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Hierarchical Bayesian Models to Assess Between- and Within-Batch Variability of Pathogen Contamination in Food

机译：评估食品中病原菌污染批次间和批次内变异性的多层贝叶斯模型

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Assessing within-batch and between-batch variability is of major interest for risk assessors and risk managers in the context of microbiological contamination of food. For example, the ratio between the within-batch variability and the between-batch variability has a large impact on the results of a sampling plan. Here, we designed hierarchical Bayesian models to represent such variability. Compatible priors were built mathematically to obtain sound model comparisons. A numeric criterion is proposed to assess the contamination structure comparing the ability of the models to replicate grouped data at the batch level using a posterior predictive loss approach. Models were applied to two case studies: contamination by Listeria monocytogenes of pork breast used to produce diced bacon and contamination by the same microorganism on cold smoked salmon at the end of the process. In the first case study, a contamination structure clearly exists and is located at the batch level, that is, between batches variability is relatively strong, whereas in the second a structure also exists but is less marked.
机译：对于食品的微生物污染而言，评估批内和批间变异性是风险评估人员和风险管理人员的主要兴趣所在。例如，批内变异性与批间变异性之间的比率对采样计划的结果有很大的影响。在这里，我们设计了分层贝叶斯模型来表示这种可变性。可以通过数学方式建立兼容的先验，以获得声音模型的比较。提出了一个数字标准来评估污染结构，并使用后验预测损失方法比较模型在批次级别复制分组数据的能力。该模型被用于两个案例研究：在生产过程结束时，被用于生产切成丁肉的猪胸肉的李斯特菌引起的李斯特菌污染和在冷熏鲑鱼中被同一微生物污染。在第一个案例研究中，明显存在污染结构，并且该污染结构位于批次级别，也就是说，批次之间的变异性相对较强，而在第二个案例研究中，也存在一个污染结构，但标记程度较低。

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