Microbial dynamics versus mathematical model dynamics: The case of microbial heat resistance induction

Valdramidis, Vasilis; Geeraerd, A.H.; Bernaerts, Kristel; Impe, Jan Van

doi:10.1016/j.ifset.2005.09.005

Cited by 74 publications

(78 citation statements)

References 31 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of the organism's ability to adapt at sublethal temperatures, all the predictions based on the unmodified model were off mark. As expected and as stressed by Valdramidis et al (28), they were all to the left of the actual survival curve. Also, the magnitude of the shift increased as the heating rate was decreased, again in agreement with the model's prediction.…”

Section: When the Newly Defined Rate Parameter In The Form Of B[t(t)supporting

confidence: 84%

“…(i) Escherichia coli. Six nonisothermal temperature profiles and corresponding survival data of E. coli K-12 MG1655, originally reported by Valdramidis et al (27,28), are shown in Fig. 5 and 6, respectively.…”

Section: When the Newly Defined Rate Parameter In The Form Of B[t(t)mentioning

confidence: 83%

“…Regardless of the log-linearity issue, none of the abovementioned models accounts for adaptation, the ability of certain bacterial cells to adjust their metabolism in response to stress in order to increase their survivability (2,10,26,27,28). A notable example is Escherichia coli.…”

mentioning

confidence: 99%

“…Several investigators have reported and discussed the quantitative aspects of adaptation (25,27,28). When it occurs, adaptation is noticed as a gap between survival curves determined at low heating rates and those predicted by kinetic models whose parameters had been determined at high lethal temperatures (7,8,9,27,28).…”

mentioning

confidence: 99%

“…When it occurs, adaptation is noticed as a gap between survival curves determined at low heating rates and those predicted by kinetic models whose parameters had been determined at high lethal temperatures (7,8,9,27,28). The question is how to modify the inactivation kinetic model so that it can properly account for adaptation at low heating rates while maintaining its predictive ability at high rates and clearly lethal temperatures.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Dynamic Model of Heat Inactivation Kinetics for Bacterial Adaptation

Corradini

Peleg

2009

Appl Environ Microbiol

View full text Add to dashboard Cite

The Weibullian-log logistic (WeLL) inactivation model was modified to account for heat adaptation by introducing a logistic adaptation factor, which rendered its "rate parameter" a function of both temperature and heating rate. The resulting model is consistent with the observation that adaptation is primarily noticeable in slow heat processes in which the cells are exposed to sublethal temperatures for a sufficiently long time. Dynamic survival patterns generated with the proposed model were in general agreement with those of Escherichia coli and Listeria monocytogenes as reported in the literature. Although the modified model's rate equation has a cumbersome appearance, especially for thermal processes having a variable heating rate, it can be solved numerically with commercial mathematical software. The dynamic model has five survival/adaptation parameters whose determination will require a large experimental database. However, with assumed or estimated parameter values, the model can simulate survival patterns of adapting pathogens in cooked foods that can be used in risk assessment and the establishment of safe preparation conditions.Combined with heat transfer data or models, microbial survival kinetics, especially of bacteria or spores, is extensively used to determine the safety of industrial heat preservation processes like canning, extant or planned. The same is true for milder heat processes such as milk and fruit pasteurization. However, survival models are also a valuable tool to assess the safety of prepared foods, especially those made of raw meats, poultry, and eggs, where surviving pathogens can be a public health issue.The heat resistance of a bacterium, or any other microorganism, is almost always determined from a set of its isothermal survival curves, recorded at several lethal temperatures. The kinetic models, which define the heat resistance parameters, may vary, but the calculation procedure itself is usually the same. First, the experimental isothermal survival data are fitted with what is known as the "primary model." Once fitted, the temperature dependence of this primary model's coefficients is described by what is known as the "secondary model." When combined with a temperature profile expression, T(t), and incorporated into the inactivation rate equation, the result is a "tertiary model," which enables its user to predict the organism's survival curve under any static or dynamic (i.e., nonisothermal) conditions.The traditional log-linear ("first-order kinetic") model is the best-known primary survival model, and it is still widely used in sterility calculations in the food, pharmaceutical, and other industries. Traditionally, it has been assumed that the D value calculated with this model has a log-linear temperature dependence or, alternatively, that the temperature effect on the exponential rate constant, k,

show abstract

Section: When the Newly Defined Rate Parameter In The Form Of B[t(t)supporting

confidence: 84%

Section: When the Newly Defined Rate Parameter In The Form Of B[t(t)mentioning

confidence: 83%

mentioning

confidence: 99%