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: vijay.juneja@ars.usda.gov

“PMIP”

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.

Practical Scenario -- Cooling Deviation

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

DO YOU HAVE

ANY

QUESTIONS?

THANK YOU!