Predicting heat inactivation of <i>Staphylococcus aureus</i> under nonisothermal treatments at different pH

Hassani, Mounir; Mañas, Pilar; Condón, S.; Pagán, Rafael

doi:10.1002/mnfr.200500171

Cited by 22 publications

(16 citation statements)

References 24 publications

(32 reference statements)

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“…4a). Previous results from different authors have shown that nonisothermal heating influences the heat resistance of vegetative bacteria (Stephens et al 1994;Hassani et al 2006), probably through the induction of heat shock protein (HSP) formation (Periago et al 2002), which can be induced very rapidly (Yura et al 2002).…”

Section: Discussionmentioning

confidence: 99%

Nonisothermal heat resistance determinations with the thermoresistometer Mastia

Conesa

Andreu

Fernández

et al. 2009

Journal of Applied Microbiology

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Aims: To design and build a thermoresistometer, named Mastia, which could perform isothermal and nonisothermal experiments. Methods and Results: In order to evaluate the thermoresistometer, the heat resistance of Escherichia coli vegetative cells and Alicyclobacillus acidoterrestris spores was explored. Isothermal heat resistance of E. coli was characterized by D60°C = 0·38 min and z = 4·7°C in pH 7 buffer. When the vegetative cells were exposed to nonisothermal conditions, their heat resistance was largely increased at slow heating and fast cooling rates. Isothermal heat resistance of A. acidoterrestris was characterized by D95°C = 7·4 min and z = 9·5°C in orange juice. Under nonisothermal conditions, inactivation was reasonably well predicted from isothermal data. Conclusions: The thermoresistometer Mastia is a very suitable instrument to get heat resistance data of micro‐organisms under isothermal and nonisothermal treatments. Significance and Impact of the Study: The thermoresistometer Mastia can be a helpful tool for food processors in order to estimate the level of safety of the treatments they apply.

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Section: Discussionmentioning

confidence: 99%

Nonisothermal heat resistance determinations with the thermoresistometer Mastia

Conesa

Andreu

Fernández

et al. 2009

Journal of Applied Microbiology

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“…7 and 8, respectively. The original authors of the data are Hassani et al (8,9,10), who also reported the organism's isothermal survival patterns at these two pH levels (10). As before, the WeLL model's parameters were calculated using data obtained at clearly lethal temperatures, i.e., in the range of 54 to 62°C.…”

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

confidence: 99%

“…Its cells can produce "heat shock proteins," which help them to survive mild heat treatments (1,11). Other organisms, Salmonella enterica and Bacillus cereus among them, can also develop defensive mechanisms that help them to survive in an acidic environment (8,9,13). Whether adaptation allows the cells to avoid injury or to repair damage once it has occurred, or both, should not concern us here.…”

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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.…”

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confidence: 99%

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Dynamic Model of Heat Inactivation Kinetics for Bacterial Adaptation

Corradini

Peleg

2009

Appl Environ Microbiol

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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,

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“…Microorganisms reveal stress adaptation, i.e., the increase of resistance to environmental conditions that would normally be lethal (e.g., heat, acidity and chemical agents), by pre-exposure to a similar stress factor (Cebrian et al, 2009;Hassani et al, 2005Hassani et al, ,2006Juneja and Marks, 2005;Valdramidis et al, 2006Valdramidis et al, ,2007 or a different kind of stress (Leyer and Johnson, 1993;Juneja and Novak, 2003;Skandamis et al, 2008Skandamis et al, ,2009. The ability of a stress-adapted microorganism to resist when exposed to another kind of environmental stress is known as cross protection (Juneja and Novak, 2003).…”

Section: Introductionmentioning

confidence: 99%