Predictions of growth for Listeria monocytogenes and Salmonella during fluctuating temperature

Effect of the storage temperature on the quality of eggs inoculated with Salmonella Enteritidis onto shell

This study explored the temperature-dependent effect on the growth characteristics of Salmonella Enteritidis (SE) on eggshell toward identifying an appropriate storage temperature for unwashed eggs in an actual distribution environment. Among the test storage temperatures (10 °C, 25 °C, and 35 °C), 25 °C was determined to be an appropriate storage temperature, with no effect of changing temperature on the control of SE on eggshell. Regarding the effect of the temperature on egg quality, the quality indicators of egg such as Haugh unit, yolk index, albumin index, and albumin pH were significantly maintained. These results indicated that unwashed eggs should be distributed at 25 °C for SE control, and the storage temperature should be below 10 °C from at least day 4 onward after the start of distribution to maintain egg quality. This study will assist for safety management of unwashed egg in an actual distribution environment.

Multifractal long-range dependence pattern of functional magnetic resonance imaging in the human brain at rest

Long-range dependence is a prevalent phenomenon in various biological systems that characterizes the long-memory effect of temporal fluctuations. While recent research suggests that functional magnetic resonance imaging signal has fractal property, it remains unknown about the multifractal long-range dependence pattern of resting-state functional magnetic resonance imaging signals. The current study adopted the multifractal detrended fluctuation analysis on highly sampled resting-state functional magnetic resonance imaging scans to investigate long-range dependence profile associated with the whole-brain voxels as specific functional networks. Our findings revealed the long-range dependence's multifractal properties. Moreover, long-term persistent fluctuations are found for all stations with stronger persistency in whole-brain regions. Subsets with large fluctuations contribute more to the multifractal spectrum in the whole brain. Additionally, we found that the preprocessing with band-pass filtering provided significantly higher reliability for estimating long-range dependence. Our validation analysis confirmed that the optimal pipeline of long-range dependence analysis should include band-pass filtering and removal of daily temporal dependence. Furthermore, multifractal long-range dependence characteristics in healthy control and schizophrenia are different significantly. This work has provided an analytical pipeline for the multifractal long-range dependence in the resting-state functional magnetic resonance imaging signal. The findings suggest differential long-memory effects in the intrinsic functional networks, which may offer a neural marker finding for understanding brain function and pathology.

Modeling the Growth of Six Listeria monocytogenes Strains in Smoked Salmon Pâté

In this study, the growth of six L. monocytogenes strains isolated from different fish products was quantified and modeled in smoked salmon pâté at a temperature ranging from 2 to 20 °C. The experimental data obtained for each strain was fitted to the primary growth model of Baranyi and Roberts to estimate the following kinetic parameters: lag phase (λ), maximum specific growth rate (μ_max), and maximum cell density (N_max). Then, the effect of storage temperature on the obtained μ_max values was modeled by the Ratkowsky secondary model. In general, the six L. monocytogenes strains showed rapid growth in salmon pâté at all storage temperatures, with a relatively short lag phase λ, even at 2 °C. The growth behavior among the tested strains was similar at the same storage temperature, although significant differences were found for the parameters λ and μ_max. Besides, the growth variations among the strains did not follow a regular pattern. The estimated secondary model parameter T_min ranged from -4.25 to -3.19 °C. This study provides accurate predictive models for the growth of L. monocytogenes in fish pâtés that can be used in shelf life and microbial risk assessment studies. In addition, the models generated in this work can be implemented in predictive modeling tools and repositories that can be reliably and easily used by the fish industry and end-users to establish measures aimed at controlling the growth of L. monocytogenes in fish-based pâtés.

Application of deep neural techniques in predictive modelling for the estimation of Escherichia coli growth rate

AIMS

To develop a predictive model for Escherichia coli using deep neural networks.

METHODS AND RESULTS

Batch experiments are conducted at different temperatures closer to optimum value (36·5°C, 37°C, 37·5°C, 38°C and 38·5°C) to obtain the growth curves of E .coli K-12. Two primary models namely modified Gompertz and new logistic are chosen. Three secondary models namely Gaussian, nonlinear autoregressive eXogenous (NARX) model and long short-term memory (LSTM) are developed. The novelty in this paper is the development of secondary models using artificial neural network (ANN) and deep network. The performance measures chosen to compare the developed primary and secondary models are correlation coefficient (R² ), root-mean-square error (RMSE) and accuracy factor (A_f ). Results show that modified Gompertz model has better R² (0·99) and RMSE (0·019) when compared to new logistic model. Also, the deep network model outperforms other secondary models. Based on the primary and novel secondary model, a predictive model (tertiary model) is developed with improved accuracy and is validated.

CONCLUSIONS

The proposed predictive model exhibit good validation results in terms of RMSE and R² values and can be applied for determining the growth rate of E. coli at a particular temperature value.

SIGNIFICANCE AND IMPACT OF THE STUDY

The proposed model can be used in food processing industries during enzyme production such as Chymosin, to predict the growth rate of E. coli as a function of temperature. Also, the developed LSTM and NARX models can be used to predict maximum specific growth rate of other microbial strains with proper training.

Punica granatum and Citrus spp. Extract Mix Affects Spoilage Microorganisms Growth Rate in Vacuum-Packaged Cooked Sausages Made from Pork Meat, Emmer Wheat (Triticum dicoccum Schübler), Almond (Prunus dulcis Mill.) and Hazelnut (Corylus avellana L.)

Sausage made from pork meat, emmer wheat (Triticum dicoccum Schübler), almond (Prunus dulcis Mill.), and hazelnut (Corylus avellana L.) was integrated with a mix of Punica granatum and Citrus spp. extracts to evaluate the possible effects on the growth and oxidation of spoilage microorganisms. Two concentrations of the mix were added, respectively, during sausage-making, and the final products were compared with a control group, without the extract mix, during storage. The use of the mix, especially at 10 g/1000 g of the whole ingredients, delayed the pH drop and thiobarbituric acid-reactive substances (TBARs) value during storage. Total viable count, lactic acid bacteria and psychrotrophic microbial counts were also affected, as the extract mix lowered the maximum growth rate of the microbial population considered. The sensory analyses revealed an improvement in the shelf-life of 6 and 16 days, respectively, when 5‰ and 10‰ of the mix were used.

Predictive Model of Listeria monocytogenes Growth in Queso Fresco

Listeria monocytogenes is a hardy psychrotrophic pathogen that has been linked to several cheese-related outbreaks in the United States, including a recent outbreak in which a fresh cheese (queso fresco) was implicated. The purpose of this study was to develop primary, secondary, and tertiary predictive models for the growth of L. monocytogenes in queso fresco and to validate these models using nonisothermal time and temperature profiles. A mixture of five strains of L. monocytogenes was used to inoculate pasteurized whole milk to prepare queso fresco. Ten grams of each fresh cheese sample was vacuum packaged and stored at 4, 10, 15, 20, 25, and 30°C. From samples at each storage temperature, subsamples were removed at various times and diluted in 0.1% peptone water, and bacteria were enumerated on Listeria selective agar. Growth data from each temperature were fitted using the Baranyi model as the primary model and the Ratkowsky model as the secondary model. Models were then validated using nonisothermal conditions. The Baranyi model was fitted to the isothermal growth data with acceptable goodness of fit statistics (R² = 0.928; root mean square error = 0.317). The Ratkowsky square root model was fitted to the specific growth rates at different temperatures (R² = 0.975). The tertiary model developed from these models was validated using the growth data with two nonisothermal time and temperature profiles (4 to 20°C for 19 days and 15 to 30°C for 11 days). Data for these two profiles were compared with the model prediction using an acceptable prediction zone analysis; >70% of the growth observations were within the acceptable prediction zone (between -1.0 and 0.5 log CFU/g). The model developed in this study will be useful for estimating the growth of L. monocytogenes in queso fresco. These predictions will help in estimation of the risk of listeriosis from queso fresco under extended storage and temperature abuse conditions.

Assessment of intraspecies variability in fungal growth initiation of Aspergillus flavus and aflatoxin B1 production under static and changing temperature levels using different initial conidial inoculum levels

Intraspecies variability in fungal growth and mycotoxin production has important implications for food safety. Using the Bioscreen C we have examined spectrophotometrically intraspecies variability of A. flavus using 10 isolates under different environments, including temperature shifts, in terms of growth and aflatoxin B₁ (AFB₁) production. Five high and five low AFB₁ producers were examined. The study was conducted at 5 isothermal conditions (from 15 to 37 °C) and 4 dynamic scenarios (between 15 and 30 °C). The experiments were carried out in a semisolid YES medium at 0.92 a_w and two inoculum levels, 10² and 10³ spores/mL. The Time to Detection (TTD) of growth initiation was determined and modelled as a function of temperature through a polynomial equation and the model was used to predict TTD under temperature upshifts conditions using a novel approach. The results obtained in this study have shown that a model can be developed to describe the effect of temperature upshifts on the TTD for all the studied isolates and inoculum levels. Isolate variability increased as the growth conditions became more stressful and with a lower inoculum level. Inoculum level affected the intraspecies variability but not the repeatability of the experiments. In dynamic conditions, isolate responses depended both on the temperature shift and, predominantly, the final temperature level. AFB₁ production was highly variable among the isolates and greatly depended on temperature (optimum temperature at 30-35 °C) and inoculum levels, with often higher production with lower inoculum. This suggests that, from an ecological point of view, the potential isolate variability and interaction with dynamic conditions should be taken into account in developing strategies to control growth and predicting mycotoxin risks by mycotoxigenic fungi.

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.

Predicting bacterial growth in raw, salted, and cooked chicken breast fillets during storage

Growth curves were evaluated for aerobic mesophilic and psychrotrophic bacteria, Pseudomonas spp. and Staphylococcus spp., grown in raw, salted, and cooked chicken breast at 2, 4, 7, 10, 15, and 20 ℃, respectively, using the modified Gompertz and modified logistic models. Shelf life was determined based on microbiological counts and sensory analysis. Temperature increase reduced the shelf life, which varied from 10 to 26 days at 2 ℃, from nine to 21 days at 4 ℃, from six to 12 days at 7 ℃, from four to eight days at 10 ℃, from two to four days at 15 ℃, and from one to two days at 20 ℃. In most cases, cooked chicken breast showed the highest microbial count, followed by raw breast and lastly salted breast. The data obtained here were useful for the generation of mathematical models and parameters. The models presented high correlation and can be used for predictive purposes in the poultry meat supply chain.

Validation of mathematical models for Salmonella growth in raw ground beef under dynamic temperature conditions representing loss of refrigeration

Temperature is a primary factor in controlling the growth of microorganisms in food. The current U. S. Food and Drug Administration Model Food Code guidelines state that food can be kept out of temperature control for up to 4 h without qualifiers, or up to 6 h, if the food product starts at an initial 41 °F (5 °C) temperature and does not exceed 70 °F (21 °C) at 6 h. This project validates existing ComBase computer models for Salmonella growth under changing temperature conditions modeling scenarios using raw ground beef as a model system. A cocktail of Salmonella serovars isolated from different meat products ( Salmonella Copenhagen, Salmonella Montevideo, Salmonella Typhimurium, Salmonella Saintpaul, and Salmonella Heidelberg) was made rifampin resistant and used for all experiments. Inoculated samples were held in a programmable water bath at 4.4 °C (40 °F) and subjected to linear temperature changes to different final temperatures over various lengths of time and then returned to 4.4 °C (40 °F). Maximum temperatures reached were 15.6, 26.7, or 37.8 °C (60, 80, or 100 °F), and the temperature increases took place over 4, 6, and 8 h, with varying cooling times. Our experiments show that when maximum temperatures were lower (15.6 or 26.7 °C), there was generally good agreement between the ComBase models and experiments: when temperature increases of 15.6 or 26.7 °C occurred over 8 h, experimental data were within 0.13 log CFU of the model predictions. When maximum temperatures were 37 °C, predictive models were fail-safe. Overall bias of the models was 1.11. and accuracy was 2.11. Our experiments show the U.S. Food and Drug Administration Model Food Code guidelines for holding food out of temperature control are quite conservative. Our research also shows that the ComBase models for Salmonella growth are accurate or fail-safe for dynamic temperature conditions as might be observed due to power loss from natural disasters or during transport out of temperature control.

Microbiological quality and safety assessment of the Rwandan milk and dairy chain

Milk is a valuable and nutritious food product that can partially fulfill the rising food demand of the growing African population. The microbiological status of milk and derived products was assessed throughout the milk and dairy chain in Rwanda by enumeration of the total mesophilic count, coliforms, and Staphylococcus aureus and detection of Salmonella and Listeria monocytogenes. The quality of raw milk was satisfactory for the majority of samples, but 5.2% contained Salmonella. At the processing level, the total mesophilic count and coliform numbers indicated ineffective heat treatment during pasteurization or postpasteurization contamination. Increasing bacterial counts were observed along the retail chain and could be attributed to insufficient temperature control during storage. Milk and dairy products sold in milk shops were of poor and variable microbiological quality in comparison with the pasteurized milk sold in supermarkets. In particular, the microbiological load and pathogen prevalence in cheese were unacceptably high.

Cold shock CspA and CspB protein production during periodic temperature cycling in Escherichia coli

Background

Temperature is an important environmental factor which can dramatically affect biochemical processes in bacteria. Temperatures above optimal cause heat shock, while low temperatures induce cold shock. Since the physiological response of the bacterium Escherichia coli to slow temperature fluctuation is not well known, we investigated the effect of periodic temperature cycling between 37° and 8°C with a period of 2 h on proteome profile, cold shock CspA and CspB protein and gene production.

Results

Several proteins (i.e. succinyl-CoA synthetase subunit alpha, periplasmic oligopeptide-binding protein, maltose-binding periplasmic protein, outer membrane porin protein, flavodoxin-1, phosphoserine aminotransferase) were up or down regulated during temperature cycling, in addition to CspA and CspB production. The results indicate that transcription of cspA and cspB increased during each temperature downshift and consistently decreased after each temperature upshift. In sharp contrast CspA-FLAG and CspB-FLAG protein concentrations in the cell increased during the first temperature down-shift and remained unresponsive to further temperature fluctuations. The proteins CspA-FLAG and CspB-FLAG were not significantly degraded during the temperature cycling.

Conclusion

The study demonstrated that slow periodic temperature cycling affected protein production compared to cells constantly incubated at 37°C or during classical cold shock. Bacterial cspA and cspB mRNA transcript levels fluctuated in synchrony with the temperature fluctuations. There was no corresponding pattern of CspA and CspB protein production during temperature cycling.

Utilization of mathematical models to manage risk of holding cold food without temperature control

This document describes the development of a tool to manage the risk of the transportation of cold food without temperature control. The tool uses predictions from ComBase predictor and builds on the 2009 U.S. Food and Drug Administration Model Food Code and supporting scientific data in the Food Code annex. I selected Salmonella spp. and Listeria monocytogenes as the organisms for risk management. Salmonella spp. were selected because they are associated with a wide variety of foods and grow rapidly at temperatures >17°C. L. monocytogenes was selected because it is frequently present in the food processing environment, it was used in the original analysis contained in the Food Code Annex, and it grows relatively rapidly at temperatures <17°C. The suitability of a variety of growth models under changing temperature conditions is largely supported by the published literature. The ComBase predictions under static temperature conditions were validated using 148 ComBase database observations for Salmonella spp. and L. monocytogenes in real foods. The times and temperature changes encompassed by ComBase Predictor models for Salmonella spp. and L. monocytogenes are consistent with published data on consumer food transport to the home from the grocery store and on representative foods from a wholesale cash and carry food service supplier collected as part of this project. The resulting model-based tool will be a useful aid to risk managers and customers of wholesale cash and carry food service suppliers, as well as to anyone interested in assessing and managing the risks posed by holding cold foods out of temperature control in supermarkets, delis, restaurants, cafeterias, and homes.

Establishing equivalence for microbial-growth-inhibitory effects ("iso-hurdle rules") by analyzing disparate listeria monocytogenes data with a gamma-type predictive model

Preservative factors act as hurdles against microorganisms by inhibiting their growth; these are essential control measures for particular food-borne pathogens. Different combinations of hurdles can be quantified and compared to each other in terms of their inhibitory effect ("iso-hurdle"). We present here a methodology for establishing microbial iso-hurdle rules in three steps: (i) developing a predictive model based on existing but disparate data sets, (ii) building an experimental design focused on the iso-hurdles using the model output, and (iii) validating the model and the iso-hurdle rules with new data. The methodology is illustrated with Listeria monocytogenes. Existing data from industry, a public database, and the literature were collected and analyzed, after which a total of 650 growth rates were retained. A gamma-type model was developed for the factors temperature, pH, a(w), and acetic, lactic, and sorbic acids. Three iso-hurdle rules were assessed (40 logcount curves generated): salt replacement by addition of organic acids, sorbic acid replacement by addition of acetic and lactic acid, and sorbic acid replacement by addition of lactic/acetic acid and salt. For the three rules, the growth rates were equivalent in the whole experimental domain (γ from 0.1 to 0.5). The lag times were also equivalent in the case of mild inhibitory conditions (γ ≥ 0.2), while they were longer in the presence of salt than acids under stress conditions (γ < 0.2). This methodology allows an assessment of the equivalence of inhibitory effects without intensive data generation; it could be applied to develop milder formulations which guarantee microbial safety and stability.

Avaliação in vitro da sensibilidade da Corynebacterium pseudotuberculosis frente a diferentes tipos de antissépticos e desinfetantes e determinação de sua curva de crescimento

RESUMO O objetivo deste estudo foi avaliar o efeito in vitro de antissépticos e desinfetantes contra a Corynebacterium pseudotuberculosis e descrever a curva de crescimento deste micro-organismo em caldo de infusão de cérebro e coração adicionado de 0,1% de Tween 80 (BHI + T), ao longo de 48 horas. Foram avaliados tintura de iodo a 10%, hipoclorito de sódio a 2,5%, permanganato de potássio a 5%, sabonete líquido antisséptico Aseptol® e álcool etílico absoluto (99,8%), por meio da metodologia da disco-difusão. Um swab estéril foi imerso na suspensão bacteriana produzida e semeado em placa de ágar Mueller-Hinton. Discos estéreis foram embebidos em cada solução a ser testada e distribuídos na superfície do ágar. Os resultados foram obtidos de acordo com o diâmetro do halo produzido ao redor dos discos. Para obtenção da curva de crescimento, colônias isoladas do micro-organismo foram inoculadas em frasco contendo BHI + T. A cada quatro horas, 2 mL eram retirados para medição da massa celular em espectrofotômetro e 1 mL para realização das diluições seriadas, plaqueamento em ágar sangue e contagem de células viáveis. Observou-se que, para a obtenção de uma concentração máxima de C. pseudotuberculosis, próxima a 1.200 x 105 células viáveis/mL, deve-se manter o inóculo sob incubação adequada por um período de 28 a 40 horas. Quanto à prova de sensibilidade, verificou-se que a tintura de iodo a 10%, seguida pelo hipoclorito de sódio a 2,5% e permanganato de potássio a 5%, foram os antissépticos e desinfetantes com maior poder bactericida in vitro contra a C. pseudotuberculosis.

Dynamic modeling of Listeria monocytogenes growth in pasteurized vanilla cream after postprocessing contamination

A product-specific model was developed and validated under dynamic temperature conditions for predicting the growth of Listeria monocytogenes in pasteurized vanilla cream, a traditional milk-based product. Model performance was also compared with Growth Predictor and Sym'Previus predictive microbiology software packages. Commercially prepared vanilla cream samples were artificially inoculated with a five-strain cocktail of L. monocytogenes, with an initial concentration of 102 CFU g(-1), and stored at 3, 5, 10, and 15 degrees C for 36 days. The growth kinetic parameters at each temperature were determined by the primary model of Baranyi and Roberts. The maximum specific growth rate (mu(max)) was further modeled as a function of temperature by means of a square root-type model. The performance of the model in predicting the growth of the pathogen under dynamic temperature conditions was based on two different temperature scenarios with periodic changes from 4 to 15 degrees C. Growth prediction for dynamic temperature profiles was based on the square root model and the differential equations of the Baranyi and Roberts model, which were numerically integrated with respect to time. Model performance was based on the bias factor (B(f)), the accuracy factor (A(f)), the goodness-of-fit index (GoF), and the percent relative errors between observed and predicted growth. The product-specific model developed in the present study accurately predicted the growth of L. monocytogenes under dynamic temperature conditions. The average values for the performance indices were 1.038, 1.068, and 0.397 for B(f), A(f), and GoF, respectively for both temperature scenarios assayed. Predictions from Growth Predictor and Sym'Previus overestimated pathogen growth. The average values of B(f), A(f), and GoF were 1.173, 1.174, 1.162, and 0.956, 1.115, 0.713 for [corrected] Growth Predictor and Sym'Previus, respectively.

Comparing uncertainty resulting from two-step and global regression procedures applied to microbial growth models

Two different microbial modeling procedures were compared and validated against independent data for Listeria monocytogenes growth. The most generally used method is two consecutive regressions: growth parameters are estimated from a primary regression of microbial counts, and a secondary regression relates the growth parameters to experimental conditions. A global regression is an alternative method in which the primary and secondary models are combined, giving a direct relationship between experimental factors and microbial counts. The Gompertz equation was the primary model, and a response surface model was the secondary model. Independent data from meat and poultry products were used to validate the modeling procedures. The global regression yielded the lower standard errors of calibration, 0.95 log CFU/ml for aerobic and 1.21 log CFU/ml for anaerobic conditions. The two-step procedure yielded errors of 1.35 log CFU/ml for aerobic and 1.62 log CFU/ ml for anaerobic conditions. For food products, the global regression was more robust than the two-step procedure for 65% of the cases studied. The robustness index for the global regression ranged from 0.27 (performed better than expected) to 2.60. For the two-step method, the robustness index ranged from 0.42 to 3.88. The predictions were overestimated (fail safe) in more than 50% of the cases using the global regression and in more than 70% of the cases using the two-step regression. Overall, the global regression performed better than the two-step procedure for this specific application.

Model on the microbial quality change of seasoned soybean sprouts for on-line shelf life prediction

The growth of aerobic bacteria on Korean seasoned soybean sprouts was modelled as a function of temperature to estimate microbial spoilage and shelf life on a real-time basis under dynamic storage conditions. Counts of aerobic bacteria on seasoned soybean sprouts stored at constant temperatures between 0 degrees C and 15 degrees C were recorded. The bootstrapping method was applied to generate many resampled data sets of mean microbial plate counts that were then used to estimate the parameters of the microbial growth model of Baranyi and Roberts. The distributions of the model parameters were quantified, and their temperature dependencies were expressed as mathematical functions. When the temperature functions of the parameters were incorporated into differential equations describing microbial growth, predictions of microbial growth under fluctuating temperature conditions were similar to observed microbial growth.

Model comparison for Escherichia coli growth in pouched food

We recently studied the growth characteristics of Escherichia coli cells in pouched mashed potatoes (Fujikawa et al., J. Food Hyg. Soc. Japan, 47, 95-98 (2006)). Using those experimental data, in the present study, we compared a logistic model newly developed by us with the modified Gompertz and the Baranyi models, which are used as growth models worldwide. Bacterial growth curves at constant temperatures in the range of 12 to 34 degrees C were successfully described with the new logistic model, as well as with the other models. The Baranyi gave the least error in cell number and our model gave the least error in the rate constant and the lag period. For dynamic temperature, our model successfully predicted the bacterial growth, whereas the Baranyi model considerably overestimated it. Also, there was a discrepancy between the growth curves described with the differential equations of the Baranyi model and those obtained with DMfit, a software program for Baranyi model fitting. These results indicate that the new logistic model can be used to predict bacterial growth in pouched food.

Dynamic modeling of Listeria monocytogenes growth in pasteurized milk

AIMS

The development and validation of a dynamic model for predicting Listeria monocytogenes growth in pasteurized milk stored at both static and dynamic temperature conditions.

METHODS AND RESULTS

Growth of inoculated L. monocytogenes in a commercial pasteurized whole milk product was monitored at various isothermal conditions from 1.5 to 16 degrees C. The kinetic parameters of the pathogen were modelled as a function of temperature using a square root type model, which was further validated using data from 92 published growth curves from eight different milk products. Compared to four published models for L. monocytogenes growth, the model developed in this study performed better, with a per cent discrepancy and bias of 49.1 and -1.01%, respectively. The performance of the model in predicting growth at dynamic temperature conditions was evaluated at four different fluctuating temperature scenarios with periodic temperature changes from -2 to 16 degrees C. The prediction of growth at dynamic storage temperature was based on the square root model in conjunction with the differential equations of the Baranyi and Roberts model, which were numerically integrated with respect to time. The per cent relative errors between the observed and the predicted growth of L. monocytogenes were less than 10% for all temperature scenarios tested.

CONCLUSIONS

Available models from experiments conducted in laboratory media may result in significant overestimation of L. monocytogenes growth in pasteurized milk because they do not take into account factors such as milk composition (e.g. natural antimicrobial compounds present in milk) and the interactions of the pathogen with the natural microflora. The product-targeted model developed in the present study showed a high performance in predicting growth of L. monocytogenes in pasteurized milk under both static and dynamic temperature conditions.

SIGNIFICANCE AND IMPACT OF THE STUDY

Temperature fluctuations often occur during the transportation and storage of pasteurized milk. A high performance, dynamic model for the growth of L. monocytogenes can be a useful tool for effective management and optimization of product safety and can lead to more realistic estimations of pasteurized-milk related safety risks.

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.

Modeling surface growth of Escherichia coli on agar plates

Surface growth of Escherichia coli cells on a membrane filter placed on a nutrient agar plate under various conditions was studied with a mathematical model. The surface growth of bacterial cells showed a sigmoidal curve with time on a semilogarithmic plot. To describe it, a new logistic model that we presented earlier (H. Fujikawa et al., Food Microbiol. 21:501-509, 2004) was modified. Growth curves at various constant temperatures (10 to 34 degrees C) were successfully described with the modified model (model III). Model III gave better predictions of the rate constant of growth and the lag period than a modified Gompertz model and the Baranyi model. Using the parameter values of model III at the constant temperatures, surface growth at various temperatures was successfully predicted. Surface growth curves at various initial cell numbers were also sigmoidal and converged to the same maximum cell numbers at the stationary phase. Surface growth curves at various nutrient levels were also sigmoidal. The maximum cell number and the rate of growth were lower as the nutrient level decreased. The surface growth curve was the same as that in a liquid, except for the large curvature at the deceleration period. These curves were also well described with model III. The pattern of increase in the ATP content of cells grown on a surface was sigmoidal, similar to that for cell growth. We discovered several characteristics of the surface growth of bacterial cells under various growth conditions and examined the applicability of our model to describe these growth curves.

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.

Prediction of pathogen growth on iceberg lettuce under real temperature history during distribution from farm to table

The growth of pathogenic bacteria Escherichia coli O157:H7, Salmonella spp., and Listeria monocytogenes on iceberg lettuce under constant and fluctuating temperatures was modelled in order to estimate the microbial safety of this vegetable during distribution from the farm to the table. Firstly, we examined pathogen growth on lettuce at constant temperatures, ranging from 5 to 25 degrees C, and then we obtained the growth kinetic parameters (lag time, maximum growth rate (micro(max)), and maximum population density (MPD)) using the Baranyi primary growth model. The parameters were similar to those predicted by the pathogen modelling program (PMP), with the exception of MPD. The MPD of each pathogen on lettuce was 2-4 log(10) CFU/g lower than that predicted by PMP. Furthermore, the MPD of pathogens decreased with decreasing temperature. The relationship between mu(max) and temperature was linear in accordance with Ratkowsky secondary model as was the relationship between the MPD and temperature. Predictions of pathogen growth under fluctuating temperature used the Baranyi primary microbial growth model along with the Ratkowsky secondary model and MPD equation. The fluctuating temperature profile used in this study was the real temperature history measured during distribution from the field at harvesting to the retail store. Overall predictions for each pathogen agreed well with observed viable counts in most cases. The bias and root mean square error (RMSE) of the prediction were small. The prediction in which mu(max) was based on PMP showed a trend of overestimation relative to prediction based on lettuce. However, the prediction concerning E. coli O157:H7 and Salmonella spp. on lettuce greatly overestimated growth in the case of a temperature history starting relatively high, such as 25 degrees C for 5 h. In contrast, the overall prediction of L. monocytogenes under the same circumstances agreed with the observed data.

Effect of refrigerating delayed shipments of raw ground beef on the detection of Salmonella Typhimurium

In eight separate trials, four groups of raw ground beef samples were inoculated with 0.04 to 0.3 CFU/g of Salmonella Typhimurium (DT 104). Each group consisted of four 25-g samples (three inoculated and one uninoculated). After inoculation, these samples were shipped by overnight courier in Shipping containers with ice packs from the U.S. Department of Agriculture (USDA), Eastern Regional Research Center, in Wyndmoor, Pa., to the U.S. Food Safety and Inspection Service (FSIS), Eastern Laboratory, in Athens, Ga. A total of 128 samples (32 in each of four groups) were shipped. A temperature data logger was placed inside each shipping container to record the temperature during shipping and storage. The first group of ground beef samples was analyzed within approximately 1 h of arrival. The second group of samples was left in the original containers, with a gel ice pack, for 24 h before processing. The third and fourth groups of samples were removed from the original shipping containers and stored at room temperature (21 +/- 2 degrees C) for 6 h and then in a refrigerator at 4 +/- 2 degrees C for 24 and 48 h, respectively, before analysis. The samples were analyzed for the presence of Salmonella according to the USDA/FSIS Microbiological Laboratory Guidebook, chapter 4.02. There was no significant difference in the presence and levels of Salmonella in ground beef among the four test groups. These data show that it is acceptable to process the late-arriving ground beef samples for the detection of Salmonella if they are kept in a refrigerator (4 +/- 2 degrees C) for 24 to 48 h or when the shipments arrive late (24 h in the container with ice pack).

Modeling Staphylococcus aureus growth and enterotoxin production in milk

Staphylococcus aureus growth and its enterotoxin production in sterilized milk were modeled with a modification of a new logistic model recently developed by us. The modified model and the Baranyi model described the early exponential phase of a growth curve more accurately than the previous model, at constant temperatures from 14 to 36.5 degrees C. The amount of toxin in milk increased linearly with time from the time the cell population reached about 10(6.5) cfu/ml. The rate of toxin production linearly increased at temperatures between 14 and 32 degrees C. From parameter values obtained at the constant temperatures, the model successfully predicted bacterial growth in the milk at a varying temperature. For toxin level estimation, we postulated that the rate of toxin production might be regulated with the temperature after the cell concentration reached 10(6.5) cfu/ml; the time point when the cell concentration reached that value was predicted with the modified growth model. Introduction of a correction factor in the toxin estimation successfully predicted the toxin level in milk at a varying temperature. These results showed that this prediction system consisting of the modified model and the toxin production algorithm might be a useful tool for modeling bacterial growth and its metabolite production in liquid foods.