5 microg/kg OTA using a 1 kg sample, Romer RAS mill, 25 g subsamples, and liquid chromatography analysis, the total, sampling, sample preparation, and analytical variances were 10.75 (coefficient of variation [CV] = 65.6%), 7.80 (CV = 55.8%), 2.84 (CV = 33.7%), and 0.11 (CV = 6.6%), respectively. The total variance for sampling, sample preparation, and analytical were 73, 26, and 1%, respectively.

Evaluation of sampling plans to detect Cry9C protein in corn flour and meal.

StarLink is a genetically modified corn that produces an insecticidal protein, Cry9C. Studies were conducted to determine the variability and Cry9C distribution among sample test results when Cry9C protein was estimated in a bulk lot of corn flour and meal. Emphasis was placed on measuring sampling and analytical variances associated with each step of the test procedure used to measure Cry9C in corn flour and meal. Two commercially available enzyme-linked immunosorbent assay kits were used: one for the determination of Cry9C protein concentration and the other for % StarLink seed. The sampling and analytical variances associated with each step of the Cry9C test procedures were determined for flour and meal. Variances were found to be functions of Cry9C concentration, and regression equations were developed to describe the relationships. Because of the larger particle size, sampling variability associated with cornmeal was about double that for corn flour. For cornmeal, the sampling variance accounted for 92.6% of the total testing variability. The observed sampling and analytical distributions were compared with the Normal distribution. In almost all comparisons, the null hypothesis that the Cry9C protein values were sampled from a Normal distribution could not be rejected at 95% confidence limits. The Normal distribution and the variance estimates were used to evaluate the performance of several Cry9C protein sampling plans for corn flour and meal. Operating characteristic curves were developed and used to demonstrate the effect of increasing sample size on reducing false positives (seller's risk) and false negatives (buyer's risk).