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用质控图和稳健统计-迭代法评估环境检测实验室测量不确定度

Evaluation of Measurement Uncertainty in an Environmental Test Laboratory by Quality Assurance, Control Charting and Robust Statistics

  • 摘要: 基于实验室长期积累的质控数据评估测量不确定度的方法具有广泛应用前景,但常见的质控图法只能处理单一浓度,而处理多浓度水平的线性校准法建立模型时需要成套、完整的质控数据,不利于基层实验室的应用。稳健统计是指不用识别、剔除离群值,直接应用全部测量数据,将离群值对统计分析结果影响降低到最小的统计分析方法。本文尝试用回收率将不同浓度数据归一化,然后用质控图方法处理。如果存在离群数据时,可用稳健统计法计算期间精密度sR。利用本实验室积累的5套和其他实验室提供的19套环境检测领域常规项目质控数据验证了新方法的可行性。验证结果表明,对单一浓度数据,不经任何处理,稳健统计-迭代法可得到与质控图法基本相符的结果,sR′(相对值)平均偏差为0.15%。对于多浓度水平数据,经归一化后,质控图法、稳健统计-迭代法与线性校准法的结果平均偏差分别为0.43%和0.20%,质控图法与稳健统计-迭代法的结果平均偏差为0.26%,三种方法计算结果基本相符;稳健统计-迭代法更接近于线性校准法计算结果,且方法原理简单,计算步骤明显简化,适用于线性校准法比例模型数据的处理。

     

    Abstract: There are broad application prospects for evaluation of measurement uncertainty in the environmental test laboratory based on quality control data accumulated in long-term routine analysis. The quality control charting method is used only for the same concentration data. Linear calibration using reference materials can be used in different concentration measurement data but the complete quality control data cover different concentrations with the same number of measurements and should be prepared before the mathematical mode is established, which makes its application in most testing laboratories unsuitable. Robust statistics is a type of statistical analysis method where it is unnecessary to identify and delete outliers but it can also reduce the effect of outliers on the final results based on all measurement data. Quality control charting methods and robust statistics (iteration method), when outliers exist, are used to calculate intermediate precision (sR′) after normalizing different concentration data by recovery rate and are described in this paper. Five sets of data collected in our laboratory and 19 sets of data from the other laboratories, which cover routine testing items in environmental protection field, were used to verify the feasibility of the new method. It can be shown that the average difference of relative intermediate precision (ΔsR-rsd) between robust statistics and quality control charting methods are almost in agreement (i.e. 0.15%) for the single concentration data. For the multi-level concentration data after normalization, the average difference (ΔsR-rsd) between quality control charting and linear calibration, between robust statistics and linear calibration, are 0.43% and 0.20%, respectively. The average of difference (ΔsR-rsd) between robust statistics and quality control charting method is 0.26%, which indicates that the results of all three methods are generally in line with each other. The principle of the new methods proposed in this paper is easy to understand and the calculation procedure is significantly simplified, making it suitable for cases of linear calibration using reference materials with direct proportion mode.

     

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