Food MicroModel Research Articles

Exposure assessment of Staphylococcus aureus in Kimbab, a ready-to-eat Korean food

The exposure assessment was carried out for Staphylococcus aureus in kimbab by predicting growth of S. aureus and the production of enterotoxin using Food MicroModel® program. Environmental parameters selected were pH 5.5, Aw 0.999, and storage temperatures in the range of 10 to 30°C. It was predicted that 6.3 hr could be a critical time for enterotoxin production while kimbab was stored at 30°C. Mild case scenario analysis showed that enterotoxin could not be produced if kimbab was kept at 10°C during preparation and distribution and then left at 25°C for 4 hr before consumption. In the worst case scenario, the keeping time at 25°C was assumed to be 7.0 hr. The level of S. aureus in the worst case was predicted to be 6.8×106 CFU/g which is lower than the critical level (7.8×106 CFU/g) for toxin production.

A quasi-chemical model for the growth and death of microorganisms in foods by non-thermal and high-pressure processing

Predictive microbial models generally rely on the growth of bacteria in laboratory broth to approximate the microbial growth kinetics expected to take place in actual foods under identical environmental conditions. Sigmoidal functions such as the Gompertz or logistics equation accurately model the typical microbial growth curve from the lag to the stationary phase and provide the mathematical basis for estimating parameters such as the maximum growth rate (MGR). Stationary phase data can begin to show a decline and make it difficult to discern which data to include in the analysis of the growth curve, a factor that influences the calculated values of the growth parameters. In contradistinction, the quasi-chemical kinetics model provides additional capabilities in microbial modelling and fits growth-death kinetics (all four phases of the microbial lifecycle continuously) for a general set of microorganisms in a variety of actual food substrates. The quasi-chemical model is differential equations (ODEs) that derives from a hypothetical four-step chemical mechanism involving an antagonistic metabolite ( quorum sensing) and successfully fits the kinetics of pathogens ( Staphylococcus aureus, Escherichia coli and Listeria monocytogenes) in various foods (bread, turkey meat, ham and cheese) as functions of different hurdles ( a w, pH, temperature and anti-microbial lactate). The calculated value of the MGR depends on whether growth-death data or only growth data are used in the fitting procedure. The quasi-chemical kinetics model is also exploited for use with the novel food processing technology of high-pressure processing. The high-pressure inactivation kinetics of E. coli are explored in a model food system over the pressure ( P) range of 207–345 MPa (30,000–50,000 psi) and the temperature ( T) range of 30–50 °C. In relatively low combinations of P and T, the inactivation curves are non-linear and exhibit a shoulder prior to a more rapid rate of microbial destruction. In the higher P, T regime, the inactivation plots tend to be linear. In all cases, the quasi-chemical model successfully fit the linear and curvi-linear inactivation plots for E. coli in model food systems. The experimental data and the quasi-chemical mathematical model described herein are candidates for inclusion in ComBase, the developing database that combines data and models from the USDA Pathogen Modeling Program and the UK Food MicroModel.

Performance evaluation of a model describing the effects of temperature, water activity, pH and lactic acid concentration on the growth of Escherichia coli

A square root-type model for Escherichia coli growth in response to temperature, water activity, pH and lactic acid was developed by Ross et al. [Int. J. Food Microbiol. (2002).]. Predicted generation times from the model were compared to the literature data using bias and accuracy factors, graphical comparisons and plots of residuals for data obtained from both liquid growth media and foods. The model predicted well for 1025 growth rate estimates reported in the literature after poor quality or unrepresentative data ( n=215) was excluded, with a bias factor of 0.92, and an accuracy factor of 1.29. In a detailed comparison to two other predictive modes for E. coli growth, Pathogen Modeling Program (PMP) and Food MicroModel (FMM), the new model generally performed better. The new model consistently gave better predictions than the other models at generation times ≤5 h. Inclusion of the lactic acid term in the model is proposed to account for the consistently good performance of the model for comparisons to growth in meat, a parameter that is not explicitly included in the other models considered in the comparisons.

Assessment of Mathematical Models for Predicting Staphylococcus aureus Growth in Cooked Meat Products

The growth of Staphylococcus aureus in commercially available vacuum-packaged cooked ham, turkey breast meat, and chicken breast meat stored at 2.3, 6.5, 10, 13.5, and 17.7°C was studied. Growth rates observed in these food products were compared with those predicted on the basis of various growth models found in the literature and with those generated by the Pathogen Modeling Program and the Food MicroModel software using graphical and mathematical analysis for performance evaluation. In general, the models studied overestimated the growth of S. aureus. The Dengremont and Membré model most closely matched the observed behavior of S. aureus in ham and chicken breast meat, with bias factors of 1.56 and 1.09, respectively. The Eifert et al. model accurately described the growth of S. aureus in turkey breast meat, with a bias factor of 1.51. The remaining models provided safe predictions of the growth rate of S. aureus, but with poor accuracy. Predictive microbiology models have an immediate practical application in improving microbial food safety and quality and are very useful decision support tools, but they should not be used as the sole determinant of product safety.

Modelling microbial growth in structured foods: towards a unified approach

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

Measurements and predictions of growth for Listeria monocytogenes and Salmonella during fluctuating temperature : II. Rapidly changing temperatures

Growth of Listeria monocytogenes and Salmonella was examined during various rates of increase and decrease in temperature from and to the minimum for growth. Growth was little affected by even the most rapid changes and injury or lag was not observed. Subsequent investigations of growth during periods of rapid variation in temperature from and to temperatures below the growth minimum again had little effect and growth was satisfactorily predicted using the dynamic model of Baranyi and Roberts [Int. J. Food Microbiol. 23 (1994) 277] in conjunction with the data of Food Micromodel.

Practical application of predictive microbiology software programs to HACCP plans.

We studied how predictive microbiology models could practically be applied to HACCP plans with two predictive software programs that are currently available. The software programs were the Food Micromodel elaborated by the Ministry of Agriculture, Fisheries, and Food, U.K. and the Pathogen Modeling Program of Eastern Regional Research Center, U.S. Department of Agriculture. They successfully provided useful information on (i) the determination of Critical Control Points (CCPs), (ii) the estimation of critical limits at CCPs, (iii) the decision of abused products, (iv) the assessment of equivalence of HACCP plans, and further (v) the development of new products. With the information simulated by the software programs, HACCP teams could make scientific and objective decisions for developing their individual plans. It was also confirmed that microbiological process standards for food processing are indispensable for the application of the predictive programs to HACCP plans.

Predictions of growth for Listeria monocytogenes and Salmonella during fluctuating temperature

We studied the predictive performance of a dynamic modelling approach, combined with predictions from the Food MicroModel software, applied to the growth of Listeria monocytogenes and Salmonella in pasteurised milk, chicken liver pâté and minced chicken, under constant as well as fluctuating temperatures. We found that, in general, the accuracy of a prediction under fluctuation temperature was similar to that under constant temperature. Generally, there was a good agreement between predictions and observations. However, the growth of Listeria monocytogenes in pasteurised milk was inhibited largely by the natural flora present.

Safety evaluation of sous vide-processed products with respect to nonproteolytic Clostridium botulinum by use of challenge studies and predictive microbiological models.

Sixteen different types of sous vide-processed products were evaluated for safety with respect to nonproteolytic group II Clostridium botulinum by using challenge tests with low (2. 0-log-CFU/kg) and high (5.3-log-CFU/kg) inocula and two currently available predictive microbiological models, Food MicroModel (FMM) and Pathogen Modeling Program (PMP). After thermal processing, the products were stored at 4 and 8 degrees C and examined for the presence of botulinal spores and neurotoxin on the sell-by date and 7 days after the sell-by date. Most of the thermal processes were found to be inadequate for eliminating spores, even in low-inoculum samples. Only 2 of the 16 products were found to be negative for botulinal spores and neurotoxin at both sampling times. Two products at the high inoculum level showed toxigenesis during storage at 8 degrees C, one of them at the sell-by date. The predictions generated by both the FMM thermal death model and the FMM and PMP growth models were found to be inconsistent with the observed results in a majority of the challenges. The inaccurate predictions were caused by the limited number and range of the controlling factors in the models. Based on this study, it was concluded that the safety of sous vide products needs to be carefully evaluated product by product. Time-temperature combinations used in thermal treatments should be reevaluated to increase the efficiency of processing, and the use of additional antibotulinal hurdles, such as biopreservatives, should be assessed.

Verification of prediction of growth of Listeria monocytogenes micro-organism in chicken meat

Raw chicken meat was comminuted and homogenised. There were measured water activity and pH (aw = 1 for temperature 25°C, pH = 5.8 for temperature 8°C). Input raw material was investigated for the presence of Listeria monocytogenes (negative) and the raw meat was inoculated by Listeria monocytogenes CCM 4699. Number of Listeria monocytogenes, total plate count and number of coliforms were determined in the range 0–7 days by bacteriological survey for the storage temperatures 4.9, 7 and 8.3°C. The increase of Listeria monocytogenes counts has been registered for temperature 4.9°C about log 1.5 CFU/g after 6 days, for temperatures 7 and 8.3°C about 2 log CFU/g (regarding to the starting counts). The prediction for listeria growth with the aid of Food MicroModel was also made. The best agreement between the experimentally analysed number of bacteria and prediction was received for the lowest incubation temperature 4.9°C.

Shelf‐life

This article highlights current research at the Leatherhead Food Research Association which is relevant to shelf‐life assessment of food products. One area is to improve the quality and safety of fish ‐ both fresh and cold‐smoked. Food MicroModel, a computer software package which aids in the prediction of the microbiological safety and spoilage potential of food products, is described. New methods capable of predicting the stability of oils and fats to oxidation, which can be a significant cause of reduced shelf‐life in fat‐containing foods, are described. Novel analytical instruments, known as electronic noses, have been shown to have potential, as has a total antioxidant activity test. Temperature control in factories is often a compromise between product safety and the need to maintain reasonable employee working conditions. This is no longer a problem with the development of glove box technology, which separates the two environments. The final study discussed in this article is the development of accelerated shelf‐life tests for chocolate products.

Predicted and observed growth and toxigenesis by Clostridium botulinum type E in vacuum-packaged fishery product challenge tests

The observed growth and toxigenesis by Clostridium botulinum type E in vacuum-packaged unprocessed, raw pickled and cold-smoked rainbow trout stored at slightly abusive temperatures were compared to predictions generated by two currently available predictive microbiological programs, Food MicroModel and Pathogen Modelling Program. In unprocessed fish there was only a 2 log increase in type E cell count at the time the toxicity first occurred after 2 weeks storage at 8°C. Neither growth or toxin production was observed in raw pickled fish with a NaCl concentration of 6.7% (w/v) during 6 weeks storage at 6°C. In cold-smoked fish with a NaCl level of 3.2% (w/v) toxic samples were detected after 3 and 4 weeks storage at 8°C and 4°C, respectively, without any increase in type E count. Both models were hampered by limitations to controlling environmental factors set by the programs which also had an adverse effect on the reliability of predictions. Most predictions generated by the models were inconsistent with the results observed in the challenge studies. In certain situations, the models seemed to be `fail-safe', in that, the growth rate predicted from the model was faster or a predicted time to toxicity shorter than that which actually occurred in the food. In other situations, the predictions showed the product to be safe when it was not. The results demonstrate the need for further development and rigorous validation of the models before they are accepted for wider use by inspecting officials and the food industry.

A model based on absorbance data on the growth rate of Listeria monocytogenes and including the effects of pH, NaCl, Na-lactate and Na-acetate

A mathematical model was developed for predicting the growth of L. monocytogenes at 9°C in the presence of 70 ppm sodium nitrite, and at different levels of pH (5.5–6.5), sodium chloride (1.0–4.0%), sodium lactate (0–0.5%) and sodium acetate (0–0.6%). Collection of the growth data was done using absorbance measurements in broth cultures and the absorbance measurement was evaluated. The model was compared to the Food MicroModel, and against the growth of L. monocytogenes in a vacuum-packed meat product stored at 9°C. A linear relationship was obtained, for the absorbance data on different dilutions of the inoculum, in the absorbance interval studied. There was also a linear relationship between the values of the maximum specific growth rates derived from the absorbance and the ones derived from viable count measurements; and corrections were made accordingly. The statistical evaluation showed that all the main factors, i.e. pH, sodium chloride, sodium lactate and sodium acetate were statistically significant for the growth rate of L. monocytogenes. Comparison to the Food MicroModel (FMM) showed a slight underprediction for the developed model (bias=0.84). The predictions were, on average, within 20% of the FMM predictions ( n=10). Validation against the observed growth of L. monocytogenes inoculated into an emulsion type of sausage ( n=4) also showed a slight underprediction by the model. The predictions were, on average, 16% below the observed values in the sausage (Bias 0.84, Accuracy 1.26).

The effect of the growth environment on the lag phase of Listeria monocytogenes

The duration of lag in Listeria monocytogenes was examined in relation to the physico–chemical properties of the growth environment. It was supposed that lag would be determined by two hypothetical quantities, the amount of work that a cell has to perform to adapt to new conditions and the rate at which it can perform that work. If the rate at which the cell can perform the necessary work is a function of the maximum specific growth rate in the new environment, the hypothesis predicts that lag time should be related in some way to growth rate, provided cells are initially in approximately the same physiological state. Literature data suggest this is true for many organisms when temperature is the sole growth limiting factor. However, lag times of L. monocytogenes displayed an unusual response to temperature in which lag times of cells precultured at 37°C were shorter at 15°C than at 20°C or 25°C. Analysis of data from the Food Micromodel in which growth of L. monocytogenes was controlled by combinations of pH, NaCl concentration and temperature, showed that there was a linear relationship between lag time and mean generation time although there was much scatter in the data. When the effects of pH, solute type and concentration were investigated individually in this work the correlation between lag time and mean generation time was often poor. It would thus appear that the relationship between growth environment and lag time is more complex than the corresponding relationship between growth environment and maximum specific growth rate.

Application of Food MicroModel predictive software in the development of Hazard Analysis Critical Control Point (HACCP) systems

A practical application of the Food MicroModel (FMM) predictive software is presented. A case study on meat-based paté is used to illustrate the various requirements needed to assure the safety of this type of foodstuff when pH is reduced. Identification of hazards was obtained from a literature review and confirmed by epidemiological links between the product and foodborne disease outbreaks. For risk assessment four different zones (safe, caution, dangerous and critical) of the level of the variable under study (pH) were defined, each zone equating to a particular level of risk. Having identified the hazards, associated risks and intrinsic parameters of the paté, a Hazard Analysis Critical Control Point (HACCP) system can be more readily established using predicted outcomes from FMM. General guidance on generic uses is also discussed.

Predicted and observed growth of Listeria monocytogenes in seafood challenge tests and in naturally contaminated cold-smoked salmon

The performance of the Pathogen Modelling Program, the Food MicroModel, the Murphy-model and the Ross-model for growth of L. monocytogenes was evaluated by comparison with data from 100 seafood challenge tests and data from 13 storage trials with naturally contaminated sliced vacuum-packed cold-smoked salmon. Challenge tests with both cured and noncured products were studied, and graphs as well as the bias- and the accuracy factors were used for comparison of the observed and predicted growth. The Pathogen Modelling Program could not be successfully validated in seafood challenge tests. Growth rates were markedly overestimated and the μ max-bias factor was as high as 3.9 in challenge tests with cured products. On the basis of the effect of temperature, NaCl/ a w and pH, the μ max-bias factor of the other three models studied, varied between 1.0 and 2.3 in the challenge tests with cured and noncured seafoods. None of the models accurately predicted the growth in both cured and noncured seafoods. However, the results indicated that a new expanded model, including the additional effect of lactate and phenol, may provide accurate predictions of the growth of L. monocytogenes in challenge tests with various types of seafoods. Storage trials clearly showed the growth of L. monocytogenes in naturally contaminated cold-smoked salmon to be markedly slower than growth in inoculated challenge tests. Consequently, all four models substantially overestimated growth in the naturally contaminated products. Temperature, pH, NaCl/ a w and lactate were measured in the storage trials and on the basis of these parameters, the Food MicroModel μ max-bias factor was 5.2. Clearly, the model could not be successfully validated with naturally contaminated cold-smoked salmon. To improve the applicability of predictive models to fish products, it is suggested to include studies with naturally contaminated products in the development and validation of models with seafood pathogens.

Validation of Predictive Mathematical Models Describing the Growth of Listeria monocytogenes

Growth of Listeria monocytogenes and Listeria innocua in commercially available sterile homogeneous foods was investigated at different temperatures, pH values, and NaCl concentrations. Growth data were fitted to the Gompertz equation and the resulting growth kinetics were compared with predictions from the Pathogen Modeling Program and Food MicroModel. In general, good agreement was obtained when comparing growth rates and generation times for both models. Differences were observed when comparing lag phases, which ranged from 117 h shorter to 4.9 h longer than predicted for L. monocytogenes. Despite differences in lag phase, under most conditions, the models gave good predictions of microbial growth. Predictive modeling appears to be a useful tool in determining growth rates of Listeria in foods.

Development of thermal inactivation models for Salmonella enteritidis and Escherichia coli O157:H7 with temperature, pH and NaCl as controlling factors

The thermal inactivation of Salmonella enteritidis phage type 4 and Escherichia coli O157:H7 as affected by temperature (54.5–64.5°C), pH (4.2–9.6 with HCl or NaOH) and NaCl concentration (0.5–8.5% w/w) was studied. Cell suspensions in modified tryptone soya broth were heated in a submerged-coil heating apparatus and survivors were enumerated on tryptone soya agar incubated aerobically. For most thermal inactivation data there was a logarithmic decrease in the viable cell concentration over the initial 4–6 log 10 reduction and D-values were fitted. In some cases, tailing of the survivor curves was observed with cells surviving longer than the D-values predicted. Models describing the effect of temperature, pH and NaCl concentration on the thermal inactivation of S. enteritidis and E. coli O157:H7 were produced. For both organisms, predicted z-values of 4.6–7.0 C° were obtained depending on conditions, with larger z-values at higher levels of NaCl. Optimum survival occurred between pH 5 and pH 7 and increasing acidity or alkalinity caused a decrease in the predicted D-values. At equivalent pH, acetic acid and lactic acid (at 0.5, 1 and 2% w/w) generally had a similar, or increased, lethal effect compared with HCl, whereas in most cases citric acid had a less lethal effect. For E. coli O157:H7, increasing NaCl concentration had a protective effect up to the maximum tested (8.5% w/w), while for S. enteritidis optimal survival at a NaCl concentration of 5–7% w/w was predicted. The models were validated in foods by comparing predictions with published data. Most (80%) of the predicted D-values from the S. enteritidis model were within the 95% confidence interval (within 2.45-fold of the published data) for different Salmonella serotypes in whole egg, egg albumen, egg yolk, beef and milk. Most (93%) of the predicted D-values from the E. coli O157:H7 model were larger than the limited published data for this organism in meat, poultry, milk and apple juice with 42% within the 95% confidence interval (within 2.05-fold of the published data). The D-value models were incorporated into Version 1, and subsequent versions, of the predictive microbiology software program, Food MicroModel.

Predictive modelling of growth of Listeria monocytogenes. The effects on growth of NaCl, pH, storage temperature and NaNO2.

The effect of NaCl concentration (5.0 115.0 g/l). pH value (4.0-7.2), temperature (1-35 degrees C) and NaNO2 concentration (0 200 mg/l) on the growth responses of Listeria monocytogenes, in laboratory medium was investigated. The growth curves generated within this matrix of conditions were fitted using the function of Baranyi and Roberts (1994) and the growth responses modelled using a quadratic polynomial to produce response surfaces. Growth curves could then be regenerated for any set of conditions within the experimental matrix and values predicted for the growth rate, doubling time, lag time and time to 1000-fold increase. The model was validated using data from published literature and was found to give realistic predictions for doubling times in foods, including meat and meat products, milk, dairy products and vegetables. Predictions from this model (Baranyi and Roberts. 1994) compared favourably with those from the models of Buchanan and Phillips (1990), Murphy et al. (1996) and the Food MicroModel.

Preparation and validation of a growth model for Bacillus cereus: the effects of temperature, pH, sodium chloride and carbon dioxide

The growth responses of a vegetative inoculum of Bacillus cereus as influenced by varying conditions of temperature, pH value and sodium chloride concentration (% w v ) and carbon dioxide concentration (% v v ) were determined in laboratory medium. Growth curves in concentrations of NaCl in the range 0.5–10.5% ( w v ), pH values in the range 4.5–7.0, CO 2 concentrations in the range 10–80% ( v v ) and storage temperatures from 10 °C to 30 °C were fitted using the regime of Baranyi et al. (1993). A response surface model was prepared and predictions of doubling time, growth rate, lag time and time to 1000-fold increase could be obtained for any set of conditions within the matrix studied. This model is included in Food MicroModel Version 1. Predicted doubling times from the model were compared to observed doubling times in the literature and the model was found to give realistic estimates of doubling time for a range of foods including milk, meat and poultry and carbohydrate-based products.