Determination of Cry9C protein in corn-based foods by enzyme-linked immunosorbent assay: interlaboratory study.

The performance of a commercially available enzyme-linked immunosorbent assay kit (Enviro-Logix) was assessed for the determination of Cry9C protein, which is produced by the genetically modified corn StarLink, in 8 types of corn-based foods (starch, refined oil, soft tortillas, tortilla chips, corn flakes, corn puffs, corn muffins, and corn bread) in an interlaboratory study involving 7 laboratories in the United States. The assay kit is a double antibody sandwich and is based on the specific interaction between antibody and antigen. The Cry9C protein analyte is sandwiched between 2 antibodies, one to capture the analyte and the other is conjugated to the enzyme, horseradish peroxidase. The enzyme uses tetramethylbenzidine/peroxide for color development. A strong acid stopping reagent is then used to change the color from blue to a stable yellow. The intensity of the color is proportional to the concentration of the Cry9C protein. In this study blind duplicates of control samples (blank material prepared from non- StarLink corn), spiked samples (blank material with the addition of Cry9C protein), and samples containing incurred analyte (products prepared with StarLink corn) were analyzed. Cry9C protein from 2 different sources was used to spike the food products. Cry9C protein produced and purified from a bacterial host was used to prepare spiked test samples at 2.72 and 6.8 ng/g. Cry9C protein from StarLink corn flour was used to prepare spiked samples at 1.97 ng/g. Average recoveries for samples spiked with corn flour Cry9C protein at 1.97 ng/g ranged from 73 to 122%, within-laboratory relative standard deviations (RSDr) ranged from 6 to 22%, and between-laboratories relative standard deviations (RSDR) ranged from 16 to 56%. Average recoveries for samples spiked with bacterial Cry9C protein at 2.72 and 6.8 ng/g ranged from 27 to 96% and from 32 to 113%, respectively; RSDr values ranged from 10 to 35% and from 7 to 38%, respectively; and the RSDR ranged from 28 to 84% and 15 to 75%, respectively. The incurred test samples were found to contain Cry9C protein at levels ranging from 0.8 to 3187 ng/g depending on the product, RSDr values ranged from 5 to 16% and RSDR values ranged from 11 to 71%. Results of the statistical analysis indicate that this method is applicable to the determination of Cry9C protein in the 8 types of collaboratively studied corn-based products containing Cry9C protein (from StarLink) at levels of > or =2 ng/g.     

Determination of Cry9C protein in processed foods made with StarLink corn

StarLink (Aventis CropScience US) hybrid corn has been genetically modified to contain a pesticidal protein, Cry9C, which makes it more resistant than traditional varieties to certain types of corn insect pests. Unlike other varieties of genetically engineered corn, the U.S. Environmental Protection Agency authorized the use of StarLink corn for animal feed and industrial use only, not for human consumption. However, some Cry9C-containing corn was mistakenly or inadvertently comingled with yellow corn intended for human food use. Because corn containing the Cry9C construct was not approved for human use, the U.S. Food and Drug Administration considers food containing it to be adulterated. Consequently, this regulatory violation resulted in hundreds of recalls of corn-based products, such as taco shells, containing cry9C DNA. Detecting the novel protein in StarLink corn is an emerging issue; therefore, there is no standardized or established analytical method for detecting Cry9C protein in processed foods. We developed a procedure for quantitation of Cry9C protein, with validation data, in processed food matrixes with a limit of quantitation at 1.7 ng/g (ppb), using a commercial polyclonal antibody-based Cry9C kit that was intended for corn grain samples. Intra- and interassay coefficients of variation were 2.8 and 11.8%, respectively. Mean recoveries were 73 and 85% at 2 and 5 ng/g Cry9C fortifications, respectively, for 19 control non-StarLink corn-based matrixes. Our data demonstrate only 0-0.5% of Cry9C protein survived the processing of tortilla chips and soft tortillas made from 100% StarLink corn, resulting in levels from below the detection limit to 45 ppb.     

Suitability of real-time quantitative polymerase chain reaction and enzyme-linked immunosorbent assay for cry9C detection in Mexican corn tortillas: fate of DNA and protein after alkaline cooking

Alkaline-cooked corn, called nixtamal, is the basis for many traditional corn products such as tortillas, chips, and taco shells that are used widely in Mexico and Central America and in the preparation of snack foods that are consumed globally. To assess the effects of alkaline and thermal treatments on the detectability of DNA and protein for the presence of genetically modified sequences, various nixtamalized products were prepared from blends of conventional white corn containing 0.1, 1.0, and 10% transgenic corn (event CBH 351, StarLink). Real-time quantitative polymerase chain reactions (RTQ-PCR) and immunoassays were used to determine the cry9C gene and protein, respectively, in unprocessed corn kernels, freshly prepared alkaline-cooked and ground corn (masa), masa flour, tortillas prepared from masa by heat treatment, chips prepared from damp masa dough by deep frying, and from tortillas processed at high (200 degrees C) and low temperatures (70 degrees C). In spite of progressive degradation of genomic DNA during processing, RTQ-PCR genetic analysis allowed detection and quantification of the cry9C gene in all products prepared from 10, 1, and 0.1% StarLink corn, except deep-fried chips containing 0.1% StarLink. Enzyme-linked immunosorbent assays readily detected <1 ppm cry9C protein in all blends of unprocessed corn (10, 1, and 0.1% StarLink) as well as in nonfried tortilla and masa products. This technique was not suitable for thermally treated nixtamalized products containing <1% transgenic corn.     

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.     

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.     

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.     

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 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.     

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.     

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.     

Protein immunoassay methods for detection of biotech crops: applications,limitations, and practical considerations

Immunoassay methods are available for detection and quantitation of proteins expressed by most biotechnology-derived crops in commercial production. The 2 most common test formats are enzyme-linked immunosorbent assay (ELISA) and immunochromatographic (lateral flow) strip tests. Two ELISA methods, one for Roundup Ready soybeans and one for MON810 CrylAb corn, were the subject of large international collaborative studies and were demonstrated to quantitatively determine the concentrations of biotech crops in samples of ground grain. Quantitative ELISA methods are also useful for analysis of processed fractions of agricultural commodities such as soybean toasted meal or corn flour. Both strip tests and ELISAs for biotech crops are currently being used on a large scale in the United States to manage the sale and distribution of grain. In these applications, tests are used to determine if the concentration of biotech grain is above or below specified threshold limits. Using existing U.S. Department of Agriculture sampling techniques, the reliability of the threshold determination is expressed in terms of statistical confidence rather than analytical precision. Combining the use of protein immunoassays with Identity Preservation systems provides an effective means of characterizing the raw and processed agricultural inputs to the food production system in a way that allows food producers to comply with labeling laws.     

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.     

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.     

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.     

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.     

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.     

计算机模拟地质化验室分样及取样常数Ks的估算

提出了计算机模拟地质化验室取样过程,考察取样误差与取样量、样品粒度之间关系,并估算取样常数.实验所得的误差与取样量之间的关系与Ingamells的取样方程一致,取样常数及取样常数和样品粒度关系式也与Ingamells推导的相符.由于计算机模拟是一颗颗取样,不用预设分布模式,不存在分析方法误差和分样操作误差的叠加,误差完...     

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。     

粒状物质的微观均匀度及其对取样误差的影响分析

研究物质的取样属性与以取样误差之间的关系是分析化学取样学的重要内容。从微观角度探讨了物质的理论性质及其对取样误差的影响。以碳化硅为例,考察了粒度分布、组分随粒度的变化以及均匀度因子等,分析了取样误差的来源。首次通过粒度分级成功地对碳化硅进行了分层,并对分层取样和随机取样的误差进行了分析和讨论,为制定合理的取样方案提供了有利的依据。本文的研究方法可适用于所有粒状物质的取样,同时也为分析化学取样学的深     

Method of determination of appropriate heat treatment of animal meal by immunoassay developed for detection of cooked beef: interlaboratory study.

An interlaboratory trial was conducted for the validation of an enzyme-linked immunosorbent assay (ELISA) method for determination of appropriate heat treatment of animal meal. A commercially available ELISA test kit developed for the identification of beef in cooked food was used in the study. Twelve laboratories from 7 European countries examined 2 different analytical protocols to establish the most appropriate analytical method. Three different samples were used, 2 animal waste materials sterilized at 129 and 134 degrees C (wet conditions), respectively, and a meat and bone meal material processed at dry conditions (maximum temperature, 140 degrees C). Statistical evaluation applying t-statistics showed that the animal meal treated according to European legislation (>133 degrees C) was clearly distinguishable from the 2 other test materials at a 99% confidence level using both analytical protocols. This method can be considered as a complementary test to the immunoassay developed for the detection of pork in cooked food that is already applied in routine analysis for the surveillance of rendering plants.