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Predicting the microbial safety of food products stored in modified atmosphere packaging implies taking into account the effect of oxygen reduction on microbial growth. According to their respiratory-type, the micro-organisms are not impacted similarly by the oxygen concentration. The aim of this article was to quantify and model the oxygen effect on the growth rates of 5 bacterial species: Listeria monocytogenes and Bacillus weihenstephanensis (facultative anaerobic), Pseudomonas fluorescens (strict aerobic), Clostridium perfringens and Clostridium sporogenes (strict anaerobic). The results showed the oxygen concentration doesn't modify the behavior of both facultative anaerobic strains. The growth rate of P. fluorescens decreased with the oxygen concentration, but the effect is only noticeable when the oxygen concentration fell below 3% in the gaseous phase. Conversely, the oxygen acted as a growth inhibitor for both Clostridium species. But total inhibition is reached only for 3.26% and 6.61% respectively for C. sporogenes and C. perfringens. Two models have been fitted for both respiratory-types, the first is the Monod model considering oxygen as a substrate for growth, and the second is the classic inhibitory model based on minimal inhibitory concentration.

The effect of carbon dioxide, temperature, and pH on growth of Listeria monocytogenes and Pseudomonas fluorescens was studied, following a protocol to monitor microbial growth under a constant gas composition. In this way, the CO2 dissolution didn't modify the partial pressures in the gas phase. Growth curves were acquired at different temperatures (8, 12, 22 and 37 °C), pH (5.5 and 7) and CO2 concentration in the gas phase (0, 20, 40, 60, 80, 100% of the atmospheric pressure, and over 1 bar). These three factors greatly influenced the growth rate of L. monocytogenes and P. fluorescens, and significant interactions have been observed between the carbon dioxide and the temperature effects. Results showed no significant effect of the CO2 concentration at 37 °C, which may be attributed to low CO2 solubility at high temperature. An inhibitory effect of CO2 appeared at lower temperatures (8 and 12 °C). Regardless of the temperature, the gaseous CO2 is sparingly soluble at acid pH. However, the CO2 inhibition was not significantly different between pH 5.5 and pH 7. Considering the pKa of the carbonic acid, these results showed the dissolved carbon under HCO3- form didn't affect the bacterial inhibition. Finally, a global model was proposed to estimate the growth rate vs. CO2 concentration in the aqueous phase. This dissolved concentration is calculated according to the physical equations related to the CO2 equilibriums, involving temperature and pH interactions. This developed model is a new tool available to manage the food safety of MAP.

Predicting microbial safety of fresh products in modified atmosphere packaging implies to take into account the dynamic of O2, CO2 and N2 exchanges in the system and its effect on microbial growth. In this paper a mechanistic model coupling gas transfer and predictive microbiology was validated using dedicated challenge-tests performed on poultry meat, fresh salmon and processed cheese, inoculated with either Listeria monocytogenes or Pseudomonas fluorescens and packed in commercially used packaging materials (tray + lid films). The model succeeded in predicting the relative variation of O2, CO2 and N2 partial pressure in headspace and the growth of the studied microorganisms without any parameter identification. This work highlighted that the respiration of the targeted microorganism itself and/or that of the naturally present microflora could not be neglected in most of the cases, and could, in the particular case of aerobic microbes contribute to limit the growth by removing all residual O2 in the package. This work also confirmed the low sensitivity of L. monocytogenes toward CO2 while that of P. fluorescens permitted to efficiently prevent its growth by choosing the right combination of packaging gas permeability value and initial % of CO2 initially flushed in the pack.

dans le cadre de la 4. International Conference on Predictive Modelling in Foods à Quimper du 15-19 juin 2003; Temperature effect on growth rates of Listeria monocytogenes, Salmonella, Escherichia coli, Clostridium perfringens and Bacillus cereus, was studied. Growth rates were obtained in laboratory medium by using a binary dilutions method in which 15 optical density curves were generated to determine one mu value. The temperature was in the range from 2 to 48 degrees C, depending on the bacterial species. Data were analysed after a square root transformation. No large difference between the strains of a same species was observed, and therefore all the strains of a same species were analysed together with the same secondary model. The variability of the residual error, including both measurements errors and biological strain difference, was homogenous for sub-optimal temperature values. To represent this variability in bacterial kinetic simulation, the 95% confidence interval based on an asymptotic Normal distribution, around the growth rate value was determined. With this modelling approach, the behaviour of bacterial species on food, irrespective of the strain or the laboratory, was described. This growth simulation with confidence limits has several applications, such as to facilitate comparisons between a challenge-test and simulation results, and, to appreciate if the temperature change has or has not a significant effect on a bacterial growth profile, with regard to the uncontrolled factors. The integration of this piece of work in the Sym&39;Previus software is now in process. Results obtained in five French laboratories will be extended by working on new food and new microbial species and improved by further work on variability estimation.

Depending on environmental factors, the prediction of bacterial growth is made difficult by the complexity of foodstuff. Although the influence of temperature, pH, and water activity are usually taken into account, models have to be completed with the influence of acid mixture. Nine strains of Listeria spp., four Salmonella spp., one Staphylococcus aureus, one Escherichia coli, and Listeria innocua ATCC 33090 were used for this study to extend model proposed by [Le Marc, Y., Huchet, V., Bourgeois, C., Guyonnet, J., Mafart, P., Thuault, D., 2002. Modelling the growth kinetics of Listeria as a function of temperature, pH and organic acid concentration. International Journal of Food Microbiology 73, 219-237]. Derived from data of [Houtsma, P.C., Kusters, B.J., De Wit, J.C., Rombouts, F.M., Zwietering, M.H., 1994. Modelling growth rates of Listeria innocua as a function of lactate concentration. International Journal of Food Microbiology 24, 113-123] and our own data, the extended model described accurately different effects of addition of acid salts in the medium (decrease of water activity and pH, variation of undissociated weak acid form, and variation of synergetic effect between environmental factors). This previous model was implemented to describe the observed variability of behaviour of the different studied strains. alpha reflected the general behaviour of species (sensitiveness to low or high undissociated acid concentration), and MIC(U) reflected the various resistances of strains. From this simple model, a new model was built for describing the effects of concentrations of several mixed acids on bacterial growth rates. Simulations of growth were carried out from three acids mixtures by inputting parameter estimates previously obtained. Despite a very variable effect of investigated acids on growth, the new model yielded fair predictions.

The combined effects of temperature, pH and organic acids (lactic, acetic and propionic) on the growth kinetics of Listeria innocua ATCC 33090 were studied. First, a multiplicative model was built assuming independent effects of all environmental factors. Thus, the model was expanded by the inclusion of a novel term describing the effects of interactions on the growth/no growth limits. The proposed approach allows an accurate description of the boundary between growth and no growth of Listeria.

Historically, the ability of foods to support the growth of spoilage organisms and food-borne pathogens has been assessed by inoculating a food with an organism of interest, and following its growth over a period of time. Information gained from such challenge tests, together with knowledge of the organoleptic stability of the product, can then be used to determine an appropriate shelf-life for the food. Whilst this approach may be seen as the "gold-standard" of microbiological assessment of food, it is both time-consuming and costly. A major advance to complement challenge testing was the development of predictive modelling, when it was demonstrated that the growth of a wide range of organisms of interest could be quite accurately modelled as a function of only a few environmental parameters-primarily temperature, pH and water activity (a(w)), with perhaps other factors such as nitrite, organic acids and oxygen. This approach to predictive microbiology is embodied in software tools such as the UK Food MicroModel and the Pathogen Modeling Program from the USA. Whilst modelling of this form yields accurate predictions of the growth of organisms in the majority of foods, there are occasions when there are discrepancies between the model and the observed growth. These discrepancies are most often described as "fail-safe", i.e. the observed growth is slower than predicted by the model. This paper examines the role of food structure in the development of microbial populations and communities, and describes the methodologies we propose to begin to tackle some of these complex and interlinked issues.

The aim of this study was to model the growth, in a model system, of toxigenic strains of Bacillus cereus as a function of temperature, pH and water activity. Optical density (OD) values were transformed into numbers of colony-forming units (CFU) by the use of a `calibrating' relation. The growth curves were then fitted to the Gompertz function which allowed the estimation of the lag-time ( λ ) and the growth rate ( μ ). These two parameters were then modelled according to the controlling factors which were pH and water activity at 20 and 30°C.

A study of the factors influencingClostridium tyrobutyricumgrowth is necessary to improve the control of butyric fermentation and avoid late blowing during cheese making. Lactic acid, one of the substrates of this fermentation is present in two optical forms in cheeses and their relative amounts may be a controlling factor. In order to determine whether these proportions could influenceC. tyrobutyricumgrowth, the micro-organism was inoculated in culture media containing different proportions of these isomers. In the racemic mix, the results show the consumption of 90% of D (−) and 25% of L (+) lactic acid during the exponential growth phase. When both isomers are present,C. tyrobutyricumpreferentially uses the D (−) lactic acid, and if only one of the two isomers is present its growth rate is three times higher with the D (−) than with the L (+) isomer.

Molds are responsible for spoilage of bakery products during storage. A modeling approach to predict the effect of water activity (aw) and temperature on the appearance time of Aspergillus candidus was developed and validated on cakes. The gamma concept of Zwietering was adapted to model fungal growth, taking into account the impact of temperature and aw. We hypothesized that the same model could be used to calculate the time for mycelium to become visible (tv), by substituting the matrix parameter by tv. Cardinal values of A. candidus were determined on potato dextrose agar, and predicted tv were further validated by challenge-tests run on 51 pastries. Taking into account the aw dynamics recorded in pastries during reasonable conditions of storage, high correlation was shown between predicted and observed tv when the aw at equilibrium (after 14 days of storage) was used for modeling (Af = 1.072, Bf = 0.979). Validation studies on industrial cakes confirmed the experimental results and demonstrated the suitability of the model to predict tv in food as a function of aw and temperature.

International audience; A stochastic modelling approach was developed to describe the distribution of Listeria monocytogenes contamination in foods throughout their shelf life. This model was designed to include the main sources of variability leading to a scattering of natural contaminations observed in food portions: the variability of the initial contamination, the variability of the biological parameters such as cardinal values and growth parameters, the variability of individual cell behaviours, the variability of pH and water activity of food as well as portion size, and the variability of storage temperatures. Simulated distributions of contamination were compared to observed distributions obtained on 5 day-old and 11 day-old cheese curd surfaces artificially contaminated with between 10 and 80 stressed cells and stored at 14°C, to a distribution observed in cold smoked salmon artificially contaminated with approximately 13 stressed cells and stored at 8°C, and to contaminations observed in naturally contaminated batches of smoked salmon processed by 10 manufacturers and stored for 10 days a 4°C and then for 20 days at 8°C. The variability of simulated contaminations was close to that observed for artificially and naturally contaminated foods leading to simulated statistical distributions properly describing the observed distributions. This model seems relevant to take into consideration the natural variability of processes governing the microbial behaviour in foods and is an effective approach to assess, for instance, the probability to exceed a critical threshold during the storage of foods like the limit of 100 CFU/g in the case of L. monocytogenes.

Lactococcin, a bacteriocin produced by Lactococcus lactis subsp. lactis ADRIA 85LO30, was purified as a 2.3-2.4 kDa peptide. Six non-bacteriocin-producing (Bac-) and non-immune (Imm-) strains were isolated after curing experiments. These strains had in common the loss or modification of two plasmids: pOS4 (32 kb) and pOS5 (70 kb). By comparing pOS5 and several modified plasmids, a DNA region from pOS5 of about 10 kb, which was necessary for wild-type bacteriocin production and immunity, was identified.

5th International Conference on Predictive Modelling in Foods; International audience; Time temperature integrators or indicators (TTIs) are effective tools making the continuous monitoring of the time temperature history of chilled products possible throughout the cold chain. Their correct setting is of critical importance to ensure food quality. The objective of this study was to develop a model to facilitate accurate settings of the CRYOLOG biological TTI, TRACEO®. Experimental designs were used to investigate and model the effects of the temperature, the TTI inoculum size, pH, and water activity on its response time. The modelling process went through several steps addressing growth, acidification and inhibition phenomena in dynamic conditions. The model showed satisfactory results and validations in industrial conditions gave clear evidence that such a model is a valuable tool, not only to predict accurate response times of TRACEO®, but also to propose precise settings to manufacture the appropriate TTI to trace a particular food according to a given time temperature scenario.

Lactic acid bacteria were selected for their inhibitory activity against Clostridium tyrobutyricum under conditions that eliminate the effects of lactic acid and hydrogen peroxide. Four strains were isolated belonging to the species Lactococcus lactis ssp. lactis. The sensitivity of the inhibitory substances to pronase and trypsine indicates that they are proteins or peptides different from nisin. Their resistance to phospholipase D indicates that they are also different from lactostrepcin. The inhibitory substances are produced during the exponential phase of growth. Their activity is bactericidal and directed toward some strains of Clostridium tyrobutyricum, Lactobacillus helveticus, and Streptococcus thermophilus, but strains used as dairy starters, Lactobacillus lactis, Streptococcus thermophilus, and Propionibacterium shermanii, are not all affected by the inhibition.