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.

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.

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.

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 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 Reliability Sampling Procedure For High Priced Products

2021 ◽  
Vol 12 (4) ◽  
pp. 1117-1120
Author(s):  
V. Jemmy Joyce, Et. al.
Keyword(s):  
Characteristic Function ◽  
Sample Size ◽  
Exponential Distribution ◽  
Operating Characteristic ◽  
Design Procedure ◽  
Sampling Procedure ◽  
Sampling Plan ◽  
Life Testing ◽  
Operating Characteristic Function ◽  
Very High

Life testing for very high priced products with least of sample size can be done using the procedure of sampling plan designed in this paper. The required sample size for various of operating characteristic function using new design procedure is obtained using program in OCTAVE based on Lomaxdistribution and is compared with sample size obtained based on exponential distribution.

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%.

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 Distribution among Sample Test Results when Testing Shelled Corn Lots for Fumonisin

2001 ◽  
Vol 84 (3) ◽  
pp. 770-776 ◽  
Author(s):  
Thomas B Whitaker ◽  
Winston M Hagler ◽  
Anders S Johansson ◽  
Francis G Giesbrecht ◽  
Mary W Trucksess
Keyword(s):  
Gamma Distribution ◽  
Gamma Function ◽  
Characteristic Curve ◽  
Test Procedure ◽  
Test Results ◽  
Significance Level ◽  
Gamma Distributions ◽  
Sample Test ◽  
The Mean ◽  
Power Divergence

Abstract 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 dried figs for aflatoxin - Part II: effect of sampling plan design on reducing the risk of misclassifying lots

2017 ◽  
Vol 10 (2) ◽  
pp. 99-109 ◽  
Author(s):  
H. Ozer ◽  
H.I. Oktay Basegmez ◽  
T.B. Whitaker ◽  
A.B. Slate ◽  
F.G. Giesbrecht
Keyword(s):  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Sampling Plan ◽  
Export Market ◽  
Test Results ◽  
Sampling Plans ◽  
Binomial Distributions ◽  
Sample Test ◽  
Codex Standard

Because aflatoxin limits vary widely among regulating countries, the Codex Committee on Contaminants in Foods (CCCF) began work in 2006 to harmonise maximum levels (MLs) and sampling plans for aflatoxin in dried figs. Studies were developed to measure the variability and distribution among replicated sample aflatoxin test results taken from the same aflatoxin contaminated lot of dried figs so that a model could be developed to evaluate the risk of misclassifying lots of dried figs by aflatoxin sampling plan designs. The model was then be used by the CCCF electronic working group (eWG) to recommend MLs and aflatoxin sampling plan designs to the full CCCF membership for lots traded in the export market. Sixteen 10 kg samples were taken from each of 20 dried fig lots with varying levels of contamination. The observed aflatoxin distribution among the 16-aflatoxin sample test results was compared to the normal, 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 aflatoxin distributions among sample test results taken from the same contaminated lot. Using the negative binomial distribution, a computer model was developed to show the effect of the number and size of samples and the accept/reject limits on the chances of rejecting good lots (seller's risk) and accepting bad lots (buyer's risk). The information was shared with the CCCF eWG and in March 2012, the 6th session of CCCF adopted at step 5/8 an aflatoxin sampling plan where three 10 kg samples must all test less than an ML of 10 µg/kg total aflatoxins to accept a dried fig lot. The 35th Session of the Codex Alimentarius Commission met in July 2012 and adopted the CCCF recommendations for the ML and the sampling plan as an official Codex standard.

Designing Sampling Plans to Detect Foreign Material in Bulk Lots of Shelled Peanut1

2008 ◽  
Vol 35 (2) ◽  
pp. 159-164 ◽  
Author(s):  
T. B. Whitaker ◽  
A. B. Slate ◽  
F. G. Giesbrecht
Keyword(s):  
Binomial Distribution ◽  
Small Sample ◽  
Sampling Plan ◽  
Test Results ◽  
Foreign Material ◽  
Sampling Plans ◽  
Sample Test ◽  
The Mean ◽  
Mean Variance ◽  
Food Manufacturer

Abstract When food manufacturers specify a maximum limit for the amount of foreign material (FM) in the lot, handlers estimate the true percent FM in a commercial lot by measuring FM in a small sample taken from the lot before shipment to a food manufacturer. Because of the uncertainty (variability) in FM among samples taken from the same lot, it is difficult to obtain a precise estimate of the true FM in the lot. The objectives of this study were to (1) measure the variability and FM distribution among sample test results when estimating the true lot proportion of FM in a lot of shelled peanuts, (2) compare the measured variability and FM distribution among sample test results to that predicted by the binomial distribution, (3) develop a computer model, based upon the binomial distribution, to evaluate the performance (buyer's risk and seller's risk) of sampling plan designs used to estimate FM in a bulk lot of shelled peanuts, and (4) demonstrate with the model the effect of increasing sample size to reduce misclassification of lots. Eighty-eight samples, 9 kg (20 lb) each, were selected at random from each of six commercial lots of shelled medium runner peanuts. The percent FM (PFM), based upon number of kernels was determined for each sample. The mean, variance, and distribution among the 88 sample test results were calculated for each of the six lots. Results indicated that the variance and distribution among the 88 sample test results are very similar to that predicted by the binomial distribution. The performance of various sampling plan designs was demonstrated using the binomial distribution.

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