An optimization algorithm for estimation of microbial survival parameters during thermal processing.

Isothermal microbial survival curves are usually described by either linear or nonlinear time-dependent models, from which non-isothermal survival curves can be generated if the parameters describing the survival kinetics of the microbial population are known. In order to estimate these parameters, an algorithm based on the steepest decent optimization method was developed. The algorithm searches the values of the survival parameters which minimize the sum of the squared differences between the experimental data and the calculated values provided by the model. The difference of the proposed algorithm with a typical optimization technique is that each data point used is not necessarily coming from the same thermal treatment; instead, data from different non-isothermal processes can be used. The developed algorithm was tested by using published non-isothermal survival data of Salmonella. The data showed that the survival curves can be described by the Weibull model, an already accepted and frequently used nonlinear model. Salmonella's survival parameters were estimated from the end points and all data points, respectively, of three non-isothermal survival curves. The results obtained showed that the number of survival data points must be sufficiently large to obtain true or statistically sound values of the survival parameters. A suitable way to achieve this is to implement the algorithm using all data points of multiple non-isothermal survival curves or a large number of end points of non-isothermal treatments. Mathematically, the developed algorithm should be applicable to any microbial survival kinetics accurately describing the inactivation of the microorganisms because no specific survival kinetics has to be pre-assumed to run the algorithm. Published by Elsevier B.V.