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Microbial spoilage of shelf-stable acidified sauces is predominantly caused by lactic acid bacteria and yeasts. A specific spoilage yeast in these products is Zygosaccharomyces bailii, as this fructophilic, osmotolerant, and weak acid resistant yeast is difficult to control. A growth/no growth model was developed describing the influence of (i) pH in a range from pH 3.0 to pH 5.0 (5 levels), (ii) acetic acid in a range from 0 to 3.5% (w/v), and (iii) lactic acid in a range from 0 to 3.0% (w/v). aw was fixed at a level of 0.95 which is representative for acidified sauces with high sugar content. Modified Sabouraud medium was inoculated at +/- 10(4) CFU/ml, incubated at 30 degrees C and growth was assessed by optical density measurements. All combinations of environmental conditions were tested in at least twelve replicates, yielding precise values for the probability of growth. Results showed that replacing acetic acid by lactic acid, which has a milder taste, may imply some risks on food spoilage because, under some conditions, stimulation of growth by lactic acid was observed. This stimulation had also consequences on the model development: (i) only ordinary logistic regression models were able to describe this phenomenon due to their flexible behaviour, (ii) it was necessary to split up the data set into two subsets to have the best description of the obtained data. Two different ordinary logistic regression models were fitted on these data sets taking either the total acid concentration as one of the explanatory variables or differentiating between the undissociated and dissociated acid concentrations. The obtained models were compared with the CIMSCEE code [CIMSCEE, 1992. Code for the production of microbiologically safe and stable emulsified and non-emulsified sauces containing acetic acid. Comité des Industries des Mayonnaise et Sauces Condimentaires, de la Communauté Economique Européenne, Brussels, Belgium], a formula which is nowadays often used by the food industry to predict the stability of acidified products based on the undissociated acetic acid, NaCl and sugars concentration. Comparing this formula and the newly developed models showed that the CIMSCEE code made a slight underestimation of the growth probability. Advantages of the newly developed models are the description of the gradual transition zone between growth and no growth and the incorporation of the effect of lactic acid, alone or in combination with acetic acid.

Aims: The dynamics of Escherichia coli near the maximum temperature for growth in a rich medium are analysed. The effects of temperature history, medium composition and physiological state of the inoculum are evaluated., Methods and Results: Kinetics of E. coli K12 MG1655 is studied in 'brain-heart infusion' broth in a temperature controlled environment. Based on viable counts, 'smooth' growth curves are observed at 40, 41, 42 and 43 degrees C. The exponential growth phase at 44 and 45 degrees C is interrupted. At 46 degrees C, a period of exponential growth is followed by inactivation. Neither the physiological state of the inoculum nor medium enrichment alters the dynamics, whilst temperature pre-adaptation or chemical chaperones restore regular cell growth and division ('smooth' exponential growth)., Conclusions: Atypical, nonexponential growth at 44, 45 and 46 degrees C seems related to protein destabilization and can (partly) be restored by an appropriate medium design (i.e. addition of chemical chaperones) or temperature history (i.e. selection of a more resistant subpopulation)., Significance and Impact of the Study: This study indicates that the maximum temperature for growth is dependent on the temperature history and the chemical environment. These observations and the nonexponential kinetics have important implications for the development of predictive models for food safety and quality.

Onsite individual wastewater treatment systems can provide a financially attractive alternative to a sewer connection in locations far from the existing sewer network. These systems are, however, relatively new, and practical experiences, especially long-term field studies, are lacking. Therefore, a thorough study of two compact biofilm-based, aerobic onsite systems, both of five population equivalents, was started in 2001. The assessment of the treatment performance of these systems, as well as the maintenance requirements and the characterisation of the feed are of great importance for the better understanding of the systems in order to optimise their design and performance. This paper presents an evaluation and discussion of the start-up and a starvation period of the two studied systems, followed by a characterisation of the incoming wastewater using activated sludge respirometry experiments in the context of the assessment and improvement of the denitrification process. Individual wastewater treatment systems are characterised by a rather long start-up period of 70-120 days. An important characteristic during the start-up is the nitrite peak, which indicates the initiation of the nitrification process. The respirometric experiments reveal that the failing denitrification is probably caused by an insufficient amount of readily biodegradable COD in the influent., (IWA Publishing 2008.)

A new class of predictive model developed in a liquid system is extended in order to quantify gelatin gel matrix structure effects on the growth of Listeria innocua and Lactococcus lactis (both in mono- and coculture, and both producing mainly lactic acid). It was observed that gelatin does not only act as a structuring agent but also alters the buffering capacity of the medium. Model extension occurs in two stages, describing chemical and microbiological processes, respectively. Firstly, equations relating undissociated lactic acid concentration and total lactic acid concentration on the one hand, and undissociated lactic acid concentration and pH on the other hand, are extended to account for the effects of gelatin concentration. Secondly, these equations are incorporated into the growth model to describe the combined effect of gelatin concentration, (undissociated) lactic acid and pH on the growth of either microorganism. The description of the model is in good agreement with the experimental data acquired in monoculture conditions. In a subsequent model validation step, when gelatin concentration and total lactic acid profile of the coculture experiments are used as inputs, the developed growth model consisting of condensed knowledge extracted from the monoculture experiments, is able to predict accurately the interaction effect occurring in coculture. The study suggests that, on the one hand, the extent of the effects of undissociated lactic acid and pH on microbial growth in structured food systems can be modified by the increase in buffering capacity, which can protect microorganisms and eventually promote higher levels of cell growth in comparison with liquid culture conditions. On the other hand, food matrix structure, in casu the gelatin, reduces the rate of microbial multiplication. Both effects are incorporated in the growth model developed in this research.

Aims: In previous studies the microbial kinetics of Escherichia coli K12 have been evaluated under static and dynamic conditions (Valdramidis et al. 2005, 2006). An acquired microbial thermotolerance following heating rates lower than 0.82 degrees C min(-1) for the studied micro-organism was observed. Quantification of this induced physiological phenomenon and incorporation, as a model building block, in a general microbial inactivation model is the main outcome of this work., Methods and Results: The microbial inactivation rate observed (k(obs)) under time-varying temperature conditions is studied and expressed as a function of the heating rate (dT/ dt). Hereto, a model building block related to the microbial physiology (k(phys)) under stress conditions is developed. Evaluation of the performance of the developed mathematical approach depicts that physiological adaptation is an essential issue to be considered when modelling microbial inactivation., Conclusions: Consideration, at a mathematical level, of microbial responses resulting in physiological adaptations contribute to the reliable quantification of the safety risks during food processing., Significance and Impact of the Study: By taking into account the physiological adaptation, the microbiological evolution during heat processing can be accurately assessed, and overly conservative or fail dangerous food processing designs can be avoided.

Growth/no growth models were developed for two spoilage bacteria typical for acidified sauces, L. plantarum and L. fructivorans. Influencing factors embedded in the model are also those typically encountered in these acidified sauces. The pH was varied between 3.0 and 5.0 (5 levels), and the acetic and lactic acid concentration ranged from 0 to 3% (6 levels). Modified MRS broth was inoculated at a high inoculation level (10(6) CFU/ml), incubated at 30 degrees C and growth was assessed by optical density measurements. All combinations of environmental conditions were tested in twelvefold yielding precise values for the probability of growth. Data were modelled by means of ordinary logistic regression. A comparison was made between a model containing the total acid concentrations as explanatory variables, on the one hand, and a model differentiating between the dissociated and undissociated concentrations, on the other hand. Results showed that (i) L. plantarum and L. fructivorans behave differently, resulting in a clearly distinct growth/no growth interface, (ii) there was no great difference between the established models with different explanatory variables, (iii) in some cases, growth/no growth boundaries at very low probabilities (which are more practical in industry) show illogical behaviour. The results of this study were also compared with the CIMSCEE code, which is often used by food producers to determine the stability of their acidified food products.

A full factorial design of five temperatures (16, 22, 25, 30 and 37 degrees C) and seven a(w) values between 0.801 and 0.982 was used to investigate the growth of the two major aflatoxin producing Aspergillus isolates on corn. The colony growth rates (g, mmd(-1)) and lag phases (lambda, d) were estimated by fitting a flexible primary growth model. Subsequently, secondary models relating g or lambda to a(w) or temperature or a(w) and temperature combined, were developed and validated by using independently collected data. The Gibson and linear Arrhenius-Davey model describing the individual effects of a(w) or temperature on g or lambda proved an adequate predictor of either growth parameter. Based on the validation criteria, a quadratic polynomial function proved to be more suitable than a Gaussian function or extended Davey model for describing the combined effect of a(w) and temperature on g or lambda. Both isolates studied had optimum growth temperatures of approximately 30 degrees C. No growth was observed for both isolates at a(w) 0.801, growth only occurring at 25 and 30 degrees C at a(w) 0.822. Significant interaction between a(w) and temperature on g and lambda was observed for both isolates. The developed models can be applied in the preservation of corn and the development of models that incorporate other factors important to mould growth on corn.

Aims: Determination of the minimum requirements (time-temperature relationship and moisture content) that are needed for a sufficient eradication of an indicator organism., Methods and Results: To determine the hygienic safety of composting processes, the indicator organism Salmonella enterica ssp. enterica serotype Senftenberg strain W 775 (further abbreviated as W 775) was artificially inoculated on a meat carrier and monitored subsequently. Different types of composting processes, e.g. composting in enclosed facilities, in open-air and in-vessel composting, were investigated. The waste feedstocks used in this work were either biowastes (i.e. vegetable, fruit and garden wastes; also called source-separated household wastes) or pure garden wastes. Beside these large-scale trials, we also conducted some lab experiments in order to determine the impact of temperature, moisture content and the presence of an indigenous microflora on the eradication of W 775. We found the temperature to be the most important parameter to eradicate W 775 from compost. When the temperature of the compost heap is 60 degrees C and the moisture content varies between 60-65%, W 775 (10(8) CFU g(-1)) will be inactivated within 10 h of composting. The moisture content is, beside temperature, a second parameter that influences the survival of W 775. When the water content of the composting materials or meat carriers is reduced, a higher survival rate of W 775 was observed (survival rate increases 0.5 log(10) unit when there is a reduction of 5% in moisture content). In addition, other parameters (such as microbial antagonism, toxic compounds, etc.) have an influence on the survival of W 775 as well., Conclusions: Our study demonstrates that all types of composting processes tested in this work were sufficient to eradicate W 775 providing that they are well managed in terms of temperature and moisture content., Significance and Impact of the Study: To give a better view on the parameters of importance for the eradication of W 775 during composting.

Thermal pasteurization is a well known and old technique for reducing the microbial count of foods. Traditional thermal processing, however, can destroy heat-sensitive nutrients and food product qualities such as flavor, color and texture. For more than 2 decades now, the use of high-pressure carbon dioxide (HPCD) has been proposed as an alternative cold pasteurization technique for foods. This method presents some fundamental advantages related to the mild conditions employed, particularly because it allows processing at much lower temperature than the ones used in thermal pasteurization. In spite of intensified research efforts the last couple of years, the HPCD preservation technique has not yet been implemented on a large scale by the food industry until now. This review presents a survey of published knowledge concerning the HPCD technique for microbial inactivation, and addresses issues of the technology such as the mechanism of carbon dioxide bactericidal action, the potential for inactivating vegetative cells and bacterial spores, and the regulatory hurdles which need to be overcome. In addition, the review also reflects on the opportunities and especially the current drawbacks of the HPCD technique for the food industry.

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.

Several model types have already been developed to describe the boundary between growth and no growth conditions. In this article two types were thoroughly studied and compared, namely (i) the ordinary (linear) logistic regression model, i.e., with a polynomial on the right-hand side of the model equation (type I) and (ii) the (nonlinear) logistic regression model derived from a square root-type kinetic model (type II). The examination was carried out on the basis of the data described in Vermeulen et al. [Vermeulen, A., Gysemans, K.P.M., Bernaerts, K., Geeraerd, A.H., Van Impe, J.F., Debevere, J., Devlieghere, F., 2006-this issue. 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. International Journal of Food Microbiology. .]. These data sets consist of growth/no growth data for Listeria monocytogenes as a function of water activity (0.960-0.990), pH (5.0-6.0) and acetic acid percentage (0-0.8% (w/w)), both for a monoculture and a mixed strain culture. Numerous replicates, namely twenty, were performed at closely spaced conditions. In this way detailed information was obtained about the position of the interface and the transition zone between growth and no growth. The main questions investigated were (i) which model type performs best on the monoculture and the mixed strain data, (ii) are there differences between the growth/no growth interfaces of monocultures and mixed strain cultures, (iii) which parameter estimation approach works best for the type II models, and (iv) how sensitive is the performance of these models to the values of their nonlinear-appearing parameters. The results showed that both type I and II models performed well on the monoculture data with respect to goodness-of-fit and predictive power. The type I models were, however, more sensitive to anomalous data points. The situation was different for the mixed strain culture. In that case, the type II models could not describe the curvature in the growth/no growth interface which was reversed to the typical curvatures found for monocultures. This unusual curvature may originate from the fact that (i) an interface of a mixed strain culture can result from the superposition of the interfaces of the individual strains, or that (ii) only a narrow range of the growth/no growth interface was studied (the local trend can be different from the trend over a wider range). It was also observed that the best type II models were obtained with the flexible nonlinear logistic regression, although reasonably good models were obtained with the less flexible linear logistic regression with the nonlinear-appearing parameters fixed at experimentally determined values. Finally, it was found that for some of the nonlinear-appearing parameters, deviations from their experimentally determined values did not influence the model fit. This was probably caused by the fact that only a limited part of the growth/no growth interface was studied.

This article describes a fully automatic image analysis procedure for fast and reliable characterization of the activated sludge composition, that is, the floc and filament features. The algorithms developed for each of the analysis steps, that is, segmentation, object recognition, and characterization, are described in detail. Although the application range of the recognition method is a priori expanded by introducing a number of control parameters, the procedure proves to be intrinsically robust as it produces satisfactory results for a fixed set of parameter values for a wide variety of image types.

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.

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.

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.

Aims: This paper studies and models the effect of the amplitude of a sudden temperature upshift DeltaT on the adaptation period of Escherichia coli, in terms of the work to be done by the cells during the subsequent lag phase (i.e., the product of growth rate mumax and lag phase duration lambda)., Methods and Results: Experimental data are obtained from bioreactor experiments with E. coli K12 MG1655. At a predetermined time instant during the exponential growth phase, a sudden temperature upshift is applied (no other environmental changes take place). The length of the (possibly) induced lag phase and the specific growth rate after the shift are quantified with the growth model of Baranyi and Roberts (Int J Food Microbiol 23, 1994, 277). Different models to describe the evolution of the product lambda x mumax as a function of the amplitude of the temperature shift are statistically compared., Conclusions: The evolution of lambda x mumax is influenced by the amplitude of the temperature shift DeltaT and by the normal physiological temperature range. As some cut-off is observed, the linear model with translation is preferred to describe this evolution., Significance and Impact of the Study: This work contributes to the characterization of microbial lag phenomena, in this case for E. coli K12 MG1655, in view of accurate predictive model building.

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.

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.

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.

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.

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.

The performance of the activated sludge process is limited by the ability of the sedimentation tank (1) to separate the activated sludge from the treated effluent and (2) to concentrate it. Apart from bad operating strategies or poorly designed clarifiers, settling failures can mainly be attributed to filamentous bulking. Image analysis is a promising technique that can be used for early detection of filamentous bulking. The aim of this paper is therefore twofold. Foremost, correlations are sought between image analysis information (i.e., the total filament length per image, the mean form factor, the mean equivalent floc diameter, the mean floc roundness and the mean floc reduced radius of gyration) and classical measurements (i.e., the Sludge Volume Index (SVI)). Secondly, this information is both explored and exploited in order to identify dynamic ARX and state space-type models. Their performance is compared based on two criteria.

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).

This paper is the second in a series of two, and studies microbial lag in cell number and/or biomass measurements caused by temperature changes with an individual-based modeling approach. For this purpose, the theory of cell division, as discussed in the first part of this series of research papers, was implemented in the individual-based modeling framework BacSim. Simulations of this model are compared with experimental data of Escherichia coli, growing in an aerated, glucose-rich medium and subjected to sudden temperature shifts. The premise of a constant cell volume under changing temperature conditions predicts no lag in cell numbers after the shift, in contrast to the experimental observations. Based on literature research, two biological mechanisms that could be responsible for the observed lag phenomena are proposed. The first assumes that the average cell volume depends on temperature while the second assumes that a lag in biomass growth occurs after the temperature shift. For a lag in cell number caused by an increased average cell volume, the cell biomass always increases at the maximal rate. Therefore, cells are evidently not stressed and do not have to adapt to the new conditions, as opposed to a lag in biomass growth. Implementation and simulation of both mechanisms are found to describe the experimental observations equally well. Therefore, further research is needed to distinguish between the two mechanisms. This can be done by observing, in addition to cell numbers, a measure for the average cell volumes. In conclusion, the individual-based modeling approach is a good methodology to investigate and test biological theories and assumptions. Also, based on the simulations, suggestions for further experimental observations can be made.

This series of two papers deals with the theory of cell division and its implementation in an individual-based modeling framework. In this first part, the theory of cell division is studied on an individual-based level in order to learn more about the mechanistic principles behind microbial lag phenomena. While some important literature on cell division theory dates from 30 to 40 years ago, until now it has hardly been introduced in the field of predictive microbiology. Yet, it provides a large amount of information on how cells likely respond to changing environmental conditions. On the basis of this theory, a general theory on microbial lag behavior caused by a combination of medium and/or temperature changes has been developed in this paper. The proposed theory then forms the basis for a critical evaluation of existing modeling concepts for microbial lag in predictive microbiology. First of all, a more thorough definition can be formulated to define the lag time lambda and the previously only vaguely defined physiological state of the cells in terms of mechanistically defined parameters like cell mass, RNA or protein content, specific growth rate and time to perform DNA replication and cell division. On the other hand, existing predictive models are evaluated with respect to the newly developed theory. For the model of , a certain fitting parameter can also be related to physically meaningful parameters while for the model of [Augustin, J.-C., Rosso, L., Carlier, V.A. 2000. A model describing the effect of temperature history on lag time for Listeria monocytogenes. Int. J. Food Microbiol. 57, 169-181] a new, mechanistically based, model structure is proposed. A restriction of the proposed theory is that it is only valid for situations where biomass growth responds instantly to an environment change. The authors are aware of the fact that this assumption is not generally acceptable. Lag in biomass can be caused, for example, by a delayed synthesis of some essential growth factor (e.g., enzymes). In the second part of this series of papers [Dens, E.J., Bernaerts, K., Standaert, A.R., Kreft, J.-U., Van Impe, J.F., this issue. Cell division theory and individual-based modeling of microbial lag: part II. Modeling lag phenomena induced by temperature shifts. Int. J. Food Microbiol], the theory of cell division is implemented in an individual-based simulation program and extended to account for lags in biomass growth. In conclusion, the cell division theory applied to microbial populations in dynamic medium and/or temperature conditions provides a useful framework to analyze microbial lag behavior.

Batch and fed batch cultures of Azospirillum brasilense Sp245 were conducted in a bioreactor. Growth response, IAA biosynthesis and the expression of the ipdC gene were monitored in relation to the environmental conditions (temperature, availability of a carbon source and aeration). A. brasilense can grow and produce IAA in batch cultures between 20 and 38 degrees C in a standard minimal medium (MMAB) containing 2.5 gl(-1)l-malate and 50 microgml(-1) tryptophan. IAA synthesis requires depletion of the carbon source from the growth medium in batch culture, causing growth arrest. No significant amount of IAA can be detected in a fed batch culture. Varying the concentration of tryptophan in batch experiments has an effect on both growth and IAA synthesis. Finally we confirmed that aerobic growth inhibits IAA synthesis. The obtained profile for IAA synthesis coincides with the expression of the indole-3-pyruvate decarboxylase gene (ipdC), encoding a key enzyme in the IAA biosynthesis of A. brasilense.

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.

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.

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.

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.

Optimal experiment design for parameter estimation (OED/PE) is an interesting technique for modelling practices when aiming for maximum parameter estimation accuracy. Nowadays, experimental designs for secondary modelling within the field of predictive microbiology are mostly arbitrary or based on factorial design. The latter type of design is common practice in response surface modelling approaches. A number of levels of the factor(s) under study are selected and all possible treatment combinations are performed. It is however not always clear which levels and treatment combinations are most relevant. An answer to this question can be obtained from optimal experiment design for-in this particular case-parameter estimation. This technique is based on the extremisation of a scalar function of the Fisher information matrix. The type of scalar function determines the final focus of the optimised design. In this paper, optimal experiment designs are computed for the cardinal temperature model with inflection point (CTMI) and the cardinal pH model (CPM). A model output sensitivity analysis (depicting the sensitivity of the model output to a small change in the model parameters) yields a first indication of relevant temperature or pH treatments. Performed designs are: D-optimal design aiming for a maximum global parameter estimation accuracy (by minimising the determinant of the Fisher information matrix), and E-optimal design improving the confidence in the most uncertain model parameter (by maximising the smallest eigenvalue of the Fisher information matrix). Although lowering the information content of a set of experiments, boundary values on the design region need to be imposed during optimisation to exclude unworkable experiments and partly account for incorrect nominal parameter values. Opposed to the frequently applied equidistant or arbitrary treatment placement, optimal design results show that typically four informative temperature or pH levels are selected and replicate experiments are to be performed at these points. Informative experiments are typically placed at points with an extreme model output sensitivity.

The microbial lag phase is a complex and yet not completely understood phenomenon. Many studies on the microbial lag phase have been published but few report a systematic study; moreover, previous lag studies have involved the effect of multiple confounded factors. Here, the effect of sudden temperature rises on an exponentially growing Escherichia coli culture is systematically investigated. Experiments are performed in a computer-controlled bioreactor where E. coli K12 MG1655 is grown under aerobic conditions in Brain Heart Infusion (BHI) broth. This experimental set-up is used to characterise the effect of (i) the amplitude of the temperature shift, (ii) the pre-shift temperature level and (iii) the post-shift temperature level on the occurrence and length of a lag phase. Besides temperature, no other environmental changes take place at the moment of the temperature shift. To quantify the length of the induced lag phase, the experimental data are described with a common growth model. Depending on the three factors tested, a lag phase of more or less 1 h is induced or not. This lag/no lag behaviour can largely be explained by the existence of a normal physiological temperature range but also the amplitude of the temperature rise plays a role. It can be concluded that for the microorganism under study the lower boundary of the normal range lies approximately between 22.78 and 23.86 degrees C. It is shown that this boundary is no cut-off point, but rather a transition zone. Even more, repeated experiments at this boundary have yielded different results (lag or no lag). This observation points out that the mechanism triggering this lag phase is not absolute but may be subject to biological variability.

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.

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.

Expression of the Azospirillum brasilense ipdC gene, encoding an indole-3-pyruvate decarboxylase, a key enzyme in the production of indole-3-acetic acid (IAA) in this bacterium, is upregulated by IAA. Here, we demonstrate that the ipdC gene is the promoter proximal gene in a bicistronic operon. Database searches revealed that the second gene of this operon, named iaaC, is well conserved evolutionarily and that the encoded protein is homologous to the Escherichia coli protein SCRP-27A, the zebrafish protein ES1, and the human protein KNP-I/GT335 (HES1), all of unknown function and belonging to the DJ-1/PfpI superfamily. In addition to this operon structure, iaaC is also transcribed monocistronically. Mutation analysis of the latter gene indicated that the encoded protein is involved in controlling IAA biosynthesis but not ipdC expression. Besides being upregulated by IAA, expression of the ipdC-iaaC operon is pH dependent and maximal at acidic pH. The ipdC promoter was studied using a combination of deletion analyses and site-directed mutagenesis. A dyadic sequence (ATTGTTTC(GAAT)GAAACAAT), centered at -48 was demonstrated to be responsible for the IAA inducibility. This bacterial auxin-responsive element does not control the pH-dependent expression of ipdC-iaaC.

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.

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.

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.

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.

Two types of small wastewater treatment systems were studied for their performance under normal conditions, including the hydraulic peak flows associated with small systems connected to just one house. Furthermore, the systems were subjected to a 7-day starvation period to simulate the effect of a holiday from home. The systems studied are (1) a combined submerged aerated filter-activated sludge system and (2) a rotating biological contactor system. Both the organic removal and the nitrification process were closely monitored. During normal operation, very good treatment results were achieved. The combined SAF-AS system realized 95% BOD removal, 88% COD removal and 94% NH4-N removal. The RBC system removed 92% of the BOD, 89% of the COD and 99% of the ammonium nitrogen. Both systems do not experience severe problems dealing with the lack of influent for a duration of seven days. The effluent concentrations did not change much, except for a small peak of nitrite which was present in all tests. However, both the ammonium oxidizing and the nitrite oxidizing bacterial populations were still active, as evidenced by the continued removal of ammonium and formation of nitrate.

In the field of predictive microbiology, mathematical models are developed to describe and predict the behaviour and possible outgrowth of spoilage and pathogenic microorganisms in food products. Research has mainly focused on the development of macroscopic models, describing the evolution of macroscopical quantities like the total cell number N. This macroscopic approach has a number of inherent disadvantages. This paper proposes the individual-based modelling methodology as a complement to macroscopic models to overcome some of these issues. As a case study, the paper proposes a new bacterial growth model to describe the stationary phase of bacterial growth. The new model is implemented in an individual-based framework and exploratory results are presented.

In this work, the growth of Listeria innocua was studied responding to the addition of different concentrations of gelatin (see text) model gel system in a modi&#95;ed Brain Heart Infusion medium at 12 C and an initial pH of 6.2. The global number of viable cells as a function of incubation time and the corresponding pH, lactic acid concentration and glucose concentration were measured. Each set of data was fitted with the growth model of Baranyi and Roberts (1994) to estimate the maximum specific growth rate and the maximum cell concentration. Gelatin had a significant e&#95;ect on the growth rate of Listeria innocua, which reduced as the gelatin concentration increased. A tail was observed after a certain concentration of gelatin indicating that there exists a maximum concentration beyond which no further reduction could be observed. There was, however, within the gelatin concentration range studied, no appreciable effect on the maximum cell concentration. A distinct morphological change of colonies was also observed with increasing gelatin concentration.

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.

In food technology, there is a need for models taking into account the interactions between microorganisms, in order to correctly predict the safety and shelf life of food products. When leaving these interactions out of consideration, a discrepancy between the model prediction and the actual microbial evolution may occur for certain types of food products. In this study, a model describing the inhibition of the pathogenic Yersinia enterocolitica in mono- and coculture with Lactobacillus sakei was extended to describe also the subsequent inactivation of Y. enterocolitica. During the development of a suitable model structure to describe the inactivation process, biological knowledge about this process was incorporated. The extended model was able to predict evolution of Y. enterocolitica in coculture as well as in monoculture.

Mathematical models used to predict the behavior of genetically modified organisms require 1). a (rather) large number of state variables, and 2). complicated kinetic expressions containing a large number of parameters. Since these models are hardly identifiable and of limited use in model-based optimization and control strategies, a generic methodology based on sensitivity function analysis is presented to reduce the model complexity at the level of the kinetics, while maintaining high prediction power. As a case study to illustrate the method and results obtained, the influence of the dissolved oxygen concentration on the cytN gene expression in the bacterium Azospirillum brasilense Sp7 is modeled. As a first modeling approach, available mechanistic knowledge is incorporated into a mass balance equation model with 3 states and 14 parameters. The large differences in order of magnitude of the model parameters identified on the available experimental data indicate 1). possible structural problems in the kinetic model and, associated with this, 2). a possibly too high number of model parameters. A careful sensitivity function analysis reveals that a reduced model with only seven parameters is almost as accurate as the original model., (Copyright 2002 Wiley Periodicals, Inc.)