1. Predictive microbiology Tom Ross Food Safety Centre, University of Tasmania and International Commission on Microbiological Specifications for Foods

2. Seafast Symposium, Bogor, December, 2009 A question… if I left a piece of chicken at 10°C for 6 hours, would that allow Salmonella to grow to dangerous levels? would the shelf life be greatly reduced?

3. Seafast Symposium, Bogor, December, 2009 ‘new’ microbial food safety management science-based ‘farm-to-fork’ relies on being able to estimate changes in numbers of pathogens from farm-to-fork, i.e. is quantitative

4. Seafast Symposium, Bogor, December, 2009 The “ICMSF Equation” Initial Contamination level less the sum of reductions (e.g. dilution, inactivation) plus the sum of increases (e.g. recontamination, growth) should not exceed the Performance Objective (or Food Safety Objective) H o - ∑R + ∑I ≤ PO / FSO

5. Seafast Symposium, Bogor, December, 2009 HACCP, complemented by GMP, is almost universally endorsed as the most rational approach to the production of safe food Sooner or later, if you do HACCP properly, you end up asking some hard questions... HACCP

6. Seafast Symposium, Bogor, December, 2009 HACCP: setting critical limits How much control is needed? e.g. what are: –the critical times and temperatures of processes or steps –appropriate product formulations for desired safety (and shelf life) –storage and packaging needs that are required to achieve control? … and, if control is lost –how much did the risk increase? –could control be ‘regained’ and, if so, –how much reprocessing/storage would be required to return quality/safety to an acceptable level?

7. Seafast Symposium, Bogor, December, 2009 managing microbial food safety and quality effects of microorganisms are related to their number –risk of illness increases with number of pathogens ingested –quality decreases as number of spoilage organisms increases we need to know about numbers of microorganisms in the food, and how they change over time

8. Seafast Symposium, Bogor, December, 2009 microbial ecology of foods microbes in foods can: grow, survive, die but these processes are not instantaneous and the amount of growth or death, or whether survival occurs, depend on: food composition and additives, other microbes in the food, processing steps, storage conditions, etc. and time

9. Seafast Symposium, Bogor, December, 2009 microbial ecology of foods is predictable collectively, responses to these factors constitute the ecology of the microorganism in the food the interactions and effects can be complex but are predictable, and can be described and quantified this is the domain of predictive microbiology

10. Overview what is predictive microbiology? what can it do? what can’t it do ? where are the resources? how is it being used?

11. Seafast Symposium, Bogor, December, 2009 Predictive Microbiology - concepts microorganisms react reproducibly to environmental conditions –the fundamental premise is that microorganisms can’t think, so that they behave reproducibly (or “predictably”) in ways dictated by their environment. thus –if we can measure their environment, we can predict what they will do and how quickly they will do it.

12. Seafast Symposium, Bogor, December, 2009 Predictive Microbiology - concepts in foods, there is a small number of environmental factors that determine microbial growth rate, namely: –temperature –pH –water activity for some foods this works, but for processed foods its probably an oversimplification, so –other factors sometimes need to be considered: e.g. organic acid type and level, nitrite, gaseous atmosphere, smoke compounds, other microbes in the food

13. Seafast Symposium, Bogor, December, 2009 Predictive Microbiology - concepts i.e., it is assumed that the actual food is less important than the physico-chemical properties of the environment (i.e. the food and its storage conditions), so long as basic (microbial) nutritional needs are met and nutrients are non-limiting

14. Seafast Symposium, Bogor, December, 2009 Predictive Microbiology - concepts its also assumed that death rate is affected by physicochemical conditions in the food, but normally death rate is most strongly governed by the treatment, e.g., high temperature pressure irradiation (UV, gamma etc.) electric field strength

15. Building predictive models

16. Seafast Symposium, Bogor, December, 2009 How predictive models are made based on measurements of changes in microbial numbers over time and environmental conditions data can be from –deliberately designed studies –“data mining” –studies in broths, or in foods

17. Seafast Symposium, Bogor, December, 2009 How models are made data are analysed and patterns of response are identified these are expressed in the form of mathematical relationships the relationships are turned into equations by finding the best values of the parameters to describe individual sets of data, i.e specific to a particular organism - this is the process of ‘model fitting’ performance of the model is then evaluated and, if necessary, model revised or new models constructed equations are incorporated into ‘user-friendly’ software

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19. Uses and limitations

20. Seafast Symposium, Bogor, December, 2009 What can we ‘predict’ amount of microbial growth after time, (from temperature and product formulation; includes lag time, growth rate) reduction in microbial numbers over time, from knowledge of treatment conditions and product formulations (includes delay, death rate) probability of growth/toxin production –stability of foods (absolute or within defined time)

21. Seafast Symposium, Bogor, December, 2009 What is modelled? growth rates –bacteria –yeasts and moulds inactivation (death) rates –bacteria –yeasts and moulds –viruses –protozoa –microbial toxins? probability of growth/toxin formation –bacteria –yeasts and moulds –micro-algae*

22. Seafast Symposium, Bogor, December, 2009 Example of model performance: E. coli growth under fluctuating temperature and water activity

23. Seafast Symposium, Bogor, December, 2009 uses of predictive microbiology models “reactive” (assessing what we have) –identifying CCPs (in Food Safety Programs) –assessment of food safety implications of a loss of “control” –assessment of equivalence of processes –risk assessment/risk management decisions “pro-active” (identifying what we could do…) –product and process design to meet objectives (e.g. current consumer expectations with safety) –i.e. “innovation”

24. Seafast Symposium, Bogor, December, 2009 limitations (i) models –don’t tell us whether the pathogen is present –don’t tell us how many there were to start with

25. Seafast Symposium, Bogor, December, 2009 limitations (ii) models: often don’t indicate level of confidence that users should have in the prediction, (or the range of variability that could be expected) don’t usually indicate the limits of their application

26. Seafast Symposium, Bogor, December, 2009 reasons for limitations: variability/system complexity don’t know which strain –significant differences between some strains of some pathogens don’t always really know the environment –micro-environments can exist around the cell i.e. a problem of not always having enough relevant data to make an accurate prediction nonetheless, in many situations appropriate models can perform very well

27. Seafast Symposium, Bogor, December, 2009 where models work well defined, controlled systems with few variables predicting the relative effects of change

28. Seafast Symposium, Bogor, December, 2009 “state of the art” used by industry –HACCP, product/process design beginning to be used by regulators e.g., “Refrigeration Index” in Australia often a key part of microbial food safety risk assessment several large internet-accessible databases and tools –e.g. Pathogen Modelling Program, ComBase, Symprevius, SSSP

29. Predictive microbiology resources

30. Seafast Symposium, Bogor, December, 2009 Roberts and Jarvis (1983) in proposing the concept of predictive microbiology, advocated a more systematic and cooperative approach to food safety microbiology within which: ‘the growth responses of the microbes of concern would be modelled with respect to the main controlling factors such as temperature, pH and a w ’ to generate models that would “enable predictions of quality and safety to be made speedily with considerable financial benefit.”

31. Seafast Symposium, Bogor, December, 2009 a model

32. Seafast Symposium, Bogor, December, 2009 models and databases: on-line ComBase (database) (http://www.combase.cc) ComBase Predictor (models) (http://www.combase.cc) Pathogen Modeling Program (on-line) (http://pmp.arserrc.gov/PMPOnline.aspx)) Seafood Spoilage Predictor (http://www.dfu.min.dk/micro/sssp/Home/Home.aspx) Refrigeration Index (ijenson@mla.com.au; http://www.mla.com.au)

33. Seafast Symposium, Bogor, December, 2009 ComBase www.combase.cc large, searchable, database of microbiological raw data still growing, users can add data web-based, free access integrates “Food Micromodel” and “Pathogen Modeling Program” data, and many more includes new models in “ComBase Predictor”

34. Seafast Symposium, Bogor, December, 2009 Data in ComBase >40,000 records on growth and survival of pathogens and spoilage organisms –~28,000 records on pathogens –~4,000 on spoilage organisms, including –‘total spoilage bacteria’ (346) –‘bacillus spoilage bacteria’ (65) –Brocothrix thermosphacta (741) –enterobacteriaceae (338) –lactic acid bacteria (701) –Shewenella putrefasciens (57) –“spoilage yeast” (44) –~22,000 full log-count curves –~10,000 growth/death rates

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40. FREE DOWNLOAD: http://portal.arserrc.gov/ Pathogen Modeling Program

41. Seafast Symposium, Bogor, December, 2009 Pathogen Modeling Program pmp.arserrc.gov/PMPOnline.aspx USDA program can also be downloaded suite of models for –various pathogen –growth –death by various treatments part of the predictive microbiology information portal - an on-line predictive microbiology resource: portal.arserrc.gov/

45. Seafast Symposium, Bogor, December, 2009 seafood spoilage and safety predictor www.dfu.min.dk/micro/sssp/ predicts growth of bacteria in different fresh and lightly preserved seafoods allows prediction of: –rates of spoilage of seafood –shelf life of various seafoods –effect of fluctuating conditions –simultaneous growth of Listeria monocytogenes (a pathogen) and spoilage bacteria in cold-smoked salmon

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48. “refrigeration index” www.mla.com.au * Australian product with regulatory approval for use under Australian Export Meat Orders predicts growth of E. coli (as an indicator of safe temperature control) from continuous temperature history using the idea of time-temperature function integration * before downloading please contact Mr. Ian Jenson ijenson@mla.com.au

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53. ‘home made’ software there are many more models in the published literature relatively easy to translate these into user-friendly software tools using spreadsheet software

54. Seafast Symposium, Bogor, December, 2009 summary best microbial food safety/quality systems rely on knowledge of microbial ecology, not testing predictive microbiology models provide condensed, quantitative, expert knowledge models provide ‘decision support’ for many practical problems/questions and/or an alternative/adjunct to microbiological testing predictive microbiology models are now being used by industry and regulators to improve productivity and food safety

55. Seafast Symposium, Bogor, December, 2009 Summary correct use of models requires microbiology understanding and basic mathematical skills but that knowledge is critical to appropriate application users should be aware of the current limits of models –both in terms of range of application and confidence intervals on model predictions

56. Seafast Symposium, Bogor, December, 2009 thank you for your attention, and for your questions and comments