The low-quality of automotive fuels may lead to the generation of pollutants harmful to both environmental and human health. The quality evaluation of automotive fuels requires a multiparameter conformity assessment, which may lead to an increased total risk of false conformity decisions even if all parameters comply with the acceptance limits. Thus, the aim of this work was to propose the establishment of multivariate acceptance limits in order to ensure a reduced total risk of false conformity decisions applied to automotive fuels analysis. Particular and total (consumers’ and/or producers’) risks were estimated using frequentist (specific) and Bayesian (global) approaches. Multivariate acceptance limits were estimated using Monte Carlo method, adopting an appropriate multivariate coverage factor (k’) defined using MS Excel Solver function. The definition of multivariate acceptance limits ensures a total risk below the maximum admissible risk (typically 5%) and was successfully employed in the conformity assessment of automotive fuels (diesel and gasoline). The employment of the multivariate acceptance limits may be useful in the conformity assessment of several multiparameter products.

汽车燃料的质量低下可能导致产生有害于环境和人类健康的污染物。汽车燃料的质量评估需要多参数合格性评估，即使所有参数都符合可接受的限值，也可能导致错误的合格性决策的总风险增加。因此，这项工作的目的是建议建立多元接受极限，以确保降低应用于汽车燃料分析的不合格决定的总风险。特殊风险和总风险（消费者和/或生产者的风险）使用常客（特定）和贝叶斯（全局）方法进行估算。采用蒙特卡洛方法，采用适当的多元覆盖因子（k'）使用MS Excel规划求解功能定义。多元接受极限的定义可确保总风险低于最大允许风险（通常为5％），并已成功用于汽车燃料（柴油和汽油）的合格评定。多元接受极限的采用可能对几种多参数产品的合格评定有用。