Ava Amideina, Petranka Petrova, Mitja Kolar, Jernej Imperl, Petko Mandjukov
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引用次数: 0
摘要
环境分析中的测量不确定性(MU)通常被认为是取样、样品制备和分析测量(通常是仪器测量)的叠加影响的综合结果。由于样品的天然异质性和/或所需的复杂预处理程序,此类研究的目标 MU 通常相对较高。在分析实践中,很少评估样品制备对 MU 的贡献。因此,它仍然是与分析相关的一个隐藏部分。然而,了解样品制备的贡献可能会为优化整个分析程序和减少分析结果的扩大 MU 提供重要的信息和进一步的可能性。从统计学的角度来看,分离来自不同步骤的不确定性贡献通常不是一件小事。选择合适的统计方法取决于数据结构和质量、变量分布等。本研究采用了三种不同的统计方法来评估样品制备的不确定性贡献,并对其进行了比较和讨论。所考虑的方法基于经典和稳健的方差分析(ANOVA),应用于对海洋藻类和蜜蜂蜂蜜样品进行微波消解的仪器分析数据。在实际实验数据集的基础上,提出了一些关于统计方法选择的一般性建议。
EVALUATION OF MEASUREMENT UNCERTAINTY ASSOCIATED WITH THE SAMPLE PREPARATION PROCEDURE
The measurement uncertainty (MU) in environmental analyses is usually considered as a combination of the additive contributions from sampling, sample preparation and analytical measurement (usually instrumental). The target MU for such studies is usually relatively high due to the natural sample heterogeneity and/or the complicated pretreatment procedure required. In the analytical practice, the contribution of the sample preparation to MU is rarely evaluated. Thus, it remains a hidden part of the one related to the analysis. However, the knowledge for the contribution of sample preparation might provide important information and further possibility for optimization of the entire analytical procedure and reduction of the expanded MU of the analytical result. From statistical point of view, the separation of uncertainty contributions from the different steps is, generally, not a trivial task. Selection of the proper statistical approach depends on the data structure and quality, variables distribution, etc. In the present study, three different statistical methods for evaluation of the uncertainty contribution of the sample preparation were applied, compared, and discussed. The considered approaches are based on, both, classical and robust analysis of variances (ANOVA) applied to data from instrumental analysis of marine algae and bee honey samples, undergoing microwave digestion. Some general recommendations on the statistical approach selection are revealed based on real experimental data set.