Predictive food microbiology

The need to assure the microbiological safety and quality of increasingly complex food products has stimulated interest in the use of mathematical modeling to quantify and predict microbial behavior. During the past several years there has been substantial advancement in both the concepts and methods used in predictive microbiology. Coupled with ‘user-friendly’ applications software and the development of expert system, these models are providing powerful new tools for rapidly estimating the effects of formulation and storage factors on the microbiological relations in foods.

[1]  M H Zwietering,et al.  Comparison of definitions of the lag phase and the exponential phase in bacterial growth. , 1992, The Journal of applied bacteriology.

[2]  J F Van Impe,et al.  Dynamic mathematical model to predict microbial growth and inactivation during food processing , 1992, Applied and environmental microbiology.

[3]  M. Griffiths,et al.  Prediction of the shelf‐life of pasteurized milk at different storage temperatures , 1988 .

[4]  K. Davey Applicability of the Davey (linear Arrhenius) predictive model to the lag phase of microbial growth , 1991 .

[5]  T. A. Roberts,et al.  The effect of sodium chloride and temperature on the rate and extent of growth of Clostridium botulinum type A in pasteurized pork slurry. , 1987, The Journal of applied bacteriology.

[6]  C. Genigeorgis,et al.  Predicting the Safe Storage of Fresh Fish Under Modified Atmospheres with Respect to Clostridium botulinum Toxigenesis by Modeling Length of the Lag Phase of Growth. , 1990, Journal of food protection.

[7]  C. Genigeorgis,et al.  Risk of Growth and Toxin Production by Clostridium botulinum Nonproteolytic Types B, E, and F in Salmon Fillets Stored Under Modified Atmospheres at Low and Abused Temperatures. , 1987, Journal of food protection.

[8]  D. Kilsby,et al.  Hazard analysis applied to microbial growth in foods: development of mathematical models describing the effect of water activity. , 1983, The Journal of applied bacteriology.

[9]  T. Montville Quantitation of pH- and salt-tolerant subpopulations from Clostridium botulinum , 1984, Applied and environmental microbiology.

[10]  K van't Riet,et al.  Modeling of bacterial growth as a function of temperature , 1991, Applied and environmental microbiology.

[11]  D. Ratkowsky,et al.  Model for combined effect of temperature and salt concentration/water activity on the growth rate of Staphylococcus xylosus. , 1987, The Journal of applied bacteriology.

[12]  K. Davey,et al.  A predictive model for combined temperature and water activity on microbial growth during the growth phase. , 1989, The Journal of applied bacteriology.

[13]  K. Dodds Combined effect of water activity and pH on inhibition of toxin production by Clostridium botulinum in cooked, vacuum-packed potatoes , 1989, Applied and environmental microbiology.

[14]  A. N. Stokes,et al.  Model for bacterial culture growth rate throughout the entire biokinetic temperature range , 1983, Journal of bacteriology.

[15]  R. Leffler,et al.  Growth of Clostridium perfringens in cooked chili during cooling , 1988, Applied and environmental microbiology.

[16]  B. Lund,et al.  The combined effect of incubation temperature, pH and sorbic acid on the probability of growth of non-proteolytic, type B Clostridium botulinum. , 1990, The Journal of applied bacteriology.

[17]  John G Phillips,et al.  Response Surface Model for Predicting the Effects of Temperature pH, Sodium Chloride Content, Sodium Nitrite Concentration and Atmosphere on the Growth of Listeria monocytogenes. , 1990, Journal of food protection.

[18]  C. Genigeorgis,et al.  Quantitative Evaluation of Clostridium botulinum Nonproteolytic Types B, E, and F Growth Risk in Fresh Salmon Tissue Homogenates Stored under Modified Atmospheres. , 1987, Journal of food protection.

[19]  John G Phillips,et al.  Model for Aerobic Growth of Shigella flexneri Under Various Conditions of Temperature, pH, Sodium Chloride and Sodium Nitrite Concentrations. , 1992, Journal of food protection.

[20]  John G Phillips,et al.  Model for the Aerobic Growth of Aeromonas hydrophila K144. , 1991, Journal of food protection.

[21]  M R Adams,et al.  Modelling the effect of pH, acidulant and temperature on the growth rate of Yersinia enterocolitica. , 1991, The Journal of applied bacteriology.

[22]  David Baker,et al.  Behavior of Nonproteolytic Clostridium botulinum Type B and E Spores in Cooked Turkey and Modeling Lag Phase and Probability of Toxigenesis , 1991 .

[23]  M. B. Cole,et al.  Comparison of a quadratic response surface model and a square root model for predicting the growth rate of Yersinia enterocolitic , 1992 .

[24]  M. Parish,et al.  Survival of Listeria monocytogenes in Low pH Model Broth Systems 1. , 1989, Journal of food protection.