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A H Geeraerd

Publications and source records attributed to A H Geeraerd.

32 records · Page 2Linked to original sources
PubMed2004

Modelling the individual cell lag phase: effect of temperature and pH on the individual cell lag distribution of Listeria monocytogenes.

The individual-based approach of the lag phase is gaining interest, especially for pathogens that initially contaminate food products in low amounts. In this paper, the effect of temperature (30, 10, 7, 4 and 2 degrees C) and pH (7.4, 6.1, 5.5, 5.0, 4.7 and 4.4) on the individual cell lag phase of Listeria monocytogenes was examined in a factorial design, using OD measurements. Individual lag phases of about 100 individual cells per condition were examined and calculated using a linear extrapolation method. Generation times were calculated out of the slope. The obtained data were analyzed at three different levels: in a first approach, the mean values were calculated for each set of environmental conditions and compared to predictions made by the USDA's Pathogen Modeling Program (PMP) for analogous growth conditions. The PMP predictions of the generation times were in the same order of magnitude as the obtained data, although a persistent underestimation could be observed. The observed individual cell lag data differed from lag phase predictions by PMP. Possible reasons for this discrepancy are discussed. Secondly, histograms of individual lag phase measurements were constructed for the different temperature-pH combinations. In this way, the influence of both factors on the variability of individual lag phases could be estimated. At low stress levels, most individual cells showed a short lag phase resulting in a compression of the histograms at the zero-lag level, while, at high stress levels, the histograms shifted to longer lag phases with a significant increase in variability. Thirdly, 37 different distribution types were fitted to the datasets to reveal the distributions that fitted best the obtained data. The gamma distribution was preferred at moderate stress levels, while the Weibull distribution was chosen for harsher growth conditions.

Colony Count, Microbial↗
PubMed2004

Predictive modelling of the microbial lag phase: a review.

This paper summarises recent trends in predictive modelling of microbial lag phenomena. The lag phase is approached from both a qualitative and a quantitative point of view. First, a definition of lag and an analysis of the prevailing measuring techniques for the determination of lag time is presented. Furthermore, based on experimental results presented in literature, factors influencing the lag phase are discussed. Major modelling approaches concerning lag phase estimation are critically assessed. In predictive microbiology, a two-step modelling approach is used. Primary models describe the evolution of microbial numbers with time and can be subdivided into deterministic and stochastic models. Primary deterministic models, e.g., Baranyi and Roberts [Int. J. Food Microbiol. 23 (1994) 277], Hills and Wright [J. Theor. Biol. 168 (1994) 31] and McKellar [Int. J. Food Microbiol. 36 (1997) 179], describe the evolution of microorganisms, using one single (deterministic) set of model parameters. In stochastic models, e.g., Buchanan et al. [Food Microbiol. 14 (1997) 313], Baranyi [J. Theor. Biol. 192 (1998) 403] and McKellar [J. Appl. Microbiol. 90 (2001) 407], the model parameters are distributed or random variables. Secondary models describe the relation between primary model parameters and influencing factors (e.g., environmental conditions). This survey mainly focuses on the influence of temperature and culture history on the lag phase during growth of bacteria.

Bacteria↗
PubMed2004

Development of a novel approach for secondary modelling in predictive microbiology: incorporation of microbiological knowledge in black box polynomial modelling.

This research deals with the development of a novel secondary modelling procedure within the framework of predictive microbiology. The procedure consists of three steps: (i) careful formulation of the available microbiological information, both from literature and from the experimental case study at hand, (ii) translation of these requirements in mathematical terms under the form of partial derivatives throughout the complete interpolation region of the experimental design, and (iii) determination of parameter values with suitable optimisation techniques for a flexible black box modelling approach, e.g., a polynomial model or an artificial neural network model. As a vehicle for this procedure, the description of the maximum specific growth rate of Lactobacillus sakei in modified BHI-broth as influenced by suboptimal temperature, water activity, sodium lactate and dissolved carbon dioxide concentration is under study. The procedure results in a constrained polynomial model with excellent descriptive and interpolating features in comparison with an extended Ratkowsky-type model and classical polynomial model, by combining specific properties of both model types. The developed procedure is illustrated on the description of the lag phase as well. It is stressed how the confrontation with experimental data is very important to appreciate the descriptive and interpolating capacities of new or existing models, which is nowadays not always carefully performed. Alternatively, the first two steps of the novel procedure can be used as a tool to demonstrate clearly (possible) interpolative shortcomings of an existing model with straightforward spreadsheet calculations.

Carbon Dioxide↗
PubMed2004

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.

Bacteria↗
PubMed2003

Combinations of pulsed white light and UV-C or mild heat treatment to inactivate conidia of Botrytis cinerea and Monilia fructigena.

The use of pulses of intense white light to inactivate conidia of the fungi Botrytis cinerea and Monilia fructigena, responsible for important economical losses during postharvest storage and transport of strawberries and sweet cherries, was investigated in this study. In the first stage, a light treatment applying pulses of 30 micros at a frequency of 15 Hz was investigated, resulting in a treatment duration varying from 1 to 250 s. The conidia of both fungi showed similar behaviour to pulsed light, with a maximal inactivation of 3 and 4 log units for B. cinerea and M. fructigena, respectively. The inactivation of the conidia increased with increasing treatment intensity, but no complete inactivation was achieved. The sigmoidal inactivation pattern obtained by the pulsed light treatment was described using a modification of the model of Geeraerd et al. [Int. J. Food Microbiol. 59 (2000) 185]. Hereto, the shoulder length was incorporated explicitly and relative values for the microbial populations were used. In the second stage, combinations of light pulses and ultraviolet-C or heat were applied. The UV light used in the experiments is the short-wave band or UV-C, running from 180 to 280 nm with a peak at 254 nm (UV-B runs from 280 to 320 nm and UV-A from 320 to 380 nm). The UV-C doses were 0.025, 0.05 and 0.10 J/cm(2), and the temperatures for the thermal treatment ranged from 35 to 45 degrees C during 3-15 min. When combining UV-C and light pulses, there was an increase in inactivation for both B. cinerea and M. fructigena, and synergism was observed. There was no effect of the order of the treatments. For the heat-light pulses combination, there was a difference between both fungi. The order of the treatments was highly significant for B. cinerea, but not for M. fructigena. Combining heat and light treatments improved the inactivation, and synergism between both methods was again observed. Complete inactivation of M. fructigena conidia was obtained after, e.g., a 40-s pulsed light treatment and 15 min at 41 degrees C, or after an 80-s light treatment and 10 min at 41 degrees C.

Botrytis↗
PubMed2003

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. METHODS AND RESULTS: Single cells were obtained using 1/2 dilution series in microtitre plates. Seventy-two Lactococcus lactis dilution series were checked by plate counting. When the last five columns of the plates were observed, the chance of having one single cell was 80%, while the yield was 75 wells containing cells. A simulation model confirmed these results. This method was compared with the commonly applied method. CONCLUSIONS: This method makes it possible to combine a higher chance of having one cell in a microtitre well with a slightly higher yield. SIGNIFICANCE AND IMPACT OF THE STUDY: A tool is developed to isolate single cells to provide a suitable base for investigating and modelling the individual cell lag phase.

Cell Division↗
PubMed2002

Inactivation of conidia of Botrytis cinerea and Monilinia fructigena using UV-C and heat treatment.

The effect of UV-C (lambda = 254 nm) and heat treatment was investigated on the inactivation of conidia of Botrytis cinerea and Monilinia fructigena, two major postharvest spoilage fungi of strawberries and cherries, respectively. Both fungi were grown at 21 degrees C in the dark and conidia were isolated after 1 week by washing the mycelium with a mild detergent solution. After filtration and resuspension in phosphate buffer to a titer of 10(5) to 10(6) cfu/ml, the conidia were subjected to different treatments. The applied UV-C doses varied from 0.01 to 1.50 J/cm2, and the conditions for the thermal treatment were 3, 5, 10, 15 and 20 min at temperatures ranging from 35 to 48 degrees C. Both techniques were applied individually and in combination. Spore inactivation increased with increasing intensity of single treatments. No surviving spores of B. cinerea were observed after 15 min at 45 degrees C or an UV-C treatment of 1.00 J/cm2. M. fructigena was more sensitive and a thermal treatment of 3 min at 45 degrees C or an UV-C treatment of 0.50 J/cm2 resulted in complete spore inactivation. Combination of both techniques reduced the required intensity of the treatment for inactivation of both fungi. The order of the applications had a significant effect on the degree of inactivation. The inactivation of B. cinerea conidia was greater when the heat treatment came first, and for M. fructigena, most inactivation was achieved when the heat treatment was preceded with an UV-C irradiation.

Ascomycota↗
PubMed2002

Using survival analysis to investigate the effect of UV-C and heat treatment on storage rot of strawberry and sweet cherry.

Ultraviolet light and heat treatment are proposed as alternative techniques for the use of chemicals to reduce the development of the spoilage fungi Botrytis cinerea and Monilinia fructigena on strawberry and sweet cherry, respectively, during storage. In order to investigate the effect of both physical techniques on microbial inactivation and on fruit quality, inoculated berries were subjected to different temperatures (40-48 degrees C) and UV-C doses (0.05-1.50 J/cm2). For each condition, 20 berries were used. After the treatment, fungal growth, visual damage (holes, stains) and fruit firmness were evaluated during a period of 10 days. The experimental data were analysed statistically using survival analysis techniques. Fungal growth on strawberries was significantly retarded using UV-C doses of 0.05 J/cm2 and higher. The same treatment had no significant effect when applied to cherries. The highest doses (1.00 and 1.50 J/cm2) had a negative effect on the calyx of the strawberry, causing browning and drying of the leaves. No beneficial effect of a low temperature treatment (40-48 degrees C) on the shelf life of strawberries was observed, but fungal development on cherries was retarded at temperatures of 45 and 48 degrees C. These temperatures caused severe damage on strawberries (soft stains, holes, decreased firmness), but had no influence on the quality of sweet cherries.

Ascomycota↗
PubMed2001

Current trends in predictive modelling of microbial lag phenomena.

This paper summarises recent trends in predictive modelling of microbial lag phenomena. The lag phase is approached from both a qualitative and quantitative point of view. Major modelling approaches and experimental results are critically assessed. This review mainly focuses on the influence of temperature and culture history on the lag phase during growth of bacteria.

Bacteria↗
PubMed2000

Structural model requirements to describe microbial inactivation during a mild heat treatment.

The classical concept of D and z values, established for sterilisation processes, is unable to deal with the typical non-loglinear behaviour of survivor curves occurring during the mild heat treatment of sous vide or cook-chill food products. Structural model requirements are formulated, eliminating immediately some candidate model types. Promising modelling approaches are thoroughly analysed and, if applicable, adapted to the specific needs: two models developed by Casolari (1988), the inactivation model of Sapru et al. (1992), the model of Whiting (1993), the Baranyi and Roberts growth model (1994), the model of Chiruta et al. (1997), the model of Daughtry et al. (1997) and the model of Xiong et al. (1999). A range of experimental data of Bacillus cereus, Yersinia enterocolitica, Escherichia coli O157:H7, Listeria monocytogenes and Lactobacillus sake are used to illustrate the different models' performances. Moreover, a novel modelling approach is developed, fulfilling all formulated structural model requirements, and based on a careful analysis of literature knowledge of the shoulder and tailing phenomenon. Although a thorough insight in the occurrence of shoulders and tails is still lacking from a biochemical point of view, this newly developed model incorporates the possibility of a straightforward interpretation within this framework.

Bacteria↗
PubMed2000

Shelf life of modified atmosphere packed cooked meat products: addition of Na-lactate as a fourth shelf life determinative factor in a model and product validation.

Cooked meat products are often post-contaminated because of a packaging and/or slicing step after the pasteurisation process. The shelf life is therefore limited and can be extended by adding Na-lactate. A previously developed model for the spoilage of gas packed cooked meat products, including temperature, water activity and dissolved CO2 as independent variables, was extended with a fourth factor: the Na-lactate concentration in the aqueous phase of the meat product. Models were developed for the maximum specific growth rate mu(max) and the lag phase lambda of the specific spoilage organism Lactobacillus sake subsp. carnosum. Quadratic response surface equations were compared with extended Ratkowsky models. In general, response surface equations fitted the experimental data best but in the case of mu(max) the response surface model predicted illogical growth behaviour at low water activities and high Na-lactate concentrations. A extensive product validation of the mathematical models was performed by means of inoculated as well as naturally contaminated industrially prepared cooked meat products. The deviations of the experimentally determined versus predicted growth parameters in inoculated cooked meat products were in general small. Both types of models were also able to predict the shelf life of naturally contaminated cooked meat products, except for pâté where an under-estimation of the shelf life was predicted by the response surface equations. The validation studies revealed higher accuracy of the extended Ratkowsky models in comparison to the response surface equations. A significant shelf life extending effect of Na-lactate was predicted, which was more pronounced at low refrigerated temperatures. A synergistic effect has also been noticed between Na-lactate and carbon dioxide which, at least partly, could be explained by the pH-decreasing effect of CO2.

Animals↗
PubMed1999

Introducing optimal experimental design in predictive modeling: a motivating example.

Predictive microbiology emerges more and more as a rational quantitative framework for predicting and understanding microbial evolution in food products. During the mathematical modeling of microbial growth and/or inactivation, great, but not always efficient, effort is spent on the determination of the model parameters from experimental data. In order to optimize experimental conditions with respect to parameter estimation, experimental design has been extensively studied since the 1980s in the field of bioreactor engineering. The so-called methodology of optimal experimental design established in this research area enabled the reliable estimation of model parameters from data collected in well-designed fed-batch reactor experiments. In this paper, we introduce the optimal experimental design methodology for parameter estimation in the field of predictive microbiology. This study points out that optimal design of dynamic input signals is necessary to maximize the information content contained within the resulting experimental data. It is shown that from few dynamic experiments, more pertinent information can be extracted than from the classical static experiments. By introducing optimal experimental design into the field of predictive microbiology, a new promising frame for maximization of the information content of experimental data with respect to parameter estimation is provided. As a case study, the design of an optimal temperature profile for estimation of the parameters D(ref) and z of an Arrhenius-type model for the maximum inactivation rate kmax as a function of the temperature, T, was considered. Microbial inactivation by heating is described using the model of Geeraerd et al. (1999). The need for dynamic temperature profiles in experiments aimed at the simultaneous estimation of the model parameters from measurements of the microbial population density is clearly illustrated by analytical elaboration of the mathematical expressions involved on the one hand, and by numerical simulations on the other.

Bacteria↗
PubMed1998

Application of artificial neural networks as a non-linear modular modeling technique to describe bacterial growth in chilled food products.

In many chilled, prepared food products, the effects of temperature, pH and %NaCl on microbial activity interact and this should be taken into account. A grey box model for prediction of microbial growth is developed. The time dependence is modeled by a Gompertz model-based, non-linear differential equation. The influence of temperature, pH and %NaCl reflected in the model parameters is described by using low-complexity, black box artificial neural networks (ANN's). The use of this non-linear modeling technique makes it possible to describe more accurately interacting effects of environmental factors when compared with classical predictive microbiology models. When experimental results on the influence of other environmental factors become available, the ANN models can be extended simply by adding more neurons and/or layers.

Bacteria↗