Testing shelled corn for aflatoxin, Part I: estimation of variance components

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 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.     

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.     

Sampling, sample preparation, and analytical variability associated with testing wheat for deoxynivalenol

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.     

Determining the variability associated with testing shelled corn for aflatoxin using different analytical procedures in Louisiana in 1998

The number of elevator facilities with laboratories to test shelled corn for aflatoxin on site is increasing. The inherent difficulty in accurately determining the true aflatoxin concentration of a lot of corn may have serious implications. Deviations from the true value are of even greater significance at busy locations where a high throughput is desired. This study was instituted to measure (1) the differences in aflatoxin test results between elevator laboratories and the Louisiana Agricultural Chemistry (LAC) laboratory and (2) the variability in aflatoxin test results associated with sampling, sample preparation, and analysis of shelled corn at such locations. One hundred lots of shelled corn from 10 elevators in Louisiana were analyzed for aflatoxin using the Aflatest method (at elevators and at the LAC laboratory) and high-performance column liquid chromatography (HPLC; LAC laboratory only). Mean aflatoxin levels determined at elevator laboratories were significantly (P < 0.05) lower from those obtained in the LAC laboratory using the Aflatest method. Overall, Aflatest method results were lower than those obtained by HPLC. This difference may be attributed to analyst technical dexterity, difficulty in providing careful attention to detail in a high throughput environment, and/or substandard facilities found at elevators. The total variance was partitioned into the combined sampling plus subsampling variance and analytical variance. The sampling and sample preparation steps accounted for about 91.5% of the total variability. When using the HPLC analytical method, the analytical step contributed only 8.5% to the total variance.     

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.     

Sampling almonds for aflatoxin, part II: estimating risks associated with various sampling plan designs

About 100 nations have established regulatory limits for aflatoxin in food and feeds. Because these limits vary widely from one country to another, the Codex Alimentarius Commission, working through the Codex Committee on Food Additives and Contaminants, has initiated work to harmonize aflatoxin limits and sampling plans for almonds, pistachios, hazelnuts, and Brazil nuts. Studies were developed to measure the uncertainty and distribution among test results for replicate samples taken from aflatoxin-contaminated almond shipments. The uncertainty and distribution information was used to develop a model to evaluate the performance of aflatoxin sampling plans so that harmonized sampling plans can be developed for almonds that reduce the misclassifying of lots in the export trade. Twenty lots of shelled almonds were sampled according to an experimental protocol in which sixteen 10 kg samples were taken from each lot. The observed aflatoxin distribution among the 16 sample test results was compared with 3 theoretical distributions. The negative binomial distribution was selected to model aflatoxin distribution among sample test results because it gave acceptable fits across all 20 observed sample distributions. By using the variance and distribution information, operating characteristics curves were developed to predict the effect of sample size and accept/reject limits on the probability of rejecting good lots and accepting bad lots.     

Distribution of deoxynivalenol in wheat,wheat flour,bran, and gluten,and variability associated with the test procedure

Analytical data obtained on deoxynivalenol (DON) concentration in naturally contaminated wheat during processing in an industrial mill were statistically analyzed, and the distribution functions of DON concentration in lots of wheat, bran, wheat flour, and gluten were estimated. The analytical method had acceptable precision (HORRAT 0.25-0.32) for each test sample. The total variance combined sampling, sample preparation, and analytical variances were 0.188, 0.033, 0.42, and 0.0014 ppm2 for wheat, 1.93; flour, 0.99; bran, 4.68; and gluten, 0.29, respectively. The distribution function of DON contamination presented an asymmetric tail for high values of concentration in wheat grains and wheat flour; in bran it seemed to be bimodal with 2 separated peaks of different concentrations; in gluten the normal distribution function gave a reasonably good fit to empirical data. The function eta(c) = -In(-Inp), where p (c) is the cumulative distribution function was linear with c in the so-called extreme-value type I distribution and could be fitted by a cubic polynomial in c in the distributions determined for all the products. This variability and distributional information contributes to the design of better sampling plans in order to reduce the total variability and to estimate errors in the evaluation of DON concentration in lots of wheat and wheat products.     

Performance of sampling plans to determine aflatoxin in farmers' stock peanut lots by measuring aflatoxin in high-risk-grade components

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 U.S. Department of Agriculture grade components: sound mature kernels plus sound splits (SMKSS), other kernels (OK), loose shelled kernels (LSK), and damaged kernels (DAM). The kernel mass (g), aflatoxin mass (ng), and aflatoxin concentration (ng of aflatoxin/g of peanuts) were measured for each of the 2400 component samples. The variabilities associated with measuring aflatoxin mass (ng) in OK + LSK + DAM, or A(OLD)ng, and in LSK + DAM, or A(LD)ng, and aflatoxin concentration (ng/g) in OK + LSK + DAM, or A(OLD)ng/g, and in LSK + DAM, or A(LD)ng/g, were determined. The variance associated with measuring aflatoxin in each of the 4 combinations of components increased with aflatoxin, and functional relationships were developed from regression analysis. The variability associated with estimating the lot concentration from each of the 4 combinations of components was also determined. The coefficients of variation (CV) associated with estimating the aflatoxin for a lot with aflatoxin at 100 ng/g were 90, 86, 94 and 96% for aflatoxin masses A(OLD)ng and A(LD)ng and aflatoxin concentrations A(OLD)ng/g and A(LD)ng/g, respectively. The performance of aflatoxin sampling plans using the combination of aflatoxin masses in OK + LD + DAM and LD + DAM components was evaluated with a 2 kg test sample and a 50 ng/g accept/reject limit.     

增量取样量接近分析试样量时的取样——理论修正与结果

化学分析取样几乎总是一个多步骤过程,所有的步骤都会导致分析结果的总体不确定性。样品采取之后,不论后续采样过程如何精细,前期采样阶段的误差都无法在后续采样过程中更正。第一次取样是最重要的,通常其方差远远超过实验室测量的方差。但这不意味着在最终实验室分析试样制备阶段可以忽略采样理论的原理。现代分析仪器旨在处理小样本(从毫克到几克)。在这种情况下,如果样品是包含少量分析物的混合颗粒,则物料的不均匀性可能会很大以至于破坏整个分析过程。不均匀性计算和样品制备过程中基本采样误差方差的估计对于开发适用的分析程序至关重要。在样本制备的最后步骤中,新的增量本是父增量本的重要组成部分,在估算样本方差时必须考虑到这种影响。TOS提供了用于处理这些情况的工具。通过两个案例阐明了不均匀性计算的应用。在第一个例子中,评估了鸡饲料中低含量添加剂的成分不均匀性,在第二个例子中,对样品制备进行了优化,以校正用于分析硅灰石精矿中矿物杂质含量的红外仪器。在处理颗粒混合物和评估混样效率时,不均匀性评估也很重要。     

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).     

Sampling and analytical variability associated with the determination of aflatoxins and ochratoxin A in bulk lots of powdered ginger marketed in 1-lb bags

Ginger has been used as a food, dietary supplement, and condiment for centuries. Mycotoxins such as the aflatoxins (AF) and ochratoxin A (OTA) have been reported in ginger roots in several studies. It is important to design effective sampling methods that will accurately and precisely predict the true mycotoxin level in a bulk lot. The objective of this study was to measure the sampling and analytical variability associated with the test procedure used to measure AF and OTA in a bulk lot of powdered ginger using a 5-g laboratory sample and HPLC analytical methods. Twelve 5-g laboratory samples were taken from each of two lots. Duplicate aliquots were removed from each 5-g laboratory sample/solvent blend, and each aliquot was simultaneously analyzed for AF and OTA by HPLC analytical methods. Using a balanced nested design, the total variance associated with the above AF and OTA test procedures was partitioned into sampling and analytical variance components for each lot. Averaged across both lots, the sampling and analytical variances accounted for 87% and 13% of the total variance, respectively, for AF and 97% and 3%, respectively, for OTA. The sampling and analytical coefficients of variation were 9.5% and 3.6%, respectively, for AF, and 16.6% and 2.9%, respectively, for OTA when using a single 5-g laboratory sample and HPLC analytical methods. Equations are derived to show the effect of increasing laboratory sample size and/or number of aliquots on reducing the variability of the test procedures used to estimate OTA and AF in powdered ginger.     

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.     

Distribution among sample test results when testing shelled corn lots for fumonisin.

The statistical distribution known as the compound gamma function was studied for suitability in describing the distribution of sample test results associated with testing lots of shelled corn for fumonisin. Thirty-two 1.1 kg test samples were taken from each of 16 contaminated lots of shelled corn. An observed distribution consisted of 32 sample fumonisin test results for each lot. The mean fumonisin concentration, c, and the variance, s2, among the 32 sample fumonisin test results along with the parameters for the compound gamma function were determined for each of the 16 observed distributions. The 16 observed distributions of sample fumonisin test results were compared with the compound gamma function using the Power Divergence test. The null hypothesis that the observed distribution could have resulted from sampling a family of compound gamma distributions was not rejected at the 5% significance level for 15 of the 16 lots studied. Parameters of the compound gamma distribution were calculated from the 32-fumonisin sample test results using the method of moments. Using regression analysis, equations were developed that related the parameters of the compound gamma distribution to fumonisin concentration and the variance associated with a fumonisin test procedure. An operating characteristic curve was developed for a fumonisin sampling plan to demonstrate the use of the compound gamma function.     

Sampling hazelnuts for aflatoxin: effect of sample size and accept/reject limit on reducing the risk of misclassifying lots

About 100 countries have established regulatory limits for aflatoxin in food and feeds. Because these limits vary widely among regulating countries, the Codex Committee on Food Additives and Contaminants began work in 2004 to harmonize aflatoxin limits and sampling plans for aflatoxin in almonds, pistachios, hazelnuts, and Brazil nuts. Studies were developed to measure the uncertainty and distribution among replicated sample aflatoxin test results taken from aflatoxin-contaminated treenut lots. The uncertainty and distribution information is used to develop a model that can evaluate the performance (risk of misclassifying lots) of aflatoxin sampling plan designs for treenuts. Once the performance of aflatoxin sampling plans can be predicted, they can be designed to reduce the risks of misclassifying lots traded in either the domestic or export markets. A method was developed to evaluate the performance of sampling plans designed to detect aflatoxin in hazelnuts lots. Twenty hazelnut lots with varying levels of contamination were sampled according to an experimental protocol where 16 test samples were taken from each lot. The observed aflatoxin distribution among the 16 aflatoxin sample test results was compared to lognormal, compound gamma, and negative binomial distributions. The negative binomial distribution was selected to model aflatoxin distribution among sample test results because it gave acceptable fits to observed distributions among sample test results taken from a wide range of lot concentrations. Using the negative binomial distribution, computer models were developed to calculate operating characteristic curves for specific aflatoxin sampling plan designs. The effect of sample size and accept/reject limits on the chances of rejecting good lots (sellers' risk) and accepting bad lots (buyers' risk) was demonstrated for various sampling plan designs.     

Testing shelled corn for aflatoxin, Part III: evaluating the performance of aflatoxin sampling plans

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.     

Testing shelled corn for aflatoxin, Part II: modeling the observed distribution of aflatoxin test results

The suitability of several theoretical distributions to predict the observed distribution of aflatoxin test results in shelled corn was investigated. Fifteen positively skewed theoretical distributions were each fitted to 18 empirical distributions of aflatoxin test results for shelled corn. The compound gamma distribution was selected to model aflatoxin test results for shelled corn. The method of moments technique was chosen to estimate the parameters of the compound gamma distribution. Mathematical expressions were developed to calculate the parameters of the compound gamma distribution for any lot aflatoxin concentration and test procedure. Observed acceptance probabilities were compared to operating characteristic curves predicted from the compound gamma distribution, and all 18 observed acceptance probabilities were found to lie within a 95% confidence band. The parameters of compound gamma were used to calculate the fraction of aflatoxin-contaminated kernels in contaminated lots. At 20 ppb, it was estimated that about 6 in 10,000 kernels are contaminated.     

Evaluating the performance of sampling plans to detect hypoglycin A in ackee fruit shipments imported into the United States

Hypoglycin A (HGA) is a toxic amino acid that is naturally produced in unripe ackee fruit. In 1973, the U.S. Food and Drug Administration (FDA) placed a worldwide import alert on ackee fruit, which banned the product from entering the United States. The FDA has considered establishing a regulatory limit for HGA and lifting the ban, which will require development of a monitoring program. The establishment of a regulatory limit for HGA requires the development of a scientifically based sampling plan to detect HGA in ackee fruit imported into the United States. Thirty-three lots of ackee fruit were sampled according to an experimental protocol in which 10 samples, i.e., ten 19 oz cans, were randomly taken from each lot and analyzed for HGA by using liquid chromatography. The total variance was partitioned into sampling and analytical variance components, which were found to be a function of the HGA concentration. Regression equations were developed to predict the total, sampling, and analytical variances as a function of HGA concentration. The observed HGA distribution among the test results for the 10 HGA samples was compared with the normal and lognormal distributions. A computer model based on the lognormal distribution was developed to predict the performance of sampling plan designs to detect HGA in ackee fruit shipments. The performance of several sampling plan designs was evaluated to demonstrate how to manipulate sample size and accept/reject limits to reduce misclassification of ackee fruit lots.     

Evaluation of Sampling and Analytical Errors in the Determination of Manganese in Soils

Sampling variances and analytical variances were estimated in a study of five chemical forms of Mn in nine soil types from a greenhouse experiment. A sampling and analytical quality control scheme and a robust analysis of variance were used for this purpose and proved appropriate. The resulting statistics were then subjected to an assessment of analytical precision and sampling precision according to certain criteria. The results showed that the sampling variances and analytical variances in this experiment were good enough to describe the natural geochemical variances.