A mathematical model for bacterial inactivation

• Published in: International journal of food microbiology Vol. 46; no. 1; pp. 45 - 55
• Main Authors: Xiong, R., Xie, G., Edmondson, A.E., Sheard, M.A.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 12.01.1999; Elsevier
• Subjects: Bacterial inactivation; Biological and medical sciences; ECOLOGIA MICROBIANA; ECOLOGIE MICROBIENNE; FOOD HYGIENE; Food industries; Food Microbiology; Fundamental and applied biological sciences. Psychology; HIGIENE DE LOS ALIMENTOS; HYGIENE DES ALIMENTS; Kinetics; Linear Models; Mathematical modelling; MATHEMATICAL MODELS; MICROBIAL ECOLOGY; MODELE MATHEMATIQUE; MODELOS MATEMATICOS; Models, Biological; Numerical Analysis, Computer-Assisted; Regression Analysis; STAPHYLOCOCCUS AUREUS; Staphylococcus aureus - growth & development; Bacterial inactivation; Mathematical modelling; Kinetic model; Decontamination; Goodness of fit test; Theoretical study; Bacteria; Inactivation; Mathematical model; Survival
• ISSN: 0168-1605; 1879-3460
• DOI: 10.1016/S0168-1605(98)00172-X

The first order kinetic model, the Buchanan model and Cerf's model, can model a linear survival curve, a survival curve with a shoulder and a survival curve with a tailing, respectively. However, they are not suitable for fitting a sigmoidal survival curve. The three models were integrated into a new model that was capable of fitting the four most commonly observed survival curves: linear curves, curves with a shoulder, curves with a tailing (biphasic curves) and sigmoidal curves. The new model was compared with the Whiting–Buchanan model using the survival curves of Staphylococcus aureus. The goodness-of-fit of the proposed model is practically as good as that of the Whiting–Buchanan model. Compared with the Whiting–Buchanan model, the proposed model has a more mechanistic background. Since for non-linear survival curves, such as biphasic and sigmoidal curves, the t m−D value (the time required for an m-log-cycle reduction of microorganisms under a given condition) cannot be estimated accurately by the existing or traditional method, a new method is also proposed to predict accurately the t m−D value for non-linear survival curves.