Risk management, Risk assessment and Predictive microbiology in the meat industry. Paul Vanderlinde and Patricia Desmarchelier

Transcription

1 Risk management, Risk assessment and Predictive microbiology in the meat industry Paul Vanderlinde and Patricia Desmarchelier

2 Risk Management Codex definition The process of weighing policy alternatives considering risk assessment and other factors relevant for the health protection of consumers and for the promotion of fair trade practices, and, if needed, selecting appropriate prevention and control options Option 2 Option 1

3 Risk Management Structure Problem Risk Assessment Evaluation Managers Stakeholders Risk Actions Options Decisions

4 Risk Assessment Characteristics of a risk assessment: Should include a clear statement of purpose Comprised of four parts Hazard identification Hazard characterisation (dose response) Exposure assessment Risk characterisation

5 Risk Assessment Qualitative or Quantitative

6 Risk Assessment Qualitative Everyday risk assessments Broad categories High, medium or low Subjective

7 Risk Assessment Quantitative Costly and time consuming Typically carried out by governments or international agencies Preferred option Quantifies the magnitude of the risk Allows stages in the process to be assessed individually for: impact priority, resource allocation etc.

8 Example Risk Assessment Purpose: To estimate the risk of E. coli O157 infection from consumption of Australian frozen boxed beef Risk managers want to know: Where during production can the risk be controlled or reduced? or What are the control points and how effective are they likely to be?

9 Example: Hamburgers produced from frozen boxed beef Production Processing Post-processing Retail and cooking Consumption Cattle to boxed beef Boxed beef to fresh ground beef Ground beef to cooked hamburgers

10 Maximum Likelihood Analysis Concentration in faeces Host susceptibility Dilution factor Cooking preference Retail storage temperature Growth during chilling Time on retail display Amount consumed Prevalence in faeces Rank Correlation

11 Interventions identified On-farm reduction in pathogens Hot water decontamination Irradiation of frozen boxed beef Better control of retail chilling temperatures

12 Intervention Strategies Intervention Control variable Predicted reduction in disease On-farm (<10,000/g faeces) Decontamination Triang(1,3,4) Irradiation 1kGy Uni(1,2) Retail display (2 0 C lower) Conc n in faeces Conc n on carcase Conc n in boxed beef Growth during retail display 25% 99.7% 97% 80%

13 Risk Management Problem Evaluation Managers Stakeholders Risk Actions Options Decisions Social & economic factors

14 Risk Management Problem Evaluation Managers Stakeholders Risk Actions Options Decisions

15 Scenario: Refrigeration breakdown We know chilling is an important risk factor and control point Has this deviation in temperature control had an effect on the final health risk? Compare this new scenario with your usual good practice

16 Scenario Temperature is not at 6 0 C! dispatch.. 48h later.. re-freeze.. 72h later.. Temperature is 6 0 C but we have failed the EMO! But have we created a health risk?

17 Scenario Purpose of the RA What are the food safety risks related to product failing to reach -6 0 C in 48h? Hazard identification Human enteric pathogens Hazard characterisation Small numbers can cause disease Product is cooked before consumption

18 Scenario Exposure assessment History of the product Bacterial growth,death,unchanged? T 0 C Time

19 Scenario 11 0 C 11 0 C -1 0 C -6 0 C delivered dispatch 2h later.. 48h later.. 72h later.. Power failed after 12h, came back 12 h later Check records.. At failure C Blast not >0 0 C during power failure

20 Example Risk characterisation Enteric pathogen Low number but product cooked No growth during outage (<2 0 C) Safety not compromised

21 What is the role of predictive microbiology? Scenario: Exposure assessment Will the bacteria increase during the refrigeration breakdown? How do you determine this? Microbiological testing Predict growth based on time-temperature records

22 Predictive Microbiology Objectively measure the growth rate of bacteria under a variety of conditions Develop a model based on the observed growth rates Three types of models Primary Secondary Tertiary

23 Secondary Models Model bacterial growth under changing intrinsic and extrinsic factors Intrinsic ph Water activity Nutrients Antimicrobials Extrinsic Temperature RH Atmosphere Light intensity

24 Tertiary Models Final stage in modelling Incorporate primary and secondary models Usually software packages Pathogen Modelling Program (US) Food Micro Model (UK) Spoilage predictor (Australia)

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26 Limitations of Predictive Microbiology Not really modelling microbial behaviour Fitting observed data to mathematical equations Generally growth is slower in food systems than predicted using the models ( fail safe )

27 Summary Sometimes things will go wrong You can minimise the consequences by keeping good records and by knowing your process Predictive microbiology can provide a cost effective measure of the relative safety of the product