predictive microbiology tom ross food safety centre, university of tasmania and international...

Predictive microbiology

Tom Ross

Food Safety Centre, University of Tasmania and

International Commission on Microbiological Specifications for Foods

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?


‘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


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)

Ho - ∑R + ∑I ≤ PO / FSO


HACCP, complemented by GMP, is almost universally HACCP, complemented by GMP, is almost universally

endorsed as the most rational approach to the endorsed as the most rational approach to the

production of safe foodproduction of safe food

Sooner or later, if you do HACCP properly, you end up Sooner or later, if you do HACCP properly, you end up

asking some hard questions . . . asking some hard questions . . .

HACCP


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?


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


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


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

Overview

• what is predictive microbiology?

• what can it do?

• what can’t it do ?

• where are the resources?

• how is it being used?


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.



• 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



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



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

Building predictive models


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


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

Uses and limitations


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)


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*


Example of model performance: E. coli growth under fluctuating temperature and water activity

2.0

3.0

4.0

5.0

6.0

7.0

8.0

0 5 10 15 20 25 30 35

Time (hours)

E. coli

[LogCFU.ml

-

0

5

10

15

20

25

30

35

40

Temperature [°C]

Observed E. coli GrowthPredicted E. coli GrowthawTemperature Profile


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”


limitations (i)

models

– don’t tell us whether the pathogen is present

– don’t tell us how many there were to start with


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


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


where models work well

• defined, controlled systems with few variables

• predicting the relative effects of change


“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

Predictive microbiology resources


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 aw’ to generate models that would “enable

predictions of quality and safety to be made speedily with

considerable financial benefit.”


a model


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([email protected]; http://www.mla.com.au)

http://pmp.arserrc.gov/PMPOnline.aspx


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”


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

Seafast Symposium, Bogor, December, 2009FREE DOWNLOAD: http://portal.arserrc.gov/

Pathogen Modeling Program


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/


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


“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

[email protected]

mailto:[email protected]


‘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


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


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


thank you for your attention,

and for your questions and comments

predictive microbiology tom ross food safety centre, university of tasmania and international...

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