Modellierung der thermischen Inaktivierung vegetativer Mikroorganismen

Pardey, Kevin K.; Schuchmann, Heike P.; Schubert, Helmar

doi:10.1002/cite.200500018

Cited by 13 publications

(10 citation statements)

References 44 publications

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“…(i) Weibullian inactivation without adaptation. Published isothermal survival curves of Escherichia coli, Salmonella, and other pathogens indicate that their heat inactivation does not follow the traditionally assumed firstorder kinetics and that their curvilinear semilogarithmic survival curves can be described by the Weibullian model (14,15,16,23,29).…”

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confidence: 99%

“…Thus, the discussion below will be relevant to both linear and nonlinear heat inactivation patterns. For several organisms, the power n(T) in equation 1 was found to be practically constant or could be assigned a fixed numerical value with only minor effect on the model's fit (6,14,16,22). We will assume that this is true and for what follows use the model with n(T) ϭ n, i.e.,…”

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confidence: 99%

“…The temperature dependence of the "rate parameter," b(T), can be described by the empirical log-logistic model (16,17,20):…”

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confidence: 99%

“…(Equation 7 can also be converted into a difference equation and solved with general-purpose software like MS Excel [19].) The validity of the WeLL model has been demonstrated by its ability to correctly predict dynamic inactivation patterns from experimental isothermal survival data (6,16,24) or from dynamic data not used in its parameter calculations (4,17,20).…”

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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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confidence: 99%

“…The temperature dependence of the "rate parameter," b(T), can be described by the empirical log-logistic model (16,17,20):…”

mentioning

confidence: 99%

mentioning

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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“…Man siehe dazu z. B. den sehr umfassenden Artikel von Pardey et al [1] und die dort zitierte Literatur, weiterhin Kopf et al [2], Peleg et al [3], Moats et al [4], Anderson et al [5], Stephens et al [6] sowie die Arbeiten der Verfasser [7 -11]. Die zu Grunde liegende Fragestellung lässt sich -für den Fall der thermischen Inaktivierung -wie folgt formulieren: Eine Probe mit Anfangskeimzahl N 0 wird ab einem Zeitpunkt t = 0 einer keimtötend hohen Temperatur T (in Grad Celsius) ausgesetzt.…”

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