7th Dubai International Food Safety Conference&

IAFP’s 1st Middle East Symposium on Food Safety

EXAMPLES OF EXISTING MODELLING TOOLS FOR TRACKING MICROBIAL HAZARDS IN FOOD CHAIN

Moez SANAA & Ewen TODD

QRA

MO

DEL

S“PRO

DU

CTIO

N-TO

-CON

SUM

PTION

”

Cross-contamination and

Recontaminationmodels

Dynamic models for predictive microbiology including Growth & Survival Specific to the food matrix

Consumption patternsPanels, Health status

Starting materialManagement of the primary productionPre-harvest activitiesPrimary production models

Quantitative analysis of raw material quality data / farm release modelsstatistical analysis procedures

RISK

RawMaterials

Transport Retail ConsumerTransportProcess/

Food packaging

Thermal transfer models

MODEL GENERAL PRINCIPLESEXAMPLE MILK PRODUCTION

1. Raw Milk contamination• Growth during transport and storage• During processing: reduction/survival/Growth

2. Contamination during processing• Recontamination• Transfer of organisms from plant environment to cheeses

• Cross contamination• transfer of microorganisms from one cheese to another caused

by direct or indirect contact• Bacteria fate

1. Products/Environment2. Growth/stress

3. Detection / Response: detect and respond to “incidents”

COMPARTMENTAL MODEL

Cheese processing room

Ripeningroom 1

Ripeningroom 2

Ripeningroom 3

Ripeningroom 4

Passageway

Smearing machine

room

Packagingroom

Presence of bacteria colonies in different compartments : milk (cells/Liter), Products (colonies/Product), Environment (colonies), Machines (colonies)

State of compartment C at time t: Ct= (ai, bi), i = 1 to nai = size of the colony i (cells) bi = Latency specific to colony i n = number of colonies

STEP S+1

COMPARTMENTAL MODEL

STEP S

Lot K1

Cheese

Machine

Environment S

Lot K2

Cheese

Environment S+1

Transfer of colonies Intra-lot and inter-lot contaminationsIntra-step and Inter-steps contaminations

MODEL THE TRANSFER OF COLONIES

Machine

Environment

Cheese

pme

pcm

pmc

7th Dubai International Food Safety Conference&

IAFP’s 1st Middle East Symposium on Food Safety

Moez SANAA

FATE OF THE MICRO-ORGANISM IN FOODSTUFFS (PREDICTIVE MICROBIOLOGY MODELS)IMPACT OF FOOD TECHNOLOGY

OUTLINE Primary growth models

Classical modelsMicrobial interactions

Secondary growth modelsCardinal modelsGrowth/no Growth boundaryLag times models

Model validation

GROWTH PHASESENVIRONMENT CONDITIONS ARE CONSIDERED CONSTANT

Time

ln(x)

Lag(latency)

exponantial

Stationary

Death

ln xmax

ln x0

m

0 100 200 300 4005

6

7

8

9

10

0 100 200 300 4005

6

7

8

9

10

temps (h)

log 10

ufc

.ml-1

0 100 200 300 4005

6

7

8

9

10

0 100 200 300 4005

6

7

8

9

10

0 100 200 300 4005

6

7

8

9

10

exponential Gompertz

logistic Baranyi

Rosso

log10 x0 = 5.90lag = 39.9 h

max = 0.037 h-1

log10 x0 = 5.86log10 xmax = 9.54

lag = 50.3 hmax = 0.043 h-1

log10 x0 = 5.60log10 xmax = 9.42

lag = 38.1 hmax = 0.042 h-1

log10 x0 = 5.85log10 xmax = 9.32

lag = 47.5 hmax = 0.040 h-1

log10 x0 = 5.90log10 xmax = 9.35

lag = 39.7 hmax = 0.037 h-1

),,( maxmax xxlagfxdt

dx

FACTORS THAT AFFECT GROWTHBiotic environmentCompetition for nutrients, production of specific inhibitors

(bacteriocins), alteration of the environment

Abiotic environmentTemperature, oxygen levels, specific preservatives (e.g.

nitrite, organic acids, smoke components), space limitation, diffusion of nutriments, etc.

Strain differences

MICROBIAL INTERACTION

Giménex & Dalgaard 2004, Mejlholm & Dalgaard 2007

maxmax

11/

LAB

LAB

Lm

Lm

Lm

dtdLm tLm

t

COM

PARING

TWO

CON

STANT EN

VIRON

MEN

T CON

DITIO

NS

Time (h)

ln x

ln xmax

lag1

ln x0

m1

m2

lag2

pH1 = pH2

aW1 = aW2

Topt= 37°C, Tmin= 2°C

T1 = 25°C, T2 = 15°C

xmax = xmax1 = xmax2

x0 = x01 = x02

SECON

DARY GRO

WTH

MO

DELS

Environmental Factors

"CARDINAL Model"

max (h-1)

0 10 20 30 40 500

0.2

0.4

0.6

0.8

1

1.2

1.4

température (°C)

pH = 7

pH = 6

pH= 5.5

pH = 5

4 6 8 10pH

T = 37°C

T = 25°C

T = 15°C

T = 10°C

SECON

DARY GRO

WTH

MO

DELS

Cardinal temperature model

min

min mi

2max

man minx

( ).( )( )

( ). ( ).( ) ( ).( 2. )opt opt opt opt opt

T T TT

T T T T

T

T T TT T T T

m miax n

min mia nm x

( ).( )( )

( ).( ) ( ).( )

opt opt opt

pH pH pHpH

pH pH pH pH p

pH

pH pHH pH

FULL CARDINAL MODEL

),,()().().().(. 12max awpHTcSRawSRpHCMTCMopt

nXXXnXXXXXXXX

XXXX

optoptoptoptn

opt

n

minmaxmin

1min

minmaxmax ).1().()).((.)(

)).((

min

min)(awaw

awawawSR

opt

MIC

ccSR 1)(

1ψ,0

1ψ5.0,)ψ1(2

5.0ψ,1

)aw,pH,T(ξ

i

ijj

i

φ12

φψ

3

minopt

optX XX

XXφ

CARDINAL MODEL ASSUMPTIONS

Optimal growth is a characteristic of microbial strain specific to food matrixCardinal parameters are strain specific

Could be assessed using broth mediaStrain variability could be captured by varying cardinal parameters

pHT°C

Aw = 0.997

EX: LISTERIA MO

NO

CYTOG

ENES

pHT°C

Aw = 0.95pHT°C

Aw = 0.93

µ = µopt . (T°, pH, aw)

GROWTH BOUNDARY

AUGUSTIN ET AL 2005

))t(θ019.0(1.11exp1

1)t(P

i j j

j

3

min,iopt,i

iopt,i

MIC

)t(c

XX

)t(XX1)t(θ

X : are environment factors (pH, T, aw…)C : are inhibitor factors concentration such as organic acids

LAG TIM

E MO

DELS

Lag time for a microorganism depend onEnvironment parameterPhysiological stage of the microorganism

Relative lag time (RLT)

RLT=lag time/generation time RLT=lag time x Ln2/Growth rate

POPULATION VS CELLULAR LAG TIME

Growth rate of populations and single cells do not differLag time for populations (> 10-100 cfu/g) are shorter and less variable than for single cellsLag time of single cells (corresponding to contamination of some foods) can be predicted from population based data of similar physiological condition

lag time x μmax = 3.9 ± 2.5 (single cells)Ln(Mean lagpopulation) = 0.907 x Ln(Mean lag single cell) – 0.311

Single cell lag is about two times the population lag

DISTRIBU

TION

OF CELL LAG

: OSM

OTIC STRESS (N

ACL 25%

FO 24 H

)Cellular lag time variability

DISTRIBU

TION

OF CELL LAG

: HEAT STRESS (55°C FO

R 4 M

IN)

Cellular lag time variability

EVALUATION AND VALIDATION

Secondary models can be evaluated by comparing measured and predict values of kinetics parametersIn real world the environment conditions vary during time, the experiment should be deigned to allow the combination of secondary and primary model

Measurement of the organism concentrationsAnd all the relevant physical and chemical

parameters during time

SIMPLIFICATION?

The model should take into the account for the food complexity! Example Listeria monocytogenes in smoked fish

Ross & Dalgaard 2004, Mejlholm & Dalgaard 2007

MICROBIAL GROWTH MODELING

Processing conditions

Product characteristics

Storage conditions

0123456789

0 10 20 30 40 50 60

Log

CFU

/g

temps de staockage

Flore d'altération

Micro-organismes pathogène

Shelflife Critical concentration of spoilage micro-organisms

Safe shel-life

Critiacal concentration of pathogenic micro-organisms

Spoilage micro-organismsPathogenic micro-organisms

Storage time

7th Dubai International Food Safety Conference&

IAFP’s 1st Middle East Symposium on Food Safety

MODELING BACTERIAL SURVIVAL OR INACTIVATION KINETICS

BACTERIAL SURVIVAL OR INACTIVATION KINETICS

“Survival curve”Same

Micro-organismMediumTemperature

Graph of the number of survivors according to time

SURVIVAL CURVE, INACTIVATION KINETICS

t, time

log10N1

log10N2

log10N

D

t1 t2

EQUATION

if N2= N1/10, log10(N1/N2)= 1,

t1 – t2 = time to divide the population by 10 = D

slope = -1/D

D = decimal reduction time

21

2

110

21

210110

logloglog

tt

NN

tt

NNslope

t, time

log10N

N

N - 1

D

OTHER WRITING

log10(N) = log10(N0) - t/D

N = N0 • 10-t/D

E = t/D = log10(N0/N) = « efficiency »

= number of decimal reductions= number of log reductions

= log kill

AN INTERESTING CONSEQUENCEN = N0 • 10-E

Consider a lot of units of volume V, the expected number of survivors per unit is given

by:N . V= N0 • V . 10-E

If N . V 1, then the unit is not sterileIf N . V < 1, then the unit is sterile

SHOULDER

timealog10N0

log10N

SHOULDERMulti target theorye.g. clumps

Multi hit theoryActivation taking precedence over inactivation mechanismCells loosing their resistance

e.g. neutral spores in acid suspension medium

e.g. inactivation of catalase

TAILING

OFF

timealog10N0

log10N

TAILING OFF

TAILING OFFMixed populationsClumpingActivation of a secondary spore germination pathwayProtective effect of the suspension medium

e.g. acid spores in neutral medium

S-SHAPED CURVES

Ababouch, L. et al., J. Appl. Bacteriol. 1987 62:503-11

GENERALIZED EQUATION FOR EFFICIENCY

DatE /)( timeatimea

log10N0

log10N

CONCAVITY UPWARD

L'Haridon, R. & Cerf, O. Revue de l'Institut Pasteur de Lyon 1978 11: 445-456.

b

D

t

NN)(

0

*10

b

D

tE

*

NON LINEAR SURVIVAL CURVES

2/3 of experimental studiesMany other equations can be used

INFLUENCE OF TEMPERATUREBIGELOW

T, temperature

log10D

n

n - 1

z

z

TT

réf

réf

DD

10

temperature

timeEqual ti

TEMPERATURE CHANGES

MODELING INDUSTRIAL TREATMENTSEach ti achieves a number of decimal reductions

Ei = (ti – a)/DTi

The total treatment achieves a total number of decimal reductions

i

itotal EE

MODELING INDUSTRIAL TREATMENTSPasteurizing value

The F value for a process is the number of minutes required to kill a known population of microorganisms in a given food under specified conditionSterilizing value

z

T

ii

i

tVP70

10

z

T

ii

i

tF121

10

i

iVPVP

i

iz FF121

EXAMPLE