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Smet C.,CPMF Flemish Cluster Predictive Microbiology in Foods | Noriega E.,CPMF Flemish Cluster Predictive Microbiology in Foods | Van Mierlo J.,BioTeC Chemical and Biochemical Process Technology and Control | Valdramidis V.P.,University of Malta | Van Impe J.F.,CPMF Flemish Cluster Predictive Microbiology in Foods
Food Research International | Year: 2015

The microbial growth morphology, as a consequence of the food structure, has been acknowledged to play a key role on the growth behavior. While in liquid media planktonic growth is observed, in a solid(like) environment cells are immobilized and forced to grow as (surface) colonies. This immobilization could affect the cell physiology and metabolism. Apart from the growth morphology, other intrinsic factors influence microbial growth. Osmotic stress and acidic stress have an impact on microbial growth. Moreover, the combination of intrinsic factors could result in a synergetic inhibitory effect on the growth behavior (hurdle technology). In this paper, the growth dynamics of Salmonella Typhimurium and Listeria monocytogenes under stressing conditions are studied for two different growth morphologies, i.e., (i) planktonic cells and (ii) surface colonies. Microorganisms are grown in petri dishes, incubated at 20 °C under static conditions. To create the solid(like) environment, 5% (w/v) gelatin is added. Stressing growth conditions are created by adding salt [0-8% (w/v)] and adapting the pH [5.5-7.0]. Cell density is determined via the viable plate count technique. While the studied pH-range has a negligible effect, the addition of salt significantly reduces growth. The growth morphology, resulting from the intrinsic food (model) structure, affects the microbial growth dynamics under static incubation conditions. In contrast to literature, surface colonies have higher or similar maximum specific growth rates than planktonic cells under most of the selected experimental conditions. For example, for S. Typhimurium at pH. 6.5 and 2% (w/v) NaCl, planktonic cells have a maximum specific growth rate of 0.435. 1/h while μmax for surface colonies has a value of 0.464 1/h. For L monocytogenes at pH 6.0 and 0% (w/v) NaCl, μmax, planktonic cells is 0.375 1/h and μmax, surface colonies is 0.424 1/h. This is due to limited oxygen availability as a result of the experimental protocol implemented, leading to lower μmax values for planktonic cells as opposed to μmax values for shaken cultures in other studies appearing in literature. This indicates that under static incubation, the effect of the microstructure is often negligible as compared to the oxygen availability. However, for the most stressing experimental conditions, the combination of high salt concentrations and a solid(like) growth environment inhibits growth, and the order μmax, planktonic cells≥. μmax, surface colonies is respected. This is for instance illustrated in the case of S. Typhimurium at pH. 5.5 and 6% (w/v) NaCl: μmax, planktonic cells reaches a value of 0.183 1/h while μmax, surface colonies is only 0.142 1/h. Also for L. monocytogenes at pH 6.0 and 8% (w/v) NaCl, μmax, planktonic cells (0.145 1/h) is higher than μmax, surface colonies (0.113 1/h). This study indicates the relevance of intrinsic factors on the growth dynamics and addresses the importance to include growth morphology, determined by the intrinsic food structure, in predictive models. © 2015 Elsevier Ltd.

Busschaert P.,CPMF Flemish Cluster Predictive Microbiology in Foods | Busschaert P.,Catholic University of Leuven | Geeraerd A.H.,CPMF Flemish Cluster Predictive Microbiology in Foods | Geeraerd A.H.,Catholic University of Leuven | And 3 more authors.
Risk Analysis | Year: 2011

The aim of quantitative microbiological risk assessment is to estimate the risk of illness caused by the presence of a pathogen in a food type, and to study the impact of interventions. Because of inherent variability and uncertainty, risk assessments are generally conducted stochastically, and if possible it is advised to characterize variability separately from uncertainty. Sensitivity analysis allows to indicate to which of the input variables the outcome of a quantitative microbiological risk assessment is most sensitive. Although a number of methods exist to apply sensitivity analysis to a risk assessment with probabilistic input variables (such as contamination, storage temperature, storage duration, etc.), it is challenging to perform sensitivity analysis in the case where a risk assessment includes a separate characterization of variability and uncertainty of input variables. A procedure is proposed that focuses on the relation between risk estimates obtained by Monte Carlo simulation and the location of pseudo-randomly sampled input variables within the uncertainty and variability distributions. Within this procedure, two methods are used-that is, an ANOVA-like model and Sobol sensitivity indices-to obtain and compare the impact of variability and of uncertainty of all input variables, and of model uncertainty and scenario uncertainty. As a case study, this methodology is applied to a risk assessment to estimate the risk of contracting listeriosis due to consumption of deli meats. © 2011 Society for Risk Analysis.

Valdramidis V.P.,Dublin Institute of Technology | Geeraerd A.H.,CPMF Flemish Cluster Predictive Microbiology in Foods | Geeraerd A.H.,Catholic University of Leuven | Tiwari B.K.,Manchester Metropolitan University | And 4 more authors.
Food Control | Year: 2011

Traditional and novel approaches for the calculation of the heat treatment efficiency are compared in this work. The Mild Heat value (MH-value), an alternative approach to the commonly used sterilisation, pasteurisation and cook value (F, P, C-value), is calculated to estimate the efficiency of a mild heat process. MH-value is the time needed to achieve a predefined microbial reduction at a reference temperature and a known thermal resistant constant, z, for log-linear or specific types of non-log-linear microbial inactivation kinetics. An illustrative example is given in which microbial inactivation data of Listeria innocua CLIP 20-595 are used for estimating the inactivation parameters under isothermal conditions of 58, 60, 63 and 66 °C by the use of the log-linear and the Geeraerd et al. (2000) model. Thereafter, dynamic temperature profiles (targeting at 54 and 57 °C) representing milk thermisation are exploited for illustrating the application of MH-value. Finally, the equivalent holding times of different temperatures are calculated taking into account the observed non-linearity. © 2010 Elsevier Ltd.

Mertens L.,CPMF Flemish Cluster Predictive Microbiology in Foods | Mertens L.,Catholic University of Leuven | Van Derlinden E.,CPMF Flemish Cluster Predictive Microbiology in Foods | Van Derlinden E.,Catholic University of Leuven | And 2 more authors.
Food Microbiology | Year: 2012

Recently, the focus of predictive food microbiology has shifted towards more mechanistically-inspired modelling. Together with this trend, the need for methods that allow rapid data collection at the (intra)cellular level, as well as the intermediate subpopulation/colony level, has emerged. Although several experimental techniques are currently available to study colony dynamics in/on solid media, their widespread implementation as high-throughput methods remains a challenge. In this research, a novel method is presented to study colony growth based on optical density measurements performed in microtiter plates. An area scan procedure was applied to monitor individual Escherichia coli colonies in 48-well plates at 30 °C. Based on a fixed threshold value to separate the object (colony) from the background, the colony area was determined as a function of time. With this technique, expansion of the colony in radial direction could be monitored. Practical limitations (i.e., maximum achievable resolution and colony size) of the proposed method were investigated. A comparison was made with existing methods at the level of hardware requirements, data acquisition and data processing. Overall, the novel optical density method proved to be a flexible, high-throughput tool for monitoring (the mechanisms of) microbial colony growth in solid(like) systems. © 2012 Elsevier Ltd.

Boons K.,CPMF Flemish Cluster Predictive Microbiology in Foods | Boons K.,Catholic University of Leuven | Noriega E.,CPMF Flemish Cluster Predictive Microbiology in Foods | Noriega E.,Catholic University of Leuven | And 5 more authors.
International Journal of Food Microbiology | Year: 2015

As most food systems are (semi-)solid, the effect of food structure on bacterial growth has been widely acknowledged. However, studies on the growth dynamics of yeasts have neglected the effect of food structure.In this paper, the growth dynamics of the spoilage yeast Saccharomyces cerevisiae was investigated at 23.5. °C in broth, singular, homogeneous biopolymer systems and binary biopolymer systems with a heterogeneous microstructure. The biopolymers gelatin and dextran were used to introduce the different levels of structure. The metabolizing ability of gelatin and dextran by S. cerevisiae was examined. To study microbial behavior in the binary systems at the micro level, mixtures were imaged with confocal laser scanning microscopy (CLSM). Growth dynamics and microscopic images of S. cerevisiae were compared with those obtained for Escherichia coli in the same model system (Boons et al., 2014). Different phase-separated, heterogeneous microstructures were obtained by changing the amount of added gelatin and dextran. Regardless of the microstructure, S. cerevisiae was preferentially located in the dextran phase. Metabolizing ability-tests indicated that gelatin could be consumed by S. cerevisiae but in the presence of glucose, no change in gelatin concentration was observed. No indication of dextran metabolizing ability was observed. When supplementing broth with gelatin or dextran alone, an enhanced growth rate and maximum cell density were observed. This enhancement was further increased by adding a second biopolymer, introducing a heterogeneous microstructure and hence increasing the medium structure complexity.The results obtained indicate that food structure complexity plays a significant role in the growth dynamics of S. cerevisiae, an important food spoiler. © 2014.

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