Formation of biofilms by Listeria monocytogenes under various growth conditions

Eight strains of Listeria monocytogenes (7644, 19112, 15313, Scott A, LCDC, 10403S, SLCC, and 1370) produce biofilms when grown on polyvinyl chloride microtiter well plates. The growth medium (tryptic soy broth [TSB] or modified Welshimer's broth [MWB] at 32 degrees C) influenced the amount of biofilm formed; maximum biofilms were formed in MWB by six strains and in TSB by the remaining two strains. This result suggests that the growth medium is critical in development of L. monocytogenes biofilm. This organism also produced biofilms on stainless steel chips. Biofilm formation on these chips was observed following growth in TSB at 4, 20, and 37 degrees C. After 20 h of incubation at 20 or 37 degrees C, the cell density was approximately 10(6) CFU per chip, and after 4 days incubation at 4 degrees C, the cell density was 10(5) CFU per chip. L. monocytogenes strain Scott A biofilm formation on stainless steel chips was visualized using scanning electron microscopy, which revealed dense aggregates of cells held together by meshlike webbing.     

环境因子及接种量对单增李斯特菌生长/非生长界面的影响

单增李斯特菌在实验设定的环境条件下,通过10倍梯度稀释将菌悬液分别稀释到101、103、105、107CFU/m L四个接种水平,然后接种到TSB-YE肉汤中,培养基置于恒温培养箱中进行培养,然后通过肉眼观察培养基浊度并结合涂布TSA-YE平板对其生长/非生长情况进行判定,通过Logistics多项式回归模型对处理的数据建立了单增李斯特菌生长/非生长的界面模型。实验结果表明不同生长温度,p H和盐度的交互作用对单增李斯特菌的生长/非生长界面的影响较大,接种量的大小也会影响单增李斯特菌生长/非生长过渡区域的具体位置,但具体原因和作用机制还有待进一步研究。该研究为抑制单增李斯特菌生长的环境因子条件范围和实际产品中的污染严重程度提供一定的参考依据,对于有潜在单增李斯特菌污染的产品来说,这为加强产品的栅栏因子,优化工艺条件以提高其安全度也提供了重要的参考。     

鲜切结球莴苣中单增李斯特菌生长预测模型的建立

为建立不同温度下鲜切结球莴苣中单增李斯特菌生长模型,将单增李斯特菌接种到鲜切结球莴苣表面,并于不同温度下贮藏,获得其在4、8、16、24和32℃下的生长数据,选用Gompertz模型进行拟合,建立初级生长模型。在此基础上建立二级模型研究温度对初级模型中单增李斯特菌生长动力学参数的影响,并进行数学检验。结果表明,对最大比生长速率和延滞时间建立平方根模型,结果呈良好的线性关系,相关系数R2分别为0.977 2和0.984 7,所建立的预测模型能很好地描述不同温度下单增李斯特菌的生长动态。     

Comparison of Listeria monocytogenes virulence in a mouse model

Listeriosis results from exposure to the foodborne pathogen Listeria monocytogenes. Although many different strains of L. monocytogenes are isolated from food, no definitive tests currently predict which isolates are most virulent. The objectives of this study were to address two major data gaps for risk assessors, variability among L. monocytogenes strains in pathogenicity and virulence. Strains used in our monkey clinical trial or additional food isolates were evaluated for their virulence and infectivity in mice. All strains were equally pathogenic to immunocompromised mice, causing deaths to 50% of the population 3 days after exposure to doses ranging from 2 to 3 log CFU. Doses resulting in 50% deaths on the fifth day after administration were 1 to 2 log lower than those on the third day, indicating that the full course of pathogenicity exceeds the 3-day endpoint in immunocompromised mice. Three strains were chosen for further testing for their virulence and infectivity in liver and spleen in normal (immunocompetent) mice. Virulence was not significantly different (P > 0.05) among the three strains, all resulting in deaths to 50% of mice at 5 to 7 log CFU by 5 days after administration. All strains were equally infective in liver or spleen, with higher numbers of L. monocytogenes directly correlated with higher doses of administration. In addition, there was no preference of organs by any strains. The lack of strain differences may reflect the limitation of the mouse model and suggests the importance of using various models to evaluate the pathogenicity and virulence of L. monocytogenes strains.     

Development and validation of a dynamic growth model for Listeria monocytogenes in fluid whole milk

Listeria monocytogenes, a psychrotrophic microorganism, has been the cause of several food-borne illness outbreaks, including those traced back to pasteurized fluid milk and milk products. This microorganism is especially important because it can grow at storage temperatures recommended for milk (< or =7 degrees C). Growth of L. monocytogenes in fluid milk depends to a large extent on the varying temperatures it is exposed to in the postpasteurization phase, i.e., during in-plant storage, transportation, and storage at retail stores. Growth data for L. monocytogenes in sterilized whole milk were collected at 4, 6, 8, 10, 15, 20, 25, 30, and 35 degrees C. Specific growth rate and maximum population density were calculated at each temperature using these data. The data for growth rates versus temperature were fitted to the Zwietering square root model. This equation was used to develop a dynamic growth model (i.e., the Baranyi dynamic growth model or BDGM) for L. monocytogenes based on a system of equations which had an intrinsic parameter for simulating the lag phase. Results from validation of the BDGM for a rapidly fluctuating temperature profile showed that although the exponential growth phase of the culture under dynamic temperature conditions was modeled accurately, the lag phase duration was overestimated. For an alpha0 (initial physiological state parameter) value of 0.137, which corresponded to the mean temperature of 15 degrees C, the population densities were underpredicted, although the experimental data fell within the narrow band calculated for extreme values of alpha0. The maximum relative error between the experimental data and the curve based on an average alpha0 value was 10.42%, and the root mean square error was 0.28 log CFU/ml.     

Estimation of uncertainty and variability in bacterial growth using Bayesian inference. Application to Listeria monocytogenes

The usefulness of risk assessment is limited by its ability or inability to model and evaluate risk uncertainty and variability separately. A key factor of variability and uncertainty in microbial risk assessment could be growth variability between strains and growth model parameter uncertainty. In this paper, we propose a Bayesian procedure for growth parameter estimation which makes it possible to separate these two components by means of hyperparameters. This model incorporates in a single step the logistic equation with delay as a primary growth model and the cardinal temperature equation as a secondary growth model. The estimation of Listeria monocytogenes growth parameters in milk using literature data is proposed as a detailed application. While this model should be applied on genuine data, it is highlighted that the proposed approach may be convenient for estimating the variability and uncertainty of growth parameters separately, using a complete predictive microbiology model.     

Survival of Listeria monocytogenes strains in a dry sausage model

The survival of five inoculated Listeria monocytogenes strains (DCS 31, DCS 184, AT3E, HT4E, and HR5E) was studied in dry fermented sausages prepared using two different starter cultures (starter A and B) with or without a protective Lactobacillus plantarum DDEN 2205 strain. L. monocytogenes was detected throughout ripening in every sausage sample in which the L. plantarum DDEN 2205 strain had not been used. The use of either starter A, with a high concentration of protective culture, or starter B, with a low concentration of protective culture, resulted in L. monocytogenes-negative sausages after 17 days of ripening. Differential survival was noted among the L. monocytogenes strains during fermentation. Strains AT3E and DCS 31 survived in sausages with protective cultures more often than did the other strains, whereas HT4E and HR5E were inhibited during ripening by all starter and protective cultures used. Protective cultures such as L. plantarum may be used as part of a hurdle strategy in dry sausage processing, but variations in susceptibility of different L. monocytogenes strains can create problems if other hurdles are not included.     

Use of Bayesian modelling in risk assessment: application to growth of Listeria monocytogenes and food flora in cold-smoked salmon

An attempt to use a Bayesian approach to model variability and uncertainty separately in microbial growth in a risk assessment is presented. It was conducted within the framework of a French project aiming at assessing the exposure to Listeria monocytogenes in cold-smoked salmon. The chosen model describes the effect of time and temperature on bacterial growth. A Bayesian approach close to the one proposed by Pouillot et al. [Int. J. Food Microbiol. 81 (2003) 87] is used to estimate the variability and uncertainty of growth parameters from both literature data and data experimentally acquired during the project. Variability between strains and between products is taken into account. The growth of the food flora of cold-smoked salmon is also modelled by the same method. The results obtained for both models are used to predict the simultaneous growth of L. monocytogenes and food flora in cold-smoked salmon with a competitive model, expressing variability and uncertainty through a second-order Monte Carlo simulation.     

Mathematical model of Listeria monocytogenes cross-contamination in a fish processing plant

Listeriosis is a foodborne disease caused by the bacterium Listeria monocytogenes. The food industry and government agencies devote considerable resources to reducing contamination of ready-to-eat foods with L. monocytogenes. Because inactivation treatments can effectively eliminate L. monocytogenes present on raw materials, postprocessing cross-contamination from the processing plant environment appears to be responsible for most L. monocytogenes food contamination events. An improved understanding of cross-contamination pathways is critical to preventing L. monocytogenes contamination. Therefore, a plant-specific mathematical model of L. monocytogenes cross-contamination was developed, which described the transmission of L. monocytogenes contamination among food, food contact surfaces, employees' gloves, and the environment. A smoked fish processing plant was used as a model system. The model estimated that 10.7% (5th and 95th percentile, 0.05% and 22.3%, respectively) of food products in a lot are likely to be contaminated with L. monocytogenes. Sensitivity analysis identified the most significant input parameters as the frequency with which employees' gloves contact food and food contact surfaces, and the frequency of changing gloves. Scenario analysis indicated that the greatest reduction of the within-lot prevalence of contaminated food products can be achieved if the raw material entering the plant is free of contamination. Zero contamination of food products in a lot was possible but rare. This model could be used in a risk assessment to quantify the potential public health benefits of in-plant control strategies to reduce cross-contamination.     

Quantifying the robustness of a broth-based model for predicting Listeria monocytogenes growth in meat and poultry products

Given the importance of Listeria monocytogenes as a risk factor in meat and poultry products, there is a need to evaluate the relative robustness of predictive growth models applied to meat products. The U.S. Department of Agriculture-Agricultural Research Service Pathogen Modeling Program is a tool widely used by the food industry to estimate pathogen growth, survival, and inactivation in food. However, the robustness of the Pathogen Modeling Program broth-based L. monocytogenes growth model in meat and poultry application has not, to our knowledge, been specifically evaluated. In the present study, this model was evaluated against independent data in terms of predicted microbial counts and covered a range of conditions inside and outside the original model domain. The robustness index was calculated as the ratio of the standard error of prediction (root mean square error of the model against an independent data set not used to create the model) to the standard error of calibration (root mean square error of the model against the data set used to create the model). Inside the calibration domain of the Pathogen Modeling Program, the best robustness index for application to meat products was 0.37; the worst was 3.96. Outside the domain, the best robustness index was 0.40, and the worst was 1.22. Product type influenced the robustness index values (P < 0.01). In general, the results indicated that broth-based predictive models should be validated against independent data in the domain of interest; otherwise, significant predictive errors can occur.     

Modelling the growth rate of Listeria monocytogenes with a multiplicative type model including interactions between environmental factors

A multiplicative secondary model previously published to describe independently the effects of environmental factors on the growth rate of Listeria monocytogenes (Augustin and Carlier, 2000) was improved by taking into account interactions between these environmental factors. The proposed model allowed to decrease the rate of fail-safe growth predicted from 13.5% to 12.1% and the rate of fail-dangerous no growth predicted from 16.1% to 7.1%.     

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.     

Modelling the competitive growth of Listeria monocytogenes and Listeria innocua in enrichment broths

The overgrowth of Listeria innocua in enrichment broths designed for the isolation of Listeria monocytogenes is believed to result from two factors: a selective growth advantage of L. innocua, and/or an inhibitory interspecies interaction. The generation times of 13 isolates of L. innocua and L. monocytogenes were determined in Brain Heart Infusion (BHI) and a variety of enrichment media. No significant differences were found in growth characteristics between either species in the various media, suggesting that the growth advantage of L. innocua in enrichment media was not as significant as previously described. Kinetic analysis of mixed cultures of L. monocytogenes and isolates of L. innocua producing a variety of inhibitory activities demonstrated the possibility of an inhibitory interaction between these two species resulting in the overgrowth of the enrichment culture with L. innocua. Modelling the evolution of the ratio between two populations in an enrichment process was used to analyze the impact of a selective growth advantage in L. innocua in an enrichment process for growth of L. monocytogenes. These findings support the widely held view that an overgrowth of L. innocua in the enrichment process can result from both a selective growth advantage as well as the production of inhibitory compounds. From a practical perspective, these interactions can result in an increase in false negatives.     

Modelling the growth of Listeria monocytogenes in dynamic conditions

A recurrent neural network for the prediction of Listeria monocytogenes growth under pH and a(w) variable conditions was developed. The use of this model offered the possibility to take into account the consequences of the variations of the factors on L. monocytogenes growth. The effects of solutions, such as NaCl, acetic acid and NaOH, and their interactions on the response of L. monocytogenes cells were studied. Furthermore, the results showed the capacity of the recurrent neural network to predict growths carried out in different experimental conditions without using those used for its elaboration.     

Control of Listeria monocytogenes growth in a ready-to-eat poultry product using a bacteriophage

A bacteriophage (phage) that infected strains of the species Listeria monocytogenes as well as Listeria ivanovii and Listeria welshimeri, but not Listeria grayi or Listeria innocua, was isolated from sheep faeces. The phage had a contractile tail and an icosohedral head indicating that it was a myovirus, and was morphologically similar to phage A511. At 30 °C, phages added at 5.2 × 10⁷ PFU ml⁻¹ prevented the growth in broth of L. monocytogenes present at approximately twice this concentration for 7 h, but re-growth occurred such that the concentration after 24 h incubation was similar in both control and phage-treated cultures. At the same temperature, but on the surface of vacuum-packed ready-to-eat chicken breast roll, there was an immediate 2.5 log₁₀ CFU cm⁻² reduction in pathogen concentration following addition of phages and then re-growth. However, at a temperature reflecting that at which a chilled food might be held (5°C), this re-growth was prevented over 21 days incubation. The data suggest a dose-dependent rapid reduction in pathogen concentration followed by no continued phage-mediated effect. These results, alongside other published data, indicate that a high concentration of phages per unit area is required to ensure significant inactivation of target pathogens on food surfaces.     

Predicting heat inactivation of Listeria monocytogenes under nonisothermal treatments

The aim of this study was to find a model that accurately predicts the heat inactivation of Listeria monocytogenes (ATCC 15313) at constantly rising heating rates (0.5 to 9 degrees C/min) in media of different pH values (4.0 to 7.4). Survival curves of L. monocytogenes obtained under isothermal treatments at any temperature were nearly linear. Estimations of survival curves under nonisothermal treatments obtained from heat resistance parameters of isothermal treatments adequately fit experimental values obtained at pH 4.0. On the contrary, survivors were much higher than estimations at pH 5.5 and 7.4. The slower the heating rate and the longer the treatment time, the greater the differences between the experimental and estimated values. An equation based on the Weibullian-like distribution, log S(t) = (t/delta)p, accurately described survival curves of L. monocytogenes obtained under nonisothermal conditions within the range of heating rates investigated. A nonlinear relationship was observed between the scale parameter (delta) and the heating rate, which allowed the development of an equation capable of predicting the inactivation rate of L. monocytogenes under nonisothermal treatments at pH 5.5 and 7.4. The model predictions were a good fit to the measured data independent of the magnitude of the thermotolerance increase. This work might contribute to the increase in safety of those food products that require long heating lag phases during the pasteurization process.     

Effects of epiphytic Enterobacteriaceae and pseudomonads on the growth of Listeria monocytogenes in model media

Four Enterobacteriaceae (Enterobacter agglomerans and Rhanella aquatilis) and six pseudomonads (Pseudomonas fluorescens, Pseudomonas chlororaphis, Pseudomonas putida) isolated from minimally processed green endive were coinoculated at 10 degrees C with Listeria monocytogenes in a minimal medium. Pseudomonads did not modify the growth of L. monocytogenes, whereas Enterobacteriaceae reduced its maximal population by 2 to 3 log CFU/ml. The same effect was observed in a diluted yeast extract medium supplemented with amino acids and glucose, in which L. monocytogenes grown alone reached 10(9) to 10(10) CFU/ml. In the same diluted yeast extract medium, not supplemented with glucose and amino acids, the maximal population of L. monocytogenes in the presence of both Enterobacteriaceae and pseudomonads was only slightly reduced (less than 0.5 log CFU/ml). Culture filtrates of the Enterobacteriaceae had no inhibitory activity on L. monocytogenes. The effect of the Enterobacteriaceae on L. monocytogenes growth was presumably due to a competition for glucose and/or amino acids.     

Modeling the growth of Listeria monocytogenes based on a time to detect model in culture media and frankfurters

In the current study, temperature dependent growth of Listeria monocytogenes strain H7776, with an initial inoculum level of approximately 0.1 CFU/mL or g, was modeled based on "time to detect" (TTD) using the Arrhenius equation. The activation energies (E(a)) obtained from specific growth rate and lag phase duration in tryptic soy broth (TSB) and frankfurters were compared. At 4 degrees C, the TTD for L. monocytogenes was 217 h in TSB and 48 h in portions of frankfurters. The TTD decreased as temperature increased from 4 to 36 degrees C in both culture media and frankfurters, and this relationship was modeled using the Arrhenius equation. Based on this model, the E(a) values for the TTD in TSB and frankfurters averaged 22.7 and 18.7 kcal/mol, respectively, and were not significantly different (p<0.05). Linear regression was performed on the exponential part of the growth curve to evaluate specific growth rate constants at each temperature using the Monod model. The E(a) values were also calculated based on the specific growth rate and the lag phase of L. monocytogenes in TSB and frankfurters incubated in the same range of temperatures. The average E(a) values for the specific growth rates and the lag phase durations in TSB cultures were 19 and 21 kcal/mol, respectively. In frankfurters, the average E(a) values were slightly greater for both specific growth rate and lag phase duration (29 and 35 kcal/mol, respectively), but these values were not significantly different from the E(a) calculated for the TTD in each medium. These results indicate that the TTD concept can be used to develop and validate safety-based shelf life models.     

Modeling the growth of Listeria monocytogenes in pasteurized white asparagus

Growth of Listeria monocytogenes in pasteurized white asparagus was monitored at different storage temperatures (4, 10, 20, and 30 degrees C). Among the main microbial kinetic parameters, growth rate (mu) per hour was calculated at each temperature using the Baranyi-Roberts model. L. monocytogenes was able to grow at all temperatures, although at 4 degrees C only a slight increment of the microbial population was observed (approximately 1 log CFU/g) after 300 h of storage. Subsequently, two different secondary modeling approaches were proposed to study the relationship between mu and storage temperature: the Arrhenius and Ratkowsky models. Although both models properly described the data observed, smaller values of root mean square error (RMSE) and standard error of prediction (SEP) were obtained with the Ratkowsky model, providing a better goodness of fit (Ratkowsky model: RMSE = 0.010, SEP = 21.23%; Arrhenius model: RMSE = 0.026, SEP = 54.37%). The maximum population density (MPD) was calculated at each temperature studied. A clear dependence between MPD and temperature was found; lower temperatures produced lower values of MPD. This finding confirmed the Jameson effect, indicating that multiple hurdles in the food-processing chain plus lower temperatures reduced L. monocytogenes growth. Predicting the growth of L. monocytogenes along the food chain will help to reduce microbial risks associated with consumption of pasteurized white asparagus.     

Validation of predictive models describing the growth of Listeria monocytogenes

In this study, predictions for growth rate of Listeria on food products were evaluated by both general applicable models and specific growth models. Literature values, obtained from a large number of publications, for growth rates in/on a variety of foods were compared by graphical and mathematical analysis with predictions given by various models. Apart for the great advantage of being generally applicable, the general models performed best. However, only small differences between the various models were observed. Model predictions were accurate within a factor of about two to four, depending on the type of product. The predictions should therefore not be considered as absolute; it is important to understand the limitations of the performance of models. All results and all assumptions should be criticised, but in many cases the accuracy will be sufficient to use these types of models as a tool in management decisions.