Concia2012_Cristina Miranda Silva

OBTENTION AND DATA MANAGEMENT IN PREDICTIVE MICROBIOLOGY MODELS

Cristina L.M. Silva

5th September 2012

CBQF – Centro de Biotecnologia e Química Fina,Escola Superior de Biotecnologia,Centro Regional do Porto da Universidade Católica Portuguesa

OUTLINE Objectives of food industry The challenge Predictive microbiology How to obtain the data Available software Validation studies Acknowledgement by regulation The complexity of dynamic conditions Conclusions

OUTLINE Objectives of food industry The challenge Predictive microbiology How to obtain the data Available software Validation studies Acknowledgement by regulation The complexity of dynamic conditions Conclusions

Objectives of food industry

Prediction of shelf life

Preservation of foods

Control/monitor the growth of microorganisms

Predict microbial death

6

Product Design

Primary Production

IndustrialProduction

Product

TransportLogistics

Trade

Home Storage

Consumption

Disposal

Distribution chain

7

Product Design

Primary Production

IndustrialProduction

Product

TransportLogistics

Trade

Home Storage

Consumption

Disposal

Distribution chain

OUTLINE Objectives of food industry The challenge Predictive microbiology How to obtain the data Available software Validation studies Acknowledgement by regulation The complexity of dynamic conditions Conclusions

The challenge Microorganisms response depends on:

- Intrinsic factors

- Extrinsic factors

- System dynamics

pHaw

others

TpHhumidity saltgas concentrationothers

• microbial interaction

• natural strains diversity

• history of initial population

• complexity of food structure

• interaction food/microorganism

• predictions in real and varying environmental conditions

OUTLINE Objectives of food industry The challenge Predictive microbiology How to obtain the data Available software Validation studies Acknowledgement by regulation The complexity of dynamic conditions Conclusions

Predictive microbiology

The use of mathematical models in the description of

microbial responses to environmental stressing factors

Thermal lethality - D and z values – Bigelow model

F dtcT T ztpc refm m 10

0

( )/

The idea is not even recent!

mathematics

microbiology

statistics

predictivemicrobiology

Model mathematical expression

i=1,2,...,n (number of experimental runs/observations)

j=1,2,...,vk=1,2,...,p

yi = f(xij, k) + i

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 1000 2000 3000 4000 5000

x

y

ymodel

yexp

q*Precise ? Accurate ?

Minimize differences

precise and accurate description of observations

model adequacy

quality of model parameters

objective

knowledge of the process

process effects on product

control of process variables

advantages

Processes

ChemicalPhysical

Food Processes

Transport Phenomena• heat• mass• momentum

Reaction kinetics

Properties

Modelling

mathematical function

variablesparameters

data

regression schemes

experimental design

design Criterium

validation

control

optimization Objectives

quality safety

prediction / simulation

development of efficient processes

contribution to safety

aplication

sigmoidal behaviour

presence of aggregated microorganisms or sub populations

more heat (or other stress factor) resistent

inactivation

0

1

2

3

4

5

6

7

8

9

0 500 1000 1500 2000

time(s)

log

N

0

1

2

3

4

5

6

7

8

0 20 40 60 80 100 120 140

0

1

2

3

4

5

6

7

8

0 20 40 60 80 100 120 140

Miller (2004)

52.5 ºC

Listeria innocualiquid medium

55 ºC57.5 ºC

60 ºC

62.5 ºC

65 ºC

log N

time (min)

0

1

2

3

4

5

6

7

8

0 20 40 60 80 100 120 140

52.5 ºC55 ºC57.5 ºC

60 ºC

62.5 ºC

65 ºC

log N

time (min)

Miller (2004)

Listeria innocualiquid medium

primary

0

1

2

3

4

5

6

7

8

0 0.5 1 1.5 2 2.5Time (min)

Lo

g C

FU

/g

kinetics

parameters

Types of models

primary

secondary

0

1

2

3

4

5

6

7

8

0 0.5 1 1.5 2 2.5Time (min)

Lo

g C

FU

/g

parameters

pH aw

temperature

primary

secondary

terciary - integration of the previous models - software

0

1

2

3

4

5

6

7

8

0 0,5 1 1,5 2 2,5Time (min)

Lo

g C

FU

/g

parameters

pH aw

temperature

primary

0

1

2

3

4

5

6

7

8

9

0 500 1000 1500 2000

logN

k

logN0

logNres

L

Time (s)

empirical fundamental

N0 number of initial viable spore cellsNres number of residual spore cellsk maximum inactivation rate L lag or shoulder

Inactivation models

primary

First order ktexpNN 0 Dt

NloglogN 0

tkexpF1tkexpFNN

21110

Cerf (1977)

Kamau et al. (1990)

tkexp1F12

tkexp1F2

logNN

log2

1

1

1

0

D – decimal reduction time

F1 – fraction of inactivated microorganismsk1 e k2– kinetic constants biphasic

0

1

2

3

4

5

6

7

8

0 2 4 6 8Time (min)

Lo

gC

FU/g

0

1

2

3

4

5

6

7

8

0 1 2 3 4 5 6

Time (min)

Lo

g C

FU/m

l

primary

Cole et al. (1993)

σwtlogλσ4

exp1

αwαNlog

0

1

2

3

4

5

6

7

8

0 0.5 1 1.5 2 2.5Time (min)

Lo

g C

FU/g

Ltkexp1Lkexp1F1

Ltkexp1Lkexp1F

logNN

log2

21

1

11

0

Whiting & Buchanan

(1992)

distribution of heat sensibility of

microbial populations

L – lag or shoulder

primary

Baranyi et al.(1993)

0N0tN

NttkdtdN

‘lag’ function

‘tail’ function

0

1

2

3

4

5

6

7

8

9

0 500 1000 1500 2000

tempo (s)

log

N

primary

Baranyi et al.(1993)

0N0tN

NttkdtdN

‘lag’ function

‘tail’ function

0

1

2

3

4

5

6

7

8

9

0 500 1000 1500 2000

tempo (s)

log

N

tkexp0Q1

0Q1tkexplog

NN

logmax

max0

Geeraerd et al.(2000)

QkdtdQ

NQkkdtdN

max

Qmax

Q – variable related to the physiological state of the cells

primary

Gompertz

0

1

2

3

4

5

6

7

8

9

0 500 1000 1500 2000

tempo (s)

log

N

Bhaduri et al (1991)Linton et al. (1995, 1996)Xiong et al. (1999)

1tL

NN

log

ek expexp

NN

loglogNlogN

res

0res

00

Logistic

Listeria monocytogenes

L-tkexp1c

logN

c – constant

reparameterized for inactivation based in Zwitering (1990)

secondary

Arrhenius

Davey / Arrhenius modified

“Square-root type models”

Ratkowsky et al. (1982)

McMeekin et al. (1987)

Adams et al. (1991)

McMeekin et al. (1992)

)Tb(Tk min

)a(a)Tb(Tk minwwmin

)pH(pH)Tb(Tk minmin

RTE

-expkk a0 RT

Elnklnk a

0

2

W4W3221

0 aCaCT

CTC

Clnk

)pH(pH)a(a)Tb(Tk minminwwmin

min – minimal value for growth

ref

aref T

1T1

RE

-expkk

tertiary

softwares

Microbial growth

Shelf life prediction

Microbial inactivation

Difficulties in food processes modelling:

Dynamic processes

Complexity and heterogeneity of products

Structural and physicochemical changes

Gompertz

)time(d

Nlogd

'dt1'tL

NN

log

)1exp(kexpexp1'tL

NN

log

)1exp(kexp)1exp(kNlogNlog

t

0res

0

res

00

dynamic situation of temperature

ref

aref T

1T1

RE

-expkk

refT1

T1

bexpaL

Gill (2011)

Linear

)time(d

Nlogd

dynamic situation of temperature

refTTz1

ref 10DD

PT

0

ZrefTT

refTdt10

D1

0

10NN

approach by Vieira et al. (2002)Cupuaçu nectar

different temperature histories

Case studies:

1tL

NN

log

ekexpexp

N

NloglogNlogN

res

0

max

res

00

Square-root

dTckmax

Arrhenius

ref

arefmax T

1T1

RE

-expkk

Williams-Landel-Ferry

min

min

T-Tb

T-Ta

10L

Arrhenius

refT1

T1

bexpaL

c, dconstants

Kref reaction rate at Tref

Ea activation energy

a, bconstants

1

2

4

3

Gill (2011)

log N

time (s)

0

1

2

3

4

5

6

7

8

9

0 1000 2000 3000 4000 5000 6000 7000

T=52ºC

T=56ºC

logN

time (s)

0

1

2

3

4

5

6

7

8

9

0 100 200

T=60ºCT=64ºC

T=68ºC

0

500

1000

1500

2000

2500

3000

320 325 330 335 340 345

T (K)

L (s

)

0

0,05

0,1

0,15

0,2

0,25

0,3

320 325 330 335 340 345

T (K)

km

ax

(s-1

)

0

1

2

3

4

5

6

7

8

9

0 1000 2000 3000 4000 5000 6000 7000time (s)

log N

T=52ºC

T=56ºC

0

1

2

3

4

5

6

7

8

9

0 100 200

time (s)

log

N

T=60ºCT=64ºCT=68ºC

Gompertz

Two-step

L= f(T)

kmax= f(T)

One-stepEquations 1 and 4 selected

L. Monocytogenes in half-cream at 52, 56, 60, 64 e 68ºC (Casadei et al. 1998)

Tank with water +

L. innocua

15 minutes

Weibull model

Inumeration

Ozonation

OUTLINE Objectives of food industry The challenge Predictive microbiology How to obtain the data Available software Validation studies Acknowledgement by regulation The complexity of dynamic conditions Conclusions

How to obtain the data

Experiments in broth at various conditions (pH, T, aw, [growth inhibitors], etc.)

Inoculation studies in foods under various conditions

Derive model parameters

Heuristic sampling

Experimental design Minimize variance of:

predicted response

parameter estimates

Sampling:

Regression schemes

Analysis of residuals

2n

1ikiji

n

1i

2i ,xfyeSSR

Least-squares method

Data analysis:

Mathematical complexity

Adequate description

model parameters

quality

OUTLINE Objectives of food industry The challenge Predictive microbiology How to obtain the data Available software Validation studies Acknowledgement by regulation The complexity of dynamic conditions Conclusions

Available software

predicts shelf-life as well as growth of spoilage and pathogenic bacteria in seafood

evaluates the effect of constant or fluctuating temperature storage conditions (Dalgaard et al. 2002, 2003, 2008)

more than 40 models for different bacterial pathogens

the software allows growth or inactivation of pathogens to be predicted for different combinations of constant temperature, pH, NaCl/aw and, in some cases, other conditions such as organic acid type and concentration, atmosphere, or nitrate

includes more than 40.000 curves/data on growth, survival or inactivation of microorganism in foods.

data has been obtained from the literature or provided by supporting institutions

the modelling toolbox within ComBase includes the Combase Predictor (previously Growth Predictor and Food MicroMoodel).

French decision support system that includes (i) a database with growth and inactivation responses of microorganisms in foods and (ii) predictive models for growth and inactivation of pathogenic bacteria and some spoilage microorganisms

predicts the effect of organic acids, temperature, pH and moisture on growth of Listeria monocytogenes in products

predict the growth of Listeria monocytogenes and Staphylococcus aureus on Ready-To-Eat meat products as a function of pH and water activity

OUTLINE Objectives of food industry The challenge Predictive microbiology How to obtain the data Available software Validation studies Acknowledgement by regulation The complexity of dynamic conditions Conclusions

Validation studies

Before market introduction, validation has to be carried out for new or altered products

After the concept/prototype

acceptance tests refinement of models final formulation validations in challenge test

have to be performed

Milkowski (2012)

This is the main difference between free and paied software

A complete challenge test takes aproximately 3 months + evaluation of process variations + identification of acceptable limits for formulation limits establish a theoretical shelf life

Industry saves costs and time when using reliable predictive micro modelling

Milkowski (2012)

Industry has to perform validation studies, for final verification

The analytical values for pH, water activity, moisture, etc are crucial

However, predictive microbiology does not replace hygiene measure or Good Manufacturing Practices

models can not be the only hurdle to pathogens

Milkowski (2012)

OUTLINE Objectives of food industry The challenge Predictive microbiology How to obtain the data Available software Validation studies Acknowledgement by regulation The complexity of dynamic conditions Conclusions

Acknowledgement by regulation

Milkowski (2012)

United States: - U.S. 9CFR Regulations - 2008 USDA Supplementary Guidance

European Union: - 2005 & 2010 EU regulation

OUTLINE Objectives of food industry The challenge Predictive microbiology How to obtain the data Available software Validation studies Acknowledgement by regulation The complexity of dynamic conditions Conclusions

The complexity of dynamic conditions

The greatest modeller’s effort has been given to data obtained under constant (or static) environmental conditions

From a realistic point of view this is somehow restrictive, since the majority of thermal processes occur under time-varying environmental conditions, and kinetic parameters obtained under such circumstances may differ from the ones estimated at static conditions, which compromises safety control

Case study

OUTLINE Objectives of food industry The challenge Predictive microbiology How to obtain the data Available software Validation studies Acknowledgement by regulation The complexity of dynamic conditions Conclusions

Conclusions

Great progresses in the past 20 years

There are many models available, each with its benefits and limitations

Yet much work has still to be developed, particularly kinetic studies under dynamic conditions

Validation studies have to be carried out for new or altered products, due to the complexity of the systems

Predictive microbiology is a powerful tool, but does not replace hygiene measures or Good Manufacturing Practices

Thank you!

Teresa BrandãoMª Manuel GilMª Fátima MillerElisabeth Alexandre