József Baranyi IAFP’s fith European Symposium on Food Safety, Berlin, 7 th October 2009

1. ComBase WorkshopIntroduction. Objectives of the meeting.Introduction to Predictive microbiology. The ComBase story. József Baranyi IAFP’s fith European Symposium on Food Safety, Berlin, 7th October 2009

2. Outline of Presentation 1 • Introducing the presenter and its organisation • Introduction to predictive microbiology • The ComBase story

3. … ranked second in the world and first in EU for the impact of its research in agricultural and food sciences (Inst. Sci. Inform. 2008; see http://www.timeshighereducation.co.uk/story.asp?storycode=404872); … the UK’s only integrated basic science provider focused on food; … a not-for-profit company with charitable status; an Institute of the Biotechnology & Biological Sciences Research Council. ... output feeds into national and international strategies, delivering advice and solutions for UK Government, public sector bodies, regulatory authorities, industry and consumers Institute of Food Research www.ifr.ac.ukA world-leading contributor to harnessing food for health and controlling food-related disease

4. - Allows industry access to the knowledge transfer activities of the Institute of Food Research - Coordinates training Institute of Food ResearchFood and Health Networkwww.foodandhealthnetwork.com • lesley.swift@bbsrc.ac.uk • Tel: +44 (0)1603 255082 • Fax: +44 (0)1603 507723 • enquiries@foodandhealthnetwork.com

5. Part 1Introduction to predictive microbiology

6. Introduction to Predictive MicrobiologyQuantitative Microbial Ecology of Food • Food environment  Microbial response • Differs from biotechnology: • Lower and broader range of cell concentration • Aim: prevent bacterial growth, not optimise • More inaccurate data in a less controlled environment

7. Progress based on two pillars Predictive microbiology Biomathematics (mathematical models) Bioinformatics (database)

8. Observed growth curves of Listeria monocytogenes in laboratory medium 8 15 oC 6 10 oC 4 log cell conc. (log cfu/ml) 5 oC 2 0 0 100 200 300 400 500 time (h)

9. 12 oC 8 oC Growth predicted bymulti-step mathematical interpolation 10 8 10 oC 15 oC 6 conc. (log10 cfu/ml) 4 5 oC 2 0 0 50 100 150 200 time (h)

10. After a lag period, cells grow exponentially Log cell.no Lag period is affected by the cells’ history! inoc. time lag

11. History-dependence of lag 10 log cells/ml 5 0 0 50 100 time(h) Growth of Listeria monocytogenes in broth, at 15 oC, after different subculturing procedures. The max.spec. growth rates are the same, but the lag times are different.

12. Growth phases of a batch culture traditionally divided into ... - adjustment (lag) - exponential - stationary phases spec.rate max. population stationary phase Ln cell.no exponential phase Sigmoid shape adjustment inoc. lag time

13. Observed, fitted and predicted log conc. of salmonellae at different pH and temperature values Error of sampling 1 < ~ 0.5logcfu Rel.error of fitted specific rate/ < ~ 5% Error of predicted specific rate /< ~ 30 % Data in www.combase.cc Gibson et al. (1988). Int. J. Food Microbiol.

14. Response surface fitted to the logarithm of observed growth rates Ln μ (1/h) Fitting a quadratic multivariate polynomial. Data: Salmonellae at 15oC (Gibson et al, 1988) NaCl (%) pH

15. Part 2The ComBase story

16. ComBase : A FREE internet tool to predict microbial responses to food environments The world’s No.1 database for predictive microbiology www.combase.cc

17. EU support: 2yrs Accompanying Measures project to populate ComBase by data from European Supporting Partners; 2004-5. Australian Food Safety Centre joined the initiative, 2006. ComBase Consortium to set up a database on microbial responses to food environments, 2003 US Department of Agriculture, Agricultural Research Service FSA, UK Institute of Food Research, Norwich, UK Eastern Regional Research Center Wyndmoor, PA, USA www.combase.cc

18. Collect the observations in a systematically organised database Organism, data source, material & methods, temperature, pH, water availability, atmosphere composition, preservatives, observation time, specific rate, logcounts, comments

19. Lab-books Spreadsheets From data to ComBase: Checking, modelling Easy programming Familiar interface Central relational database Browser, combined with predictions Big capacity, fast data retrieve

20. Users of ComBase Tools(telling from email addresses)

21. ComBase invited seminars and workshops "ComBase can be a watershed in the development of Predictive Microbiology and its applications" Tom McMeekin, Director of Australian Food Safety Centre Norwich London Quimper Budapest Brescia Philadelphia Pamplona Bologna Valencia Madrid Slovenia Denver Washington Crete Athens New Orleans Monterrey Queretaro Bangkok Bogotá Kuala Lumpur "ComBase is an exemplar of the way that governments and the research community can successfully work together to help improve the safety of food products“ Jon Bell, CEO, Food Standards Agency Sydney Melbourne Hobart

22. www.combase.cc Choose ComBase Browser to find records satisfying constraints on: - Food; - Organism; - Environmental conditions; (temp; pH, aw...)

23. Set up query www.combase.cc

24. Display selected records www.combase.cc

25. Link to a curve fitting module www.combase.cc

26. Compare observations with results from ComBase Predictor www.combase.cc

27. Browsing among 40,000 observed growth / survival curves

28. ComBase offers a depository of raw data and the use of predictive software tools built on those data • Browse observed data (currently ca 40,000 curves); • Get predictions and compare with observations; • - Offers training via downloadable demos; • - Specific (food + bacteria + environment) scenarios; • - Training.