UMR 518 AgroParisTech-INRA MIA, 16 rue Claude Bernard 75005 Paris, France.
Int J Food Microbiol. 2012 Apr 16;155(3):146-52. doi: 10.1016/j.ijfoodmicro.2012.01.023. Epub 2012 Feb 3.
To fit a lognormal distribution to a complex set of microbial data, including detection data (e.g. presence or absence in 25g) and enumeration data (e.g. 30cfu/g), we compared two models: a model called M(CLD) based on data expressed as concentrations (in cfu/g) or censored concentrations (e.g. <10cfu/g, or >1cfu/25g) versus a model called M(RD) that directly uses raw data (presence/absence in test portions, and plate colony counts). We used these two models to simulated data sets, under standard conditions (limit of detection (LOD)=1cfu/25g; limit of quantification (LOQ)=10cfu/g) and used a maximum likelihood estimation method (directly for the model M(CLD) and via the Expectation-Maximisation (EM) algorithm for the model M(RD). The comparison suggests that in most cases estimates provided by the proposed model M(RD) are similar to those obtained by model M(CLD) accounting for censorship. Nevertheless, in some cases, the proposed model M(RD) leads to less biased and more precise estimates than model M(CLD).
为了将复杂的微生物数据（包括检测数据（例如 25g 中的存在或不存在）和计数数据（例如 30cfu/g））拟合到对数正态分布中，我们比较了两种模型：一种称为 M(CLD)的模型，该模型基于表示为浓度（cfu/g）或截尾浓度（例如 <10cfu/g 或 >1cfu/25g）的数据；另一种称为 M(RD)的模型，该模型直接使用原始数据（测试部分的存在/不存在以及平板菌落计数）。我们使用这两种模型模拟了数据集，在标准条件下（检测限 (LOD)=1cfu/25g；定量限 (LOQ)=10cfu/g），并使用最大似然估计方法（直接用于模型 M(CLD)，并通过期望最大化 (EM) 算法用于模型 M(RD)。比较表明，在大多数情况下，拟议模型 M(RD)提供的估计值与考虑到截尾的模型 M(CLD)的估计值相似。然而，在某些情况下，拟议的模型 M(RD)比模型 M(CLD)导致的偏差更小且更精确。
