Biomedical subjects

A H Geeraerd

Publications and source records attributed to A H Geeraerd.

At least 19 recordsLinked to original sources

PubMed2007

Individual and combined effects of ph and lactic acid concentration on Listeria innocua inactivation: development of a predictive model and assessment of experimental variability.

In food technology, organic acids (e.g., lactic acid, acetic acid, and citric acid) are popular preservatives. The purpose of this study was to separate the individual effects of the influencing factors pH and undissociated lactic acid on Listeria innocua inactivation. Therefore, the inactivation process was investigated under controlled, initial conditions of pH (pH0) and undissociated lactic acid ([LaH]0). The resulting inactivation curves consisted of a (sometimes negligible) shoulder period followed by a descent phase. In a few cases, a tailing phase was observed. Depending on the conditions, the descent phase contained one or two log-linear parts or had a convex or concave shape. In addition, the inactivation process was characterized by a certain variability, dependent on the severity of the conditions. Furthermore, in the neighborhood of the growth/no growth interface sometimes contradictory observations occurred. Overall, the individual effects of the influencing factors pH and undissociated lactic acid could clearly be distinguished and were also apparent based on fluorescence microscopy. Appropriate model types were developed and enabled prediction of which conditions of pH0 and [LaH]0 are necessary to obtain a predetermined inactivation (number of decimal reductions) within a predetermined time range.

Food Preservatives↗

PubMed2006

Influence of pH, water activity and acetic acid concentration on Listeria monocytogenes at 7 degrees C: data collection for the development of a growth/no growth model.

Growth/no growth models can be used to determine the chance that microorganisms will grow in specific environmental conditions. As a consequence, these models are of interest in the assessment of the safety of foods which can be contaminated with food pathogens. In this paper, growth/no growth data for Listeria monocytogenes (in a monoculture and in a mixed strain culture) are presented. The data were gathered at 7 degrees C in Nutrient Broth with different combinations of environmental factors pH (5.0-6.0, six levels), water activity (0.960-0.990, six levels) and acetic acid concentration (0-0.8% (w/w), five levels). This combination of environmental factors for the development of a growth/no growth model was based on the characteristics of sauces and mayonnaise based salads. The strains used were chosen from screening experiments in which the pH, water activity and acetic acid resistance of 26 L. monocytogenes strains (LFMFP culture collection) was determined at 30 degrees C in Brain Heart Infusion broth. The screening showed that most L. monocytogenes strains were not able to grow at a(w)<0.930, pH<4.3 or a total acetic acid concentration >0.4% (w/w). Among these strains, the ones chosen were the most resistant to one of these factors in the hope that, if the resulting model predicted no growth at certain conditions for those more resistant strains, then these predictions would also be valid for the less resistant strains. A mixed strain culture was also examined to combine the strains that were most resistant to one of the factors. A full factorial design with the selected strains was tested. The experiments were performed in microtiter plates and the growth was followed by optical density measurements at 380 nm. The plates were inoculated with 6 log CFU/ml and twenty replicates were made for each treatment combination. These data were used (1) to determine the growth/no growth boundary and (2) to estimate the influence of the environmental conditions on the time to detection. From the monoculture and mixed strain data, the growth boundary of L. monocytogenes is shown not to be a straight cut-off but a rather narrow transition zone. The experiments also showed that in the studied region, a(w) did not have a pronounced influence on the position of the growth/no growth boundary while a low concentration of acetic acid (0.2% (w/w)) and a pH decrease from 6.0 to 5.8 was sufficient to significantly reduce the possibility of growth. The determination of the time to detection showed a significant increase at the combinations of environmental conditions near the 'no growth zone'. For example, at 0.2% (w/w) acetic acid, there was an increase from +/-10 days to 30 days by lowering pH from 5.8 to 5.6 at a(w) values of 0.985 and 0.979, while at pH 5.4 less than 50% growth occurred for all a(w) values.

Acetic Acid↗

PubMed2006

Modelling Yersinia enterocolitica inactivation in coculture experiments with Lactobacillus sakei as based on pH and lactic acid profiles.

In food processing and preservation technology, models describing microbial proliferation in food products are a helpful tool to predict the microbial food safety and shelf life. In general, the available models consider microorganisms in pure culture. Thus, microbial interactions are ignored, which may lead to a discrepancy between model predictions and the actual microbial evolution, particularly for fermented and minimally processed food products in which a background flora is often present. In this study, the lactic acid mediated negative microbial interaction between the lactic acid bacterium Lactobacillus sakei and the psychrotrophic food pathogen Yersinia enterocolitica was examined. A model describing the lactic acid induced inhibition (i.e., early induction of the stationary phase) of the pathogen [Vereecken, K.M., Devlieghere, F., Bockstaele, A., Debevere, J., Van Impe, J.F., 2003. A model for lactic acid induced inhibition of Yersinia enterocolitica in mono- and coculture with Lactobacillus sakei. Food Microbiology 20, 701-713.] was extended to describe the subsequent inactivation (i.e., decrease of the cell concentration to values below the detection limit). In the development of a suitable model structure to describe the inactivation process, critical points in the variation of the specific evolution rate mu [1/h] with the dynamic (time-varying) pH and undissociated lactic acid profiles were taken into account. Thus, biological knowledge, namely, both pH and undissociated lactic acid have an influence on the microbial evolution, was incorporated. The extended model was carefully validated on new data. As a result, the newly developed model is able to accurately predict the growth, inhibition and subsequent inactivation of Y. enterocolitica in coculture as based on the dynamic pH and lactic acid profiles of the medium.

Antibiosis↗

PubMed2006

Validation of predictive growth models describing superatmospheric oxygen effects on Pseudomonas fluorescens and Listeria innocua on fresh-cut lettuce.

Two microbial growth models predicting the growth of Pseudomonas fluorescens and Listeria innocua at superatmospheric oxygen and carbon dioxide concentrations at 7 degrees C were validated on fresh-cut butterhead lettuce. Cut lettuce was inoculated with the same strain of L. innocua as the in vitro experiments. The P. fluorescens strain was tagged with a gene encoding green fluorescent protein (GFP) in order to distinguish the inoculated strain from contaminating Pseudomonaceae. Also growth of aerobic mesophilic and lactic acid bacteria was monitored during the experiments. The suggested P. fluorescens model was appropriate to predict growth on cut lettuce. L. innocua on the other hand, grew considerably slower under in vivo circumstances than predicted. CO(2) had a growth promoting effect on L. innocua growing on cut lettuce, whereas in vitro an inhibiting effect was observed. Validation parameters are calculated and hypotheses to explain the discrepancy between predicted and observed growth of L. innocua are provided.

Carbon Dioxide↗

PubMed2006

Effect of preincubation temperature and pH on the individual cell lag phase of Listeria monocytogenes, cultured at refrigeration temperatures.

The impact of precultural temperature and pH on the distribution of the lag phase of individual Listeria monocytogenes cells was assessed during preincubation at 7 degrees C, using a dilution protocol to obtain single cells, and optical density measurements to estimate the individual lag phase. Firstly, the pure temperature effect (37, 15, 10, 7, 4 and 2 degrees C) was investigated on a subsequent growth at 7 degrees C and pH 7.4. Secondly, low precultural temperatures (10, 7 and 4 degrees C) were combined with a controlled pH at 7.4 and 5.7 with a subsequent growth at 7 degrees C and at different pH values (7.4, 6.0 and 5.5). For all temperature-pH combinations, the individual cell lag phase was determined using a three-phase linear growth model. It was observed that at low precultural temperatures (2, 4 and 7 degrees C), a high proportion of L. monocytogenes cells were able to grow at 7 degrees C with almost no lag phase, consequently, the resulting distributions were positively skewed. Beside this, the variability observed was lower than at higher precultural temperatures. Regarding the precultural pH effect, at pH 7.4 the mean values of the lag phases were shorter at lower preincubation temperatures; while at pH 5.7 small pH transitions produced shorter individual lag phases at all precultural temperatures. The quantification of the effect of precultural conditions on the individual cell lag phase duration would improve the accuracy of the existing growth models, especially when a series of processing and storage steps are linked together in a process model or exposure assessment. Distributions will be fitted to the data for every set of conditions, generating useful tools for further risk assessment purposes.

Colony Count, Microbial↗

PubMed2006

Effect of environmental parameters (temperature, pH and a(w)) on the individual cell lag phase and generation time of Listeria monocytogenes.

The effect of the individual environmental factors temperature (2-30 degrees C), pH (4.4-7.4) and a(w) (0.947-0.995) as well as the combinations of these factors on the individual cell lag phase and the generation time of Listeria monocytogenes was investigated. Individual cells were isolated using a serial dilution protocol in microtiter plates, and subsequent growth was investigated by optical density (OD) measurements at 600 nm. About 100 replicates were made for each set of environmental conditions. Part of the data were previously published in Francois et al. (Francois, K., Devlieghere, F., Smet, K., Standaert, A.R., Geeraerd, A.H., Van Impe, J.F., Debevere, J., 2005a. Modelling the individual cell lag phase: effect of temperature and pH on the individual cell lag distribution of Listeria monocytogenes. Int. J. Food Microbiol. 100, 41-53.), but were recalculated here using the calibration curves for transformation of optical density to colony forming units/ml from Francois et al. (Francois, K., Devlieghere, F., Standaert, A.R., Geeraerd, A.H., Cools, I., Van Impe, J.F., Debevere, J., 2005b. Environmental factors influencing the relationship between optical density and cell count for Listeria monocytogenes. J. Appl. Microbiol. 99, 1503-1515), as this calibration curve appeared to be dependent on the environmental parameters. The previous dataset was also extended with a factor a(w), observed individually and combinations with the above mentioned environmental factors. Individual cell lag phases and subsequent growth rates were calculated assuming an exponential growth model. The results are discussed as mean values to determine the general trends and in addition, histograms are made and statistical distributions are fitted to the different data sets. When stress levels increased, the mean values and the variability observed for the individual cell lag phases increased, resulting in broader histograms and distributions that were shifting to the right. Also the gravity point of the distributions was shifting from a skewed left type to a more symmetrical type. The best description of the data is obtained with an exponential distribution for low stress levels, a gamma distribution for intermediate stress and a Weibull distribution for severe stress levels. When only low stress levels were applied, a significant percentage of the cells showed no lag phase. In those cases, a new approach was used to obtain better fits: cells with a lag phase and those without a lag phase were separated using a binomial distribution while in a second step, a gamma or a Weibull distribution is fitted to the fraction of cells showing a lag phase. A normal distribution is used to describe the variability of the generation times. These distributions can be applied to refine the exposure assessment part of the risk assessment concerning L. monocytogenes by incorporating intercellular variability.

Colony Count, Microbial↗

PubMed2006

Single cell variability of L. monocytogenes grown on liver pâté and cooked ham at 7 degrees 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). METHODS AND RESULTS: Single cells were isolated by a dilution protocol and inoculated on 15 g samples of liver pâté and cooked ham, pasteurized in the packaging. Of each product, 250 samples were inoculated, of which 50 samples were analysed for L. monocytogenes on each analysis day. Results were compared to simulations, based on distributions that describe the variability of the individual cell lag phases and generation times of L. monocytogenes cultivated in broths. Based on the same simulation techniques, the variability effect was investigated for different inoculum levels (10, 100, 10,00 and 10,000 cells). It was demonstrated that the expected variability of the outgrowth of L. monocytogenes in a challenge test is very high for low inoculum levels. CONCLUSIONS: The variability in growth characteristics observed between different single L. monocytogenes cells on foods is very large. The simulations based on the previously collected optical density data in broths, could be confirmed by foods inoculated with single L. monocytogenes cells. SIGNIFICANCE AND IMPACT OF THE STUDY: The large variability between different individual L. monocytogenes cells has serious consequences for the experimental design of a challenge test. One thousand cells per portion are necessary in order to reduce the variability to acceptable levels and quantify the behaviour of the pathogen consistently with a reasonable number of challenge tests.

Animals↗

PubMed2006

Use of image analysis for sludge characterisation: studying the relation between floc shape and sludge settleability.

This paper starts by presenting a fully automatic image analysis procedure for characterisation of flocs and filaments in activated sludge images. Thereafter the attention is directed towards the results of four lab-scale experiments, in which image information is related to sludge settleability in terms of sludge volume index. This relation is statistically confirmed by applying a principal component analysis to the data. In addition, the redundancy in the data sets is studied with regard to floc shape descriptors and the monitoring potential of image analysis is demonstrated by means of a multiple linear regression exercise.

Image Processing, Computer-Assisted↗

PubMed2005

Reflections on the use of robust and least-squares non-linear regression to model challenge tests conducted in/on food products.

In this research, we question the straight-forward use of the classical sum of squared error criterion for identifying the typical parameters of a primary model (like growth rate mumax and lag time lambda) when applied to growth curves obtained in and on food products. Firstly, we base our reflections on 62 Listeria monocytogenes laboratory challenge tests collected in various environments (broth, crushed cold-smoked salmon, and surface of cold-smoked salmon slices). Whereas growth data in broth resulted in residual values consistent with a Gaussian distribution, growth data in the crushed product and even more on the surface of slices appeared different. Secondly, we propose the use of an alternative so-called robust non-linear regression method suitable when experimental error is non-normally distributed, which seems, according to this research, typical for microbial challenge tests in/on food products, and which lead to apparent outliers or leverage points in the experimental data. Properties of the robust regression procedure are illustrated on simulated data first, whereafter its use on the considered challenge tests is illustrated. To conclude, reflections on the assumptions and related realism underlying challenge tests and recommendations for fitting growth curves obtained in and on food products are presented.

Animals↗

PubMed2005

Predictive modelling and validation of Listeria innocua growth at superatmospheric oxygen and carbon dioxide concentrations.

The effect of superatmospheric oxygen and carbon dioxide concentrations on the growth of Listeria innocua, which was used as a model organism for the pathogen Listeria monocytogenes, was evaluated. The bacteria were grown on a nutrient agar surface at 7 degrees C. Three carbon dioxide levels (0%, 12.5% and 25%) were combined with different levels of high oxygen concentrations (above 20%) based on a mixture design. The applied oxygen concentrations did not significantly influence the growth. High CO2 concentrations, on the contrary, reduced the maximum specific growth rate and prolonged the lag time. An overall model to describe the growth of L. innocua under high carbon dioxide conditions was constructed based on nine growth experiments, using a weighted one-step regression procedure. The influence of carbon dioxide on lag time and maximum specific growth rate was described using Ratkowsky-type models and inserted in the Baranyi equation. The model described the growth very well. To assess the validity of the model, 14 additional experiments were carried out. There was a good correlation of the model predictions and observed validation data.

Carbon Dioxide↗

PubMed2005

Predictive modelling of the individual and combined effect of water activity and temperature on the radial growth of Fusarium verticilliodes and F. proliferatum on corn.

The major objective of this study was to develop validated models to describe the effect of a(w) and temperature on the radial growth on corn of the two major fumonisin producing Fusaria, namely Fusarium verticilliodes and F. proliferatum. The growth of these two isolates on corn was therefore studied at water activities between 0.810-0.985 and temperatures between 15 and 30 degrees C. Minimum a(w) for growth was 0.869 and 0.854 for F. verticilliodes and F. proliferatum, respectively. No growth took place at a(w) values equal to 0.831 and 0.838 for F. verticilliodes and F. proliferatum, respectively. The colony growth rates, g (mm d(-1)) were determined by fitting a flexible growth model describing the change in colony diameter (mm) with respect to time (days). Secondary models, relating the colony growth rate with a(w) or a(w) and temperature were developed. A third order polynomial equation and the linear Arrhenius-Davey model were used to describe the combined effect of temperature and a(w) on g. The combined modelling approaches, predicting g (mm d(-1)) at any a(w) and/or temperature were validated on independently collected data. All models proved to be good predictors of the growth rates of both isolates on maize within the experimental conditions. The third order polynomial equation had bias factors of 1.042 and 1.054 and accuracy factors of 1.128 and 1.380 for F. verticilliodes and F. proliferatum, respectively. The linear Arrhenius-Davey model had bias factors of 0.978 and 1.002 and accuracy factors of 1.098 and 1.122 for F. verticilliodes and F. proliferatum, respectively. The results confirm the general finding that a(w) has a greater influence on fungal growth than temperature. The developed models can be applied for the prevention of Fusarium growth on maize and the development of models that incorporate other factors important to mould growth on maize.

Adsorption↗

PubMed2005

GInaFiT, a freeware tool to assess non-log-linear microbial survivor curves.

This contribution focuses on the presentation of GInaFiT (Geeraerd and Van Impe Inactivation Model Fitting Tool), a freeware Add-in for Microsoft Excel aiming at bridging the gap between people developing predictive modelling approaches and end-users in the food industry not familiar with or not disposing over advanced non-linear regression analysis tools. More precisely, the tool is useful for testing nine different types of microbial survival models on user-specific experimental data relating the evolution of the microbial population with time. As such, the authors believe to cover all known survivor curve shapes for vegetative bacterial cells. The nine model types are: (i) classical log-linear curves, (ii) curves displaying a so-called shoulder before a log-linear decrease is apparent, (iii) curves displaying a so-called tail after a log-linear decrease, (iv) survival curves displaying both shoulder and tailing behaviour, (v) concave curves, (vi) convex curves, (vii) convex/concave curves followed by tailing, (viii) biphasic inactivation kinetics, and (ix) biphasic inactivation kinetics preceded by a shoulder. Next to the obtained parameter values, the following statistical measures are automatically reported: standard errors of the parameter values, the Sum of Squared Errors, the Mean Sum of Squared Errors and its Root, the R(2) and the adjusted R(2). The tool can help the end-user to communicate the performance of food preservation processes in terms of the number of log cycles of reduction rather than the classical D-value and is downloadable via the KULeuven/BioTeC-homepage at the topic "Downloads" (Version 1.4, Release date April 2005).

Bacteria↗

PubMed2005

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

AIMS: The effect of temperature (2-30 degrees 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. METHODS AND RESULTS: Calibration curves, relating OD with plate counts, were collected by measuring the OD of consecutive one-half dilution series, before determining the cell density by classic plate count methods. The calibration curves were observed to be shifting in a parallel way, with increasing stress levels. Especially pH influenced the curve in a great extent, while the other variables were showing more synergetic effects. The reason for the shift was investigated by a microscopic viability test, showing a viability decrease with increasing stress levels, causing the shift of the calibration curve. In a last step a model was made describing the effect of environmental factors on the calibration curve, with different data transformations being tested. A polynomial equation was fitted to the data, taking into account a set of constraints to incorporate microbiological knowledge in the black box model. Hence, illogical interpolation results and overfitting of the data could be avoided. CONCLUSIONS: Different stress factors are affecting the relationship between the OD and the cell count of L. monocytogenes by lowering the cell viability. These effects could be modelled using a constrained polynomial model. SIGNIFICANCE AND IMPACT OF THE STUDY: The observed phenomena are important when calculating growth parameters, like growth rate and lag phase, based on OD data.

Calibration↗

PubMed2004

Obtaining single cells: analysis and evaluation of an experimental protocol by means of a simulation model.

The research presented in this paper analyses a newly developed experimental protocol for isolating single cells by constructing a simulation model of the process. The protocol involves sequential 50% dilutions of a cell suspension in a microtiter plate, so that eventually, wells are obtained containing exactly one cell. The aim of this modelling study is (i) to gain insight in the governing mechanisms of the dilution process, (ii) to confirm experimental findings and (iii) to enable the prediction of an average outcome for future experiments. The model construction process is presented chronologically. The initial basic model simulates the experiment as a sequence of binomial processes, using Monte Carlo techniques. Statistical analysis of the results shows that aggregational factors need to be taken into account in the form of a lognormal distribution. Several issues involved in this adaptation are discussed. To fully account for cell aggregation in the dilution process, a cell clumping algorithm is built into the simulation model. Simulation data from the resulting model show similar statistical characteristics as the experimental data and yield reliable prediction intervals for the available experimental data. The simulation model is a useful tool to support experimental findings and predict the outcome of future experiments. Even more importantly, this study emphasises the importance of careful statistical analysis in single cell research. The impact of stochastic effects is considerably amplified at the low cell concentrations involved and needs to be taken into account in any modelling effort.

Bacteria↗

PubMed2004

Towards a novel class of predictive microbial growth models.

Food safety and quality are influenced by the presence (and possible proliferation) of pathogenic and spoilage microorganisms during the life cycle of the product (i.e., from the raw ingredients at the start of the production process until the moment of consumption). In order to simulate and predict microbial evolution in foods, mathematical models are developed in the field of predictive microbiology. In general, microbial growth is a self-limiting process, principally due to either (i) the exhaustion of one of the essential nutrients, and/or (ii) the accumulation of toxic products that inhibit growth. Nowadays, most mathematical models used in predictive microbiology do not explicitly incorporate this basic microbial knowledge. In this paper, a novel class of microbial growth models is proposed. In contrast with the currently used logistic type models, e.g., the model of Baranyi and Roberts [Baranyi, J., Roberts, T.A., 1994. A dynamic approach to predicting bacterial growth in food. International Journal of Food Microbiology 23, 277-294], the novel model class explicitly incorporates nutrient exhaustion and/or metabolic waste product effects. As such, this novel model prototype constitutes an elementary building block to be extended in a natural way towards, e.g., microbial interactions in co-cultures (mediated by metabolic products) and microbial growth in structured foods (influenced by, e.g., local substrate concentrations). While under certain conditions the mathematical equivalence with classical logistic type models is clear and results in equal fitting capacities and parameter estimation quality (see Poschet et al. [Poschet, F., Vereecken, K.M., Geeraerd, A.H., Nicolai, B.M., Van Impe, J.F., 2004. Analysis of a novel class of predictive microbial growth models and application to co-culture growth. International Journal of Food Microbiology, this issue] for a more elaborated analysis in this respect), the biological interpretability and extendability represent the main added value.

Bacteria↗

PubMed2004

Analysis of a novel class of predictive microbial growth models and application to coculture growth.

In this paper, a novel class of microbial growth models is analysed. In contrast with the currently used logistic type models (e.g., the model of Baranyi and Roberts [Baranyi, J., Roberts, T.A., 1994. A dynamic approach to predicting bacterial growth in food. International Journal of Food Microbiology 23, 277-294]), the novel model class, presented in Van Impe et al. (Van Impe, J.F., Poschet, F., Geeraerd, A.H., Vereecken, K.M., 2004. Towards a novel class of predictive microbial growth models. International Journal of Food Microbiology, this issue), explicitly incorporates nutrient exhaustion and/or metabolic waste product effects inducing stationary phase behaviour. As such, these novel model types can be extended in a natural way towards microbial interactions in cocultures and microbial growth in structured foods. Two illustrative case studies of the novel model types are thoroughly analysed and compared to the widely used model of Baranyi and Roberts. In a first case study, the stationary phase is assumed to be solely resulting from toxic product inhibition and is described as a function of the pH-evolution. In the second case study, substrate exhaustion is the sole cause of the stationary phase. Finally, a more complex case study of a so-called P-model is presented, dealing with a coculture inhibition of Listeria innocua mediated by lactic acid production of Lactococcus lactis.

Coculture Techniques↗

PubMed2004

Estimating the probability and level of contamination with Salmonella of feed for finishing pigs produced in Switzerland--the impact of the production pathway.

Contaminated feed is a source of infection with Salmonella for livestock, including pigs. Because pigs rarely show clinical signs of salmonellosis, undetected carriers can enter the food production chain. In a "Farm to Fork" food safety concept, safe feed is the first step for ensuring safe food. Heat treatment or adding organic acids are process steps for reducing or eliminating a contamination with Salmonella. The aims of this study were (I) to estimate the probability and the level of Salmonella contamination in batches of feed for finishing pigs in Swiss mills and (II) to assess the efficacy of specific process steps for reducing the level of contamination with Salmonella. A quantitative release assessment was performed by gathering and combining data on the various parameters having an influence on the final contamination of feed. Fixed values and probability distributions attributed to these parameters were used as input values for a Monte Carlo simulation. The simulation showed that-depending on the production pathway-the probability that a batch of feed for finishing pigs contains Salmonella ranged from 34% (for feed on which no specific decontaminating step was applied) to 0% (for feed in which organic acids were added and a heat treatment was implemented). If contamination occurred, the level of contamination ranged from a few Salmonella kg(-1) feed to a maximum of 8E+04 Salmonella kg(-1) feed. Probability and levels of contamination were highest when no production process able to reduce or eliminate the pathogen was implemented. However, most of the Swiss production was shown to undergo some kind of decontaminating step. A heat treatment, in combination with the use of organic acids, was found as a solution of choice for the control of Salmonella in feed.

Animal Feed↗

PubMed2004

Development of predictive modelling approaches for surface temperature and associated microbiological inactivation during hot dry air decontamination.

This research deals with the development of predictive modelling approaches in the field of heat transfer and microbial inactivation. Upon making some backstage microbiological considerations, surface temperature predictions during hot dry air decontaminations are incorporated in a microbial inactivation model, in order to describe inactivation kinetics under realistic (time-varying) temperature conditions. In the present study, the following parts are presented. (i) First, a one-dimensional heat transfer model is developed taking into account exchanges by convection, radiation and evaporation. The model is subsequently validated on a laboratory setup and on a test rig, assuming no water activity changes. This test rig is developed for studying-at a later stage-surface pasteurisation treatment on food products with the use of hot dry air. (ii) Isothermal inactivation data of Escherichia coli K12 MG1655 have been collected and inactivation parameters are accurately estimated by using a primary and a secondary model in a global modelling approach. (iii) Microbiological considerations such as microbial growth effects during come-up times, initial temperature of inactivation, and heat resistance effects, based on experimental observations and on literature studies, are formulated in order to evaluate possible microbial effects arising under the dynamic temperature conditions modelled in step (i). (iv) Microbial inactivation simulations with the incorporation of surface temperature predictions are presented. (v) Finally, the level of the microbial decontamination in an example based on the design of an industrial installation is presented, outlining the importance of the combination of surface temperature and microbial inactivation modelling approaches.

Escherichia coli↗