Université Européenne de Bretagne, France-Université de Brest, EA3882, Laboratoire Universitaire de Biodiversité et Ecologie Microbienne, IFR148 ScInBioS, UMT 08.3 PHYSI'Opt, 6 rue de l'Université, F-29334 Quimper, France.
Int J Food Microbiol. 2012 Jan 16;152(3):139-52. doi: 10.1016/j.ijfoodmicro.2011.09.023. Epub 2011 Oct 2.
Growth, growth boundary and inactivation models have been extensively developed in predictive microbiology and are commonly applied in food research nowadays. Few studies though report the development of models which encompass all three areas together. A tiered modelling approach, based on the Gamma hypothesis, is proposed here to predict the behaviour of Listeria. Datasets of Listeria spp. behaviour in laboratory media, meat, dairy, seafood products and vegetables were collected from literature, unpublished sources and from the databases ComBase and Sym'Previus. The explanatory factors were temperature, pH, water activity, lactic and sorbic acids. For the growth part, 697 growth kinetic datasets were fitted. The estimated growth rates and 2021 additional growth primary datasets were used to fit the secondary growth models. In a second step, the fitted model was used to predict the growth/no-growth boundary. For the inactivation modelling phase, 535 inactivation curves were used. Gamma models with and without interactions between the explanatory factors were used for the growth and boundary models. The correct prediction percentage (predicted growth when growth is observed+predicted inactivation when inactivation is observed) varied from 62% to 81% for the models without interactions, and from 85% to 87% for the models with interactions. The median error for the predicted population size was less than 0.34 log(10)(CFU/mL) for all models. The kinetics of inactivation were fitted with modified Weibull primary models and the estimated bacterial resistance was then modelled as a function of the explanatory factors. The error for the predicted microbial population size was less than 0.71 log(10)(CFU/mL) with a median value of less than 0.21 for all foods. The model enables the quantification of the increase or decrease in the bacterial population for a given formulation or storage condition. It might also be used to optimise a food formulation or storage condition in the case of a targeted increase or decrease of the bacterial population.
生长、生长边界和失活动力学模型已在预测微生物学中得到广泛发展，目前在食品研究中也得到广泛应用。然而，很少有研究报告开发涵盖这三个领域的模型。本文提出了一种基于伽马假设的分层建模方法来预测李斯特菌的行为。从文献、未发表的来源以及 ComBase 和 Sym'Previus 数据库中收集了李斯特菌在实验室培养基、肉类、乳制品、海鲜产品和蔬菜中的行为数据集。解释因素包括温度、pH 值、水分活度、乳酸和山梨酸。对于生长部分，拟合了 697 个生长动力学数据集。估计的生长速率和 2021 个额外生长原始数据集用于拟合二次生长模型。在第二步中，使用拟合模型预测生长/不生长边界。在失活动力学建模阶段，使用了 535 条失活动力学曲线。对于生长和边界模型，使用了具有和不具有解释因素相互作用的伽马模型。对于没有相互作用的模型，正确预测百分比（观察到生长时的预测生长+观察到失活时的预测失活）从 62%到 81%不等，对于具有相互作用的模型，从 85%到 87%不等。对于所有模型，预测种群大小的中位数误差小于 0.34log10（CFU/mL）。使用修正的 Weibull 原始模型拟合失活动力学，并将估计的细菌抗性建模为解释因素的函数。预测微生物种群大小的误差小于 0.71log10（CFU/mL），所有食品的中位数值小于 0.21。该模型能够量化给定配方或储存条件下细菌种群的增加或减少。它也可以用于优化食品配方或储存条件，以实现细菌种群的有针对性增加或减少。
