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.     

Determination of aflatoxins B1, B2, G1, and G2 and ochratoxin A in ginseng and ginger by multitoxin immunoaffinity column cleanup and liquid chromatographic quantitation: collaborative study

The accuracy, repeatability, and reproducibility characteristics of a method using multitoxin immunoaffinity column cleanup with liquid chromatography (LC) for determination of aflatoxins (AF; sum of aflatoxins B1, B2, G1, and G2) and ochratoxin A (OTA) in powdered ginseng and ginger have been established in a collaborative study involving 13 laboratories from 7 countries. Blind duplicate samples of blank, spiked (AF and OTA added) at levels ranging from 0.25 to 16.0 microg/kg for AF and 0.25 to 8.0 microg/kg for OTA were analyzed. A naturally contaminated powdered ginger sample was also included. Test samples were extracted with methanol and 0.5% aqueous sodium hydrogen carbonate solution (700 + 300, v/v). The extract was centrifuged, diluted with phosphate buffer (PB), filtered, and applied to an immunoaffinity column containing antibodies specific for AF and OTA. After washing the column with water, the toxins were eluted from the column with methanol, and quantified by high-performance LC with fluorescence detection. Average recoveries of AF from ginseng and ginger ranged from 70 to 87% (at spiking levels ranging from 2 to 16 microg/kg), and of OTA, from 86 to 113% (at spiking levels ranging from 1 to 8 microg/kg). Relative standard deviations for within-laboratory repeatability (RSDr) ranged from 2.6 to 8.3% for AF, and from 2.5 to 10.7% for OTA. Relative standard deviations for between-laboratory reproducibility (RSDR) ranged from 5.7 to 28.6% for AF, and from 5.5 to 10.7% for OTA. HorRat values were < or = 2 for the multi-analytes in the 2 matrixes.     

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.     

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.     

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 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.     

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).     

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.     

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.     

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.     

A survey of ochratoxin A and aflatoxins in domestic and imported beers in Japan by immunoaffinity and liquid chromatography.

Analyses of ochratoxin A (OTA) and aflatoxins (AFs) in 94 imported beer samples from 31 producing countries and in 22 Japanese beer samples were performed by immunoaffinity column and reversed-phase liquid chromatography (LC) with fluorescence detection. Recoveries of OTA from beer samples spiked at 25 and 250 pg/mL were 86.1 and 88.2%, respectively. Recoveries of AFs were 98.4 and 98.9%, 95.4 and 95.5%, 101.2 and 97.8%, and 98.9 and 96.0%, respectively, from beer samples spiked at 4.1 and 41 pg AF B1, 4.45 and 44.5 pg AF B2, 4.7 and 47 pg AF G1, and 4.65 and 46.5 pg AF G2/mL. Detection limits were 1.0 pg/mL for OTA, 0.5 pg/mL for AFs B1 and B2, and 1.0 pg/mL for AFs G1 and G2. OTA was detected in 86 (91.5%) of 94 imported beer samples at a mean level of 10.1 pg/mL and in 21 (95.5%) of 22 Japanese beer samples at a mean level of 12.5 pg/mL. AF B1 was detected in 11 of 94 imported beer samples at a level of 0.5-83.1 pg/mL and in 2 of 22 Japanese beer samples at 0.5 and 0.8 pg/mL. Except for one beer sample from Peru, the samples contaminated with AFs were also contaminated with OTA. Although OTA was detected in most samples from various countries, AFs were detected in the beer samples from only a limited number of countries where AF contamination might be expected to occur because of their warm climate.     

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。     

Comparison of slurry mixing and dry milling in laboratory sample preparation for determination of ochratoxin A and deoxynivalenol in wheat

The significance of laboratory sample preparation for the determination of two important mycotoxins, ochratoxin A (OTA) and deoxynivalenol (DON), in wheat was investigated by comparing water-slurry mixing and dry-milling procedures. The distribution of OTA and DON in 10 kg samples of naturally contaminated wheat was established by analyzing one hundred 100 g subsamples of each sample. A normal distribution and a good repeatability of DON measurements was observed for both water-slurry mixing (mean 2290 microg/kg, CV 4.6%, median 2290 microg/kg) and dry milling (mean 2310 microg/kg, CV 6.4%, median 2290 microg/kg) procedures. For OTA determinations, reliable results could be obtained only by slurry mixing sample preparation (mean 2.62 microg/kg, CV 4.0%, median 2.62 microg/kg), whereas dry-milling comminution resulted in an inhomogeneous distribution with a high variability (mean 0.83 microg/kg, CV 75.2%, median 0.60 microg/kg) and a positive skewness (2.12). Ad hoc experiments were performed on different size portions of the same sample (10 kg) to assess accuracy and precision of the comminution/homogenization procedures (slurry mixing and dry milling). Very good results were obtained for DON determination with both procedures in terms of accuracy (>98.7% of the "weighted value") and precision (CV <3%). For OTA determination good results were only obtained by slurry mixing (99.4% of the "weighted value," CV 10%), whereas dry milling provided results with low accuracy (43.2% of the "weighted value") and high variability (CV 110%). This study clearly demonstrated that sample preparation by slurry mixing is strictly necessary to obtain reliable laboratory samples for OTA determination in wheat to minimize misclassification of acceptable/rejectable lots, mainly within official control.     

Contribution of sampling to the variability of pesticide residue data

The uneven distribution of pesticide residues among the treated objects leads to an inevitable variability of pesticide residue levels measured in the samples, which may significantly contribute to the combined uncertainty of the analytical results. A total of 8844 unit-crop residue data derived from 57 lots and 19 field trials were evaluated to determine the characteristic features of residue distribution in unit crops and composite samples. The average residue levels and the corresponding coefficient of variation (CV) values obtained for individual units taken from a given lot showed wide variation from lot to lot. There was no significant difference between the CVs of residue levels in sample sets of various unit crops or composite sample populations of different sizes taken from various crops. The CV values for levels of residues taken from individual lots followed normal distribution. Very good correlation was found between the CVs of the parent and sample populations. The experimentally obtained values were very close to those expected on the basis of the central limit theorem. The estimated typical relative standard uncertainties of sampling medium-size crops for pesticide residue analysis in the cases of sample sizes of 5, 10, and 25 were 37, 25, and 16%, respectively.     

Evaluating the performance of sampling plans to detect fumonisin B1 in maize lots marketed in Nigeria

Fumonisins are toxic and carcinogenic compounds produced by fungi that can be readily found in maize. The establishment of maximum limits for fumonisins requires the development of scientifically based sampling plans to detect fumonisin in maize. As part of an International Atomic Energy Agency effort to assist developing countries to control mycotoxin contamination, a study was conducted to design sampling plans to detect fumonisin in maize produced and marketed in Nigeria. Eighty-six maize lots were sampled according to an experimental protocol in which an average of 17 test samples, 100 g each, were taken from each lot and analyzed for fumonisin B1 by using liquid chromatography. The total variability associated with the fumonisin test procedure was measured for each lot. Regression equations were developed to predict the total variance as a function of fumonisin concentration. The observed fumonisin distribution among the replicated-sample test results was compared with several theoretical distributions, and the negative binomial distribution was selected to model the fumonisin distribution among test results. A computer model was developed by using the variance and distribution information to predict the performance of sampling plan designs to detect fumonisin in maize 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 maize lots.     

Application of static and dynamic liquid-phase microextraction in the determination of polycyclic aromatic hydrocarbons

Two modes of liquid-phase microextraction (LPME), static and semi-automated dynamic, have been developed for the HPLC analysis of polycyclic aromatic hydrocarbons. In static LPME, a small drop (3 microl) of organic solvent was held at the tip of a microsyringe needle and exposed to the sample containing the analytes, permitting extraction to occur. In semi-automated dynamic LPME, a syringe pump was used to automate the repetitive procedure of filling a microsyringe barrel that functioned as a microseparatory funnel, with fresh aliquots of sample, and expelling them after extraction. The factors influential to both techniques such as the type of organic solvent, extraction time, sampling volume, number of samplings, salt concentration and temperature were investigated. Static LPME provided high enrichment (60- to 180-fold) and simplicity. The analytical data exhibited a relative standard deviation range of 4.7-9.0%. Dynamic LPME provided higher (>280-fold) enrichment within nearly the same extraction time (approximately 20 min) and better precision (< or = 6.0%). Both methods allow the detection of polycyclic aromatic hydrocarbons at microg/l levels in water by HPLC. Water samples collected from two rivers were analyzed using the methods, respectively. The results demonstrated that both modes of LPME were fast, simple and accurate.     

Aptamer‐affinity column clean‐up coupled with ultra high performance liquid chromatography and fluorescence detection for the rapid determination of ochratoxin A in ginger powder

Aptamers are single‐stranded oligonucleotides with high affinity and specificity and are widely used in targets separation and enrichment. Here, an aptamer‐affinity column (AAC) was firstly prepared in‐house through a covalent immobilization strategy. Then, ochratoxin A (OTA) in ginger powder was absorbed and enriched using the new aptamer‐based clean‐up technology for the first time, and was further analyzed by ultra high performance liquid chromatography with fluorescence detection. After optimization, the average recoveries for blank samples spiked with OTA at 5, 15, and 45 μg/kg ranged from 85.36 to 96.83%. Furthermore, the AAC exhibited a similar accuracy as an immunoaffinity column to clean up OTA in ginger powder. Above all, it exhibited better reusability, twice that of the immunoaffinity column, had lower toxicity and cost, and took less time. Of 25 contaminated ginger powder samples, OTA contamination levels ranged from 1.51 to 4.31 μg/kg, which were lower than the European Union (EU) regulatory limits. All the positive samples were further confirmed by ultra‐fast LC with MS/MS. In conclusion, the method of clean‐up based on the AAC coupled to ultra‐HPLC with fluorescence detection was rapid, specific, and sensitive for the quantitative analysis of OTA in a complex matrix.     

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).     

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.