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.     

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.     

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.     

Measurement uncertainty from physical sample preparation: estimation including systematic error

A methodology is proposed, which employs duplicated primary sampling and subsequent duplicated physical preparation coupled with duplicated chemical analyses. Sample preparation duplicates should be prepared under conditions that represent normal variability in routine laboratory practice. The proposed methodology requires duplicated chemical analysis on a minimum of two of the sample preparation duplicates. Data produced from the hierarchical design is treated with robust analysis of variance (ANOVA) to generate uncertainty estimates, as standard uncertainties ('u' expressed as standard deviation), for primary sampling (ssamp), physical sample preparation (sprep) and chemical analysis (sanal). The ANOVA results allow the contribution of the sample preparation process to the overall uncertainty to be assessed. This methodology has been applied for the first time to a case study of pesticide residues in retail strawberry samples. Duplicated sample preparation was performed under ambient conditions on two consecutive days. Multi-residue analysis (quantification by GC-MS) was undertaken for a range of incurred pesticide residues including those suspected of being susceptible to loss during sample preparation procedures. Sampling and analytical uncertainties dominated at low analyte concentrations. The sample preparation process contributed up to 20% to the total variability and had a relative uncertainty (Uprep%) of up to 66% (for bupirimate at 95% confidence). Estimates of systematic errors during physical sample preparation were also made using spike recovery experiments. Four options for the estimation of measurement uncertainty are discussed, which both include and exclude systematic error arising from sample preparation and chemical analysis. A holistic approach to the combination and subsequent expression of uncertainty is advised.     

废弃线路板化学分析采样、制样及检测方法

介绍废弃线路板中有回收价值元素和有害物质分析的采样、制样及检测技术。样品经过分类采样、剪切破碎和高温灰化制样,采用样品全分析或副批混合样分析。通过提高称样量、多次测定求平均值的办法,火试金富集-重量法测定贵金属金、银。湿法王水溶解样品,碘量法测定主体元素铜。电感耦合等离子体发射光谱(ICP-AES)法测定其它杂质元素,被测元素质量浓度在0~10μg/mL范围内与光谱强度呈良好线性关系,相关系数均大于0.9998。测定结果的相对标准偏差小于10%(n=5),加标回收率为97.0%~102.5%。该方法简单、快速,有效地解决了线路板样品不均匀而难采样,以及硬度、韧性强制样难,金属易包裹难分解的技术瓶颈,测定结果准确,具有代表性。该方法适用于废弃线路板化学成分分析,其它废弃电子产品检测可参考此方法。     

The uncertainty of the chemical sample preparation is part of the uncertainty of the measurement result

Accreditation and Quality Assurance -     

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 and sampling strategies for environmental analysis

Sampling errors are generally believed to dominate the errors of analytical measurement during the entire environmental data acquisition process. Unfortunately, environmental sampling errors are hardly quantified and documented even though analytical errors are frequently yet improperly reported to the third decimal point in environmental analysis. There is a significant discrepancy in directly applying traditional sampling theories (such as those developed for the binary particle systems) to trace levels of contaminants in complex environmental matrices with various spatial and temporal heterogeneities. The purpose of this critical review is to address several key issues in the development of an optimal sampling strategy with a primary goal of sample representativeness while minimizing the total number of samples and sampling frequencies, hence the cost for sampling and analysis. Several biased and statistically based sampling approaches commonly employed in environmental sampling (e.g. judgmental sampling and haphazard sampling vs. statistically based approaches such as simple random, systematic random, and stratified random sampling) are examined with respect to their pros and cons for the acquisition of scientifically reliable and legally defensible data. The effects of sample size, sample frequency and the use of compositing are addressed to illustrate the strategies for a cost reduction as well as an improved representativeness of sampling from spatially and temporally varied environmental systems. The discussions are accompanied with some recent advances and examples in the formulation of sampling strategies for the chemical or biological analysis of air, surface water, drinking water, groundwater, soil, and hazardous waste sites.     

Combined uncertainty factor for sampling and analysis

Accreditation and Quality Assurance - Measurement uncertainty that arises from primary sampling can be expressed as an uncertainty factor, which recognises its sometimes approximately log-normal...     

Correction to: Combined uncertainty factor for sampling and analysis

Accreditation and Quality Assurance - In the penultimate paragraph of the original publication, a confidence interval of 93 mg/kg to 971 mg/kg was reported. These values should be...     

Recent developments in solid-phase microextraction for on-site sampling and sample preparation

On-site sampling and sample preparation favor portable, solventless or even solvent-free techniques. Solid-phase microextraction (SPME) has these advantages. This review focuses on developments between 2007 and early 2011 in microextraction techniques for on-site sampling and sample preparation, including fiber SPME, stir-bar sorptive extraction (SBSE), thin-film microextraction (TFME) and different types of in-needle SPME. The major trends in on-site applications of SPME appear to be fiber and thin-film SPME, microextraction by packed sorbent (MEPS) and the sorbent-packed needle-trap device (NTD). We discuss and compare several aspects of these types of SPME in on-site applications. We also describe sorbent phases for SPME that benefit on-site applications. Finally, we provide a perspective on SPME-based techniques for on-site applications.     

Uncertainty from sampling: workshop to launch a Nordtest handbook on sampling uncertainty estimation and control

A workshop on uncertainty in sampling was held in Hillerød, Denmark, on 12–13 April 2007 to launch a new handbook on sampling quality assurance and uncertainty estimation. The participants of the workshop were approximately 60 delegates from 15 European countries, representing institutions performing sampling, users of the data, research institutions, as well as accreditation bodies. Materials from the workshop, including examples, tools, and calculation aids for the work can be found at http://www.samplersguide.com. The Nordtest handbook Uncertainty from sampling will be made available on the Nordtest web site at http://www.nordicinnovation.net/nordtest.cfm under NT technical reports, report number NT tec 604. Until the final report is available on the Nordtest web site, an advance draft of the Nordtest handbook is available from http://www.samplersguide.com.     

Ethylene oxide pollution evaluation part I: Sampling with active charcoal tubes

Summary Air sampling of ethylene oxide on active charcoal tubes followed by GC analysis is a frequently proposed method. This paper studies just to what extent it can be used, defining its limitations (concentration range, influence of ambient temperature and relative humidity), which are seldom taken seriously into account. This method proves inadequate to determine low pollution levels, i.e. around 2 mg m⁻³.     

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.     

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.     

Cost effective, robust estimation of measurement uncertainty from sampling using unbalanced ANOVA

There is an increasing appreciation that the uncertainty in environmental measurements is vitally important for their reliable interpretation. However, the adoption of methods to estimate this uncertainty has been limited by the extra cost of implementation. One method that can be used to estimate the random components of uncertainty in the sampling and analytical processes requires the collection of duplicate samples at 10% of the primary sampling locations and duplicating the analyses of these samples. A new program has been written and applied to a modified experimental design to enable a 33% reduction in the cost of analysing this 10% subset, whilst accommodating outlying values. This unbalanced robust analysis of variance (U-RANOVA) uses an unbalanced rather than the balanced experimental design usually employed. Simulation techniques have been used to validate the results of the program, by comparison of the results between the proposed unbalanced and the established balanced designs. Comparisons are also made against the seed parameters (mean and standard deviation) used to simulate the parent population, prior to the addition of a proportion (up to 10%) of outlying values. Application to a large number of different simulated populations shows that U-RANOVA produces results that are effectively indistinguishable from the results produced by the accepted balanced approach and are equally close to the true (seed) parameters of the parent normal population.     

铁矿石、锰矿石和铬矿石取样和样品制备技术概况

我国钢铁工业的主要原料铁矿石、锰矿石和铬矿石进口量大,对外依存度分别在80%,55%,95%以上,贸易中经常发生质量纠纷,有些质量纠纷的焦点为取样和样品制备技术。从影响样品代表性的角度概述了铁矿石(包括直接还原铁和热压块)、锰矿石和铬矿石取样和样品制备所涉及的方法标准、品质波动、样品部位、最小份样数、最小份样量、缩分方法、最小留样量和设备的技术要求,分析了取样和样品制备中存在的问题,提出了确保取样和样品制备人员能正确执行方法标准、采取有代表性样品应采取的措施。     

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.     

Compliance with legal requirements: definition of limits, sampling and measurement uncertainty

In many cases compositional requirements for foodstuffs (e.g. limits for the fat, protein, dry matter, or water content) are established by legislation. Adequate compliance testing is possible only if limits are clearly defined, taking measurement and sampling uncertainty into consideration. Furthermore, decisions on compliance must be based on samples which reflect the composition of the quantity to be evaluated. The resulting sample sizes are normally regarded by food inspection authorities as being much larger than acceptable. Consequently, an alternative strategy should be developed. Autocontrol data (i.e. inspection results obtained by the factory) in principle provide an adequate data basis for decisions on compliance. However, they must be reliable and the food inspection authority must have access to these data on request. Using these data and on condition that they show an approximate normal distribution, an inspection strategy based on arithmetic mean and standard deviation can be developed. Reliable and transparent decisions on compliance can thus be made. In many cases an adequate verification of food authenticity requires a comparison of raw material and product composition. Maximum acceptable differences, taking the relevant sources of variation into consideration, have to be defined and should be used instead of limits. Received: 17 April 2002 Accepted: 23 June 2002     

Fusion with lithium borate as sample preparation for ICP and AAS analysis

Summary Different approaches to sample fusion are investigated. The advantages of a device consisting of graphite plate and graphite rings are demonstrated. A method is proposed for ICP and AAS analysis of geological samples and samples from ore dressing after fusion with lithium borates and dissolution with acids.

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Probenvorbereitung für die ICP und AAS-Analyse durch Schmelzen mit Lithiumborat