Predicting the kinetics of Listeria monocytogenes and Yersinia enterocolitica under dynamic growth/death-inducing conditions, in Italian style fresh sausage

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• 作者：Luigi Iannetti^a ; ^{l.iannetti@izs.it} ; Romolo Salini^a ; Anna Franca Sperandii^a ; Gino Angelo Santarelli^a ; Diana Neri^a ; Violeta Di Marzio^a ; Romina Romantini^a ; Giacomo Migliorati^a ; Jó ; zsef Baranyi^b
• 关键词：Listeria monocytogenes ; Yersinia enterocolitica ; Predictive model ; Water activity ; Dynamic environment ; Sausage
• 刊名：International Journal of Food Microbiology
• 出版年：2017
• 出版时间：2 January 2017
• 年：2017
• 卷：240
• 期：Complete
• 页码：108-114
• 全文大小：559 K
• 卷排序：240

文摘

Traditional Italian pork products can be consumed after variable drying periods, where the temporal decrease of water activity spans from optimal to inactivating values. This makes it necessary to A) consider the bias factor when applying culture-medium-based predictive models to sausage; B) apply the dynamic version (described by differential equations) of those models; C) combine growth and death models in a continuous way, including the highly uncertain growth/no growth range separating the two regions.This paper tests the applicability of published predictive models on the responses of Listeria monocytogenes and Yersinia enterocolitica to dynamic conditions in traditional Italian pork sausage, where the environment changes from growth-supporting to inhibitory conditions, so the growth and death models need to be combined. The effect of indigenous lactic acid bacteria was also taken into account in the predictions.Challenge tests were carried out using such sausages, inoculated separately with L. monocytogenes and Y. enterocolitica, stored for 480 h at 8, 12, 18 and 20 °C. The pH was fairly constant, while the water activity changed dynamically. The effects of the environment on the specific growth and death rate of the studied organisms were predicted using previously published predictive models and parameters.Microbial kinetics in many products with a long shelf-life and dynamic internal environment, could result in both growth and inactivation, making it difficult to estimate the bacterial concentration at the time of consumption by means of commonly available predictive software tools. Our prediction of the effect of the storage environment, where the water activity gradually decreases during a drying period, is designed to overcome these difficulties. The methodology can be used generally to predict and visualise bacterial kinetics under temporal variation of environments, which is vital when assessing the safety of many similar products.