Occurrence, distribution and diversity of Listeria monocytogenes contamination on beef and pig carcasses after slaughter

In this study we investigated the prevalence and location of Listeria monocytogenes and hygiene indicator bacteria on beef and pig carcasses. Carcasses were sampled after slaughter and before cooling at eight and nine sites on the carcass, respectively. For each sample, detection and enumeration of Listeria was performed, as well as the enumeration of Total Aerobic Counts (TAC) and Enterobacteriaceae. The L. monocytogenes isolates were also typed to determine pulsotypes and clonal complexes (CC). L. monocytogenes was detected on 46% [95% CI: 35-56%] of beef and 22% [95% CI: 11-32%] of pig carcasses. Contamination levels at the different carcass sites differed considerably between beef and pigs. Genetic typing of strains suggests that carcass contamination originates from both incoming animals with transmission during slaughter practices as well as persistent (CC9) contamination from the slaughterhouse environment. These findings can be used to understand the complexity of introduction and persistence of this pathogen in slaughter facilities. Accurate correlation of L. monocytogenes presence proved unfeasible with any of the tested hygiene indicator bacteria.

An educationally inspired illustration of two-dimensional Quantitative Microbiological Risk Assessment (QMRA) and sensitivity analysis

Quantitative Microbiological Risk Assessment (QMRA) is a structured methodology used to assess the risk involved by ingestion of a pathogen. It applies mathematical models combined with an accurate exploitation of data sets, represented by distributions and - in the case of two-dimensional Monte Carlo simulations - their hyperparameters. This research aims to highlight background information, assumptions and truncations of a two-dimensional QMRA and advanced sensitivity analysis. We believe that such a detailed listing is not always clearly presented in actual risk assessment studies, while it is essential to ensure reliable and realistic simulations and interpretations. As a case-study, we are considering the occurrence of listeriosis in smoked fish products in Belgium during the period 2008-2009, using two-dimensional Monte Carlo and two sensitivity analysis methods (Spearman correlation and Sobol sensitivity indices) to estimate the most relevant factors of the final risk estimate. A risk estimate of 0.018% per consumption of contaminated smoked fish by an immunocompromised person was obtained. The final estimate of listeriosis cases (23) is within the actual reported result obtained for the same period and for the same population. Variability on the final risk estimate is determined by the variability regarding (i) consumer refrigerator temperatures, (ii) the reference growth rate of L. monocytogenes, (iii) the minimum growth temperature of L. monocytogenes and (iv) consumer portion size. Variability regarding the initial contamination level of L. monocytogenes tends to appear as a determinant of risk variability only when the minimum growth temperature is not included in the sensitivity analysis; when it is included the impact regarding the variability on the initial contamination level of L. monocytogenes is disappearing. Uncertainty determinants of the final risk indicated the need of gathering more information on the reference growth rate and the minimum growth temperature of L. monocytogenes. Uncertainty in the dose-response relationship was not included in the analysis, hence the level of its influence cannot be assessed in the present research. Finally, a baseline global workflow for QMRA and sensitivity analysis is proposed.

Variability in growth/no growth boundaries of 188 different Escherichia coli strains reveals that approximately 75% have a higher growth probability under low pH conditions than E. coli O157:H7 strain ATCC 43888

This study investigated the variation in growth/no growth boundaries of 188 Escherichia coli strains. Experiments were conducted in Luria-Bertani media under 36 combinations of lactic acid (LA) (0 and 25 mM), pH (3.8, 3.9, 4.0, 4.1, 4.2 and 4.3 for 0 mM LA and 4.3, 4.4, 4.5, 4.6, 4.7 and 4.8 for 25 mM LA) and temperature (20, 25 and 30 °C). After 3 days of incubation, growth was monitored through optical density measurements. For each strain, a so-called purposeful selection approach was used to fit a logistic regression model that adequately predicted the likelihood for growth. Further, to assess the growth/no growth variability for all the strains at once, a generalized linear mixed model was fitted to the data. Strain was fitted as a fixed factor and replicate as a random blocking factor. E. coli O157:H7 strain ATCC 43888 was used as reference strain allowing a comparison with the other strains. Out of the 188 strains tested, 140 strains (∼75%) presented a significantly higher probability of growth under low pH conditions than the O157:H7 strain ATCC 43888, whereas 20 strains (∼11%) showed a significantly lower probability of growth under high pH conditions.

The metabolic response of cultured tomato cells to low oxygen stress

The storage of fruits and vegetables under a controlled atmosphere can induce low oxygen stress, which can lead to post-harvest losses through the induction of disorders such as core breakdown and browning. To gain better understanding of the metabolic response of plant organs to low oxygen, cultured tomato cells (Lycopersicum esculentum) were used as a model system to study the metabolic stress response to low oxygen (0 and 1 kPa O2). By adding 13C labelled glucose, changes in the levels of polar metabolites and their 13C label accumulation were quantified. Low oxygen stress altered the metabolite profile of tomato cells, with the accumulation of the intermediates of glycolysis in addition to increases in lactate and sugar alcohols. 13C label data showed reduced label accumulation in almost all metabolites except lactate and some sugar alcohols. The results showed that low oxygen stress in tomato cell culture activated fermentative metabolism and sugar alcohol synthesis while inhibiting the activity of the TCA cycle and the biosynthesis of metabolites whose precursors are derived from central metabolism, including fluxes to most organic acids, amino acids and sugars.

Survival of Escherichia coli O157:H7 and Salmonella enterica serovar Typhimurium in manure and manure-amended soil under tropical climatic conditions in Sub-Saharan Africa

AIMS

  To establish the fate of Escherichia coli O157:H7 and Salmonella Typhimurium in manure and manure-amended agricultural soils under tropical conditions in Sub-Saharan Africa.

METHODS AND RESULTS

  Survival of nonvirulent E. coli O157:H7 and Salm. Typhimurium at 4 and 7 log CFU g(-1) in manure and manure-amended soil maintained at ≥80% r.h. or exposed to exclusive field or screen house conditions was determined in the Central Agro-Ecological Zone of Uganda. Maintaining the matrices at high moisture level promoted the persistence of high-density inocula and enhanced the decline of low-density inocula in the screen house, but moisture condition did not affect survival in the field. The large majority of the survival kinetics displayed complex patterns corresponding to the Double Weibull model. The two enteric bacteria survived longer in manure-amended soil than in manure. The 7 log CFU g(-1) E. coli O157:H7 and Salm. Typhimurium survived for 49-84 and 63-98 days, while at 4 log CFU g(-1) , persistence was 21-28 and 35-42 days, respectively.

CONCLUSIONS

  Under tropical conditions, E. coli O157:H7 and Salm. Typhimurium persisted for 4 and 6 weeks at low inoculum density and for 12 and 14 weeks at high inoculum density, respectively.

SIGNIFICANCE AND IMPACT OF THE STUDY

  Persistence in the tropics was (i) mostly shorter than previously observed in temperate regions thus suggesting that biophysical conditions in the tropics might be more detrimental to enteric bacteria than in temperate environments; (ii) inconsistent with published data isothermally determined previously hence indicating the irrelevance of single point isothermal data to estimate survival under dynamic temperature conditions.

On the critical evaluation of growth/no growth assessment of Zygosaccharomyces bailii with optical density measurements: liquid versus structured media

Growth/no growth (G/NG) studies that include the effect of medium structure have typically been performed for (pathogenic) bacteria and on the basis of gelatin/agar as a gelling agent. In this study, the growth potential of the spoilage yeast Zygosaccharomyces bailii was investigated in two model systems that resemble the macroscopic physicochemical and rheological properties of acidic sauces. In a Carbopol model system, the effect of pH (3.5-4.5), glycerol concentration (17-32%), acetic acid concentration (1.5-2.0%) and medium structure (3 levels) was investigated. In xanthan gum, the behavior of the yeast was studied at different levels of pH (3.5-4.5), NaCl concentration (0.5-13.5%), acetic acid concentration (0-2.0%) and medium structure (2 levels). Rheologically, viscoelastic moduli failed to discriminate between different forms of microbial growth, whereas yield stress data appeared to provide a better indication. In general, G/NG results revealed an unexpected increase of growth probability as a function of medium structure, both at 22 and 30 °C. Whether this behavior is the result of an underlying growth-promoting mechanism could not be explained from a macroscopic point of view (e.g., macrorheology, a(w)), but may be more related to the local microscopic properties of the gels. In a second part of this study, the potential use and information content of optical density measurements for G/NG data collection in structured media were critically evaluated and confronted with their practical relevance to the food industry.

Membrane permeabilization and cellular death of Escherichia coli, Listeria monocytogenes and Saccharomyces cerevisiae as induced by high pressure carbon dioxide treatment

In this study, the relationship between (irreversible) membrane permeabilization and loss of viability in Escherichia coli, Listeria monocytogenes and Saccharomyces cerevisiae cells subjected to high pressure carbon dioxide (HPCD) treatment at different process conditions including temperature (35-45 degrees C), pressure (10.5-21.0 MPa) and treatment time (0-60 min) was examined. Loss of membrane integrity was measured as increased uptake of the fluorescent dye propidium iodide (PI) with spectrofluorometry, while cell inactivation was determined by viable cell count. Uptake of PI by all three strains indicated that membrane damage is involved in the mechanism of HPCD inactivation of vegetative cells. The extent of membrane permeabilization and cellular death increased with the severity of the HPCD treatment. The resistance of the three tested organisms to HPCD treatment changed as a function of treatment time, leading to significant tailing in the survival curves, and was dependent on pressure and temperature. The results in this study also indicated a HPCD-induced damage on nucleic acids during cell inactivation. Transmission electron microscopy showed that HPCD treatment had a profound effect on the intracellular organization of the micro-organisms and influenced the permeability of the bacterial cells by introducing pores in the cell wall.

Design of an experimental viscoelastic food model system for studying Zygosaccharomyces bailii spoilage in acidic sauces

Within the field of predictive microbiology, the number of studies that quantify the effect of food structure on microbial behavior is very limited. This is mainly due to impracticalities related to the use of a nonliquid growth medium. In this study, an experimental food model system for studying yeast spoilage in acid sauces was developed by selecting a suitable thickening/gelling agent. In a first step, a variety of thickening/gelling agents was screened, with respect to the main physicochemical (pH, water activity, and acetic acid and sugar concentrations) and rheological (weak gel viscoelastic behavior and presence of a yield stress) characteristics of acid sauces. Second, the rheological behavior of the selected thickening/gelling agent, Carbopol 980, was extensively studied within the following range of conditions: pH 4.0 to 5.0, acetic acid concentration of 0 to 1.0% (vol/vol), glycerol concentration of 0 to 15% (wt/vol), and Carbopol concentration of 1.0 to 1.5% (wt/vol). Finally, the applicability of the model system was illustrated by performing growth experiments in microtiter plates for Zygosaccharomyces bailii at 0, 0.5, 1.0, and 1.5% (wt/vol) Carbopol, 5% (wt/vol) glycerol, 0% (vol/vol) acetic acid, and pH 5.0. A shift from planktonic growth to growth in colonies was observed when the Carbopol concentration increased from 0.5 to 1.0%. The applicability of the model system was illustrated by estimating mu(max) at 0.5% Carbopol from absorbance detection times.

Stress-adaptive responses by heat under the microscope of predictive microbiology

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.

Modelling of the individual and combined effects of water activity and temperature on the radial growth of Aspergillus flavus and A. parasiticus on corn

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.

Modelling the combined effects of structured food model system and lactic acid on Listeria innocua and Lactococcus lactis growth in mono- and coculture

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.

High pressure carbon dioxide inactivation of microorganisms in foods: The past, the present and the future

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.

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.

Exploring the performance of logistic regression model types on growth/no growth data of Listeria monocytogenes

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.

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.

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.

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.

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.

Single cell variability of L. monocytogenes grown on liver pate and cooked ham at 7oC: 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.

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.

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.

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.

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.

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.

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.

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).

Estimating the probability and level of contamination with 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.

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.

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.

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.

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.

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

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

Predictive modelling of the microbial lag phase: a review

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

Effect of chemicals on the microbial evolution in foods

In contrast with most chemical hazardous compounds, the concentration of food pathogens changes during processing, storage, and meal preparation, making it difficult to estimate the number of microorganisms or the concentration of their toxins at the moment of ingestion by the consumer. These changes are attributed to microbial proliferation, survival, and/or inactivation and must be considered when exposure to a microbial hazard is assessed. The number of microorganisms can also change as a result of physical removal, mixing of food ingredients, partitioning of a food product, or cross-contamination (M. J. Nauta. 2002. Int. J. Food Microbiol. 73:297-304). Predictive microbiology, i.e., relating these microbial evolutionary patterns to environmental conditions, can therefore be considered a useful tool for microbial risk assessment, especially in the exposure assessment step. During the early development of the field (late 1980s and early 1990s), almost all research was focused on the modeling of microbial growth over time and the influence of temperature on this growth. Later, modeling of the influence of other intrinsic and extrinsic parameters garnered attention. Recently, more attention has been given to modeling of the effects of chemicals on microbial inactivation and survival. This article is an overview of different applied strategies for modeling the effect of chemical compounds on microbial populations. Various approaches for modeling chemical growth inhibition, the growth-no growth interface, and microbial inactivation by chemicals are reviewed.

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

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

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

AIMS

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

METHODS AND RESULTS

Single cells were obtained using 1/2 dilution series in microtitre plates. Seventy-two Lactococcus lactis dilution series were checked by plate counting. When the last five columns of the plates were observed, the chance of having one single cell was 80%, while the yield was 75 wells containing cells. A simulation model confirmed these results. This method was compared with the commonly applied method.

CONCLUSIONS

This method makes it possible to combine a higher chance of having one cell in a microtitre well with a slightly higher yield.

SIGNIFICANCE AND IMPACT OF THE STUDY

A tool is developed to isolate single cells to provide a suitable base for investigating and modelling the individual cell lag phase.

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

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

Predicting inhibition and inactivation of Yersinia enterocolitica through lactic acid production by Lactobacillus sakei

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.

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

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

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

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

Current trends in predictive modelling of microbial lag phenomena

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

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

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

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

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

Introducing optimal experimental design in predictive modeling: a motivating example

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

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

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