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Use of Predictive Microbiology Information Portal,
the USDA-Pathogen Modeling Program and
ComBase
Vijay K. Juneja, Ph.D.
USDA-ARS-ERRC
600 East Mermaid Lane
Wyndmoor, Pennsylvania 19038
Phone: 215-233-6500
Fax: 215-233-6697
e-mail: [email protected]
Predictive Microbiology Information Portal
http://portal.arserrc.gov
Regulations Models Useful Links
•Final Rule on Listeria monocytogenes in RTE Meat and Poultry Products
•“Zero Tolerance” Policy
Pathogen Modeling Program (PMP) 7.0
The PMP is a repository of models that estimate the behavior of bacterial pathogens in specific environments. Through a user interface, information is provided about the effects of environmental factors on: - growth - inactivation (thermal and non-thermal) - toxin production
PMP
• The PMP 7.0 currently contains: - 40 models
- 15 food and 25 broth models
- static and dynamic temperature models
• Used by ~50% of FSIS-inspected companies
• >7,000 downloads per year
ARS Pathogen Modeling Program 7.0
• Types of Models (Total 40)
– Growth: 23
– Heat Inactivation: 3
– Survival: 4
– Cooling: 4
– Irradiation: 6
• www.ars.usda.gov/naa/errc/mfsru/pmp
Growth Models
• Pathogen Growth Models (Aerobic)
– Aeromonas hydrophila
– Bacillus cereus
– E. coli O157:H7
– Listeria monocytogenes
– Salmonella spp.
– Salmonella typhimurium
– Shigella flexneri
– Staphylococcus aureus
– Yersinia enterocolitica
Growth Models
• Pathogen Growth Models (Anaerobic)
– Aeromonas hydrophila
– Bacillus cereus
– Clostridium perfringens
– Clostridium botulinum
– E. coli O157:H7
– Listeria monocytogenes
– Shigella flexneri
– Staphylococcus aureus
-- Web-based database for food microbiology data consisting of thousands of
microbial growth and survival models
-- Major international initiative to coordinate collection and dissemination of food
microbiology data – ERRC and Institute of Food Research
Predictive Models For Foodborne
Pathogens (Applications)
Combase Database Browser
The ComBase Database consists of thousands of microbial growth
and survival curves that have been collated in research
establishments and from publications.
3 Ways to Access the Combase Browser:
• http:combase.arserrc.gov
• www.combase.cc
• www.arserrc.gov, Click on PMIP on the left
ComBase Predictive Models
•Combase Predictor: A set of 23 growth models and 6
thermal death models for predicting the response of many
important food pathogenic and spoilage microorganisms to
key environmental factors.
•Perfringens Predictor: An application for predicting the
growth of Clostridium perfringens during the cooling of
meats.
Federal Regulation
USDA Food Safety and Inspection Services Guideline
130 – 80 F (54.4 – 26.6 C) ≤ 1.5 h
80 – 40 F (26.6 – 4.4 C) ≤ 5 h
In the event of process deviation or temperature abuse, manufacturers
must prove that growth of C. perfringens < 1 log
Cooling/Growth Models
• Scenario #1:
• Cooked roast beef plant has a cooling Critical Control Point (CCP) Confidence Limit:
• Product’s internal temperature –
– between 130 F and 80 F for not more than 1.5 and then
between 80 F and 40 F for not more than 5 hours.
• For this cooling deviation, the product cooled
– between 130 F and 80 F in 2 hours and then
between 80 F and 40 F in another 6 hours
Cooling / Growth Models
-Clostridium perfringens – Cooling Cured Beef -Clostridium perfringens in Beef Broth -Proteolytic Clostridium botulinum in Beef Broth -Clostridium perfringens – Cooling Cured Chicken
Cooling/Growth Models
• Scenario #1 - Results from PMP 7.0
– Clostridium perfringens
• Mean Net Growth = 0.18
• LCL Net Growth = 0.12
• UCL Net Growth = 0.25
– Clostridium botulinum
• Mean Net Growth = 0.00
• LCL Net Growth = - 0.01
• UCL Net Growth = 0.01
Cooling/Growth Models
• Scenario #1 - Product Disposition
– Product would be released without any further action because:
• The UCL net growth for Clostridium perfringens is 0.25 which meets the Agency performance standard/policy of no more than 1.0 log increase for the pathogen; and
• The UCL net growth for Clostridium botulinum is 0.01 which is not more a 0.3 log increase indicating there was no multiplication of the pathogen thus meeting the Agency performance standard/policy