Extracting survival parameters from isothermal, isobaric, and "iso-concentration" inactivation experiments by the "3 end points method".

Theoretically, if an organism's resistance can be characterized by 3 survival parameters, they can be found by solving 3 simultaneous equations that relate the final survival ratio to the lethal agent's intensity. (For 2 resistance parameters, 2 equations will suffice.) In practice, the inevitable experimental scatter would distort the results of such a calculation or render the method unworkable. Averaging the results obtained with more than 3 final survival ratio triplet combinations, determined in four or more treatments, can remove this impediment. This can be confirmed by the ability of a kinetic inactivation model derived from the averaged parameters to predict survival patterns under conditions not employed in their determination, as demonstrated with published isothermal survival data of Clostridium botulinum spores, isobaric data of Escherichia coli under HPP, and Pseudomonas exposed to hydrogen peroxide. Both the method and the underlying assumption that the inactivation followed a Weibull-Log logistic (WeLL) kinetics were confirmed in this way, indicating that when an appropriate survival model is available, it is possible to predict the entire inactivation curves from several experimental final survival ratios alone. Where applicable, the method could simplify the experimental procedure and lower the cost of microbial resistance determinations. In principle, the methodology can be extended to deteriorative chemical reactions if they too can be characterized by 2 or 3 kinetic parameters.

Generating microbial survival curves during thermal processing in real time.

AIMS: To develop a method to calculate and record theoretical microbial survival curves during thermal processing of foods and pharmaceutical products simultaneously with the changing temperature. Moreover, to demonstrate that the method can be used to calculate nonisothermal survival curves, with widely available software such as Microsoft Excel. METHODS AND RESULTS: It has been assumed that the targeted organism's isothermal survival curves are not log linear and hence, the inactivation rate in nonisothermal processes is a function of the momentary temperature and the corresponding survival ratio. This could be expressed by a difference equation, which is an approximation to the continuous rate model. The concept was tested with the isothermal survival parameters of Clostridium botulinum and Bacillus sporothermodurans spores, and Salmonella enteritidis cells, using different kinds of survival models and under temperature profiles resembling those of commercial processes. As expected, there was an excellent agreement between the curves produced by solving the differential equation of the continuous model and by the incremental method, which has been posted on the web as freeware. CONCLUSIONS: It is possible to calculate nonisothermal survival curves, in real time, with an algorithm that can be written in the language of general purpose software, to follow the inactivation of one or more targeted organisms simultaneously and to simulate microbial survival patterns under existing or planned industrial thermal processes. SIGNIFICANCE AND IMPACT OF THE STUDY: Replacement of the traditional 'F0-value', which requires the log linearity of the organism's isothermal survival curves, by the more realistic theoretical survival ratio estimate as a measure of the thermal process efficacy.

Estimating the frequency of high microbial counts in commercial food products using various distribution functions.

Industrial microbial count records usually form an irregular fluctuating time series. If the series is truly random or weakly autocorrelated, the fluctuations can be considered as the outcome of the interplay of numerous factors that promote or inhibit growth. These factors usually balance each other, although not perfectly, hence, the random fluctuations. If conditions are unchanged, then at least in principle the probability that they will produce a coherent effect, i.e., an unusually high (or low) count of a given magnitude, can be calculated from the count distribution. This theory was tested with miscellaneous industrial records (e.g., standard plate count, coliforms, yeasts) of various food products, including a dairy-based snack, frozen foods, and raw milk, using the normal, log normal, Laplace, log Laplace, Weibull, extreme value, beta, and log beta distribution functions. Comparing predicted frequencies of counts exceeding selected levels with those actually observed in fresh data assessed their efficacy. No single distribution was found to be inherently or consistently superior. It is, therefore, suggested that, when the probability of an excessive count is estimated, several distribution functions be used simultaneously and a conservative value be used as the measure of the risk.