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.     

Collaborative sampling trial in the context of quality assurance in the German marine monitoring programme for the North Sea and the Baltic Sea

This paper presents the assessment of a collaborative trial in sampling in the Baltic Sea in the framework of quality assurance in the German marine monitoring programme for the North Sea and the Baltic Sea. The objective of investigations was to determine the influence of sampling on analytical results for selected monitoring parameters and to harmonize the procedure for sampling of sea water to a large extent. In these studies the staff of three vessels took replicate sea water samples, 1 m below the surface and below the halocline, at two monitoring stations. Mass concentration mean values for different nutrient parameters were obtained from each sample, all in one laboratory. Data produced from the hierarchical design were treated with robust analysis of variance (ANOVA) to generate uncertainty estimates, as standard uncertainties (“u” expressed as standard deviation), for geochemical variation (s _geochem), primary sampling (s _sampling), and chemical analysis (s _analysis). Geochemical variation dominated the total variance in all cases. Sampling and analytical uncertainties contributed together up to 15% of the total variance and had a relative measurement uncertainty (u%) of less than 2% for all the parameters investigated. Thus for this study the sampling protocol and the analytical method could be regarded as fit-for-purpose. M. Gluschke was formerly affiliated to the Federal Environmental Agency, P.O. Box 33 00 22, 14191 Berlin, Germany.     

Geochemical investigation of alluvial sediments: validation of ICP-OES determination of heavy metals. A case study from the Utrata River Valley (central Poland)

The development and validation of a reliable analytical procedure for the determination of selected metals (Cd, Cr, Cu, Pb, Zn and Mn) in sediments accumulated in the Utrata River (Poland) is described. The aqua regia extraction followed by inductively coupled plasma optical emission spectrometry (ICP-OES) was used for this purpose. The optimized analytical procedure was validated, and adequate quality control actions were implemented in order to provide reliable data. The precision under’ within-laboratory’ reproducibility conditions was estimated from duplicate analysis. Certified reference material (CRM) was used in order to evaluate the accuracy of the results regarding the sewage sludge amended soil CRM 143R. The detection limits for all elements of interest were well-below their content in the investigated sediment samples. The obtained reliable data could be used for assessment of the relationship between human economic activity in the past and the geochemical features of the sediments.     

Estimating sampling and analysis uncertainties to assess the fitness for purpose of a water quality monitoring network

In order to verify that the protocols used for water quality monitoring of surface waters within the Long-term Environmental Research Monitoring and Testing System (OPE), located in the north-eastern part of France in relation with a geological disposal for radioactive waste project, are fit for purpose, a validation study was conducted following the methodology described in the Eurachem/citac and Nordtest guidance documents on uncertainty arising from sampling. As one of the objectives of the OPE water monitoring programme was to investigate the spatial and temporal variability of water quality, quality requirements were set to having a measurement variance, including sampling and analytical contributions, less than 20 % of the total variance to minimise the impact of measurement over the observed environmental variability. The replicate method was then selected in order to estimate the measurement uncertainty, including the sampling contribution, as well as the spatial and temporal variability of water quality of surface waters. To minimise costs, a single-split level was selected. Analytical uncertainties were assessed from inter-laboratory data and/or internal quality control data from the last 2–5 years. Finally, ANOVA was applied to the data sets after elimination of outliers. Results showed that for pH, electrical conductivity, turbidity and nitrate, the sampling uncertainty was negligible, whereas for other parameters such as dissolved oxygen, total suspend solids, total organic carbon, nitrite and phosphate, the sampling contribution to the measurement uncertainty was largely significant. For all parameters except calcium, the sampling and analytical protocols were considered fit for purpose.     

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 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.     

Parametric and bootstrap estimations of confidence intervals for representative sample weights

Summary The essential problem in representative sampling is a comparison of analytical and sampling errors. Application of analysis of variance (ANOVA) to this problem seems less appropriate than regression analysis based on Wilsons formula, because in the first approach the total analytical error is commonly underestimated. Furthermore, regression analysis allows derivation of confidence intervals for representative sample weights. In this context a parametric and a non-parametric procedure are described.     

Determination of soluble ions and elements in ambient air suspended particulate matter: Inter-technique comparison of XRF, IC and ICP for sample-by-sample quality control

In this paper, we describe a validation procedure for chemical fractionation analysis of elements (Al, As, Ba, Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, S, Sb, Si, Sr, Ti and V) and soluble ions (Cl⁻, NO₃⁻, SO₄²⁻, Na⁺, NH₄⁺, Mg²⁺, Ca²⁺) in suspended particulate matter (PM). The procedure applies three distinct measurement techniques (XRF, IC and ICP-OES) to the analysis of individual samples. The techniques used generate different outputs at different stages in the procedure. This makes it possible to identify the contributions of specific parameters to measurement uncertainty. On this basis, we propose a scheme for controlling the analytical quality of data from individual samples in which inter-technique comparisons is used in the same way many analytical methods use surrogates. We apply this scheme to about 310 samples of PM₁₀ and PM_2.5 identifying and assessing the main factors contributing to measurement uncertainty. This procedure successfully resolved a number of difficulties frequently encountered during the analysis of PM, including lack of appropriate reference materials and the low reliability of alternative techniques of quality control. The results demonstrate the critical importance of sample treatment prior to destructive analysis by IC and ICP.     

Sampling variability and uncertainty in total diet studies

Here, the uncertainty budget for a total diet study (TDS) was clarified by separating the total measurement uncertainty into the uncertainty arising from the compositional heterogeneity of food items between cities (referred to as inter-city variance), the heterogeneity of food items within cities (intra-city variance), and the chemical analysis of the food samples (analytical variance) at one study design. TDS samples were collected from 14 cities in Japan. Duplicate samples collected in each city were prepared from food items purchased from different shops, and the cadmium concentrations were measured individually to obtain the intra-city variance. These results were used to show the importance of sampling design in TDSs, by evaluating a sampling method known as a multi-stage design, in which multiple samples are collected from several cities. Such schemes have been applied to TDSs, but the uncertainty involved has not been assessed. An intra-city correlation was observed between the cadmium concentrations in samples from the same city, demonstrating that the effective sample size was not simply the number of cities and shops sampled. The TDS results showed a high intra-city variance, which was greater than the inter-city variance for all of the food groups studied, and particularly for the bean and potato groups. By combining the sampling and analytical uncertainties obtained, the sampling uncertainty across different primary sampling unit sizes and secondary sampling unit sizes was obtained. As suggested by the analysis of potatoes and beans, grouping food samples from different shops in the same city can improve the representativeness of the results.     

Analytical quality control program used by the trace elements in foods and diets sub-network of the FAO European cooperative network on trace elements

Summary The analytical quality control program employed by the ongoing FAO study on 14 trace elements in nationally representative staple foods of European countries is presented. The analytical quality control used is based on: 1) precautions taken to avoid trace element contaminations or losses during sampling and sample handling; and, 2) on methods used to guarantee that the actual determinations yield correct results. The precautions are presented. A number of certified biological reference materials (RMs) were used to validate the analytical methods employed. The following staple food RMs were also prepared: wheat flour, potato powder, animal muscle (pork) and milk powder. They were tested for homogeneity and subjected to an interlaboratory comparison study on the basis of which recommended values for trace element concentrations were defined. Further, the mean relative standard deviation for the 95% confidence limits of the medians in all RMs was below 5% for Ca, Mg and Zn; below 10% for Mn; below 15% for Fe, Cu and Se; and below 25% for Mo and Ni. These RMs were used to control the analytical quality of the trace element determinations in the actual samples. It is concluded that important contaminations were avoided in sampling and sample handling and that use of the RMs described was necessary to guarantee the analytical quality of the results.     

Empirical versus modelling approaches to the estimation of measurement uncertainty caused by primary sampling

Measurement uncertainty is a vital issue within analytical science. There are strong arguments that primary sampling should be considered the first and perhaps the most influential step in the measurement process. Increasingly, analytical laboratories are required to report measurement results to clients together with estimates of the uncertainty. Furthermore, these estimates can be used when pursuing regulation enforcement to decide whether a measured analyte concentration is above a threshold value. With its recognised importance in analytical measurement, the question arises of 'what is the most appropriate method to estimate the measurement uncertainty?'. Two broad methods for uncertainty estimation are identified, the modelling method and the empirical method. In modelling, the estimation of uncertainty involves the identification, quantification and summation (as variances) of each potential source of uncertainty. This approach has been applied to purely analytical systems, but becomes increasingly problematic in identifying all of such sources when it is applied to primary sampling. Applications of this methodology to sampling often utilise long-established theoretical models of sampling and adopt the assumption that a 'correct' sampling protocol will ensure a representative sample. The empirical approach to uncertainty estimation involves replicated measurements from either inter-organisational trials and/or internal method validation and quality control. A more simple method involves duplicating sampling and analysis, by one organisation, for a small proportion of the total number of samples. This has proven to be a suitable alternative to these often expensive and time-consuming trials, in routine surveillance and one-off surveys, especially where heterogeneity is the main source of uncertainty. A case study of aflatoxins in pistachio nuts is used to broadly demonstrate the strengths and weakness of the two methods of uncertainty estimation. The estimate of sampling uncertainty made using the modelling approach (136%, at 68% confidence) is six times larger than that found using the empirical approach (22.5%). The difficulty in establishing reliable estimates for the input variable for the modelling approach is thought to be the main cause of the discrepancy. The empirical approach to uncertainty estimation, with the automatic inclusion of sampling within the uncertainty statement, is recognised as generally the most practical procedure, providing the more reliable estimates. The modelling approach is also shown to have a useful role, especially in choosing strategies to change the sampling uncertainty, when required.     

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.     

Determination and interpretation of environmental water samples contaminated by uranium mining activities

Interpretation of environmental behavior of uranium is based on several steps of data analysis and statistical inference. First step is sampling and analyzing of uranium in field samples by routine laboratory methods. Such methods have to fulfill multiple requirements like robustness, efficiency, low detection limit and precision. A comparison of different approaches in assigning uncertainty to experimentally obtained analytical data shows that classical error estimation is not significantly inferior to more sophisticated modern techniques like inverse regression or orthogonal regression. A second step is the correlation of analytical data with current state of insight into environmental behavior of uranium. Such a correlation furthers the choice of adequate geochemical models and quality of geochemical data base for subsequent detailed analysis, e.g. by geochemical modeling. An appraisal of the individual steps in this complex analysis is given on the basis of statistical procedures for calibration and an E_H-pH diagram of uranium for atmospheric conditions.     

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.     

数理统计在地球化学样品分析质量控制中的应用

使用Excel2013和Minitab两种常用软件对地球化学样品中CaO含量分布情况进行了研究,运用描述性统计、正态分布、背景值与异常方法对实验室分析的广西某地地球化学样品的分析数据进行了质量评价,结果表明:综合运用计算机软件和数理统计方法,能快速找到分析数据的详细信息和数据特征,判别地球化学样品实验室分析数据的准确性,找出元素的背景值和异常值并剔除异常值,比较发现,样品中CaO含量分析数据对数转换后比原始数据的分布更趋于正态分布,相比于X射线荧光光谱方法,用电感耦合等离子体发射光谱法测定的结果更符合正态分布。方法对地球化学调查样品的分析数据质量评价作了有益的尝试,揭示了数理统计方法是地球化学样品分析质量控制的有效手段。     

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.     

Quality assurance in analytical measurement

 The peculiarities of analytical measurement require to check characteristics of the error (its components) of the obtained analysis results to assure the quality of the measurements. This article deals with the various quality assurance procedures and algorithms which are used to check the quality indices, i.e. the accuracy, reproducibility, certainty and repeatability of analytical measurements: These procedures include: laboratory rapid control; Intra-laboratory statistical control (statistical selection control by alternative attribute, statistical selection control by quantity method of periodic check of the analysis procedure for conformity to the specified requirements) and external control (inter-laboratory control checks, inter-laboratory comparison tests, and intra-laboratory control algorithms carried out by the appropriate supervisory body.) in the separately taken laboratory. The respective algorithms, control plans and control requirements, specified according to the different control aims and assurance tasks, enable the quality and certianty of analytical information obtained in laboratories in Russia to be assured. Received: 9 November 1998 / Accepted: 24 November 1998     

Determination and interpretation of environmental water samples contaminated by uranium mining activities

Interpretation of environmental behavior of uranium is based on several steps of data analysis and statistical inference. First step is sampling and analyzing of uranium in field samples by routine laboratory methods. Such methods have to fulfill multiple requirements like robustness, efficiency, low detection limit and precision. A comparison of different approaches in assigning uncertainty to experimentally obtained analytical data shows that classical error estimation is not significantly inferior to more sophisticated modern techniques like inverse regression or orthogonal regression. A second step is the correlation of analytical data with current state of insight into environmental behavior of uranium. Such a correlation furthers the choice of adequate geochemical models and quality of geochemical data base for subsequent detailed analysis, e.g. by geochemical modeling. An appraisal of the individual steps in this complex analysis is given on the basis of statistical procedures for calibration and an E_H-pH diagram of uranium for atmospheric conditions. Received: 30 July 1998 / Revised: 18 November 1998 / Accepted: 26 November 1998