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Artificial Intelligence

Selected Challenges and Opportunities in Modeling Nonthermal Preservation of Foods

Both nonthermal and thermal preservations methods attempt to make foods safe for human consumption and extend their shelf life. But unless combined as in pressure-assisted sterilization or freezing pasteurized liquid eggs, in nonthermal methods the targeted microbes are inactivated through different mechanisms, and at lower temperatures to maintain freshness and protect nutrients. A food preservation process’s efficacy is judged by the number of decades reduction in a targeted microbe that it produces, requiring knowledge of the inactivation kinetics which is frequently nonlinear. One challenge has been to compare the efficacy of a novel nonthermal process to that of a conventional thermal inactivation at a lethal temperature. In several processes, especially of very short duration, obtaining proper static survival data for the kinetic model construction is not a feasible option. The problem can be avoided by extracting the inactivation parameters from survival ratios determined after dynamic processes completion. This requires numerical solution of simultaneous nonlinear equations, themselves the numerical solutions of differential equation, which can be done with modern mathematical software. Such programs also enable to test several models, to simulate numerous contemplated nonthermal processes in a very short time, and to examine their microbial safety implications. A major challenge to future researchers will be to quantify the roles of several factors that vary simultaneously during a process and incorporate them in its differential rate equation. Currently mathematical methods to solve such equations are limited to models having three adjustable parameters. Development of software that can handle microbial inactivation and chemical degradation kinetics models having more than three adjustable parameters would also be a challenge. All the above refers to “point kinetics,” and even more challenging would be to incorporate it in models that also address the treatment’s geometry based on heat and mass transfer, and other considerations as needed. Speaker: Micha Peleg, DSc

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