Microbial food safety sampling in the food industry is generally based on single sampling schemes, with the results dictating the acceptance/rejection of the entire food lot. Resampling plans are generally only used to discern quality of ingredients or food items where food safety is not part of the evaluation. This paper developed a Bayesian model to quantify the uncertainty in the concentration of a bacterial organism in a batch of product considering the results from the primary sampling, as well as from subsequent resampling. Bayesian consideration of the primary sampling outcomes allowed formulation of a prior distribution which was then used to revise the uncertainty in the concentration present. Uncertainty distributions for microbial concentration were calculated for a range of resampling scenarios, in the event where one sample from an initial sampling tested positive. The study demonstrated that even in the event of large numbers of negative retest results, there was only a small shift in the uncertainty distributions of the concentrations of microorganisms present compared to the results from the initial sampling results. When the second set of testing includes positive outcomes, the peak of the uncertainty distribution moves to the right, as the retest outcomes revise upwards the initial estimate of the concentration of the microbial organism present. The study demonstrated the potential value of additional sampling to better estimate the likely microorganism concentration present in the food product. Especially in the events where a large proportion of the retests were negative, the magnitude of uncertainty was improved. The approach may be especially valuable for any investigation into a wider root cause analysis undertaken after the original positive test.

食品工业中的微生物食品安全抽样通常基于单个抽样方案，其结果决定了整个食品批次的接受/拒绝。重采样计划通常仅用于识别食品安全不在评估范围内的成分或食品的质量。本文开发了一种贝叶斯模型，考虑了一次采样以及后续重新采样的结果，可以对一批产品中细菌生物浓度的不确定性进行量化。贝叶斯对主要采样结果的考虑允许制定先验分布，然后用于修正当前浓度的不确定性。针对一系列重采样场景计算了微生物浓度的不确定性分布，如果初始抽样中的一个样本测试为阳性。该研究表明，即使有大量的阴性复检结果，与初始采样结果相比，存在的微生物浓度的不确定性分布也只有很小的变化。当第二组测试包含阳性结果时，不确定性分布的峰值将向右移动，因为重新测试结果会向上修正存在的微生物的浓度的初始估计值。该研究证明了额外采样的潜在价值，可以更好地估计食品中可能存在的微生物浓度。特别是在大部分重新测试为阴性的情况下，不确定性的幅度得到了改善。