Use of Loose Shelled Kernels to Estimate Aflatoxin in Farmers Stock Peanut Lots1

1999 ◽  
Vol 26 (1) ◽  
pp. 39-44 ◽  
Author(s):  
T. B. Whitaker ◽  
F. G. Giesbrecht ◽  
W. M. Hagler
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Functional Relationship ◽  
Test Sample ◽  
Sampling Plan ◽  
Sample Sizes ◽  
Total Aflatoxin ◽  
Aflatoxin Concentration

Abstract Loose shelled kernels (LSK) are a defined grade component of farmers stock peanuts and represented, on the average, 33.3% of the total aflatoxin mass and 7.7% of the kernel mass among the 120 farmers stock peanut lots studied. The functional relationship between aflatoxin in LSK taken from 2-kg test samples and the aflatoxin in farmers stock peanut lots was determined to be linear with zero intercept and a slope of 0.297. The correlation between aflatoxin in LSK and aflatoxin in the lot was 0.844 which suggests that LSK taken from large test samples can be used to estimate the aflatoxin concentration in a farmer's lot. Using only LSK allows large test samples to be used to estimate the lot concentration since LSK can be easily screened from a large test sample. If LSK accounts for 7.7% of the lot kernel mass, a 50-kg sample will yield about 3.9 kg of LSK which can be easily prepared for aflatoxin analysis. Increasing the test sample size from 2 to 50 kg reduced the coefficient of variation associated with estimating a lot with 100 parts per billion (ppb) aflatoxin from 114 to 23%, respectively. As an example, a farmers stock aflatoxin sampling plan with dual tolerances (10 and 100 ppb) that classified lots into three categories was evaluated for two test sample sizes (2 and 50 kg). The effect of increasing test sample size from 2 to 50 kg on the number of lots classified into each of the three categories was demonstrated when measuring aflatoxin only in LSK.

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.

scholarly journals Predicting Aflatoxin and Fumonisin in Shelled Corn Lots Using Poor-Quality Grade Components

2006 ◽  
Vol 89 (2) ◽  
pp. 433-440 ◽  
Author(s):  
Anders S Johansson ◽  
Thomas B Whitaker ◽  
Winston M Hagler ◽  
Daryl T Bowman ◽  
Andy B Slate ◽  
...  
Keyword(s):  
North Carolina ◽  
Total Concentration ◽  
Screening Method ◽  
Test Sample ◽  
Poor Quality ◽  
Foreign Material ◽  
Total Aflatoxin ◽  
Quality Grade ◽  
Aflatoxin Concentration ◽  
Inspection Service

Abstract 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.

scholarly journals Estimating Deoxynivalenol in Shelled Corn Barge Lots by Measuring Deoxynivalenol in Corn Screenings

2003 ◽  
Vol 86 (6) ◽  
pp. 1187-1192 ◽  
Author(s):  
Thomas B Whitaker ◽  
John L Richard ◽  
Francis G Giesbrecht ◽  
Andrew B Slate ◽  
Nelson Ruiz
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Test Sample ◽  
Regression Equations

Abstract To determine if deoxynivalenol (DON) is concentrated in small corn screenings, fourteen to twenty-three 1.1 kg test samples were taken from each of 10 barges of shelled corn. Each of the 181 test samples was divided into 2 components (fines and clean) using a 5 mm screen. The clean component sample rode the 5 mm screen and the fines component sample passed through the 5 mm screen. The DON concentration in fines component sample was about 3 times the DON concentration in the clean component sample. The DON in the 181 fines and clean component samples averaged 689.0 and 206.1 ng/g, respectively. Regression equations were developed to predict the DON in the barge based upon measurements of DON in the fines component sample. The ratio of DON in the lot to DON in the fines component sample was 0.359. The coefficient of variation (CV) associated with predicting the DON concentration in a lot with 359 ng/g using a 1.1 kg test sample was 47.0%. Increasing sample size to 4.4 kg reduced the CV to 23%.

scholarly journals Testing Shelled Corn for Aflatoxin, Part III: Evaluating the Performance of Aflatoxin Sampling Plans

2000 ◽  
Vol 83 (5) ◽  
pp. 1279-1284 ◽  
Author(s):  
Anders S Johansson ◽  
Thomas B Whitaker ◽  
Francis G Giesbrecht ◽  
Winston M Hagler ◽  
James H Young
Keyword(s):  
Sample Size ◽  
False Positives ◽  
Sample Sizes ◽  
False Negatives ◽  
Sampling Plans ◽  
Acceptance Level ◽  
Aflatoxin Concentration ◽  
Acceptance Levels ◽  
Regulatory Guideline

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

scholarly journals Sampling plan in health surveys, city of São Paulo, Brazil, 2015

2018 ◽  
Vol 52 ◽  
pp. 81 ◽  
Author(s):  
Maria Cecilia Goi Porto Alves ◽  
Maria Mercedes Loureiro Escuder ◽  
Moises Goldbaum ◽  
Marilisa Berti de Azevedo Barros ◽  
Regina Mara Fisberg
Keyword(s):  
Sample Size ◽  
Health Survey ◽  
Coefficient Of Variation ◽  
Sample Size Calculation ◽  
Sao Paulo ◽  
Sampling Plan ◽  
São Paulo ◽  
Design Effect ◽  
Sampling Procedures ◽  
The City

OBJECTIVE: To evaluate the sampling plan of the Health Survey of the City of São Paulo (ISA-Capital 2015) regarding the accuracy of estimates and the conformation of domains of study by the Health Coordinations of the city of São Paulo, Brazil. METHODS: We have described the population, domains of study, and sampling procedures, including stratification, calculation of sample size, and random selection of sample units, of the Health Survey of the City of São Paulo, 2015. The estimates of proportions were analyzed in relation to precision using the coefficient of variation and the design effect. We considered suitable the coefficients below 30% at the regional level and 20% at the city level and the estimates of the design effect below 1.5. We considered suitable the strategy of establishing the Health Coordinations as domains after verifying that, within the coordinations, the estimates of proportions for the age and sex groups had the minimum acceptable precision. The estimated parameters were related to the subjects of use of services, morbidity, and self-assessment of health. RESULTS: A total of 150 census tracts were randomly selected, 30 in each Health Coordination, 5,469 households were randomly selected and visited, and 4,043 interviews were conducted. Of the 115 estimates made for the domains of study, 97.4% presented coefficients of variation below 30%, and 82.6% were below 20%. Of the 24 estimates made for the total of the city, 23 presented coefficient of variation below 20%. More than two-thirds of the estimates of the design effect were below 1.5, which was estimated in the sample size calculation, and the design effect was below 2.0 for 88%. CONCLUSIONS: The ISA-Capital 2015 sample generated estimates at the predicted levels of precision at both the city and regional levels. The decision to establish the regional health coordinations of the city of São Paulo as domains of study was adequate.

Variability Among Peanut Subsamples Prepared for Aflatoxin Analysis with Four Mills

1993 ◽  
Vol 76 (5) ◽  
pp. 983-987 ◽  
Author(s):  
Joe W Dorner ◽  
Richard J Cole
Keyword(s):  
Liquid Chromatography ◽  
Sample Size ◽  
Coefficient Of Variation ◽  
Aflatoxin Concentration

Abstract The variability in aflatoxin concentration among peanut subsamples ground with 4 different mills was evaluated. Twenty 2 kg samples of naturally contaminated peanuts were ground in a Dickens subsampling mill (DM), a Stephan model UM-12 vertical cutter mixer (SM), and a Robot Coupe model RSI6Y-1 vertical cutter mixer (RC1). Twenty 4 kg samples were ground in the DM, SM, and a Robot Coupe model R10P vertical cutter mixer (RC2). From each 2 kg sample, ten 100 g subsamples were withdrawn, and from each 4 kg sample, ten 200 g subsamples were withdrawn. Subsamples were analyzed for aflatoxin by liquid chromatography. The coefficient of variation (CV) among each set of 10 subsamples was determined for each sample, and the CVs for each sample size were ranked and analyzed by the Kruskal-Wallis test of ranks. For 2 kg samples, the CVs for the samples ground in RC1 ranked significantly lower than those for samples ground in DM and SM. For 4 kg samples, the CVs for samples ground in RC2 and SM were significantly lower than that for samples ground in DM. The averages of the CVs for 2 kg samples were 17.2% (RC1), 32.8% (SM), and 40.6% (DM). The averages of the CVs for 4 kg samples were 21.2% (RC2), 26.0% (SM), and 47.0% (DM).

scholarly journals Estimating Aflatoxin in Farmers' Stock Peanut Lots by Measuring Aflatoxin in Various Peanut-Grade Components

1998 ◽  
Vol 81 (1) ◽  
pp. 61-67 ◽  
Author(s):  
Thomas B Whitaker ◽  
Winston M Hagler ◽  
Francis G Giesbrecht ◽  
Joe w Dorner ◽  
Floyd E Dowell ◽  
...  
Keyword(s):  
Linear Regression ◽  
Correlation Analysis ◽  
Correlation Coefficient ◽  
Test Sample ◽  
Statistical Analyses ◽  
Data Sets ◽  
Regression Equations ◽  
Total Aflatoxin ◽  
Accurate Predictor ◽  
Aflatoxin Concentration

Abstract Five, 2 kg test samples were taken from each of 120 farmers' stock peanut lots contaminated with aflatoxin. Kernels from each 2 kg sample were divided into the following grade components: sound mature kernels plus sound splits (SMKSS), other kernels (OK), loose shelled kernels (LSK), and damaged kernels (DAM). Kernel mass, aflatoxin mass, and aflatoxin concentration were measured for each of the 2400 component samples. For 120 lots tested, average aflatoxin concentrations in SMKSS, OK, LSK, and DAM components were 235, 2543, 11 775, and 69 775 ng/g, respectively. Aflatoxins in SMKSS, OK, LSK, and DAM components represented 6.9, 7.9, 33.3, and 51.9% of the total aflatoxin mass, respectively. Cumulatively, 3 aflatoxin risk components—OK, LSK, and DAM—accounted for 93.1% of total aflatoxin, but only 18.4% percent of test sample mass. Correlation analysis suggests that the most accurate predictor of aflatoxin concentration in the lot is the cumulative aflatoxin mass in the high 3 risk corn ponents OK + LSK + DAM (correlation coefficient, r = 0.996). If the aflatoxin in the combined OK + LSK + DAM components is expressed in concentration units, r decreases to 0.939. Linear regression equations relating aflatoxin in OK + LSK + DAM to aflatoxin concentration in the lot were developed. The cumulative aflatoxin in the OK + LSK + DAM components was not an accurate predictor (r = 0.539) of aflatoxin in the SMKSS component. Statistical analyses of 3 other data sets published previously yielded similar results.

Effects of Sample Size and Sample Acceptance Level on the Number of Aflatoxin-Contaminated Farmers’ Stock Lots Accepted and Rejected at the Buying Point

1994 ◽  
Vol 77 (6) ◽  
pp. 1672-1680 ◽  
Author(s):  
Thomas B Whitaker ◽  
Jeremy Wu ◽  
Floyd E Dowell ◽  
Winston M Hagler ◽  
Francis G Giesbrecht
Keyword(s):  
Sample Size ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Single Sample ◽  
Sample Sizes ◽  
Acceptance Level ◽  
Aflatoxin Concentration ◽  
Acceptance Levels

Abstract Sixteen different aflatoxin sampling plan designs were evaluated using the negative-binomial distribution. Evaluations were used to predict the effects of 4 different sample sizes and 4 different sample acceptance levels on the classification of farmers’ stock lots according to the lot aflatoxin concentration. The 4 sample sizes evaluated were 2.27 kg (5 lb), 4.54 kg (10 lb), 9.08 kg (20 lb), and 18.16 kg (40 lb). The 4 sample acceptance levels evaluated were 5,50,100, and 180 ng/g. A decrease in the sample acceptance level from 180 to 5 ng/g decreased the number of lots accepted at all lot concentrations, increased the number of lots rejected at all lot concentrations, and decreased the average aflatoxin concentrations among all lots accepted. At the highest sample acceptance level, 180 ng/g, increase in sample size from 2.27 to 18.16 kg decreased the percent of lots accepted at concentrations above the sample acceptance level and increased the percent of lots accepted at concentrations below the sample acceptance level. At the lowest sample acceptance level investigated, 5 ng/g, an increase in sample size from 2.27 to 18.16 kg decreased the percent of lots accepted at all concentrations. The effect of using sampling designs with 2 sample acceptance levels to classify lots into 3 categories was investigated. The advantages of using dual sample acceptance levels over a single sample acceptance level was demonstrated for 2.27 and 9.08 kg samples.

scholarly journals Confocal 3D DNA Cytometry: Assessment of Required Coefficient of Variation by Computer Simulation

2004 ◽  
Vol 26 (3) ◽  
pp. 93-99
Author(s):  
Lennert S. Ploeger ◽  
Jeroen A.M. Beliën ◽  
Neal M. Poulin ◽  
William Grizzle ◽  
Paul J. van Diest
Keyword(s):  
Computer Simulation ◽  
Sample Size ◽  
Coefficient Of Variation ◽  
Small Sample ◽  
Laser Scanning Microscopy ◽  
Confocal Laser ◽  
Dna Cytometry ◽  
Sample Sizes ◽  
Intact Cells ◽  
Dna Histograms

Background: Confocal Laser Scanning Microscopy (CLSM) provides the opportunity to perform 3D DNA content measurements on intact cells in thick histological sections. So far, sample size has been limited by the time consuming nature of the technology. Since the power of DNA histograms to resolve different stemlines depends on both the sample size and the coefficient of variation (CV) of histogram peaks, interpretation of 3D CLSM DNA histograms might be hampered by both a small sample size and a large CV. The aim of this study was to analyze the required CV for 3D CLSM DNA histograms given a realistic sample size. Methods: By computer simulation, virtual histograms were composed for sample sizes of 20000, 10000, 5000, 1000, and 273 cells and CVs of 30, 25, 20, 15, 10 and 5%. By visual inspection, the histogram quality with respect to resolution of G0/1 and G2/M peaks of a diploid stemline was assessed. Results: As expected, the interpretability of DNA histograms deteriorated with decreasing sample sizes and higher CVs. For CVs of 15% and lower, a clearly bimodal peak pattern with well distinguishable G0/1 and G2/M peaks were still seen at a sample size of 273 cells, which is our current average sample size with 3D CLSM DNA cytometry. Conclusions: For unambiguous interpretation of DNA histograms obtained using 3D CLSM, a CV of at most 15% is tolerable at currently achievable sample sizes. To resolve smaller near diploid stemlines, a CV of 10% or better should be aimed at. With currently available 3D imaging technology, this CV is achievable.

scholarly journals How sample size influences the replicability of task-based fMRI

2017 ◽  
Author(s):  
Benjamin O. Turner ◽  
Erick J. Paul ◽  
Michael B. Miller ◽  
Aron K. Barbey
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Large Range ◽  
Test Sample ◽  
Small Samples ◽  
Sample Sizes ◽  
Typical Sample ◽  
The Impact ◽  
Larger Sample ◽  
Different Levels

Despite a growing body of research suggesting that task-based functional magnetic resonance imaging (fMRI) studies often suffer from a lack of statistical power due to too-small samples, the proliferation of such underpowered studies continues unabated. Using large independent samples across eleven distinct tasks, we demonstrate the impact of sample size on replicability, assessed at different levels of analysis relevant to fMRI researchers. We find that the degree of replicability for typical sample sizes is modest and that sample sizes much larger than typical (e.g., N = 100) produce results that fall well short of perfectly replicable. Thus, our results join the existing line of work advocating for larger sample sizes. Moreover, because we test sample sizes over a fairly large range and use intuitive metrics of replicability, our hope is that our results are more understandable and convincing to researchers who may have found previous results advocating for larger samples inaccessible.

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