Concepts and Tools for Predictive Modeling of Microbial Dynamics

Despite the constant development of novel thermal and nonthermal technologies, knowledge on the mechanisms of microbial inactivation is still very limited. Technologies such as high pressure, ultraviolet light, pulsed light, ozone, power ultrasound and cold plasma (advanced oxidation processes) have shown promising results for inactivation of micro-organisms. The efficacy of inactivation is greatly enhanced by combination of conventional (thermal) with nonthermal, or nonthermal with another nonthermal technique. The key advantages offered by nonthermal processes in combination with sublethal mild temperature (<60°C) can inactivate micro-organisms synergistically. Microbial cells, when subjected to environmental stress, can be either injured or killed. In some cases, cells are believed to be inactivated, but may only be sublethally injured leading to their recovery or, if the injury is lethal, to cell death. It is of major concern when micro-organisms adapt to stress during processing. If the cells adapt to a certain stress, it is associated with enhanced protection against other subsequent stresses. One of the most striking problems during inactivation of micro-organisms is spores. They are the most resistant form of microbial cells and relatively difficult to inactivate by common inactivation techniques, including heat sterilization, radiation, oxidizing agents and various chemicals. Various novel nonthermal processing technologies, alone or in combination, have shown potential for vegetative cells and spores inactivation. Predictive microbiology can be used to focus on the quantitative description of the microbial behaviour in food products, for a given set of environmental conditions.

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“…The most general expression for the microbial behaviour as a function of time can be defined as follows (Bernaerts et al . ).

\begin{matrix} \frac{d N_{i} false(t false)}{dt} = & μ 1.2em1.2emtrue(N_{i} false(t false), < e n v false(t false) >, < p h y s false(t false) >, \\ < P false(t false) >, < S false(t false) >, < N_{j} false(t false) >, \dots 1.2em1.2emtrue) \cdot N_{i} false(t false) \end{matrix}

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Section: Mathematical Modelling and Optimization In Nonthermal Procesmentioning

confidence: 97%

State of the art of nonthermal and thermal processing for inactivation of micro-organisms

et al. 2018

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“…Additionally, Eq. 3 relies on the elementary dynamic model building block describing microbial evolution under batch cultivation within a homogeneous environment, as described by Bernaerts et al (2004). The effect of the temperature on parameter k max was described by the use of the Bigelow model (Bigelow 1921;Eq.…”

Section: Microbial Strain Preparationmentioning

confidence: 99%

Quantitative Evaluation of Thermal Inactivation Kinetics of Free-Floating Versus Surface-Attached Listeria innocua Cells

Valdramidis

Péroval²,

Portanguen³

et al. 2007

Food Bioprocess Technol

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Surface pasteurization is one of the decontamination treatments that can contribute to better preservation of meat products retaining most of their quality characteristics relatively intact if compared with the raw products. The current research compares the kinetics of free-floating and surface attached Listeria innocua cells by using integrated microbial and heat transfer modelling approaches. Surface pasteurization treatments are applied on a (abiotic) Teflon® model system in a novel steam surface decontamination rig. The experimental set-up prevented following four technological aspects to occur, (1) cold purge migration to the surface during the heating process, (2) inactivation kinetics of a cocktail of microbes, (3) protective effect of food components, and (4) physical distribution of bacteria throughout the depth of the product skin. Microbial load predictions are performed based on the inactivation parameters obtained during free-floating cell experiments. These predictions, when compared with the microbial data of the surface treatments, prove that the surface attached cells were much more heat resistant, despite the experimental set-up preventing the aforementioned (technological) events to occur. Indeed, surface attached cells can have different physiological/phenotypical/genetical characteristics, such as cell aggregations, colony formations, presence of flagella. In a final step, three techniques are implemented to evaluate mathematically the kinetics of the surface attached cells. Overall, this research's significance is lying in the quantitative assessment of microbial heat resistance. The technological reasons underlying the increased microbial heat resistance on biotic and abiotic surfaces should be reevaluated, taking into account possible physiological/ phenotypical/genetical characteristics.

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“…1 ( open circles ). Conventionally, for analysis of such data, the growth curve is divided into separate phases [3, 4, 6]; more promising seems to be using the above models directly. The exponential model ( curves a ) (which is the solution of , taking into account under the initial condition A (0) = A 0 ( A 0 being the inoculum size)) is quite close to experimental data points at the beginning of growth and suggests the tendency which, however, can hardly be considered as a separate “exponential phase” since the model deviates considerably from further experimental data (a larger part of the data, as a matter of fact; Fig.…”

Section: Laboratory Exercises and Model‐based Data Analysismentioning

confidence: 99%

“…see Refs. [6 and 7])). On the other hand, research in the population growth provides excellent possibilities for both laboratory exercises and qualitative and quantitative data analysis; such studies (not being highly demanding of material resources or skills or special knowledge of mathematics) can be easily conducted, whereas experience gained in these exercises and modeling will be very useful in other areas.…”

mentioning

confidence: 99%

Growth of microbial populations. Mathematical modeling, laboratory exercises, and model‐based data analysis

Juška

Gedminienė

Ivanec

2006

Biochem Molecular Bio Educ

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This paper has arisen as a result of teaching Models in Biology to undergraduates of Bioengineering at the Gediminas Technical University of Vilnius. The aim is to teach the students to use a fresh approach to the problems they are familiar with, to come up with an articulate verbal model after a mental effort, to express it in rigorous mathematical terms, to solve (with the aid of computers) corresponding equations, and finally, to analyze and interpret experimental data in terms of their (mathematical) models. Investigation of microbial growth provides excellent possibilities to combine laboratory exercises, mathematical modeling, and model-based data analysis. Application of mathematics in this field proved to be very fruitful in getting deeper insight into the processes of microbial growth. The step-by-step modeling resulted in an extended model of the growth covering conventional "lag," "exponential," and "stationary" phases. In contrast to known models (differential equations that can be solved only numerically), the present model is expressed symbolically as a finite combination of elementary functions. The approach can be applied in other areas of modern biology, such as dynamics of various cellular processes, enzyme and receptor kinetics, and others.

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Concepts and Tools for Predictive Modeling of Microbial Dynamics

Cited by 32 publications

References 60 publications

State of the art of nonthermal and thermal processing for inactivation of micro-organisms

State of the art of nonthermal and thermal processing for inactivation of micro-organisms

Quantitative Evaluation of Thermal Inactivation Kinetics of Free-Floating Versus Surface-Attached Listeria innocua Cells

Growth of microbial populations. Mathematical modeling, laboratory exercises, and model‐based data analysis

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