Microbial Survival Curves: Interpretation, Mathematical Modeling, and Utilization
Traditionally, microbial mortality has been treated as a process that follows first-order kinetics. There is a large body of evidence, however, that indicates that this need not be the case. An alternative approach is to consider the survival curve as the cumulative form of a temporal distribution of lethal events. The purpose of this review is to demonstrate that if the resistance of an individual cell or spore to a lethal agent is measured by the time to its destruction, then the survival curve is the cumulative form of the resistance's distribution within the population. If so, then the curve's slope has rate units and hence there exist a relation between the survival curve's shape and the inactivation kinetics. It is shown that if in a nonisothermal heat treatment the momentary slope of the semilogarithmic survival curve is that of the isothermal survival curve at the momentary temperature at a time that corresponds to the momentary survival ratio, then the survival curve can be constructed for the particular process, provided that the temperature history ("profile") and the survival parameters' temperature dependence can be expressed algebraically. Similar considerations apply to other lethal agents whose intensity varies with time--dissipating chemical antimicrobials, antibiotics, and gaseous disinfectants, for example. The differential equations, which produce the survival curves under variable conditions, can be solved numerically with currently available commercial software. This is demonstrated with actual and simulated microbial survival curves under a variety of changing agent intensities, which include monotonic increases or decreases and regular or random fluctuations. The method can also be used to calculate an organism's survival parameters directly from inactivation data obtained under varying agent intensities, which is demonstrated with heat-treated Salmonella as an example.
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Document Type: Research Article
Affiliations: Department of Food Science, Chenoweth Laboratory, University of Massachusetts, Amherst, Massachusetts, USA
Publication date: July 1, 2003