General Model, Based on Two Mixed Weibull Distributions of Bacterial Resistance, for Describing Various Shapes of Inactivation Curves

ABSTRACT Cells of Listeria monocytogenes or Salmonella enterica serovar Typhimurium taken from six characteristic stages of growth were subjected to an acidic stress (pH 3.3). As expected, the bacterial resistance increased from the end of the exponential phase to the late stationary phase. Moreover, the shapes of the survival curves gradually evolved as the physiological states of the cells changed. A new primary model, based on two mixed Weibull distributions of cell resistance, is proposed to describe the survival curves and the change in the pattern with the modifications of resistance of two assumed subpopulations. This model resulted from simplification of the first model proposed. These models were compared to the Whiting's model. The parameters of the proposed model were stable and showed consistent evolution according to the initial physiological state of the bacterial population. Compared to the Whiting's model, the proposed model allowed a better fit and more accurate estimation of the parameters. Finally, the parameters of the simplified model had biological significance, which facilitated their interpretation.

[1]  M. Hajmeer,et al.  Modeling the survival of Salmonella spp. in chorizos. , 2006, International journal of food microbiology.

[2]  F. Leroy,et al.  Modeling Bacteriocin Resistance and Inactivation of Listeria innocua LMG 13568 by Lactobacillus sakei CTC 494 under Sausage Fermentation Conditions , 2005, Applied and Environmental Microbiology.

[3]  J. Raso,et al.  Inactivation kinetics of Yersinia enterocolitica by citric and lactic acid at different temperatures. , 2005, International journal of food microbiology.

[4]  I. Leguerinel,et al.  Survival curves of heated bacterial spores: effect of environmental factors on Weibull parameters. , 2005, International journal of food microbiology.

[5]  I. Taub,et al.  The mathematical properties of the quasi-chemical model for microorganism growth-death kinetics in foods. , 2005, International journal of food microbiology.

[6]  M Peleg,et al.  A model of microbial survival curves in water treated with a volatile disinfectant , 2003, Journal of applied microbiology.

[7]  D. Wilson,et al.  Salmonella enterica Serovar Typhimurium and Listeria monocytogenes Acid Tolerance Response Induced by Organic Acids at 20°C: Optimization and Modeling , 2003, Applied and Environmental Microbiology.

[8]  G. Nychas,et al.  Modeling the microbial interaction and the death of Escherichia coli O157:H7 during the fermentation of Spanish-style green table olives. , 2003, Journal of food protection.

[9]  K. Koutsoumanis,et al.  A vitalistic approach for non-thermal inactivation of pathogens in traditional Greek salads , 2002 .

[10]  I. Booth,et al.  Stress and the single cell: intrapopulation diversity is a mechanism to ensure survival upon exposure to stress. , 2002, International journal of food microbiology.

[11]  M. J. Ocio,et al.  Empirical model building based on Weibull distribution to describe the joint effect of pH and temperature on the thermal resistance of Bacillus cereus in vegetable substrate. , 2002, International journal of food microbiology.

[12]  S. Libby,et al.  The alternative sigma factor σE controls antioxidant defences required for Salmonella virulence and stationary‐phase survival , 2002, Molecular microbiology.

[13]  I. Leguerinel,et al.  On calculating sterility in thermal preservation methods: application of the Weibull frequency distribution model. , 2001, International journal of food microbiology.

[14]  J. Sofos,et al.  Influence of the Natural Microbial Flora on the Acid Tolerance Response of Listeria monocytogenes in a Model System of Fresh Meat Decontamination Fluids , 2001, Applied and Environmental Microbiology.

[15]  K. Takumi,et al.  Modelling inactivation of Escherichia coli by low pH: application to passage through the stomach of young and elderly people , 2000, Journal of applied microbiology.

[16]  A H Geeraerd,et al.  Structural model requirements to describe microbial inactivation during a mild heat treatment. , 2000, International journal of food microbiology.

[17]  L. Phan-Thanh,et al.  Acid responses of Listeria monocytogenes. , 2000, International journal of food microbiology.

[18]  M. Peleg,et al.  Modeling Microbial Survival during Exposure to a Lethal Agent with Varying Intensity , 2000, Critical reviews in food science and nutrition.

[19]  G. Nychas,et al.  A predictive model for the non-thermal inactivation of Salmonella enteritidis in a food model system supplemented with a natural antimicrobial. , 1999, International journal of food microbiology.

[20]  F. Leroy,et al.  Temperature and pH Conditions That Prevail during Fermentation of Sausages Are Optimal for Production of the Antilisterial Bacteriocin Sakacin K , 1999, Applied and Environmental Microbiology.

[21]  R. Xiong,et al.  A mathematical model for bacterial inactivation. , 1999, International journal of food microbiology.

[22]  M Peleg,et al.  Reinterpretation of microbial survival curves. , 1998, Critical reviews in food science and nutrition.

[23]  A. McQuarrie,et al.  Regression and Time Series Model Selection , 1998 .

[24]  R. Buchanan,et al.  INTERACTIONS BETWEEN pH AND MALIC ACID CONCENTRATION ON THE INACTIVATION OF LISTERIA MONOCYTOGENES , 1998 .

[25]  J. Membré,et al.  Effects of Temperature, pH, Glucose, and Citric Acid on the Inactivation of Salmonella typhimurium in Reduced Calorie Mayonnaise. , 1997, Journal of food protection.

[26]  Sylvie Huet,et al.  Statistical tools for nonlinear regression : a practical guide with S-PLUS examples , 1997 .

[27]  J. Borkowski Statistical Tools for Nonlinear Regression , 1997 .

[28]  R. Buchanan,et al.  Expanded models for the non‐thermal inactivation of Listeria monocytogenes , 1997, Journal of applied microbiology.

[29]  M. Catteau,et al.  Modelling the growth, survival and death of Listeria monocytogenes , 1997, Journal of applied microbiology.

[30]  R. C. Whiting,et al.  Model for the survival of Staphylococcus aureus in nongrowth environments. , 1996, International journal of food microbiology.

[31]  C. Gahan,et al.  Adaptive acid tolerance response in Listeria monocytogenes: isolation of an acid-tolerant mutant which demonstrates increased virulence , 1996, Applied and environmental microbiology.

[32]  R. C. Whiting,et al.  Microbial modeling in foods. , 1995, Critical reviews in food science and nutrition.

[33]  J Baranyi,et al.  A dynamic approach to predicting bacterial growth in food. , 1994, International journal of food microbiology.

[34]  Robert L. Buchanan,et al.  Interaction of Citric Acid Concentration and pH on the Kinetics of Listeria monocytogenes Inactivation. , 1994, Journal of food protection.

[35]  M. B. Cole,et al.  Application of a log-logistic model to describe the survival of Yersinia enterocolitica at sub-optimal pH and temperature. , 1994, International journal of food microbiology.

[36]  J. Foster,et al.  A low-pH-inducible, stationary-phase acid tolerance response in Salmonella typhimurium , 1994, Journal of bacteriology.

[37]  R. C. Whiting,et al.  Modeling bacterial survival in unfavorable environments , 1993, Journal of Industrial Microbiology.

[38]  M. B. Cole,et al.  A vitalistic model to describe the thermal inactivation ofListeria monocytogenes , 1993, Journal of Industrial Microbiology.

[39]  R. C. Whiting,et al.  Differentiation of the Effects of pH and Lactic or Acetic Acid Concentration on the Kinetics of Listeria Monocytogenes Inactivation. , 1993, Journal of food protection.

[40]  S. Doores,et al.  Enhanced thermal destruction of Listeria monocytogenes and Staphylococcus aureus by the lactoperoxidase system , 1990, Applied and environmental microbiology.

[41]  O. Cerf,et al.  A REVIEW Tailing of Survival Curves of Bacterial Spores , 1977 .

[42]  Kristel Bernaerts,et al.  Accurate modelling of non-loglinear survival curves , 2004 .

[43]  M. V. van Boekel,et al.  On the use of the Weibull model to describe thermal inactivation of microbial vegetative cells. , 2002, International journal of food microbiology.

[44]  Sophie Bréand Étude biométrique de la réponse d'une population bactérienne à une variation défavorable de température ou de pH : applications en microbiologie prévisionnelle alimentaire , 1998 .

[45]  R. C. Whiting,et al.  Non‐Thermal Inactivation Models for Listeria monocytogenes , 1994 .

[46]  O. Cerf,et al.  Tailing of survival curves of bacterial spores. , 1977, The Journal of applied bacteriology.

[47]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[48]  I. Alberta,et al.  A modified Weibull model for bacterial inactivation , 2022 .