测定数据的质量控制:测量和监测精度和准确性的程序综述

M. Abzalov
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引用次数: 59

摘要

分析数据质量的控制在采矿业中通常被称为质量保证和质量控制(QAQC),涉及对样品质量的监测和分析准确性和精密度的量化。qqc程序通常涉及使用样品副本和专门准备的标准,其等级是已知的。许多案例研究表明,通过使用大约5%至10%的现场重复和3%至5%的纸浆重复,可以实现可靠的样品精度控制。这些重复样品应在主实验室制备和分析。分析结果中的偏差可以通过在每个样品批次中加入3%至5%的标准品来确定。使用了几种不同的标准,其值跨越实际样品中等级的实际范围。空白(样品中感兴趣的金属浓度低于检测限)也应包括在内。标准样品本身无法识别样品制备过程中引入的偏差,因此大约5%的重复样品(粗次品和纸浆)应在另一个外部信誉良好的实验室进行处理和分析。本文讨论了用于估计精度和准确度误差的技术,并概述了诊断工具。这表明,最常用的方法之一,汤普森-豪沃斯技术,产生的结果始终低于其他方法。这些结果反映了该方法的本质,该方法依赖于假设误差为正态分布,因此当误差呈偏态分布时,会产生偏倚的结果。本研究与Stanley和Lawie (2007: Exploration and Mining Geology, v. 16, p. 265-274)的建议一致,即使用平均变异系数(CV AVR(%))作为矿山地质应用中相对精度误差的通用度量:!根据案例研究,对几种不同的矿床类型提出了可接受的样品精度水平。[1]: / /嵌入内联- - 1. - gif图像
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Quality Control of Assay Data: A Review of Procedures for Measuring and Monitoring Precision and Accuracy
Control of analytical data quality is usually referred to in the mining industry as Quality Assurance and Quality Control (QAQC), and involves the monitoring of sample quality and quantification of analytical accuracy and precision. QAQC procedures normally involve using sample duplicates and specially prepared standards whose grade is known. Numerous case studies indicate that reliable control of sample precision is achieved by using approximately 5% to 10% of field duplicates and 3% to 5% of pulp duplicates. These duplicate samples should be prepared and analyzed in the primary laboratory. Bias in the analytical results can be identified by inclusion of 3% to 5% of the standard in each sample batch. Several different standards are used, with values spanning the practical range of grades in the actual samples. A blank (a sample in which the concentration of metal of interest is below detection limit) should also be included. Standard samples alone cannot identify biases introduced during sample preparation, and therefore approximately 5% of the duplicate samples (coarse rejects and pulp) should be processed and assayed at another, external, reputable laboratory. This paper discusses techniques used for estimation of errors in precision and accuracy, and overviews diagnostic tools. It is shown that one of the most commonly used methods, the Thompson-Howarth technique, produces consistently lower results than other methods. These results reflect the nature of this method, which relies on the assumption of a normally distributed error, and thus produces biased results when errors have a skewed distribution. This study concurs with the suggestion of Stanley and Lawie (2007: Exploration and Mining Geology, v. 16, p. 265–274) to use the average coefficient of variation ( CV AVR (%) ) as the universal measure of relative precision error in mine geology applications: ![Graphic][1] Based on case studies, an acceptable level of sample precision is proposed for several different deposit types. [1]: /embed/inline-graphic-1.gif
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