Abstract Risks of a false decision on conformity of the chemical composition of a multicomponent material or object due to measurement uncertainty are defined using the Bayesian approach. Even if the conformity assessment for each particular component of a material is successful, the total probability of a false decision (total consumer’s risk or producer’s risk) concerning the material as a whole might still be significant. This is related to the specific batch, lot, sample, environmental compartment, or other item of material or object (specific consumer’s and producer’s risks), or to a population of these items (global consumer’s and producer’s risks). A model of the total probability of such false decisions for cases of independent actual (‘true’) concentrations or contents of the components and the corresponding measurement results is formulated based on the law of total probability. It is shown that the total risk can be evaluated as a combination of the particular risks in the conformity assessment of components of the item. For a more complicated task, i.e. for a larger number of components under control, the total risk is greater. When the actual values of the components’ concentrations or contents, as well as the measurement results, are correlated, they are modelled by multivariate distributions. Then, a total global risk of a false decision on the material conformity is evaluated by the calculation of integrals of corresponding joint probability density function. A total specific risk can be evaluated as the joint posterior cumulative function of actual property values of a specific item lying outside the multivariate specification (tolerance) domain when the vector of measured values obtained for the item is inside this domain. The effect of correlation on the risk is not easily predictable. Examples of the evaluation of risks are provided for conformity assessment of denatured alcohols, total suspended particulate matter in ambient air, a cold/flu medication, and a PtRh alloy.

中文翻译：

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IUPAC/CITAC 指南：评估因测量不确定性而导致的多组分材料或物体合格评定错误决策的风险（IUPAC 技术报告）

摘要 使用贝叶斯方法定义了由于测量不确定性而导致对多组分材料或物体的化学成分符合性做出错误决定的风险。即使材料的每个特定成分的合格评定成功，关于整个材料的错误决定（总消费者风险或生产商风险）的总概率可能仍然很大。这与特定批次、批次、样品、环境隔间或其他材料或物体项目（特定消费者和生产商的风险）或这些项目的总体（全球消费者和生产商的风险）有关。对于独立的实际（“真实”）浓度或成分含量以及相应的测量结果的情况，这种错误决定的总概率模型是基于总概率定律制定的。结果表明，总风险可以作为项目组件合格评定中特定风险的组合进行评估。对于更复杂的任务，即对于更多数量的受控组件，总风险更大。当组分浓度或含量的实际值以及测量结果相关时，它们通过多变量分布建模。然后，通过计算相应联合概率密度函数的积分来评估材料符合性错误决策的总全局风险。当为项目获得的测量值向量在多变量规范（容差）域之外时，可以将总特定风险评估为该特定项目的实际属性值的联合后验累积函数。相关性对风险的影响不容易预测。提供了风险评估示例，用于对变性酒精、环境空气中的总悬浮颗粒物、感冒/流感药物和 PtRh 合金进行符合性评估。