Kjell Francois

Modelling the individual cell lag phase. Isolating single cells: protocol development.

Aims: To develop a protocol to isolate single cells in wells of a microtitre plate, having a high certainty of individual cells, combined with a sufficient yield.

Evaluation of a challenge testing protocol to assess the stability of ready-to-eat cooked meat products against growth of Listeria monocytogenes

Challenge testing of ready-to-eat (RTE) foods with Listeria monocytogenes is recommended to assess the potential for growth. The present study was undertaken to evaluate a protocol for challenge testing applied to RTE cooked meat products. In order to choose L. monocytogenes strains with a representative behaviour, initially, the variability of the response of multiple L. monocytogenes strains of human and food origin to different stress and growth conditions was established. The strains were not inhibited in their growth at moderate acid pH (5.25) and the four strains tested in particular showed a similar acid-adaptive response. Growth of the various strains under four different combined stress conditions indicated that no L. monocytogenes strain had consistently significant longer or shorter lag phase or higher or lower maximum specific growth rates. The effect of choice of strain and history (pre-incubation temperature 7 or 30 degrees C) on growth of L. monocytogenes under optimum conditions (Brain Heart Infusion, BHI) and modified BHI simulating conditions of cooked ham and paté was studied. In general, all four L. monocytogenes strains behaved similarly. In BHI, no difference in lag phase was observed for the cold-adapted and standard inoculum, whereas in BHI adjusted to ham and pâté conditions, a ca. 40-h reduction of the lag phase was noted for the cold-adapted inoculum. Subsequently, microbial challenge testing of L. monocytogenes in modified atmosphere packaged sliced cooked ham and paté was performed. A mixed inoculum of four L. monocytogenes strains and an inoculum level of ca. 1-10 cfu/g was used. On vacuum packed sliced cooked ham, the concentration of 100 cfu/g, the safety limit considered as low risk for causing listeriosis, was exceeded after 5 days whereas ca. 10(5) cfu/g were obtained after 14 days when also LAB spoilers reached unacceptable numbers (ca. 10(7) cfu/g) whether standard or cold-adapted inoculum was used. The concentration of sodium lactate determined the opportunities for growth of L. monocytogenes in pâté. If growth of L. monocytogenes in pâté was noticed, the threshold of 100 cfu/ml was crossed earlier for the cold-adapted inoculum compared to the standard inoculum.

Environmental factors influencing the relationship between optical density and cell count for Listeria monocytogenes

Aims:  The effect of temperature (2–30°C), pH (4·8–7·4) and water activity (0·946–0·995) on the relationship between optical density (OD) at 600 nm and the plate count (CFU ml−1) was investigated for Listeria monocytogenes.

Single cell variability of L. monocytogenes grown on liver pâté and cooked ham at 7°C: comparing challenge test data to predictive simulations

Aims:  The variability in growth between individual Listeria monocytogenes cells was investigated on liver pâté and cooked ham. These results were compared to Monte Carlo simulations based on data collected previously in broths (Francois et al., submitted for publication).

Risk Assessment of Listeria monocytogenes: Impact of Individual Cell Variability on the Exposure Assessment Step

Recently, the lag phase research in predictive microbiology is focusing more on the individual cell variability, especially for pathogenic microorganisms that typically occur in very low contamination levels, like Listeria monocytogenes. In this study, the effect of this individual cell lag phase variability was introduced in an exposure assessment study for L. monocytogenes in a liver pâté. A basic framework was designed to estimate the contamination level of pâté at the time of consumption, taking into account the frequency of contamination and the initial contamination levels of pâté at retail. Growth was calculated on pâté units of 150 g, comparing an individual-based approach with a classical population-based approach. The two different protocols were compared using simulations. If only the individual cell lag variability was taken into account, important differences were observed in cell density at the time of consumption between the individual-based approach and the classical approach, especially at low inoculum levels, resulting in high variability when using the individual-based approach. Although, when all variable factors were taken into account, no significant differences were observed between the different approaches, allowing the conclusion that the individual cell lag phase variability was overruled by the global variability of the exposure assessment framework. Even in more extreme conditions like a low inoculum level or a low water activity, no differences were created in cell density at the time of consumption between the individual-based approach and the classical approach. This means that the individual cell lag phase variability of L. monocytogenes has important consequences when studying specific growth cases, especially when the applied inoculum levels are low, but when performing more general exposure assessment studies, the variability between the individual cell lag phases is too limited to have a major impact on the total exposure assessment.

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.