Open Access
Subscription or Fee Access
Mathematical models in Predictive microbiology for food safety
Abstract
The study of predictive microbiology is predicated on the notion that populations of microorganisms react in a predictable manner to the conditions of their environment and that historical observations can be used to predict how populations will react in the future by looking at environments in terms of the dominant limitations they present. Predictive microbiology is a relatively new branch of the field of microbiology that has recently gained significant attention. The field of predictive microbiology is garnering an increasing amount of attention on a worldwide basis. Proponents of this field assert that it will lead to improvements in food microbiology. In this essay, we discuss the development of predictive microbiology as well as its advantages and disadvantages. As a result of the fact that kinetic modeling and probabilistic modeling are located at different extremities of a continuum of modeling, the distinction between the two is manufactured. Both of these approaches sit on opposite ends of the spectrum of possibilities. It is concluded that predictive modeling can be applied to complex food systems, that predictive models can help solve problems and make accurate predictions and analyses, that the method has not yet reached its full potential, and that "predictive microbiology" may be a good way to understand the microbial ecology of food.
References
K.J.J.o.f.p. Koutsoumanis, Modeling food spoilage in microbial risk assessment, 72(2) (2009) 425–427.
S.A. Dominguez, D.W.J.J.o.f.p. Schaffner, Modeling the growth of Salmonella in raw poultry stored under aerobic conditions, 71(12) (2008) 2429–2435.
F. Liu, R.-Q. Yang, Y.-F.J.F.M. Li, Correlations between growth parameters of spoilage micro-organisms and shelf-life of pork stored under air and modified atmosphere at− 2, 4 and 10° C, 23(6) (2006) 578–583.
P.J.I.j.o.f.m. Dalgaard, Modelling of microbial activity and prediction of shelf life for packed fresh fish, 26(3) (1995) 305–317.
K. Koutsoumanis, Predictive modeling of the shelf life of fish under nonisothermal conditions, Applied environmental microbiology 67(4) (2001) 1821–1829.
D. Thuault, O. Couvert, > La simulation du comportement des microorganismes dans les aliments, Revue générale du froid du conditionnement d'air (JAN-FEV) (2009) 45–50.
R. Li, X. Kou, L. Zhang, S. Wang, Inactivation kinetics of food-borne pathogens subjected to thermal treatments: a review, International Journal of Hyperthermia 34(2) (2018) 177–188.
N.J. Nagelkerke, A note on a general definition of the coefficient of determination, Biometrika 78(3) (1991) 691–692.
K. Huang, H. Tian, L. Gai, J. Wang, A review of kinetic models for inactivating microorganisms and enzymes by pulsed electric field processing, Journal of food engineering 111(2) (2012) 191–207.
J. Baranyi, C. Pin, T. Ross, Validating and comparing predictive models, International journal of food microbiology 48(3) (1999) 159–166.
T. Ross, Indices for performance evaluation of predictive models in food microbiology, Journal of Applied Bacteriology 81(5) (1996) 501–508.
R. Villa-Rojas, J. Tang, S. Wang, M. Gao, D.-H. Kang, J.-H. Mah, P. Gray, M.E. Sosa-Morales, A. Lopez-Malo, Thermal inactivation of Salmonella Enteritidis PT 30 in almond kernels as influenced by water activity, Journal of food protection 76(1) (2013) 26–32.
J. Yang, G. Bingol, Z. Pan, M.T. Brandl, T.H. McHugh, H. Wang, Infrared heating for dry-roasting and pasteurization of almonds, Journal of food engineering 101(3) (2010) 273–280.
T. Török, A. King Jr, Thermal inactivation kinetics of food‐borne yeasts, Journal of food science 56(1) (1991) 6–9.
G. Chen, O.H. Campanella, C.M. Corvalan, T.A. Haley, On-line correction of process temperature deviations in continuous retorts, Journal of food engineering 84(2) (2008) 258–269.
M. Peleg, Advanced quantitative microbiology for foods and biosystems: models for predicting growth and inactivation, CRC Press 2006.
M.G. Corradini, M. Peleg, Dynamic model of heat inactivation kinetics for bacterial adaptation, Applied and Environmental Microbiology 75(8) (2009) 2590–2597.
L. Humpheson, M. Adams, W. Anderson, M. Cole, Biphasic thermal inactivation kinetics in Salmonella enteritidis PT4, Applied and Environmental Microbiology 64(2) (1998) 459–464.
D. Kamau, S. Doores, K. Pruitt, Enhanced thermal destruction of Listeria monocytogenes and Staphylococcus aureus by the lactoperoxidase system, Applied and Environmental Microbiology 56(9) (1990) 2711–2716.
R. Tomlins, Z. Ordal, Thermal injury and inactivation in vegetative bacteria. En" Inhibition and inactivation of vegetative microbes. Skinner, FA y Hugo, WB, Society of Applied Bacteriology Symposia Series, 1976, pp. 135–190.
V. Juneja, S. Mukhopadhyay, H. Marks, T.B. Mohr, A. Warning, A. Datta, Predictive thermal inactivation model for effects and interactions of temperature, NaCl, sodium pyrophosphate, and sodium lactate on Listeria monocytogenes in ground beef, Food and Bioprocess Technology 7(2) (2014) 437–446.
V.K. Juneja, U. Gonzales-Barron, F. Butler, A.S. Yadav, M. Friedman, Predictive thermal inactivation model for the combined effect of temperature, cinnamaldehyde and carvacrol on starvation-stressed multiple Salmonella serotypes in ground chicken, International journal of food microbiology 165(2) (2013) 184–199.
V.K. Juneja, Predictive model for the combined effect of temperature, sodium lactate, and sodium diacetate on the heat resistance of Listeria monocytogenes in beef, Journal of food protection 66(5) (2003) 804–811.
V.K. Juneja, B.S. MARMER, B.S. EBLEN, Predictive model for the combined effect of temperature, pH, sodium chloride, and sodium pyrophosphate on the heat resistance of Escherichia coli O157: H7 1, Journal of Food Safety 19(2) (1999) 147–160.
M. Van Boekel, V. Fogliano, N. Pellegrini, C. Stanton, G. Scholz, S. Lalljie, V. Somoza, D. Knorr, P.R. Jasti, G. Eisenbrand, A review on the beneficial aspects of food processing, Molecular nutrition food research 54(9) (2010) 1215–1247.
M. Schwaab, J.C. Pinto, Optimum reference temperature for reparameterization of the Arrhenius equation. Part 1: Problems involving one kinetic constant, Chemical Engineering Science 62(10) (2007) 2750–2764.
Refbacks
- There are currently no refbacks.