矿石中总铁含量测定结果不确定度的评定

采用直观的因果图，对矿石中总铁含量测量不确定度的来源及其对测量不确定度的影响进行分析。建立有效的数学模型，利用相对标准不确定度分步计算及整体合成进行测定结果不确定度的评定。     

电感耦合等离子体发射光谱法测定锂离子电池石墨负极材料中铁不确定度评定

采用电感耦合等离子体发射光谱法测定锂离子电池石墨类负极材料中铁含量,对测定结果的不确定度进行评定。根据JJF 1059.1—2012《测量不确定度评定与表示》和测定过程中的风险因素,建立数学模型和因果图,分析不确定度来源,计算合成标准不确定度和扩展不确定度。结果表明,测量重复性、标准曲线拟合、提取回收率对测定结果影响较大。当样品中铁含量为495.00 mg/kg时,其扩展不确定度为U=29.89 mg/kg,k=2。     

5-Br-PADAP光度法测钒钛磁铁矿中钒的不确定度评定

5-Br-PADAP光度法是测钒钛磁铁矿中钒的主要方法之一。根据其分析过程的操作步骤确定了其影响因素。建立数学模型,并根据数学模型把不确定度分解为称样质量、溶液含量、钒标准工作曲线和重复测定引入的各不确定度,对影响测量结果的各个分量进行了分析评定和计算。结果表明,钒的标准工作曲线的建立是影响样品分析误差的重要因素。     

Uncertainty Evaluation of 5-Br-PADAP Photometric Method for Determination of Vanadium in Vanadium Titanium Magnetite

5-Br-PADAP光度法是测钒钛磁铁矿中钒的主要方法之一.根据其分析过程的操作步骤确定了其影响因素.建立数学模型,并根据数学模型把不确定度分解为称样质量、溶液含量、钒标准工作曲线和重复测定引入的各不确定度,对影响测量结果的各个分量进行了分析评定和计算.结果表明,钒的标准工作曲线的建立是影响样品分析误差的重要因素.     

纯铝中Si、 Fe、 Cu的ICP-AES法测定结果的不确定度评定

通过纯铝中Si、 Fe、 Cu的ICP-AES法测定结果的不确定度评定实践, 分析了该方法测定过程的不确定度来源、建立了数学模型并计算了各标准不确定度分量、合成标准不确定度、展伸不确定度等.     

电感耦合等离子体质谱法测定酱油中铅和总砷含量的不确定度评定

根据实验过程的数学模型,分析了电感耦合等离子体质谱法测定酱油中铅和总砷含量的不确定度来源,对不确定度分量进行计算,并计算出合成不确定度及扩展不确定度。     

1G-AFS法连续测定锌精矿中砷锑铋锡的不确定度分析

对HG-AFS法连续测定锌精矿中砷、锑、铋、锡的不确定度进行了分析。根据建立的数学模型计算出各种不确定度的分量并将其合成，最后得出HG-AFS法连续测定锌精矿中砷、锑、铋、锡的扩展不确定度。     

化学法测定锌铁合金钢板镀层中铁含量测量结果的不确定度评定

对用化学法测定锌铁合金钢板镀层铁含量测量结果的不确定度进行评定,分析了该方法测定过程的不确定度来源主要是分析锌铁合金钢板镀层中铁质量的不确定度和分析锌铁合金钢板镀层质量的不确定度,建立数学模型并计算了各不确定度分量,经合成得标准不确定度为0.21%,扩展不确定度为0.42%(k=2).     

ICP-AES法测定钯碳催化剂中钯的不确定度的评定

通过ICP-AES法测定钯碳催化剂中钯的不确定度的评定实践，分析了该方法测定过程的不确定度来源、建立了数学模型并计算了各标准不确定度分量、合成标准不确定度、展伸不确定度等。     

ICP-MS法测定高纯铝中杂质不确定度的评定

利用ICP-MS测定高纯铝中杂质。用建立数学模型的方法对结果的不确定度进行评估和计算。对试料的称重、标准溶液的配制、工作曲线的拟合、测量重复性的各分量不确定度进行分析。得到了各分量不确定度和合成不确定度,最终得出更加客观的结果。     

ICP-AES法测定油漆涂层中总铅含量的不确定度评定

根据EURACHEM/C ITAC 2000中的规定评定了ICP-AES法测定油漆中总铅含量的不确定度。结果表明,标准曲线拟合线性方程、标准工作液配制过程、样品重复性分析及消化过程中消化回收率是不确定度的主要来源。油漆涂层中的可溶性铅的检测结果可表示为:(162.7±19.0)mg/kg(k=2)。     

A model for the evaluation of uncertainty in routine multi-element analysis

A model for calculating uncertainty in routine multi-element analysis is described. The model is constructed according to the principles of GUM/EURACHEM. Control chart results are combined with other existing data and results from the actual measurement into a concentration-dependent estimate of combined standard uncertainty. Since possible sources of bias are included in the calculation, overall bias as estimated from the data is used only as a control to identify needs for modification of the model and/or the analytical procedure. For each individual sample, uncertainty can be calculated automatically based on two pre-calculated parameters together with measured concentration and instrumental standard deviation. As an example, the model is demonstrated for inductively coupled plasma-mass spectrometry (ICP-MS) analysis of sewage sludge including laboratory sub-sampling, sample preparation, and instrumental determination.     

Uncertainty in analytical results from solid materials with electrothermal atomic absorption spectrometry: a comparison of methods

The combined uncertainty in the analytical results of solid materials for two methods (ET-AAS, analysis after prior sample digestion and direct solid sampling) are derived by applying the Guide to the Expression of Uncertainty in Measurement from the International Standards Organization. For the analysis of solid materials, generally, three uncertainty components must be considered: (i) those in the calibration, (ii) those in the unknown sample measurement and (iii) those in the analytical quality control (AQC) process. The expanded uncertainty limits for the content of cadmium and lead from analytical data of biological samples are calculated with the derived statistical estimates. For both methods the expanded uncertainty intervals are generally of similar width, if all sources of uncertainty are included. The relative uncertainty limits for the determination of cadmium range from 6% to 10%, and for the determination of lead they range from 8% to 16%. However, the different uncertainty components contribute to different degrees. Though with the calibration based on reference solutions (digestion method) the respective contribution may be negligible (precision < 3%), the uncertainty from a calibration based directly on a certified reference material (CRM) (solid sampling) may contribute significantly (precision about 10%). In contrast to that, the required AQC measurement (if the calibration is based on reference solutions) contributes an additional uncertainty component, though for the CRM calibration the AQC is “built-in”. For both methods, the uncertainty in the certified content of the CRM, which is used for AQC, must be considered. The estimation of the uncertainty components is shown to be a suitable tool for the experimental design in order to obtain a small uncertainty in the analytical result.     

四氧化二氮微量水标准物质的研制

以四氧化二氮为原料,经预脱水、深度脱水和精馏等处理工艺制备了7种浓度的四氧化二氮微量水标准物质.两家实验室采用核磁共振方法对标准物质中的微量水含量进行定值分析.用方差统计方法对定值结果进行数据处理和不确定度评定.     

高纯硅溶胶成分标准物质的制备

采用离子交换法实现了高纯硅溶胶主成分与众多杂质离子的分离,并制备了高纯硅溶胶标准物质。多家实验室采用原子吸收光谱、电感耦合等离子体发射光谱、离子色谱等不同原理的方法协同定值;利用称量法对二氧化硅含量进行定值;用方差统计方法对定值结果进行数据处理和不确定度评定。     

用ISO《测量不确定度表达指南》评估ICP-AES法测定不确定度

用国际通用的方法评估出ICP－AES法测定不确定度，考虑不确定度的主要来源包括仪器的精密度、标准物质标称值的不确定度以及制备溶液过程中引起的不确定度，推导出各种传播系数表达式，计算出各种不确定度分量并将其合成，并以测定钢铁中磷含量为例，提供了计算过程所需的各参数的采集和计算方法，所用的方法同样适用于以线性回归标准曲线法获得测定结果不确定度的评估。     

Quality assurance system for a decomposition method as demonstrated for the Wickbold combustion technique

 A five-step model for a quality assurance system is developed for an internal quality control check. It includes the quality control of the decomposition method and the detection method as steps belonging together. The Wickbold combustion technique as decomposition method in combination with atomic absorption spectrometry was chosen. The vaporization of the elements mercury, arsenic, lead, antimony and selenium is based on combustion in an oxyhydrogen flame. To check the efficiency of the analytical system, the uncertainty of results was calculated on the basis of the "Guide to the Expression of Uncertainty in Measurement". Received: 13 January 1997 · Accepted: 29 March 1997     

Effect of non-significant proportional bias in the final measurement uncertainty

The trueness of an analytical method can be assessed by calculating the proportional bias of the method in terms of apparent recovery. If the apparent recovery does not differ significantly from one, the analytical method has not a significant bias. If this is the case, the bias is neglected and the uncertainty associated with this bias is included in the uncertainty budget of results. However, when assessing trueness there is always a probability of incorrectly concluding that the proportional bias is not significant. Therefore, the uncertainty of results may be underestimated. In this paper, we study how non-significant bias affects the uncertainty of analytical results. Moreover, we study how to avoid the underestimation of uncertainty by including the non-significant bias calculated in the uncertainty budget. To answer these questions, we have used the Monte-Carlo method to simulate the process of estimating the apparent recovery of a biased analytical method and, subsequently, the future results this method provides. The results of the simulation show that non-significant bias may underestimate the uncertainty of analytical results when bias contributes in more than 20% to the overall uncertainty. Uncertainty is specially underestimated when bias contributes in more than 50% to the overall uncertainty.     

Measurement uncertainty of food carotenoid determination

For consistent interpretation of an analytical method result it is necessary to evaluate the confidence that can be placed in it, in the form of a measurement uncertainty estimate. The Guide to the expression of Uncertainty in Measurement issued by ISO establishes rules for evaluating and expressing uncertainty. Carotenoid determination in food is a complex analytical process involving several mass transfer steps (extraction, evaporation, saponification, etc.), making difficult the application of these guidelines. The ISO guide was interpreted for analytical chemistry by EURACHEM, which includes the possibility of using intra- and interlaboratory information. Measurement uncertainty was estimated based on laboratory validation data, including precision and method performance studies, and also, based on laboratory participation in proficiency tests. These methods of uncertainty estimation were applied to analytical results of different food matrices of fruits and vegetables. Measurement uncertainty of food carotenoid determination was 10–30% of the composition value in the great majority of cases. Higher values were found for measurements near instrumental quantification limits (e.g. 75% for β-cryptoxanthin, and 99% for lutein, in pear) or when sample chromatograms presented interferences with the analyte peak (e.g. 44% for α-carotene in orange). Lower relative expanded measurement uncertainty values (3–13%) were obtained for food matrices/analytes not requiring the saponification step. Based on these results, the saponification step should be avoided if food carotenoids are not present in the ester form. Food carotenoid content should be expressed taking into account the measurement uncertainty; therefore the maximum number of significant figures of a result should be 2.