K.M Vereecken

Analysis and practical implementation of a model for combined growth and metabolite production of lactic acid bacteria

Next to the traditional application of lactic acid bacteria (LAB) as starter cultures for food fermentations, the use of LAB as protective cultures against microbial pathogens and spoilage organisms in other food production processes gains more and more interest. The inhibitory effect of LAB is mainly accomplished through formation of antimicrobial metabolites. In this paper, the model of Nicolaï et al. [Food Microbiol. 10 (1993) 229.], describing cell growth and production of lactic acid, which is the major end-product of LAB metabolism, is investigated. In contrast to classical predictive models, the transition of the exponential growth phase to the stationary phase is obtained through the increasing concentrations of undissociated lactic acid [LaH] and decreasing pH in the environment. To describe the variation in time of [LaH] and pH, a novel, robust calculation method is introduced. The model of Nicolaï et al. in combination with the novel method of [LaH] and pH computation is then further applied to an experimental data set of Lactococcus lactis SL05 grown in a rich medium. An accurate description of the measured values of cell concentration, total lactic acid concentration and pH is obtained.

Concepts and tools for predictive modeling of microbial dynamics

Description of microbial cell (population) behavior as influenced by dynamically changing environmental conditions intrinsically needs dynamic mathematical models. In the past, major effort has been put into the modeling of microbial growth and inactivation within a constant environment (static models). In the early 1990s, differential equation models (dynamic models) were introduced in the field of predictive microbiology. Here, we present a general dynamic model-building concept describing microbial evolution under dynamic conditions. Starting from an elementary model building block, the model structure can be gradually complexified to incorporate increasing numbers of influencing factors. Based on two case studies, the fundamentals of both macroscopic (population) and microscopic (individual) modeling approaches are revisited. These illustrations deal with the modeling of (i) microbial lag under variable temperature conditions and (ii) interspecies microbial interactions mediated by lactic acid production (product inhibition). Current and future research trends should address the need for (i) more specific measurements at the cell and/or population level, (ii) measurements under dynamic conditions, and (iii) more comprehensive (mechanistically inspired) model structures. In the context of quantitative microbial risk assessment, complexity of the mathematical model must be kept under control. An important challenge for the future is determination of a satisfactory trade-off between predictive power and manageability of predictive microbiology models.

Effect of chemicals on the microbial evolution in foods.

In contrast with most chemical hazardous compounds, the concentration of food pathogens changes during processing, storage, and meal preparation, making it difficult to estimate the number of microorganisms or the concentration of their toxins at the moment of ingestion by the consumer. These changes are attributed to microbial proliferation, survival, and/or inactivation and must be considered when exposure to a microbial hazard is assessed. The number of microorganisms can also change as a result of physical removal, mixing of food ingredients, partitioning of a food product, or cross-contamination (M. J. Nauta. 2002. Int. J. Food Microbiol. 73:297-304). Predictive microbiology, i.e., relating these microbial evolutionary patterns to environmental conditions, can therefore be considered a useful tool for microbial risk assessment, especially in the exposure assessment step. During the early development of the field (late 1980s and early 1990s), almost all research was focused on the modeling of microbial growth over time and the influence of temperature on this growth. Later, modeling of the influence of other intrinsic and extrinsic parameters garnered attention. Recently, more attention has been given to modeling of the effects of chemicals on microbial inactivation and survival. This article is an overview of different applied strategies for modeling the effect of chemical compounds on microbial populations. Various approaches for modeling chemical growth inhibition, the growth-no growth interface, and microbial inactivation by chemicals are reviewed.

Quantifying lactic acid induced inhibition and inactivation of Yersinia enterocolitica in mixed cultures

Abstract In food technology, models describing microbial proliferation in food products are a helpful tool to predict the food safety. In general, the available models consider the micro-organisms in pure culture. As such, microbial interactions are ignored, which may lead to a discrepancy between model predictions and the actual microbial evolution. In this study, a model describing the lactic acid induced inhibition of the pathogenic Yersinia enterocolitica in mixed culture was extended to describe also the subsequent inactivation. In the development of a suitable model structure to describe the inactivation process, biological knowledge was incorporated. The extended model was able to predict the evolution of Y. enterocolitica in coculture as well as in monoculture.