Temperature effect on bacterial growth rate: quantitative microbiology approach including cardinal values and variability estimates to perform growth simulations on/in food

Membré, Jeanne‐Marie; Leporq, Benoît; Vialette, Michèle; Mettler, Eric; Perrier, Louise; Thuault, Dominique; Zwietering, Marcel H.

doi:10.1016/j.ijfoodmicro.2004.10.015

Cited by 106 publications

(65 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A cardinal-value model was used, with modules of temperature (T), pH, and water activity (a w ); the parameters of this model are the cardinal values T min , T opt , T max , pH min , pH opt , pH max , a w(min) , and a w (opt) , where the subscript "min" indicates the theoretical minimal value allowing growth, the subscript "max" indicates the theoretical maximal value allowing growth, and the subscript "opt" indicates the optimal value for which growth is maximal (28). All of these parameters were therefore determined for each strain studied, resulting in a strain-dependent model for the prediction of growth in laboratory medium (20,21).…”

Section: Methodsmentioning

confidence: 99%

“…The food products and bacteria studied were selected according to food safety concerns, especially those of France (Table 1). For a given species, representative strains were chosen, whose cardinal values had been obtained in the earlier step of the SymЈPrevius program (20,21): 16 strains of L. monocytogenes from sausage (3 strains), seafood (2 strains), dairy products (6 strains), poultry (1 strain), and food plants (4 strains); 10 strains of E. coli from a meat product (1 strain), bovine feces (3 strains), dairy products (3 strains), and human isolates (3 strains); 10 strains of B. cereus from seafood (1 strain), dairy products (6 strains), egg or egg-based products (2 strains), and pasta (1 strain); 5 strains of C. perfringens from pork (1 strain), dairy products (3 strains), and poultry (1 strain); and 9 strains of Salmonella from sausage and pork meat (2 strains), dairy products (1 strain), poultry (1 strain), dairy plants (3 strains), and bakery products (2 strains). The study of the effect of temperature on growth rates demonstrated that intraspecies variability was low compared to uncertainty (21), and only one strain was retained for the validation study.…”

Section: Methodsmentioning

confidence: 99%

“…This is all the more important when creating a software application (9, 16), such as the French national program of predictive microbiology, SymЈPrevius (14). In this research program, industry, public, university, and technical center laboratories first constructed a parameter database covering 50 bacterial strains of five species grown in laboratory medium (20,21). The pathogenic bacteria selected were Escherichia coli, Listeria monocytogenes, Bacillus cereus, Clostridium perfringens, and Salmonella.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Development and Validation of Experimental Protocols for Use of Cardinal Models for Prediction of Microorganism Growth in Food Products

Pinon

Zwietering

Perrier

et al. 2004

Appl Environ Microbiol

View full text Add to dashboard Cite

An experimental protocol to validate secondary-model application to foods was suggested. Escherichia coli, Listeria monocytogenes, Bacillus cereus, Clostridium perfringens, and Salmonella were observed in various food categories, such as meat, dairy, egg, or seafood products. The secondary model validated in this study was based on the gamma concept, in which the environmental factors temperature, pH, and water activity (a w ) were introduced as individual terms with microbe-dependent parameters, and the effect of foodstuffs on the growth rates of these species was described with a food-and microbe-dependent parameter. This food-oriented approach was carried out by challenge testing, generally at 15 and 10°C for L. monocytogenes, E. coli, B. cereus, and Salmonella and at 25 and 20°C for C. perfringens. About 222 kinetics in foods were generated. The results were compared to simulations generated by existing software, such as PMP. The bias factor was also calculated. The methodology to obtain a food-dependent parameter (fitting step) and therefore to compare results given by models with new independent data (validation step) is discussed in regard to its food safety application. The proposed methods were used within the French national program of predictive microbiology, SymPrevius, to include challenge test results in the database and to obtain predictive models designed for microbial growth in food products.Predictive microbiology has proven its value for a useful model-based description of microbial growth in foods ever since its development (18,19). Data used in building a model are usually acquired in laboratory media. However, the predictions agree more or less successfully with observations of food products (6, 36), and validation of the model proves to be necessary in such cases. Salter et al. (31) underlined the importance of good prediction for food safety, although their model was not validated in their paper. Indeed, models should be validated for prediction in the product in question, to allow for risk assessment (26). This is all the more important when creating a software application (9, 16), such as the French national program of predictive microbiology, SymЈPrevius (14). In this research program, industry, public, university, and technical center laboratories first constructed a parameter database covering 50 bacterial strains of five species grown in laboratory medium (20,21). The pathogenic bacteria selected were Escherichia coli, Listeria monocytogenes, Bacillus cereus, Clostridium perfringens, and Salmonella. This initial work led to a model describing growth rates versus temperature, pH, and water activity.The objective of this study was to develop a methodology to use these first results in foods. Thus, challenge tests were carried out, and then kinetics were analyzed to (i) obtain medium-dependent parameters and (ii) validate complete models. Since temperature is the major factor of interest in the food industry (18), the studies reported focused on that aspect. MATERIALS AND METHODSPrevio...

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Development and Validation of Experimental Protocols for Use of Cardinal Models for Prediction of Microorganism Growth in Food Products

Pinon

Zwietering

Perrier

et al. 2004

Appl Environ Microbiol

View full text Add to dashboard Cite

show abstract

“…cereus in zucchini puree) to calibrate their models and thus obtained more realistic predictions (Membre et al, 2005). Using other relevant models and depending on the impacting parameters for their product, students could determine the impact of different formulations such as concentrations of preservative such as salt (in connection with water activity), acids, nitrites, etc.…”

Section: Team Work Project: Impact Of Environmental Parameters On Gromentioning

confidence: 99%

Teaching microbiological food safety through case studies

2015

IJFS

View full text Add to dashboard Cite

Higher education students usually ask for more training based on case studies. This was addressed by designing a specific food safety module (24 hours) in which students were shown how to predict microbiological risks in food products i.e. they were asked to determine product shelf-life according to product formulation, preservation methods and consumption habits using predictive microbiology tools. Working groups of four students first identified the main microbiological hazards associated with a specific product. To perform this task, they were given several documents including guides for good hygiene practices, reviews on microbiological hazards in the food sector, flow sheets, etc. . . After three-hours of work, the working groups prepared and gave an oral presentation in front of their classmates and professors. This raised comments and discussion that allowed students to adjust their conclusions before beginning the next step of their work. This second step consisted in the evaluation of the safety risk associated with the two major microbiological hazards of the product studied, using predictive microbiology. Students then attended a general lecture on the different tools of predictive microbiology and tutorials (6 hours) that made them familiar with the modelling of bacterial growth or inactivation. They applied these tools (9 hours) to predict the shelf-life of the studied product according to various scenarios of preservation (refrigeration, water activity, concentration of salt or acid, modified atmosphere, etc. . . ) and/or consumption procedures (cooking). The module was concluded by oral presentations of each working group and included student evaluation (3 hours).

show abstract

“…Parameters T min , ε 1 and ε 2 were estimated using literature data (Griffiths and Phillips, 1990;Meer et al, 1991;Dufrenne et al, 1994;Dufrenne et al, 1995;Valero et al, 2000;Borge et al, 2001;Membré et al, 2005) and ComBase (http://wyndmoor.arserrc.gov/combase/).…”

Section: Growth Model: Growth Rate and Lag Time At Low Temperaturesmentioning

confidence: 99%

Application of Modelling Techniques in the Food Industry: Determination of Shelf-Life for Chilled Foods

Membré¹,

Johnston²,

Bassett³

et al. 2005

Acta Hortic.

View full text Add to dashboard Cite

Microbiological modelling techniques (predictive microbiology, the Bayesian Markov Chain Monte Carlo method and a probability risk assessment approach) were combined to assess the shelf-life of an in-pack heat-treated, low-acid sauce intended to be marketed under chilled conditions. From a safety perspective, the product and process design for the chilled sauce was focused on the spore forming micro-organism Bacillus cereus. Different scenarios of time/temperature profiles in the food supply chain from manufacture up to the consumer were analysed in terms of growth of B. cereus (growth rate and lag phase) and of the consequence of this on the shelf-life. The end of the shelf-life was considered to be the time at which B. cereus reaches a concentration of 10 5 cfu g -1 . For example, we have found equivalence in term of model output between scenarios in which the temperature in both retail and at the consumer's home was below 6°C for 60 days, below 8°C for 28 days, and below 10°C for 17 days. These results can be used to support decisions relating to new product design, such as maximum shelf-life, target markets and labelling. INTRODUCTIONSeveral governments and international organisations such as WHO and FAO have started developing probabilistic assessments of the risks posed by particular microorganisms in foods on the market. These assessments involve predictive microbiology and microbiological risk assessment techniques. The problems tackled are very complex, since the risks are determined by a multitude of different factors including the microorganism, the food supply chain and the consumer. Despite this complexity, the risk assessment approach has been able to provide risk managers with key information to help them decide on the necessity and suitability of risk intervention methods. Full-blown governmental risk assessments can be very resource intensive and often require advanced levels of skills and expertise, in areas such food microbiology, epidemiology and modelling. However, in specific applications (e.g. deciding on the suitability of the design of a new product before it is marketed) some form of probabilistic risk assessment approach may be helpful in an industrial context, when it can provide new relevant insights. In the current paper, this possibility is explored for a new low-acid sauce, which is to be heat-treated in pack, during manufacture and marketed in a chill chain for safety.Our risk assessment approach comprises of four steps: • Identification of the micro-organism(s) of concern (hazard identification) • Development and running of a suitable exposure model • Defining and validating the application domain of the model • Applying the results to make safety recommendations.Details regarding the models and their input values are specified below.

show abstract

Temperature effect on bacterial growth rate: quantitative microbiology approach including cardinal values and variability estimates to perform growth simulations on/in food

Cited by 106 publications

References 22 publications

Development and Validation of Experimental Protocols for Use of Cardinal Models for Prediction of Microorganism Growth in Food Products

Development and Validation of Experimental Protocols for Use of Cardinal Models for Prediction of Microorganism Growth in Food Products

Teaching microbiological food safety through case studies

Application of Modelling Techniques in the Food Industry: Determination of Shelf-Life for Chilled Foods

Contact Info

Product

Resources

About