Variability and distribution among sample test results when sampling unprocessed oat lots for ochratoxin A

2015 ◽  
Vol 8 (4) ◽  
pp. 511-524 ◽  
Author(s):  
T.B. Whitaker ◽  
A.B. Slate ◽  
T.W. Nowicki ◽  
F.G. Giesbrecht
Keyword(s):  
Ochratoxin A ◽  
Negative Binomial ◽  
Test Procedure ◽  
Regression Equations ◽  
Test Results ◽  
Sampling Step ◽  
Test Portion ◽  
Sample Test ◽  
Total Variability ◽  
Sampling Statistics

In 2008, Health Canada announced it was considering the establishment of maximum levels for ochratoxin A (OTA) in a number of foods, including unprocessed wheat and oats and their products. The Canada Grains Council and Canadian National Millers Association initiated a study to measure the variability and distribution among sample test results so that scientifically based sampling plans could be designed to meet regulatory and industry requirements. Twenty lots of oats naturally contaminated with OTA were identified and sampled according to a nested experimental protocol where 16-two kg laboratory samples were taken from each lot, two 100 g test portions were taken from each comminuted laboratory sample, and two aliquots of the extract from each test portion were analysed for OTA by LC. The variance associated with each step of the OTA test procedure were found to be a function of OTA concentration and regression equations were developed to predict the functional relationship. When using the above OTA test procedure on an oat lot at 5 μg/kg, the sampling, sample preparation, analytical, and total variances were 11.26, 0.10, 0.13 and 11.49, respectively. The 2 kg sampling step accounted for 98.0% (11.26/11.49) of the total variability. The observed OTA distribution among the 16 OTA sample results was found to be positively skewed and the negative binomial distribution was selected to model the OTA distribution among sample test results. The sampling statistics were incorporated into the FAO Mycotoxin Sampling Tool where operating characteristic curves were calculated to predict the chances of rejecting good lots (seller’s risk) and accepting bad lots (buyer’s risk) for various sampling plan designs.

Variability and distribution among sample test results when sampling unprocessed wheat lots for ochratoxin A

2016 ◽  
Vol 9 (2) ◽  
pp. 163-178 ◽  
Author(s):  
T.B. Whitaker ◽  
A.B. Slate ◽  
T.W. Nowicki ◽  
F.G. Giesbrecht
Keyword(s):  
Liquid Chromatography ◽  
Sample Preparation ◽  
Ochratoxin A ◽  
Test Procedure ◽  
Test Results ◽  
Laboratory Sample ◽  
Sampling Step ◽  
Test Portion ◽  
Sample Test ◽  
Sampling Statistics

In 2008, Health Canada announced it was considering the establishment of maximum levels for ochratoxin A (OTA) in unprocessed wheat, oats, and their products. The Canada Grains Council and Canadian National Millers Association initiated two studies to measure the variability and distribution among sample test results for unprocessed wheat and oats so that scientifically based OTA sampling plans could be designed to meet regulatory and industry requirements. Sampling statistics related to detecting OTA in oats has been published. 54 OTA contaminated wheat lots representing three wheat classes were identified for the sampling study. Each lot was sampled according to a nested experimental protocol where sixteen 2-kg laboratory samples were taken from each lot, multiple 5-g test portions were taken from each comminuted 2-kg laboratory sample, and multiple OTA measurements were made on each test portion using liquid chromatography. The sampling, sample preparation, and analytical variances associated with each step of the OTA test procedure were found to be a function of OTA concentration and regression equations were developed to predict the functional relationships between variance and OTA concentration. When sampling a wheat lot containing 5 µg/kg OTA with an OTA test procedure consisting of a sampling step employing a single 2-kg laboratory sample, sample preparation step employing a single 100-g test portion, and an analytical step that used liquid chromatography to quantify OTA, the sampling step accounted for 95.3% of the total variability. The observed OTA distribution among the 16 OTA sample results was found to be positively skewed and the negative binomial distribution was selected to model the OTA distribution among sample test results. The sampling statistics were incorporated into the FAO Mycotoxin Sampling Tool and the chances of rejecting good lots and accepting bad lots were calculated for various sampling plan designs.

scholarly journals Evaluating the Performance of Sampling Plans to Detect Fumonisin B1 in Maize Lots Marketed in Nigeria

2007 ◽  
Vol 90 (4) ◽  
pp. 1050-1059 ◽  
Author(s):  
Thomas B Whitaker ◽  
M Bruno Doko ◽  
Britt M Maestroni ◽  
Andrew B Slate ◽  
Bosede F Ogunbanwo
Keyword(s):  
Negative Binomial ◽  
Test Procedure ◽  
Fumonisin B1 ◽  
Sampling Plan ◽  
Regression Equations ◽  
Test Results ◽  
Energy Agency ◽  
Sampling Plans ◽  
Sample Test ◽  
Total Variability

Abstract 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.

Comparing the USDA/AMS Subsampling Mill to a Vertical Cutter Mixer Type Mill Used to Comminute Shelled Peanut Samples for Aflatoxin Analysis1

2012 ◽  
Vol 39 (1) ◽  
pp. 69-81 ◽  
Author(s):  
Thomas B. Whitaker ◽  
Andrew B. Slate
Keyword(s):  
Operating Characteristic ◽  
Test Procedure ◽  
Sampling Plan ◽  
Test Results ◽  
Test Portion ◽  
Two Samples ◽  
The Us ◽  
Sample Test ◽  
Total Variability ◽  
Very High

Abstract The US peanut industry can use up to three 21.8 kg samples per lot to determine if shelled peanut lots are acceptable or unacceptable due to aflatoxin content. If a lot is accepted by the first 21.8 kg sample (1AB≤8 ng/g) prepared with the USDA/AMS Subsampling mill (DM), then some peanut buyers request that the sheller prepare the second sample (2AB) and in some cases the 3AB sample (called special samples in the trade) with a vertical cutter mixer (VCM) type mill. These requests to specifically use the VCM instead of the DM to prepare official (1AB) and special (2AB or 3AB) samples is based in part on a perception that analytical results associated with a test portion taken from the 21.8 kg sample comminuted with a DM does not detect the full magnitude of aflatoxin in the 21.8 kg sample and that negative aflatoxin certificates (lot acceptance) are more likely to occur when samples are prepared with a DM than a VCM. Analysis of aflatoxin test results from two shellers along with Monte Carlo simulation indicate that differences between the 1AB and special sample test results are due to the use of a cut-off limit (≤ 8 ng/g associated with the 1AB) requested by the buyer as part of the acceptance criteria and not due to any bias associated with the DM. Operating characteristic curves were used to demonstrate that the performance of the USDA/AMS aflatoxin sampling plan is about the same regardless of the use of a DM or a VCM for sample preparation. The performances are similar because the DM, with an 1100 g test portion, account for only 8% of the total variability of the aflatoxin test procedure (sampling and analysis account for about 92%). A sampling plan that requires two 21.8 kg samples to test less than a limit, regardless of mill used to prepare the two samples, has a very low risk of accepting bad lots above the FDA limit of 20 ng/g, but has a very high risk of rejecting good lots, which makes for an extremely high economic burden on the sheller.

Sampling dried figs for aflatoxin – Part 1: variability associated with sampling, sample preparation, and analysis

2017 ◽  
Vol 10 (1) ◽  
pp. 31-40 ◽  
Author(s):  
H. Ozer ◽  
H.I. Oktay Basegmez ◽  
T.B. Whitaker ◽  
A.B. Slate ◽  
F.G. Giesbrecht
Keyword(s):  
Sample Preparation ◽  
Test Procedure ◽  
Portion Size ◽  
Total Variance ◽  
Sampling Variance ◽  
Regression Equations ◽  
Sampling Step ◽  
Test Portion ◽  
Analytical Variance ◽  
Aflatoxin Concentration

The variability associated with the aflatoxin test procedure used to estimate aflatoxins in bulk shipments of dried figs was investigated. Sixteen 10 kg laboratory samples were taken from each of twenty commercial bulk lots of dried figs suspected of aflatoxin contamination. Two 55 g test portions were taken from each comminuted laboratory sample using water-slurry comminution methods. Finally, two aliquots from the test portion/solvent blend were analysed for both aflatoxin B1 and total aflatoxins. The total variance associated with testing dried figs for aflatoxins was measured and partitioned into sampling, sample preparation and analytical variance components (total variance is equal to the sum of the sampling variance, sample preparation variance, and analytical variance). Each variance component increased as aflatoxin concentration increased. Using regression analysis, mathematical expressions were developed to model the relationship between aflatoxin concentration and the total, sampling, sample preparation and analytical variances when testing dried figs for aflatoxins. The regression equations were modified to estimate the variances for any sample size, test portion size, and number of analyses for a specific lot aflatoxin concentration. When using the above aflatoxin test procedure to sample a fig lot at 10 μg/kg total aflatoxins, the sampling, sample preparation, analytical, and total variances were 47.20, 0.29, 0.13, and 47.62, respectively. The sampling, sample preparation, and analytical steps accounted for 99.1, 0.6, and 0.3% of the total variance, respectively. For the aflatoxin test procedure used in this study, the sampling step is the largest source of variability.

scholarly journals Testing Green Coffee for Ochratoxin A, Part II: Observed Distribution of Ochratoxin A Test Results

2005 ◽  
Vol 88 (3) ◽  
pp. 780-787 ◽  
Author(s):  
Eugenia Azevedo Vargas ◽  
Thomas B Whitaker ◽  
Eliene Alves dos Santos ◽  
Andrew B Slate ◽  
Francisco B Lima ◽  
...  
Keyword(s):  
Ochratoxin A ◽  
Lognormal Distribution ◽  
Operating Characteristic ◽  
Negative Binomial ◽  
Characteristic Curve ◽  
Test Procedure ◽  
Green Coffee ◽  
Test Results ◽  
Empirical Distributions ◽  
Operating Characteristic Curve

Abstract 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 μg/kg, approximately 6 beans per 10 000 beans are contaminated.

scholarly journals Sampling Hazelnuts for Aflatoxin: Effect of Sample Size and Accept/Reject Limit on Reducing the Risk of Misclassifying Lots

2007 ◽  
Vol 90 (4) ◽  
pp. 1028-1035 ◽  
Author(s):  
Guner Ozay ◽  
Ferda Seyhan ◽  
Aysun Yilmaz ◽  
Thomas B Whitaker ◽  
Andrew B Slate ◽  
...  
Keyword(s):  
Sample Size ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Food Additives ◽  
Sampling Plan ◽  
Test Results ◽  
Sampling Plans ◽  
Wide Range ◽  
Sample Test

Abstract 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.

scholarly journals Evaluation of Sampling Plans to Detect Cry9C Protein in Corn Flour and Meal

2004 ◽  
Vol 87 (4) ◽  
pp. 950-960 ◽  
Author(s):  
Thomas B Whitaker ◽  
Mary W Trucksess ◽  
Francis G Giesbrecht ◽  
Andrew B Slate ◽  
Francis S Thomas
Keyword(s):  
Normal Distribution ◽  
Test Procedure ◽  
Enzyme Linked Immunosorbent Assay ◽  
Sampling Variance ◽  
Regression Equations ◽  
Corn Flour ◽  
Test Procedures ◽  
Sampling Plans ◽  
Sample Test ◽  
Almost All

Abstract 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).

Revisiting the sampling, sample preparation, and analytical variability associated with testing wheat for deoxynivalenol

2019 ◽  
Vol 12 (4) ◽  
pp. 319-332
Author(s):  
S.A. Tittlemier ◽  
J. Chan ◽  
D. Gaba ◽  
K. Pleskach ◽  
J. Osborne ◽  
...  
Keyword(s):  
Experimental Design ◽  
Sample Preparation ◽  
Normal Distribution ◽  
Total Variance ◽  
Design Parameters ◽  
Test Results ◽  
Operating Characteristics ◽  
Test Portion ◽  
Decision Limit ◽  
Sample Test

Fifteen lots of wheat were sampled to characterise the total variance and distribution among sample test results associated with measuring deoxynivalenol (DON) in bulk wheat lots. An unbalanced nested experimental design based on past research was used to determine contributions to the total variance from sampling, sample preparation, and analysis. The wheat lots used in the study contained average DON concentrations that ranged from 0.17 to 24.5 mg/kg. Sampling was determined to be the largest contributor to the total variance of measuring DON at low mg/kg concentrations, which are relevant to existing maximum levels. With the experimental design parameters of 1 kg laboratory samples, sub-division of whole and ground grain using rotary sample division, sample comminution using a commercial-grade coffee grinder, extraction of 100 g test portions, and making one measurement of DON in the test portion by gas chromatography-mass spectrometry, the total variance of DON measurement at 2 mg/kg was 0.046 mg2/kg2 (coefficient of variation=10.7%). At this concentration, sampling contributed 67% to the total variance, followed by sample preparation (18%) and analysis (15%). The DON distribution among sample test results was accurately described by the normal distribution. The mathematical model of variance was used with the normal distribution of DON measurement results to construct operating characteristics curves to model the likelihood of mischaracterising a wheat lot as (non) compliant with a certain decision limit. With realistic laboratory sample and test portion sizes, as well as a practicable decision limit of 1.5 mg/kg, the estimated probability of mischaracterising a wheat lot containing 2 mg/kg DON as less than this concentration was reduced to 1%.

Predicting the Distribution of Aflatoxin Test Results from Farmers’ Stock Peanuts

1994 ◽  
Vol 77 (3) ◽  
pp. 659-666 ◽  
Author(s):  
Thomas B Whitaker ◽  
Francis G Giesbrecht ◽  
Jeremy Wu ◽  
Winston M Hagler ◽  
Floyd E Dowell
Keyword(s):  
Negative Binomial ◽  
Accurate Estimate ◽  
Test Results ◽  
Sample Sizes ◽  
Significance Level ◽  
Sample Test ◽  
Unknown Distribution Function ◽  
Acceptance Levels ◽  
Ks Test ◽  
Binomial Function

Abstract Suitability of the negative binomial function for use in estimating the distribution of sample aflatoxin test results associated with testing farmers1 stock peanuts for aflatoxin was studied. A 900 kg portion of peanut pods was removed from each of 40 contaminated farmers1 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, s2xamong 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. However, the negative binomial function did fit the observed distribution for sample test results at a concentration greater than 0 in 4 of the 6 cases. As a result, the negative binomial function provides an accurate estimate of the acceptance probabilities associated with accepting contaminated lots of farmers' stock peanuts for various sample sizes and various sample acceptance levels greater than 0 ng/g.

scholarly journals Testing Green Coffee for Ochratoxin A, Part III: Performance of Ochratoxin A Sampling Plan

2006 ◽  
Vol 89 (4) ◽  
pp. 1021-1026
Author(s):  
Eugenia A Vargas ◽  
Thomas B Whitaker ◽  
Eliene A Santos ◽  
Andrew B Slate ◽  
Francisco B Lima ◽  
...  
Keyword(s):  
Sample Preparation ◽  
Sample Size ◽  
Ochratoxin A ◽  
Test Procedure ◽  
Sampling Plan ◽  
Green Coffee ◽  
Sampling Step ◽  
Sampling Plans

Abstract 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 μg/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 μg/kg relative to a fixed regulatory limit of 5 μg/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).

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