scholarly journals Sampling Almonds for Aflatoxin, Part I: Estimation of Uncertainty Associated with Sampling, Sample Preparation, and Analysis

2006 ◽  
Vol 89 (4) ◽  
pp. 1027-1034 ◽  
Author(s):  
Thomas B Whitaker ◽  
Andrew B Slate ◽  
Merle Jacobs ◽  
J Michael Hurley ◽  
Julie G Adams ◽  
...  
Keyword(s):  
Sample Preparation ◽  
High Performance ◽  
Test Procedure ◽  
Aflatoxin Contamination ◽  
Total Variance ◽  
Concentration Variance ◽  
Chromatography Analysis ◽  
Aflatoxin Concentration ◽  
Variance Estimates ◽  
Mathematical Relationships

Abstract 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.

scholarly journals Sampling Hazelnuts for Aflatoxin: Uncertainty Associated with Sampling, Sample Preparation, and Analysis

2006 ◽  
Vol 89 (4) ◽  
pp. 1004-1011 ◽  
Author(s):  
Guner Ozay ◽  
Ferda Seyhan ◽  
Aysun Yilmaz ◽  
Thomas B Whitaker ◽  
Andrew B Slate ◽  
...  
Keyword(s):  
Sample Preparation ◽  
Test Procedure ◽  
Aflatoxin Contamination ◽  
Aflatoxin Level ◽  
Mathematical Expressions ◽  
Aflatoxin Concentration ◽  
Total Variability ◽  
Subsample Size ◽  
Liquid Chromatographic ◽  
The Relationship

Abstract 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 highperformance 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.

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 Shelled Corn for Aflatoxin, Part I: Estimation of Variance Components

2000 ◽  
Vol 83 (5) ◽  
pp. 1264-1269 ◽  
Author(s):  
Anders S Johansson ◽  
Thomas B Whitaker ◽  
Winston M Hagler ◽  
Francis G Giesbrecht ◽  
James H Young ◽  
...  
Keyword(s):  
Sample Preparation ◽  
Aflatoxin Contamination ◽  
Total Variance ◽  
Mathematical Expressions ◽  
Aflatoxin Concentration ◽  
Concentration Test ◽  
Liquid Chromatographic Analysis ◽  
Subsample Size ◽  
Liquid Chromatographic ◽  
Estimation Of Variance

Abstract The variability associated with testing lots of shelled corn for aflatoxin was investigated. Eighteen lots of shelled corn were tested for aflatoxin contamination. The total variance associated with testing shelled corn was estimated and partitioned into sampling, sample preparation, and analytical variances. All variances increased as aflatoxin concentration 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. Test results on a lot with 20 parts per billion aflatoxin using a 1.13 kg sample, a Romer mill, 50 g subsamples, and liquid chromatographic analysis showed that the total, sampling, sample preparation, and analytical variances were 274.9 (CV = 82.9%), 214.0 (CV = 73.1%), 56.3 (CV = 37.5%), and 4.6 (CV = 10.7%), respectively. The percentage of the total variance for sampling, sample preparation, and analytical was 77.8, 20.5, and 1.7, respectively.

Variability Among Aflatoxin Test Results on Runner Peanuts Harvested From Small Field Plots1

2004 ◽  
Vol 31 (1) ◽  
pp. 59-63 ◽  
Author(s):  
T. B. Whitaker ◽  
J. W. Dorner ◽  
F. G. Giesbrecht ◽  
A. B. Slate
Keyword(s):  
Standard Deviation ◽  
High Performance ◽  
Weighted Average ◽  
Aflatoxin Contamination ◽  
Small Field ◽  
Test Results ◽  
Field Plot ◽  
Aflatoxin Concentration ◽  
Production Practices ◽  
Standard Production

Abstract An experiment was conducted to determine the variability associated with aflatoxin contamination of peanuts from plants grown in specified row lengths. Runner peanuts (cv. Georgia Green) were planted in 10, 76.2 m rows (20 seed/m) and grown using standard production practices. Plants were exposed to natural late-season drought conditions making the peanuts susceptible to preharvest aflatoxin contamination. Plants were mechanically dug, inverted, and separated into 500 plots of 1.5 m single rows. Peanuts from each numerically identified plot were harvested with a mechanical picker, dried to 8% kernel moisture (wet basis), shelled, and analyzed for aflatoxin by high performance liquid chromatography (HPLC). The average kernel mass and weighted average aflatoxin concentration for all plots was 131 g and 2278 ng/g, respectively. The kernel mass varied among the 500 plots from a low of 4 g to a maximum of 283 g. The aflatoxin concentration among the 500 plots varied from a low of 0 ng/g to a maximum of 32,142 ng/g. The standard deviation among the 500 plot aflatoxin values was 4061. The standard deviation among sample concentrations for this field study was very similar to previous studies that measured the standard deviation among sample concentrations taken from bulk farmers' stock lots. Increasing plot length decreased the standard deviation among plot aflatoxin values as predicted by statistical theory. For example, increasing plot row length by a factor of four, or from 1.5 to 6 m, decreased the standard deviation by a factor of two, or from 4061 to 2031. A regression equation was developed to predict the effect of plot row length on the variability among aflatoxin plot values. This information is useful for designing field plot experiments to test various strategies for reducing or preventing preharvest aflatoxin contamination.

Variability Associated with Sampling, Sample Preparation, and Chemical Testing for Aflatoxin in Farmers' Stock Peanuts

1994 ◽  
Vol 77 (1) ◽  
pp. 107-116 ◽  
Author(s):  
Thomas B Whitaker ◽  
Floyd E Dowell ◽  
Winston M Hagler ◽  
Francis G Giesbrecht ◽  
Jeremy Wu
Keyword(s):  
Sample Preparation ◽  
Variance Component ◽  
Analytical Techniques ◽  
State Inspection ◽  
Total Variance ◽  
Federal State ◽  
Inspection Method ◽  
Aflatoxin Concentration ◽  
Total Variability ◽  
Inspection Service

Abstract Forty farmers’ stock lots of runner peanuts suspected of containing aflatoxin were identified by the Federal State Inspection Service by using the visual Aspergillus flavus inspection method. A 900 kg portion was removed from each lot and divided into 50 samples each of 2.27 kg (5 lb), 4.54 kg (10 lb), and 6.81 kg (15 lb) weights. For each sample, foreign material was removed, pods were shelled, and all kernels were comminuted for 7 min in a vertical cutter mixer. A100 g subsample was removed from each comminuted sample for aflatoxin analysis by liquid chromatography (LC). The total variance associated with each sample size was estimated. The total variance was also partitioned into sampling, sample preparation, and analytical variance components. Each variance component was shown to be a function of aflatoxin concentration. By using regression techniques, the relationship between variance and aflatoxin concentration was developed for each variance component. The total, sampling, sample preparation, and analytical variances associated with testing a lot at 100 ppb with a 2.27 kg sample, 100 g subsample, and using LC analytical techniques are 25 378,23 533,1830, and 15, respectively. Sampling, sample preparation, and analysis account for 92.7, 7.2, and 0.1% of the total variability, respectively.

Sampling Uncertainties for the Detection of Chemical Agents in Complex Food Matrices†‡

2005 ◽  
Vol 68 (6) ◽  
pp. 1306-1313 ◽  
Author(s):  
THOMAS B. WHITAKER ◽  
ANDERS S. JOHANSSON
Keyword(s):  
Sample Preparation ◽  
Coefficient Of Variation ◽  
Test Procedure ◽  
Total Variance ◽  
Test Results ◽  
Total Uncertainty ◽  
Chemical Agents ◽  
Relative Standard ◽  
Low Levels ◽  
Repeated Test

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.

scholarly journals Testing Green Coffee for Ochratoxin A, Part I: Estimation of Variance Components

2004 ◽  
Vol 87 (4) ◽  
pp. 884-891 ◽  
Author(s):  
Eugenia A Vargas ◽  
Thomas B Whitaker ◽  
Eliene A Santos ◽  
Andrew B Slate ◽  
Francisco B Lima ◽  
...  
Keyword(s):  
Sample Preparation ◽  
Ochratoxin A ◽  
Total Variance ◽  
Green Coffee ◽  
Chromatography Analysis ◽  
Mathematical Expressions ◽  
Coffee Beans ◽  
Subsample Size ◽  
Estimation Of Variance ◽  
The Relationship

Abstract 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 μg/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.

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