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Microbial Modeling Needs for the Nonthermal Processing of Foods

作   者:
Serment-Moreno, Vinicio
作者机构:
Suite 101Hiperbaric USA Corp FL 33122 USA 2250 NW 84th Ave Applicat & Food Proc Dept Doral
关键词:
Microbial inactivation kineticsHigh pressure processing (HPP)Weibull modelMathematical modelingNonthermal processing (NTP)Safe harbor
期刊名称:
Food Engineering Reviews
i s s n:
1866-7910
年卷期:
2021 年 13 卷 3 期
页   码:
465-489
页   码:
摘   要:
Year 2019 marked the 20th anniversary of the Nonthermal Processing Division (NPD) of the Institute of Food Technologists (IFT). The first 20 years served to understand the extents and limitations of nonthermal food processing technologies (NTP) that evolved from emerging trends to well-established commercial operations. It is now time for a new generation of contributors to rise up to the challenge of establishing safe harbors for NTP and deepening food safety regulations that meet industrial practices, where the development of simple, reliable kinetic models plays a major role. The review presents evidence against the still accepted assumption of linear microbial inactivation kinetics modeling in NTP, and questions the contributions of traditional kinetic parameters derived from this belief like the decimal reduction time (D) and z-values, in the development of NTP guidelines. Moreover, research findings continue to support the Weibull model and derived mathematical expressions as simple, reliable equations to predict microbial inactivation in thermal and NTP, allowing the reinterpretation of some kinetic parameters (D, z) within the Weibullian context. The review also aims to serve as a starting point for readers interested in mathematical modeling by summarizing related guidelines and statistical criteria, which goes beyond simply fitting equations to experimental data. Mathematical modeling is an integral approach involving careful experimental planning, knowledge of model properties and assumptions, data visualization, evaluation of model performance, assessment of model parameter estimate uncertainty and inherent data variability, model selection, and most importantly, critical judgment and common sense to provide the model a practical context.
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