Growth of Clostridium perfringens from spore inocula in cooked cured beef: development of a predictive model
نویسندگان
چکیده
The objective of this study was to develop a model to predict the growth of C. perfringens from spores at temperatures applicable to the cooling of cooked cured meat products. C. perfringens growth from spores was not observed at a temperature of Ž . 12 C for up to 3 weeks. The two parameters: germination, outgrowth, and lag GOL time and exponential growth rate, EGR, were determined using a function derived from mechanistic and stochastic considerations and the observed relative growths at specified times. A general model to predict the amount of relative growth for arbitrary temperature was determined by fitting the exponential growth rates to a square root Ratkowsky function, and assuming a constant ratio of GOL and generation times. The predicted relative growth is sensitive to the value of this ratio. A closed form equation was developed that can be used to estimate the relative growth for a general cooling scenario and determine a standard error of the estimate. The equation depends upon microbiological assumptions of the effect of history of the GOL times for gradual changes in temperature. Applying multivariate statistical procedures, a confidence interval was computed on the prediction of the amount of growth for a given temperature. The model predicts, for example, a relative growth of 3.17 with an upper 95% confidence limit of 8.50 when cooling the product from 51 to 11 C in 8 h, assuming a log linear decline in temperature with time. Published by Elsevier Science Ltd.
منابع مشابه
Predictive model for growth of Clostridium perfringens in cooked cured pork.
Mathematical models have been developed and used for predicting growth of foodborne pathogens in various food matrices. However, these early models either used microbiological media or other model systems to develop the predictive models. Some of these models have been shown to be inaccurate for applications in meat and specific food matrices, especially under dynamic conditions, such as consta...
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