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.     

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 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.     

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 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.     

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.     

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).     

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.     

Gy sampling theory in environmental studies: 2. Subsampling error estimates

Sampling can be a significant source of error in the measurement process. The characterization and cleanup of hazardous waste sites require data that meet site-specific levels of acceptable quality if scientifically supportable decisions are to be made. In support of this effort, the US Environmental Protection Agency (EPA) is investigating methods that relate sample characteristics to analytical performance. Predicted uncertainty levels allow appropriate study design decisions to be made, facilitating more timely and less expensive evaluations. Gy sampling theory can predict a significant fraction of sampling error when certain conditions are met. We report on several controlled studies of subsampling procedures to evaluate the utility of Gy sampling theory applied to laboratory subsampling practices. Several sample types were studied and both analyte and non-analyte containing particles were shown to play important roles affecting the measured uncertainty.

Gy sampling theory was useful in predicting minimum uncertainty levels provided the theoretical assumptions were met. Predicted fundamental errors ranged from 46 to 68% of the total measurement variability. The study results also showed sectorial splitting outperformed incremental sampling for simple model systems and suggested that sectorial splitters divide each size fraction independently. Under the limited conditions tested in this study, incremental sampling with a spatula produced biased results when sampling particulate matrices with grain sizes about 1 mm.     

Evaluation of extraction buffers using the current approach of detecting multiple allergenic and nonallergenic proteins in food

The detection of food allergens has been a challenge because of the increasing need to ensure the absence of undeclared allergens in foods. The current trend in the detection of some food allergens, like peanuts, is based on the detection of multiple allergenic and nonallergenic proteins, and this is the approach that kit manufacturers have adopted. Because commercial kits differ in their ability to detect allergens, regulatory agencies, the food industry, and kit manufacturers are working together to standardize the detection methods. Three kits for the detection of peanuts have been evaluated for performance by the AOAC Research Institute. For this evaluation, a peanut butter suspension was used as a reference material. Several kit components contribute to between-kit analytical variation, even when the same sample is used. One component of commercial kits, which may be contributing to this variability, is the sample extraction buffer. In this study, differences in extractability of 3 allergenic foods were evaluated by using 4 different extraction buffers. The conclusion is that optimum allergen extractability was buffer-dependent, and no single buffer is appropriate for use as a universal extraction solution for all allergenic foods. Therefore, a thorough evaluation of sample preparation buffers needs to be performed for every individual allergenic food. In light of the results obtained, the current approach used for detection of peanut allergens based on the detection of multiple allergenic and nonallergenic proteins is being analyzed.     

Sampling constants for niacin content in standard reference material 1846 Infant Formula

Modern measurement systems for food components often require use of ever smaller sample sizes, down to mg for some new microtechniques, which puts a stronger demand on development of reference materials with defined homogeneity for subsampling. One approach to evaluate the homogeneity of materials is the characterization of sampling constants, defined as that amount of material that gives a 1% error for subsampling. This approach was developed for geological sampling and has been applied in a limited way for inorganic components in food/biological materials. We have extended this approach to the determination of the sampling constants for an organic component, niacin, in the SRM 1846 Infant Formula material. This material was produced by blending of a dry vitamin mix (5% weight) into the bulk spray dried powder, for long term stability purposes. By analyzing similar aliquots of a reconstituted homogeneous fluid solution of a large sample size, in comparison to smaller portions of dry powder, an estimate of the variation due to sampling can be separated from estimates of variation due to analysis. Using either the AOAC microbiological method or a newly developed HPLC method, sampling constants for the niacin content of SRM 1846 are in the range of 1–3 g; use of smaller sub-samples can introduce significant variation into determinations using this SRM.     

Assessing sample representativeness for inorganic chemical investigation of invertebrates by INAA

The quality of environmental studies depends on the utilization of adequate sampling protocol and analytical method for obtaining reliable results and minimizing analytical uncertainties. In order to demonstrate the applicability of INAA for determining chemical element composition of invertebrates, this work evaluated sample representativeness in terms of subsampling and sample size. Br, Co, Fe, K, Na, Sc and Zn could be determined in very small samples despite increasing of analytical uncertainties. Special attention should be directed to invertebrate species with small structures because of the high chemical variation observed among different sample sizes tested.     

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.     

Preparation of peanut butter suspension for determination of peanuts using enzyme-linked immunoassay kits

Peanuts are one of the 8 most common allergenic foods and a large proportion of peanut-allergic individuals have severe reactions, some to minimal exposure. Specific protein constituents in the peanuts are the cause of the allergic reactions in sensitized individuals who ingest the peanuts. To avoid accidental ingestion of peanut-contaminated food, methods of analysis for the determination of the allergenic proteins in foods are important tools. Such methods could help identify foods inadvertently contaminated with peanuts, thereby reducing the incidence of allergic reactions to peanuts. Commercial immunoassay kits are available but need study for method performance, which requires reference materials for within- and between-laboratory validations. In this study, National Institute of Standards and Technology Standard Reference Material 2387 peanut butter was used. A polytron homogenizer was used to prepare a homogenous aqueous Peanut Butter suspension for the evaluation of method performance of some commercially available immunoassay kits such as Veratox for Peanut Allergen Test (Neogen Corp.), Ridascreen Peanut (R-Biopharm GmbH), and Bio-Kit Peanut Protein Assay Kit (Tepnel). Each gram of the aqueous peanut butter suspension contained 20 mg carboxymethylcellulose sodium salt, 643 microg peanut, 0.5 mg thimerosal, and 2.5 mg bovine serum albumin. The suspension was homogenous, stable, reproducible, and applicable for adding to ice cream, cookies, breakfast cereals, and chocolate for recovery studies at spike levels ranging from 12 to 90 microg/g.     

Determination of manganese and zinc in powdered chocolate samples by slurry sampling using sequential multi-element flame atomic absorption spectrometry

In the present work, a slurry sampling flame atomic absorption spectrometric method to determine directly manganese and zinc in powdered chocolate samples is proposed. The optimization step was performed using univariate methodology involving the following factors: nature and concentration of the acid solution, sonication time, and particle size. The established conditions led to the use of a sample mass of 150 mg, 2.0 mol L^− 1 nitric acid solution, sonication time of 15 min, and a slurry volume of 50 mL. This method allows the determination of manganese and zinc with detection limit of 52 and 61 ng g^− 1, respectively, and a precision expressed as relative standard deviation (RSD) of 2.6% and 3.2% (both, n = 10) for contents of manganese and zinc of 52.4 and 100.0 μg g^− 1, respectively. The proposed method was applied for determination of manganese and zinc in five powdered chocolate samples. In these, the manganese content varied from 42.8 to 52.7 and from 88.6 to 102.4 μg g^− 1 of zinc. The analytical results were compared with the results obtained by analysis of these samples after digestion using open vessel and acid bomb digestion procedures and determination using FS-FAAS. The statistical comparison by t-test (95% confidence level) showed no significant difference between these results.     

Molecular Modeling of Metabolism for Allergen-Free Low Linoleic Acid Peanuts

It is necessary to eliminate linoleic acid and allergenic arachins from peanuts for good health reasons. Virginia-type peanuts, harvested from plots treated with mineral salts combinations that mimic the subunit compositions of glutamate dehydrogenase (GDH) were analyzed for fatty acid and arachin compositions by HPLC and polyacrylamide gel electrophoresis, respectively. Fatty acid desaturase and arachin encoding mRNAs were analyzed by Northern hybridization using the homologous RNAs synthesized by peanut GDH as probes. There were 70?C80?% sequence similarities between the GDH-synthesized RNAs and the mRNAs encoding arachins, fatty acid desaturases, glutamate synthase, and nitrate reductase, which similarities induced permutation of the metabolic pathways at the mRNA level. Modeling of mRNAs showed there were 210, 3,150, 1,260, 2,520, and 4,200 metabolic permutations in the control, NPKS-, NS-, Pi-, NH₄Cl-, and PK-treated peanuts, respectively. The mRNA cross-talks decreased the arachin to almost zero percent in the NPKS- and PK-treated peanuts, and linoleate to ??18?% in the PK-treated peanut. The mRNA cross-talks may account for the vastly reported environmentally induced variability in the linoleate contents of peanut genotypes. These results have quantitatively unified molecular biology and metabolic pathways into one simple biotechnology for optimizing peanut quality and may encourage small-scale industry to produce arachin-free low linoleate peanuts.     

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...     

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.     

Gas-liquid chromatographic determination of milk fat and cocoa butter equivalents in milk chocolate: interlaboratory study

A collaborative trial was conducted to validate an analytical approach comprising method procedures for determination of milk fat and the detection and quantification of cocoa butter equivalents (CBEs) in milk chocolate. The whole approach is based on (1) comprehensive databases covering the triacylglycerol composition of a wide range of authentic milk fat, cocoa butter, and CBE samples and 947 gravimetrically prepared mixtures thereof; (2) the availability of a certified cocoa butter reference material for calibration; (3) an evaluation algorithm, which allows reliable quantitation of the milk fat content in chocolate; (4) a subsequent correction to take account of the triacylglycerols derived from milk fat; (5) mathematical expressions to detect the presence of CBEs in milk chocolate; and (6) a multivariate statistical formula to quantitate the amount of CBEs in milk chocolate. Twelve laboratories participated in the validation study. CBE admixtures were detected down to a level of 0.5 g CBE/100 g milk chocolate, without false-positive or -negative results. The applied quantitation model performed well at the statutory limit of 5% CBE addition to milk chocolate, with a prediction error of 0.7%, and HorRat values ranging from 0.8 to 1.5. The relative standard deviation for reproducibility (RSDR) values for quantitation of CBEs in analyses of chocolate fat solutions ranged from 2.2 to 3.8% and for analyses of real chocolate samples, from 4.1 to 4.7%, demonstrating that the whole approach, based solely on chocolate fat blends, is applicable to real milk chocolate samples.