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Fitting a lognormal distribution to enumeration and absence/presence data

Abstract : To fit a lognormal distribution to a complex set of microbial data, including detection data (e.g. presence or absence in 25 g) and enumeration data (e.g. 30 cfu/g), we compared two models: a model called M-CLD based on data expressed as concentrations (in cfu/g) or censored concentrations (e.g. <10 cfu/g, or >1 cfu/25 g) 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) = 1 cfu/25 g; limit of quantification (LOQ) = 10 cfu/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. (C) 2012 Elsevier B.V. All rights reserved.
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Natalie N. Commeau, Éric É. Parent, Marie Laure Delignette-Muller, Marie Cornu. Fitting a lognormal distribution to enumeration and absence/presence data. International Journal of Food Microbiology, Elsevier, 2012, 155 (3), pp.146 - 152. ⟨10.1016/j.ijfoodmicro.2012.01.023⟩. ⟨hal-01004205⟩



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