Biomedical subjects

Thomas B Whitaker

Publications and source records attributed to Thomas B Whitaker.

13 recordsLinked to original sources

PubMed2005

Sampling uncertainties for the detection of chemical agents in complex food matrices.

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.

Aflatoxins↗

PubMed2003

Detecting mycotoxins in agricultural commodities.

It is difficult to obtain precise and accurate estimates of the true mycotoxin concentration of a bulk lot when using a mycotoxin-sampling plan that measures the concentration in a small portion of the bulk lot. A mycotoxin-sampling plan is defined by a mycotoxin test procedure and a defined accept/reject limit. A mycotoxin test procedure is a complicated process and generally consists of several steps: (a) a sample is taken from the lot, (b) the sample is ground (comminuted) in a mill to reduce particle size, (c) a subsample is removed from the comminuted sample, and (d) the mycotoxin is extracted from the comminuted subsample and quantified. Even when using accepted test procedures, there is variability associated with each step of the mycotoxin test procedure. Because of this variability, the true mycotoxin concentration in the lot cannot be determined with 100% certainty by measuring the mycotoxin concentration in a sample taken from the lot. The variability for each step of the mycotoxin test procedure, as measured by the variance statistic, is shown to increase with mycotoxin concentration. Sampling is usually the largest source of variability associated with the mycotoxin test procedure. Sampling variability is large because a small percentage of kernels are contaminated and the level of contamination on a single seed can be very large. Methods to reduce sampling, sample preparation, and analytical variability are discussed.

Aflatoxins↗

PubMed2002

Sampling wheat for deoxynivalenol.

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

Algorithms↗

PubMedDate not supplied

Estimating deoxynivalenol in shelled corn barge lots by measuring deoxynivalenol in corn screenings.

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%.

Algorithms↗

PubMedDate not supplied

Testing green coffee for ochratoxin A, part I: estimation of variance components.

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 microg/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.

Algorithms↗

PubMedDate not supplied

Variation of analytical results for peanuts in energy bars and milk chocolate.

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.

Allergens↗

PubMedDate not supplied

Evaluation of sampling plans to detect Cry9C protein in corn flour and meal.

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).

Algorithms↗

PubMedDate not supplied

Immunochemical analytical methods for the determination of peanut proteins in foods.

Peanut proteins can cause allergenic reactions that can result in respiratory and circulatory effects in the body sometimes leading to shock and death. The determination of peanut proteins in foods by analytical methods can reduce the risk of serious reactions in the highly sensitized individual by allowing for the detection of these proteins in a food at various stages of the manufacturing process. The method performance of 4 commercially available enzyme-linked immunosorbent assay (ELISA) kits was evaluated for the detection of peanut proteins in milk chocolate, ice cream, cookies, and breakfast cereals: ELISA-TEK Peanut Protein Assay, now known as "Bio-Kit" for peanut proteins, from ELISA Technologies Inc.; Veratox for Peanut Allergens from Neogen Corp.; RIDASCREEN Peanut Kit from R-Biopharm GmbH; and ProLisa from Canadian Food Technology Ltd. The 4 test kits were evaluated for accuracy (recovery) and precision using known concentrations of peanut or peanut proteins in the 4 food matrixes. Two different techniques, incurred and spiked, were used to prepare samples with 4 known concentrations of peanut protein. Defatted peanut flour was added in the incurred samples, and water-soluble peanut proteins were added in the spiked samples. The incurred levels were 0.0, 10, 20, and 100 microg whole peanut per g food; the spiked levels were 0.0, 5, 10, and 20 microg peanut protein per g food. Performance varied by test kit, protein concentration, and food matrix. The Veratox kit had the best accuracy or lowest percent difference between measured and incurred levels of 15.7% when averaged across all incurred levels and food matrixes. Recoveries associated with the Veratox kit varied from 93 to 115% for all food matrixes except cookies. Recoveries for all kits were about 50% for cookies. The analytical precision, as measured by the variance, increased with an increase in protein concentration. However, the coefficient of variation (CV) was stable across the 4 incurred protein levels and was 7.0% when averaged across the 4 food matrixes and analytical kits. The R-Biopharm test kit had the best precision or a CV of 4.2% when averaged across all incurred levels and food matrixes. Because measured protein values varied by test kit and food matrix, a method was developed to normalize or transform measured protein concentrations to an adjusted protein value that was equal to the known protein concentration. The normalization method adjusts measured protein values to equal the true protein value regardless of the type test kit or type food matrix.

Allergens↗

PubMedDate not supplied

Testing green coffee for ochratoxin A, part II: Observed distribution of ochratoxin A test results.

The suitability of 4 theoretical distributions (normal, lognormal, negative binomial, and gamma) to predict the observed distribution of ochratoxin A (OTA) in green coffee was investigated. One symmetrical and 3 positively skewed theoretical distributions were each fitted to 25 empirical distributions of OTA test results for green coffee beans. Parameters of each theoretical distribution were calculated by using Methods of Moments. The 3 skewed theoretical distributions provided acceptable fits to each of the 25 observed distributions. Because of its simplicity, the lognormal distribution was selected to model OTA test results for green coffee. Using variance equations determined in previous studies, mathematical expressions were developed to calculate the parameters of the log normal distribution for a given OTA lot concentration and test procedure. Observed acceptance probabilities were compared to an operating characteristic curve predicted from the lognormal distribution, and all 25 observed acceptance probabilities were found to lie within the 95% confidence band associated with the predicted operating characteristic curve. The parameters of compound gamma distribution were used to calculate the fraction of OTA contamination beans within a contaminated lot. The percent-contaminated beans were a function of the lot concentration and increased with lot concentration. At a lot concentration of 5 microg/kg, approximately 6 beans per 10,000 beans are contaminated.

Buffers↗

PubMedDate not supplied

Predicting aflatoxin and fumonisin in shelled corn lots using poor-quality grade components.

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.

Aflatoxins↗

PubMedDate not supplied

Sampling hazelnuts for aflatoxin: uncertainty associated with sampling, sample preparation, and analysis.

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 high-performance 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.

Aflatoxins↗

PubMedDate not supplied

Testing green coffee for ochratoxin A, part III: performance of ochratoxin A sampling plan.

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 microg/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 microg/kg relative to a fixed regulatory limit of 5 microg/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).

Carcinogens↗

PubMedDate not supplied

Sampling almonds for aflatoxin, part I: estimation of uncertainty associated with sampling, sample preparation, and analysis.

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.

Aflatoxin B1↗