A retail and consumer phase model for exposure assessment of Bacillus cereus.

An exposure assessment is conducted for psychrotrophic and mesophilic Bacillus cereus in a cooked chilled vegetable product. A model is constructed that covers the retail and consumer phase of the food pathway, using the output of a similar model on the industrial process as input. Microbial growth is the predominant process in the model. Variability in time and temperature during transport and storage is included in the model and different domestic refrigerator temperature distributions are compared. As an end point, probable levels of B. cereus colony forming units (cfu) in packages of vegetable purée are predicted at the moment the consumer takes the product from its refrigerator, that is prior to a cooking process. The psychrotrophic strain is predicted to end up above a threshold level of 10(5) cfu/g in 0.9% to 6.3% of the vegetable purée packages, depending on domestic refrigerator temperature. Accounting for spoilage this reduces to 0.3% to 2.4%. Even if the purée is stored at 4 degrees C in the domestic refrigerator and use-by-date (UBD) is respected, the threshold level may be passed. For the mesophilic strain the threshold level is rarely passed, but in contrast to the total viable count, the spore load at the end point is predicted to be higher than in the psychrotrophic strain. Our study illustrates how an exposure assessment model, which may be used in quantitative risk assessment, can integrate expertise in modelling, food processing and microbiology over the food pathway, and thus evaluate food safety, identify gaps in knowledge and compare risk management measures. As important gaps in knowledge, the lack of sporulation and germination models and data, validated non-isothermal growth models and a spoilage model useful for risk assessment are identified. Knowledge of the dose-response relationship is limited and does not allow a full risk assessment. It is shown that exposure can be lowered by lowering domestic refrigerator temperatures, and less so much by monitoring and withdrawing contaminated products at the end of industrial processing.

[1]  Baranyi Comparison of Stochastic and Deterministic Concepts of Bacterial Lag. , 1998, Journal of theoretical biology.

[2]  R. Moezelaar,et al.  Sensitivities of Germinating Spores and Carvacrol-Adapted Vegetative Cells and Spores of Bacillus cereus to Nisin and Pulsed-Electric-Field Treatment , 2001, Applied and Environmental Microbiology.

[3]  M. Zwietering,et al.  Modelling Bacterial Growth of Lactobacillus curvatus as a Function of Acidity and Temperature , 1995, Applied and environmental microbiology.

[4]  Pablo S. Fernández,et al.  Application of nonlinear regression analysis to the estimation of kinetic parameters for two enterotoxigenic strains ofBacillus cereus spores , 1999 .

[5]  J Baranyi,et al.  A dynamic approach to predicting bacterial growth in food. , 1994, International journal of food microbiology.

[6]  Evers Eg,et al.  Risk assessment of Shiga-toxin producing Escherichiacoli O157 in steak tartare in the Netherlands , 2001 .

[7]  C. Nguyen-the,et al.  Spore-forming bacteria in commercial cooked, pasteurised and chilled vegetable purées , 2000 .

[8]  L. Gram,et al.  Micro-Organisms in Foods 6 , 2005 .

[9]  R. C. Whiting,et al.  Microbial modeling in foods. , 1995, Critical reviews in food science and nutrition.

[10]  J. McKillip Prevalence and expression of enterotoxins in Bacillus cereus and other Bacillus spp., a literature review , 2000, Antonie van Leeuwenhoek.

[11]  J Baranyi,et al.  Mathematics of predictive food microbiology. , 1995, International journal of food microbiology.

[12]  R. Beumer,et al.  Characteristics of some psychrotrophic Bacillus cereus isolates. , 1995, International journal of food microbiology.

[13]  D. Vose Risk Analysis: A Quantitative Guide , 2000 .

[14]  K Koutsoumanis,et al.  Development and assessment of an intelligent shelf life decision system for quality optimization of the food chill chain. , 2001, Journal of food protection.

[15]  P. E. Granum,et al.  A new cytotoxin from Bacillus cereus that may cause necrotic enteritis , 2000, Molecular microbiology.

[16]  K. Morgan,et al.  Food safety knowledge and practice among elderly people living at home. , 1998, Journal of epidemiology and community health.

[17]  C. Gill,et al.  Application of a Temperature Function Integration Technique to Assess the Hygienic Adequacy of a Process for Spray Chilling Beef Carcasses. , 1991, Journal of food protection.

[18]  M H Zwietering,et al.  Application of predictive microbiology to estimate the number of Bacillus cereus in pasteurised milk at the point of consumption. , 1996, International journal of food microbiology.

[19]  Finn Verner Jensen,et al.  Introduction to Bayesian Networks , 2008, Innovations in Bayesian Networks.

[20]  平田 文範,et al.  Clostridium botulinum , 2022, CABI Compendium.

[21]  M. H. Zwietering,et al.  Evaluation of Data Transformations and Validation of a Model for the Effect of Temperature on Bacterial Growth , 1994, Applied and environmental microbiology.

[22]  P. Taoukis,et al.  Applicability of Time‐Temperature Indicators as Shelf Life Monitors of Food Products , 1989 .

[23]  Summary Risk assessment of food borne bacterial pathogens: Quantitative methodology relevant for human exposure assessment , 2003 .

[24]  K. Johnson,et al.  Germination and Heat Resistance of Bacillus cereus Spores from Strains Associated with Diarrheal and Emetic Food‐Borne Illnesses , 1982 .

[25]  Judith Evans,et al.  Consumer Handling of Chilled Foods: A survey of time and temperature conditions , 1991 .

[26]  C. Genigeorgis,et al.  Temperature distribution and prevalence of Listeria spp. in domestic, retail and industrial refrigerators in Greece. , 1997, International journal of food microbiology.

[27]  K Koutsoumanis,et al.  Application of a systematic experimental procedure to develop a microbial model for rapid fish shelf life predictions. , 2000, International journal of food microbiology.

[28]  K Koutsoumanis,et al.  Use of time-temperature integrators and predictive modelling for shelf life control of chilled fish under dynamic storage conditions. , 1999, International journal of food microbiology.

[29]  M. Nauta,et al.  Research on factors allowing a risk assessment of spore-forming pathogenic bacteria in cooked chilled foods containing vegetables: a FAIR collaborative project. , 2000, International journal of food microbiology.

[30]  M H Cassin,et al.  Quantitative risk assessment for Escherichia coli O157:H7 in ground beef hamburgers. , 1998, International journal of food microbiology.

[31]  R. R. Beumer,et al.  A risk assessment study of Bacillus cereus present in pasteurized milk. , 1997 .

[32]  J Olley,et al.  Relationship between temperature and growth rate of bacterial cultures , 1982, Journal of bacteriology.

[33]  József Baranyi,et al.  A non-autonomous differential equation to model bacterial growth. , 1993 .

[34]  M. G. Smith The generation time, lag time, and minimum temperature of growth of coliform organisms on meat, and the implications for codes of practice in abattoirs , 1985, Journal of Hygiene.

[35]  Maarten J Nauta,et al.  Modelling bacterial growth in quantitative microbiological risk assessment: is it possible? , 2002, International journal of food microbiology.

[36]  P. Fernández,et al.  Growth of Bacillus cereus in natural and acidified carrot substrates over the temperature range 5–30°C , 2000 .

[37]  P. E. Granum,et al.  Prevalence, characterization and growth of Bacillus cereus in commercial cooked chilled foods containing vegetables , 2000, Journal of applied microbiology.

[38]  Mgb,et al.  A modular process risk model structure for quantitative microbiological risk assessment and its application in an exposure assessment of Bacillus cereus in a REPFED , 2001 .