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.

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 Maize meal slurry mixing: an economical recipe for precise aflatoxin quantitation

2019 ◽  
Vol 12 (3) ◽  
pp. 203-212 ◽  
Author(s):  
J. Kumphanda ◽  
L. Matumba ◽  
T.B. Whitaker ◽  
W. Kasapila ◽  
J. Sandahl
Keyword(s):  
Sample Preparation ◽  
Analytical Methods ◽  
Laboratory Sample ◽  
Test Portion ◽  
Sampling Plans ◽  
Dry Grind ◽  
Water Matrix ◽  
Analytical Variance ◽  
Aflatoxin Concentration ◽  
Maize Meal

The laboratory sample preparation for mycotoxin determination in cereals, often overlooked among sampling plans and analytical methods, was further studied. The precision of aflatoxin analysis in comminuted maize samples using 25 g slurry (prepared from 250 g test portion of comminuted maize, water/matrix (1+1, v/w)) and 12.5 g dry grind test portion were compared against the conventional 50 g dry grind test portion through replicated (10) Aflatest® immunoaffinity fluorometric tests of naturally contaminated samples with aflatoxin concentration ranging from 4.9 to 81.7 μg/kg. The overall mean aflatoxin concentration obtained from the 10 different samples tested using 12.5 g and 50.0 g dry grind procedures was 12% significantly (P<0.05) lower (poorer) compared to 25 g slurry. The sample preparation plus analytical variance associated with testing 25.0 g slurry, 50.0 g dry grind and 12.5 g dry grind test portions were in the ratio of 1:5:15, respectively.

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 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.

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.

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%.

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.

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.

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