scholarly journals Sampling Grain Shipments To Detect Genetically Modified Seed

2001 ◽  
Vol 84 (6) ◽  
pp. 1941-1946 ◽  
Author(s):  
Thomas B Whitaker ◽  
Larry Freese ◽  
Francis G Giesbrecht ◽  
Andrew B Slate
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Binomial Distribution ◽  
Genetically Modified ◽  
Seed Sample ◽  
Test Results ◽  
Sample Test

Abstract 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.

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.

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.

scholarly journals Sample size for estimating mean and coefficient of variation in species of crotalarias

2018 ◽  
Vol 90 (2) ◽  
pp. 1705-1715 ◽  
Author(s):  
MARCOS TOEBE ◽  
LETÍCIA N. MACHADO ◽  
FRANCIELI L. TARTAGLIA ◽  
JULIANA O. DE CARVALHO ◽  
CIRINEU T. BANDEIRA ◽  
...  
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation

scholarly journals Assessment of Educational Game Materials and Poster Practices with Rubrics by Expert Educators and Students

2018 ◽  
Vol 5 (13) ◽  
pp. 79
Author(s):  
Figen Altay ◽  
Kevser Bozkurt
Keyword(s):  
Correlation Analysis ◽  
Educational Games ◽  
Educational Game ◽  
Test Results ◽  
Knowledge Test ◽  
Significant Difference ◽  
Scoring Rubrics ◽  
Sample Test ◽  
The Difference ◽  
Poster Presentations

The purpose of this study was to investigate the difference between evaluations of the educational game materials and poster practices by students’ own peers and by expert educators using the rubrics created by expert educators and students together. Study included 10 students and 3 educators attended educational game materials course. Students were informed about basic skills of movement, game, game types, game equipment, analytical rubric, and educational game lectures were given to the students for 6 weeks and 80 minutes each week. 12-question knowledge test was used regarding educational games, analytical scoring rubrics, developing game materials and preparing posters. Materials and posters presented in the course were recorded. Evaluation scales were selected by students and expert teachers. Selected peers and educators evaluated 25 videos. One-way analysis of variance and correlation analysis were used for the reliability and repeatability measurements of the students and teachers. R values of 0.96-0.92 were found between students and 0.78-0.86 between educators. For knowledge tests of the groups, according to Wilcoxon paired two-sample test, there was a significant difference in test results (p<.05). The t test was used in the results of the student and educator video evaluations and there was no significant difference between the scores given by the expert educators and the students to the material and poster presentations (p>.05). In conclusion, this study showed that students could make evaluations as good as expert educators when given an answer key such as a scoring rubric that will help them in the evaluation.

A variable sample size run sum coefficient of variation chart

2022 ◽  
Author(s):  
Wai Chung Yeong ◽  
Yen Yoon Tan ◽  
Sok Li Lim ◽  
Khai Wah Khaw ◽  
Michael Boon Chong Khoo
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Variable Sample Size

Modified Automated Determination of 2,3-Diphosphoglycerate in Whole Blood

1972 ◽  
Vol 18 (9) ◽  
pp. 1001-1004 ◽  
Author(s):  
Kenneth F Atkinson
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Whole Blood ◽  
Normal Range ◽  
Blood Samples ◽  
Automated Determination

Abstract A modification is described of the automated determination of 2,3-diphosphoglycerate (DPG) in blood [Grisolia, S., et al., Anal. Biochem. 31, 235 (1969)]. Modifications in the manifold result in a sensitive, noise-free, rapid system and the modifications in the preparations of the reagents ensure stability of the diluted standards and blood samples for at least three weeks. Samples are run at the rate of 60/h and sample size can be as small as 5 µl of whole blood. The coefficient of variation of the overall determination of automated DPG and manual hemoglobin is 3.6% and the SD is ±0.77 µmol/g Hb. The normal range is 14.6 ± 2.2 (SD) µmol/g hemoglobin.

scholarly journals Inspection Strategy under Indeterminacy Based on Neutrosophic Coefficient of Variation

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 193 ◽  
Author(s):  
Muhammad Aslam ◽  
Mansour Sattam Aldosari
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Operating Characteristic ◽  
Uncertain Data ◽  
Sampling Plan ◽  
Sampling Plans ◽  
Interval Method ◽  
Inspection Strategy ◽  
Classical Statistics

The existing sampling plans which use the coefficient of variation (CV) are designed under classical statistics. These available sampling plans cannot be used for sentencing if the sample or the population has indeterminate, imprecise, unknown, incomplete or uncertain data. In this paper, we introduce the neutrosophic coefficient of variation (NCV) first. We design a sampling plan based on the NCV. The neutrosophic operating characteristic (NOC) function is then given and used to determine the neutrosophic plan parameters under some constraints. The neutrosophic plan parameters such as neutrosophic sample size and neutrosophic acceptance number are determined through the neutrosophic optimization solution. We compare the efficiency of the proposed plan under the neutrosophic statistical interval method with the sampling plan under classical statistics. A real example which has indeterminate data is given to illustrate the proposed plan.

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