2002
DOI: 10.1016/s0168-1605(01)00624-9
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On calculating sterility in thermal preservation methods: application of the Weibull frequency distribution model

Abstract: A simple and parsimonious model which originated from the Weibull frequency distribution was proposed to describe nonlinear survival curves of spores. This model was suitable for downward concavity curves (Bacillus cereus and Bacillus pumilus), as well as for upward concavity curves (Clostridium botulinum). It was shown that traditional F values calculated from this new model were no longer additive, to such an extent that a heat treatment should be better characterized by the obtained decimal reduction of spo… Show more

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Cited by 723 publications
(427 citation statements)
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References 17 publications
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“…Estimations of model accuracy (A f ) and bias (B f ) showed an excellent fit to this model when describing the germination of C. perfringens type A food poisoning isolates in buffer solutions as affected by pH (5.8 to 8), germinant concentration (10-100 mM KCl), and spore germination temperature (T SG , 30-50°C) (Paredes-Sabja and Torres 2010). A constant behavior index β can be used to predict the effect of T SG on germination (Fernández García et al 2002;Mafart et al 2002) but improved results are obtained when using the following expression:…”
Section: Inactivation Of Bacterial Sporesmentioning
confidence: 99%
See 1 more Smart Citation
“…Estimations of model accuracy (A f ) and bias (B f ) showed an excellent fit to this model when describing the germination of C. perfringens type A food poisoning isolates in buffer solutions as affected by pH (5.8 to 8), germinant concentration (10-100 mM KCl), and spore germination temperature (T SG , 30-50°C) (Paredes-Sabja and Torres 2010). A constant behavior index β can be used to predict the effect of T SG on germination (Fernández García et al 2002;Mafart et al 2002) but improved results are obtained when using the following expression:…”
Section: Inactivation Of Bacterial Sporesmentioning
confidence: 99%
“…Upward concave curves (n<1) suggests that the sensitive members of the population die quickly, leaving progressively more resistant survivors, a behavior previously described as "tailing". Finally, the Weibull model includes the survival curve for the conventional first order model (n=1; Mafart et al 2002;Buzrul et al 2005). Several authors have reported that the Weibull model performs much better than the classical linear inactivation model (Chen and Hoover 2003;Chen 2007;Pina Perez et al 2007;van Boekel 2008).…”
Section: Nonlinear Microbial Inactivation Kinetics Modelsmentioning
confidence: 99%
“…Assuming that the pressure and temperature resistance of the individual cells of a microbial population is governed by a Weibull distribution, Mafart et al [29] developed a Weibullian equation by the following equation:…”
Section: Mathematical Modelsmentioning
confidence: 99%
“…Hence, lately, some authors have considered the survival curve as the cumulative form of lethality event distribution [45,68,84,110,144], considering the death of each microorganism as a probabilistic case. In these probabilistic models, it is assumed that each microorganism of the population has a different sensitivity to an inactivating agent and dies at a specific time.…”
Section: Predictive Mathematical and Probabilistic Modelsmentioning
confidence: 99%