Modeling and predicting the simultaneous growth of Listeria monocytogenes and natural flora in minced tuna

Establishment and Application of a Predictive Growth Kinetic Model of Salmonella with the Appearance of Two Other Dominant Background Bacteria in Fresh Pork

To better guide microbial risk management and control, growth kinetic models of Salmonella with the coexistence of two other dominant background bacteria in pork were constructed. Sterilized pork cutlets were inoculated with a cocktail of Salmonella Derby (S. Derby), Pseudomonas aeruginosa (P. aeruginosa), and Escherichia coli (E. coli), and incubated at various temperatures (4-37 °C). The predictive growth models were developed based on the observed growth data. By comparing R² of primary models, Baranyi models were preferred to fit the growth curves of S. Derby and P. aeruginosa, while the Huang model was preferred for E. coli (all R² ≥ 0.997). The secondary Ratkowsky square root model can well describe the relationship between temperature and μ_max (all R² ≥ 0.97) or Lag (all R² ≥ 0.98). Growth models were validated by the actual test values, with B_f and A_f close to 1, and MSE around 0.001. The time for S. Derby to reach a pathogenic dose (10⁵ CFU/g) at each temperature in pork was predicted accordingly and found to be earlier than the time when the pork began to be judged nearly fresh according to the sensory indicators. Therefore, the predictive microbiology model can be applied to more accurately predict the shelf life of pork to secure its quality and safety.

Competitive growth kinetics of Campylobacter jejuni, Escherichia coli O157:H7 and Listeria monocytogenes with enteric microflora in a small-intestine model

Aims

The biological events occurring during human digestion help to understand the mechanisms underlying the dose–response relationships of enteric bacterial pathogens. To better understand these events, we investigated the growth and reduction behaviour of bacterial pathogens in an in vitro model simulating the environment of the small intestine.

Methods and Results

The foodborne pathogens Campylobacter jejuni, Listeria monocytogenes and Escherichia coli O157:H7 were cultured with multiple competing enteric bacteria. Differences in the pathogen's growth kinetics due to the relative amount of competing enteric bacteria were investigated. These growth differences were described using a mathematical model based on Bayesian inference. When pathogenic and enteric bacteria were inoculated at 1 log CFU per ml and 9 log CFU per ml, respectively, L. monocytogenes was inactivated over time, while C. jejuni and E. coli O157:H7 survived without multiplying. However, as pathogen inocula were increased, its inhibition by enteric bacteria also decreased.

Conclusions

Although the growth of pathogenic species was inhibited by enteric bacteria, the pathogens still survived.

Significance and Impact of the Study

Competition experiments in a small‐intestine model have enhanced understanding of the infection risk in the intestine and provide insights for evaluating dose–response relationships.

Quantifying the bioprotective effect of Lactobacillus sakei CTC494 against Listeria monocytogenes on vacuum packaged hot-smoked sea bream

In this study, the bioprotective potential of Lactobacillus sakei CTC494 against Listeria monocytogenes CTC1034 was evaluated on vacuum packaged hot-smoked sea bream at 5 °C and dynamic temperatures ranging from 3 to 12 °C. The capacity of three microbial competition interaction models to describe the inhibitory effect of L. sakei CTC494 on L. monocytogenes was assessed based on the Jameson effect and Lotka-Volterra approaches. A sensory analysis was performed to evaluate the spoiling capacity of L. sakei CTC494 on the smoked fish product at 5 °C. Based on the sensory results, the bioprotection strategy against the pathogen was established by inoculating the product at a 1:2 ratio (pathogen:bioprotector, log CFU/g). The kinetic growth parameters of both microorganisms were estimated in mono-culture at constant storage (5 °C). In addition, the inhibition function parameters of the tested interaction models were estimated in co-culture at constant and dynamic temperature storage using as input the mono-culture kinetic parameters. The growth potential (δ log) of L. monocytogenes, in mono-culture, was 3.5 log on smoked sea bream during the experimental period (20 days). In co-culture, L. sakei CTC494 significantly reduced the capability of L. monocytogenes to grow, although its effectiveness was temperature dependent. The LAB strain limited the growth of the pathogen under storage at 5 °C (<1 log increase) and at dynamic profile 2 (<2 log increase). Besides, under storage at dynamic profile 1, the growth of L. monocytogenes was inhibited (<0.5 log increase). These results confirmed the efficacy of L. sakei CTC494 for controlling the pathogen growth on the studied fish product. The Lotka-Volterra competition model showed slightly better fit to the observed L. monocytogenes growth response than the Jameson-based models according to the statistical performance. The proposed modelling approach could support the assessment and establishment of bioprotective culture-based strategies aimed at reducing the risk of listeriosis linked to the consumption of RTE hot-smoked sea bream.

Evaluation of the effect of Lactobacillus sakei strain L115 on Listeria monocytogenes at different conditions of temperature by using predictive interaction models

In this study, the inhibitory capacity of Lactobacillus sakei strain L115 against Listeria monocytogenes has been assayed at 4, 8, 11, 15 and 20 °C in broth culture. Besides, the use of predictive microbiology models for describing growth of both microorganisms in monoculture and coculture has been proposed. A preliminary inhibitory test confirmed the ability of Lb. sakei strain L115 to prevent the growth of a five-strain cocktail of L. monocytogenes. Next, the growth of microorganisms in isolation, i.e. in monoculture, was monitored and kinetic parameters maximum specific growth rate (μ_sp;max) and maximum population density (N_max) were estimated by fitting the Baranyi model to recorded data. Inhibition coefficients (α) were calculated for the two kinetic parameters tested (μ_sp:max and N_max) to quantify the percentage of reduction of growth when the microorganisms were in coculture in comparison with monoculture. The kinetic parameters were input into three interaction models, developed based on modifications of the Baranyi growth model, namely Jameson effect, new modified version of the Jameson effect and Lotka-Volterra models. Two approaches were utilized for simulation, one using the monoculture μ_sp;max, under the hypothesis that the growth potential is similar under monoculture and coculture conditions provided the environmental conditions are not modified, and the other one, based on adjusting the monoculture kinetic parameter by applying the corresponding α to reproduce the observed μ_sp;max under coculture conditions, assuming, in this approach, that the existence of a heterogeneous population can change the growth potential of each microbial population. It was observed that in coculture, μ_sp;max of L. monocytogenes decreased (e.g., α = 31% at 4 °C) and the N_max was much lower than that of monoculture (e.g., α = 36% at 4 °C). The best simulation performance was achieved applying α to adjust the estimated monoculture growth rate, with the modified Jameson and Lotka-Volterra models showing better fit to the observed microbial interaction data as demonstrated by the fact that 100% data points fell within the acceptable simulation zone (±0.5 log CFU/mL from the simulated data). More research is needed to clarify the mechanisms of interaction between the microorganisms as well as the role of temperature.

Modelling the interaction of the sakacin-producing Lactobacillus sakei CTC494 and Listeria monocytogenes in filleted gilthead sea bream (Sparus aurata) under modified atmosphere packaging at isothermal and non-isothermal conditions

The objective of this work was to quantitatively evaluate the effect of Lactobacillus sakei CTC494 (sakacin-producing bioprotective strain) against Listeria monocytogenes in fish juice and to apply and validate three microbial interaction models (Jameson, modified Jameson and Lotka Volterra models) through challenge tests with gilthead sea bream (Sparus aurata) fillets under modified atmosphere packaging stored at isothermal and non-isothermal conditions. L. sakei CTC494 inhibited L. monocytogenes growth when simultaneously present in the matrix (fish juice and fish fillets) at different inoculation ratios pathogen:bioprotector (i.e. 1:1, 1:2 and 1:3). The higher the inoculation ratio, the stronger the inhibition of L. monocytogenes growth, with the ratio 1:3 yielding no growth of the pathogen. The maximum population density (N_max) was the most affected parameter for L. monocytogenes at all inoculation ratios. According to the microbiological and sensory analysis outcomes, an initial inoculation level of 4 log cfu/g for L. sakei CTC494 would be a suitable bioprotective strategy without compromising the sensory quality of the fish product. The performance of the tested interaction models was evaluated using the Acceptable Simulation Zone approach. The Lotka Volterra model showed slightly better fit than the Jameson-based models with 75-92% out of the observed counts falling into the Acceptable Simulation Zone, indicating a satisfactory model performance. The evaluated interaction models could be used as predictive modelling tool to simulate the simultaneous behaviour of bacteriocin-producing Lactobacillus strains and L. monocytogenes; thus, supporting the design and optimization of bioprotective culture-based strategies against L. monocytogenes in minimally processed fish products.

Growth, detection and virulence of Listeria monocytogenes in the presence of other microorganisms: microbial interactions from species to strain level

Like with all food microorganisms, many basic aspects of L. monocytogenes life are likely to be influenced by its interactions with bacteria living in close proximity. This pathogenic bacterium is a major concern both for the food industry and health organizations since it is ubiquitous and able to withstand harsh environmental conditions. Due to the ubiquity of Listeria monocytogenes, various strains may contaminate foods at different stages of the supply chain. Consequently, simultaneous exposure of consumers to multiple strains is also possible. In this context even strain-to-strain interactions of L. monocytogenes play a significant role in fundamental processes for the life of the pathogen, such as growth or virulence, and subsequently compromise food safety, affect the evolution of a potential infection, or even introduce bias in the detection by classical enrichment techniques. This article summarizes the impact of microbial interactions on the growth and detection of L. monocytogenes primarily in foods and food-associated environments. Furthermore it provides an overview of L. monocytogenes virulence in the presence of other microorganisms.

Growth Kinetics of Listeria monocytogenes in Cut Produce

Cut produce continues to constitute a significant portion of the fresh fruit and vegetables sold directly to consumers. As such, the safety of these items during storage, handling, and display remains a concern. Cut tomatoes, cut leafy greens, and cut melons, which have been studied in relation to their ability to support pathogen growth, have been specifically identified as needing temperature control for safety. Data are needed on the growth behavior of foodborne pathogens in other types of cut produce items that are commonly offered for retail purchase and are potentially held without temperature control. This study assessed the survival and growth of Listeria monocytogenes in cut produce items that are commonly offered for retail purchase, specifically broccoli, green and red bell peppers, yellow onions, canned green and black olives, fresh green olives, cantaloupe flesh and rind, avocado pulp, cucumbers, and button mushrooms. The survival of L. monocytogenes strains representing serotypes 1/2a, 1/2b, and 4b was determined on the cut produce items for each strain individually at 5, 10, and 25°C for up to 720 h. The modified Baranyi model was used to determine the growth kinetics (the maximum growth rates and maximum population increases) in the L. monocytogenes populations. The products that supported the most rapid growth of L. monocytogenes, considering the fastest growth and resulting population levels, were cantaloupe flesh and avocado pulp. When stored at 25°C, the maximum growth rates for these products were 0.093 to 0.138 log CFU/g/h and 0.130 to 0.193 log CFU/g/h, respectively, depending on the strain. Green olives and broccoli did not support growth at any temperature. These results can be used to inform discussions surrounding whether specific time and temperature storage conditions should be recommended for additional cut produce items.

Predictive Modeling for Estimation of Bacterial Behavior from Farm to Table

Microbial contamination is inevitable for raw and/or minimally processed ready-to-eat foods. As a consequence of the pathogenic bacterial contamination, the risk of food-borne illness increases during distribution and storage until consumption. Prediction of microbial growth and/or inactivation in/on those foods provides important information for ensuring the microbial food safety. Although numerous predictive models for bacterial growth have been proposed for various microorganisms, this review focuses on the modeling of pathogenic bacterial growth in raw and minimally processed ready-to-eat foods such as fresh-cut produce and raw minced-tuna, a common ingredient for sushi. The growth models described here take into account both the environment temperature and microbial competition in the food matrix. Microbial competition plays a key role in real food environments. Food-based predictive models enable not only to directly estimate the microbial growth on those foods, but also to apply to validation of culture-medium-based predictive models. Furthermore, toward a development of accurate and/or realistic bacterial dose-response models, a model for inactivation of pathogenic bacteria during simulated gastric fluid is also introduced.

Prediction of the Growth of Salmonella Enteritidis in Raw Ground Beef at Various Combinations of the Initial Concentration of the Pathogen and Temperature

Recently we clarified the growth kinetics of Salmonella Enteritidis in raw ground beef at various temperatures with our growth model. Based on those results, this study aimed to build a new methodology to predict the growth of Salmonella in ground beef at given initial concentrations of the pathogen and temperatures. Namely, the maximum cell population of Salmonella at various combinations of its initial concentration and temperature was developed with a polynomial equation. The rate constants of Salmonella growth at various temperatures were estimated with the square root model studied in our recent study. A new system consisting of our growth model, the polynomial equation, and the square root model successfully predicted the growth of Salmonella inoculated at given concentrations in beef at constant and dynamic temperatures. The growth of natural microflora in beef at those temperature patterns were also successfully predicted with the growth model.

Prediction of microbial growth in mixed culture with a competition model

Prediction of microbial growth in mixed culture was studied with a competition model that we had developed recently. The model, which is composed of the new logistic model and the Lotka-Volterra model, is shown to successfully describe the microbial growth of two species in mixed culture using Staphylococcus aureus, Escherichia coli, and Salmonella. With the parameter values of the model obtained from the experimental data on monoculture and mixed culture with two species, it then succeeded in predicting the simultaneous growth of the three species in mixed culture inoculated with various cell concentrations. To our knowledge, it is the first time for a prediction model for multiple (three) microbial species to be reported. The model, which is not built on any premise for specific microorganisms, may become a basic competition model for microorganisms in food and food materials.

Development of a competition model for microbial growth in mixed culture

A novel competition model for describing bacterial growth in mixed culture was developed in this study. Several model candidates were made with our logistic growth model that precisely describes the growth of a monoculture of bacteria. These candidates were then evaluated for the usefulness in describing growth of two competing species in mixed culture using Staphylococcus aureus, Escherichia coli, and Salmonella. Bacterial cells of two species grew at initial doses of 10(3), 10(4), and 10(5) CFU/g at 28ºC. Among the candidates, a model where the Lotka-Volterra model, a general competition model in ecology, was incorporated as a new term in our growth model was the best for describing all types of growth of two competitors in mixed culture. Moreover, the values for the competition coefficient in the model were stable at various combinations of the initial populations of the species. The Baranyi model could also successfully describe the above types of growth in mixed culture when it was coupled with the Gimenez and Dalgaard model. However, the values for the competition coefficients in the competition model varied with the conditions. The present study suggested that our model could be a basic model for describing microbial competition.

Growth modeling of Listeria monocytogenes in pasteurized liquid egg

The growth kinetics of Listeria monocytogenes and natural flora in commercially produced pasteurized liquid egg was examined at 4.1 to 19.4°C, and a growth simulation model that can estimate the range of the number of L. monocytogenes bacteria was developed. The experimental kinetic data were fitted to the Baranyi model, and growth parameters, such as maximum specific growth rate (μ(max)), maximum population density (N(max)), and lag time (λ), were estimated. As a result of estimating these parameters, we found that L. monocytogenes can grow without spoilage below 12.2°C, and we then focused on storage temperatures below 12.2°C in developing our secondary models. The temperature dependency of the μ(max) was described by Ratkowsky's square root model. The N(max) of L. monocytogenes was modeled as a function of temperature, because the N(max) of L. monocytogenes decreased as storage temperature increased. A tertiary model of L. monocytogenes was developed using the Baranyi model and μ(max) and N(max) secondary models. The ranges of the numbers of L. monocytogenes bacteria were simulated using Monte Carlo simulations with an assumption that these parameters have variations that follow a normal distribution. Predictive simulations under both constant and fluctuating temperature conditions demonstrated a high accuracy, represented by root mean square errors of 0.44 and 0.34, respectively. The predicted ranges also seemed to show a reasonably good estimation, with 55.8 and 51.5% of observed values falling into the prediction range of the 25th to 75th percentile, respectively. These results suggest that the model developed here can be used to estimate the kinetics and range of L. monocytogenes growth in pasteurized liquid egg under refrigerated temperature.

Prediction of the amount and rate of histamine degradation by diamine oxidase (DAO)

Histamine is a biogenic amine that forms in a variety of foods and can cause food poisoning at high concentrations (>500 ppm). In situations where the formation of histamine in food cannot be prevented through refrigeration, diamine oxidase (DAO) enzyme may be used to degrade histamine to safe levels. The aims of this work were to apply DAO in model (buffer) and real (cooked tuna soup used in the manufacture of a fish paste product, Rihaakuru) systems, in order to obtain predictions for the rates and amounts of histamine degradation. The two systems were set up with a constant concentration of histamine (500 mg/L) and the DAO enzyme (2534 units/L) at a temperature of 37°C, agitation at 100 rpm and an incubation time of 10h with variable pH (5-7) and salt concentrations (1-5%). A total of 15 experiments were designed for each system using central composite design (CCD). The data from these experiments were fitted into regression models; initially the data were used to generate an exponential decline model and then the data from this were fitted into a secondary response surface model (RSM) to predict the rate and amount of histamine degradation by DAO. The model system results indicated that DAO activity was not significantly affected by salt (p>0.05), and that activity reached a maximum within the pH range of 6-6.5 with an optimum at pH 6.3. However, the results obtained with the tuna soup model showed that the optimum oxidation of histamine using DAO occurred between pH 6-7 and salt 1-3%. This study defined the conditions for the use of DAO to degrade 500 mg/L of histamine in tuna soup used to manufacture Rihaakuru. The models generated could also be used to predict the rate and amount of histamine degradation in other foods that have similar characteristics to tuna soup.