The effect of sample preparation and test procedure on the evaluation of tropical red soils

2016 ◽  
pp. 249-253 ◽  
Author(s):  
G Brink ◽  
F Hörtkorn
Keyword(s):  
Sample Preparation ◽  
Test Procedure ◽  
Red Soils

scholarly journals Variability Associated with Testing Shelled Corn for Fumonisin

1998 ◽  
Vol 81 (6) ◽  
pp. 1162-1168 ◽  
Author(s):  
Thomas B Whitaker ◽  
Mary W Trucksess ◽  
Anders S Johansson ◽  
Francis G Geesbrecht ◽  
Winston M Hagler ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Sample Preparation ◽  
Coefficient Of Variation ◽  
Variance Component ◽  
Test Procedure ◽  
Functional Relationships ◽  
Similar Test ◽  
Coefficients Of Variation ◽  
Order Of Magnitude

Abstract Variances associated with sampling, sample preparation, and analytical steps of a test procedure that measures fumonisin in shelled corn were estimated. The variance associated with each step of the test procedure increases with fumonisin concentration. Functional relationships between variance and fumonisin concentration were estimated by regression analysis. For each variance component, functional relationships were independent of fumonisin type (total, B1, B2, and B3 fumonisins). At 2 ppm, coefficients of variation associated with sampling (1.1 kg sample), sample preparation (Romer mill and 25 g subsample), and analysis are 16.6,9.1, and 9.7%, respectively. The coefficient of variation associated with the total fumonisin test procedure was 45% and is about the same order of magnitude as that for measuring aflatoxin in shelled corn with a similar test procedure.

scholarly journals Sampling, Sample Preparation, and Analytical Variability Associated with Testing Wheat for Deoxynivalenol

2000 ◽  
Vol 83 (5) ◽  
pp. 1285-1292 ◽  
Author(s):  
Thomas B Whitaker ◽  
Winston M Hagler ◽  
Francis G Giesbrecht ◽  
Anders S Johansson
Keyword(s):  
Sample Preparation ◽  
Sample Size ◽  
Variance Component ◽  
Small Sample Size ◽  
Test Procedure ◽  
Small Sample ◽  
Test Procedures ◽  
Analytical Variability ◽  
Total Variability ◽  
Regression Techniques

Abstract The variability associated with testing wheat for deoxynivalenol (DON) was measured using a 0.454 kg sample, Romer mill, 25 g comminuted subsample, and the Romer Fluoroquant analytical method. The total variability was partitioned into sampling, sample preparation, and analytical variability components. Each variance component was a function of the DON concentration and equations were developed to predict each variance component using regression techniques. The effect of sample size, subsample size, and number of aliquots on reducing the variability of the DON test procedure was also determined. For the test procedure, the coefficient of variation (CV) associated with testing wheat at 5 ppm was 13.4%. The CVs associated with sampling, sample preparation, and analysis were 6.3, 10.0, and 6.3%, respectively. For the sample variation, a 0.454 kg sample was used; for the sample preparation variation, a Romer mill and a 25 g subsample were used; for the analytical variation, the Romer Fluoroquant method was used. The CVs associated with testing wheat are relatively small compared to the CV associated with testing other commodities for other mycotoxins, such as aflatoxin in peanuts. Even when the small sample size of 0.454 kg was used, the sampling variation was not the largest source of error as found in other mycotoxin test procedures.

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.

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.

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 A REVIEW ON NEXT-GENERATION SEQUENCING SAMPLE PREPARATION

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
A. K. Gupta
Keyword(s):  
Next Generation Sequencing ◽  
Sample Preparation ◽  
Genome Sequencing ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Test Procedure ◽  
Next Generation ◽  
Specialized Exhibition ◽  
Generation Sequencing

To stay aware of interest for sample arrangement, numerous high-throughput sequencing labs have computerized specimen arrangement strategies. The point when selecting a robotization stage, clients might as well recognize the specialized exhibition of the fluid handler and its demonstrated exhibition for NGS test procedure. NGS test arrangement stages may as well offer clients a critical decrease in involved time and the adaptability to run numerous methodologies. In light of the fact that the sample readiness protocol for NGS are quickly developing, a perfect robotization framework ought to be good with reagents for different requirements on advancing sequencing stages.Key words: NGS, genome sequencing, BenchCel Workstation, Benchbot Robot, Bravo platform, automation protocols

scholarly journals Testing Green Coffee for Ochratoxin A, Part III: Performance of Ochratoxin A Sampling Plan

2006 ◽  
Vol 89 (4) ◽  
pp. 1021-1026
Author(s):  
Eugenia A Vargas ◽  
Thomas B Whitaker ◽  
Eliene A Santos ◽  
Andrew B Slate ◽  
Francisco B Lima ◽  
...  
Keyword(s):  
Sample Preparation ◽  
Sample Size ◽  
Ochratoxin A ◽  
Test Procedure ◽  
Sampling Plan ◽  
Green Coffee ◽  
Sampling Step ◽  
Sampling Plans

Abstract Green coffee shipments are often inspected for ochratoxin A (OTA) and classified into good or bad categories depending on whether the OTA estimates are above or below a defined regulatory limit. Because of the uncertainty associated with the sampling, sample preparation, and analytical steps of an OTA test procedure, some shipments of green coffee will be misclassified. The misclassification of lots leads to some good lots being rejected (sellers' risk) and some bad lots being accepted (buyers' risk) by an OTA sampling plan. Reducing the uncertainty of an OTA test procedure and using an accept/reject limit less than the regulatory limit can reduce the magnitude of one or both risks. The uncertainty of the OTA test procedure is most effectively reduced by increasing sample size (or increasing the number of samples analyzed), because the sampling step is the largest source of uncertainty in the OTA test procedure. The effects of increasing sample size and changing the sample accept/reject limit relative to the regulatory limit on the performance of OTA sampling plans for green coffee were investigated. For a given accept/reject limit of 5 μg/kg, increasing sample size increased the percentage of lots accepted at concentrations below the regulatory limit and increased the percentage of lots rejected at concentrations above the regulatory limit. As a result, increasing sample size reduced both the number of good lots rejected (sellers' risk) and the number of bad lots accepted (buyers' risk). For a given sample size (1 kg), decreasing the sample accept/reject limit from 5 to 2 μg/kg relative to a fixed regulatory limit of 5 μg/kg decreased the percentage of lots accepted and increased the percentage of lots rejected at all OTA concentrations. As a result, decreasing the accept/reject limit below the regulatory limit increased the number of good lots rejected (sellers' risk), but decreased the number of bad lots accepted (buyers' risk).

scholarly journals Variation of Analytical Results for Peanuts in Energy Bars and Milk Chocolate

2004 ◽  
Vol 87 (4) ◽  
pp. 943-949 ◽  
Author(s):  
Mary W Trucksess ◽  
Thomas B Whitaker ◽  
Andrew B Slate ◽  
Kristina M Williams ◽  
Vickery A Brewer ◽  
...  
Keyword(s):  
Sample Preparation ◽  
Sample Size ◽  
Test Procedure ◽  
Enzyme Linked Immunosorbent Assay ◽  
Allergic Reactions ◽  
Test Results ◽  
Elisa Method ◽  
Milk Chocolate ◽  
Powdered Milk ◽  
Subsample Size

Abstract Peanuts contain proteins that can cause severe allergic reactions in some sensitized individuals. Studies were conducted to determine the percentage of recovery by an enzyme-linked immunosorbent assay (ELISA) method in the analysis for peanuts in energy bars and milk chocolate and to determine the sampling, subsampling, and analytical variances associated with testing energy bars and milk chocolate for peanuts. Food products containing chocolate were selected because their composition makes sample preparation for subsampling difficult. Peanut-contaminated energy bars, noncontaminated energy bars, incurred milk chocolate containing known levels of peanuts, and peanut-free milk chocolate were used. A commercially available ELISA kit was used for analysis. The sampling, sample preparation, and analytical variances associated with each step of the test procedure to measure peanut protein were determined for energy bars. The sample preparation and analytical variances were determined for milk chocolate. Variances were found to be functions of peanut concentration. Sampling and subsampling variability associated with energy bars accounted for 96.6% of the total testing variability. Subsampling variability associated with powdered milk chocolate accounted for >60% of the total testing variability. The variability among peanut test results can be reduced by increasing sample size, subsample size, and number of analyses. For energy bars the effect of increasing sample size from 1 to 4 bars, subsample size from 5 to 20 g, and number of aliquots quantified from 1 to 2 on reducing the sampling, sample preparation, and analytical variance was demonstrated. For powdered milk chocolate, the effects of increasing subsample size from 5 to 20 g and number of aliquots quantified from 1 to 2 on reducing sample preparation and analytical variances were demonstrated. This study serves as a template for application to other foods, and for extrapolation to different sizes of samples and subsamples as well as numbers of analyses.

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