use of ppgyredictive microbiology in the assessment of …€¦ · natat o a ood st tute ( u...

2nd Int. Congress on Quality of Fishery Products, 17-18 Nov., Bilbao

Use of predictive microbiology in the assessment p gy

of health risks and shelf life in fishery products

Paw Dalgaard

Seafood & Predictive Microbiology

National Food Institute (DTU Food) at o a ood st tute ( U ood)

Technical University of Denmark (DTU)

pada@food dtu dk [email protected]

Use of predictive microbiology in the assessment ofhealth risks and shelf life in fishery products

Outline of presentation:

health risks and shelf life in fishery products


• Predictive microbiology

• Human health risks – assessment and management

• Histamine in marine finfish

• Listeria monocytogenes in ready-to-eat seafood

• Shelf-life of fishery products• Shelf life of fishery products

• Predictive models and software

Ti i i • Time-temperature integration tags

• Conclusions and perspectives

DTU Food 2/32


/g)


og c

fu/ Spoilage microorganisms

Pathogenic microorganisms

sms

(L

Critical concentration of

Shelf-life

oorg

anis Critical concentration of

spoilage microorganisms

of m

icro

'Safe shelf-life'

Con

c. o Critical concentration of

pathogenic microorganisms

DTU Food 3/32Storage time

C

Predictive microbiology and seafood complexity

• Temperaturep

• pH

• NaCl/water activity

• Smoke components (phenol)

• Nitrite

• CO2• CO2

• Acetic acid

• Benzoic acid

12 parameters

• Citric acid

• Diacetat

Lactic acid• Lactic acid

• Sorbic acid

• Interactions between all these parameters

DTU Food 4/32

p

Predictive microbiology and seafood complexity

910

6789

u/g)

Growthrate

3456

Log

(cfu

012

Lag time

Storage time

Primary model Secondary model

A simplified approach is needed to model the effect of the many relevant

product characteristics and storage conditions that influence growth and growth

DTU Food 5/32

boundary of pathogenic and spoilage microorganisms in seafood













DTU Food 6/32

Histamine in marine finfish

• Histamine fish poisoning is responsible for more foodborne incidents

of disease than any other hazard in fish and shell fishof disease than any other hazard in fish and shell-fish

Free histidine Histidine decarboxylase HistamineFree histidine Histidine decarboxylase Histamine

• Significant growth is required more than 1-10 million bacteria/g

• Toxic histamine concentrations (> 500 mg/kg) can be formed by:

• Mesophilic bacteria at above 7–10˚C• Mesophilic bacteria at above 7–10 C

• Psychrotolerant bacteria at above ~0˚C

Toxic histamine concentrations can be formed in marine finfish • Toxic histamine concentrations can be formed in marine finfish

when these are chilled in agreement with EU regulations

DTU Food 7/32


Morganella psychrotolerans can grow and is able to produce toxic

t ti f hi t i t 0°Cconcentrations of histamine at 0°C

10Growth Histamine

6000

7000

8000

9000

pm)7

8

9

)

3000

4000

5000

6000

stam

ine

(pp

20°C3

4

5

6

Log(

cfu/

g

20°C15°C

0

1000

2000

3000

His 15°C

10°C 5°C 0°C

0

1

2

3 15°C 10°C 5°C 0°C

0 5 10 15 20 25 30 35 40 45 50

Days0 5 10 15 20 25 30 35 40 45 50

Days

0

DTU Food 8/32Emborg & Dalgaard (2008a)


Both mesophilic and psychrotolerant bacteria have beenresponsible for incidents of histamine fish poisoning

Seafood Bacteria Place and time

Fresh tuna Morganella morganii Japan, 1955

Fresh tuna Morganella morganii Japan, 1965

Fresh tuna Hafnia sp. Prauge, 1967

Fresh tuna Raoultella planticola (Klebsiella California, 1977es tu a aou te a p a t co a ( ebs e apneumoniae)

Ca o a, 9

Dried Sardine Photobacterium phsophoreum Japan, 2002

Tuna in chilisauce Morganella psychrotolerans or Denmark, 2003Tuna in chilisauce Morganella psychrotolerans orPhotobacterium phosphoreum

Denmark, 2003

Cold smoked tuna Photobacterium phosphoreum Denmark, 2004

Cold smoked tuna Morganella psychrotolerans Denmark 2004Cold smoked tuna Morganella psychrotolerans Denmark, 2004

Tuna in flexible packaging

Morganella morganii Denmark, 2004

Fresh tuna Photobacterium phosphoreum Denmark 2006

DTU Food 9/32

Fresh tuna Photobacterium phosphoreum Denmark, 2006

Dalgaard et al. 2008


Predictive models for growth and histamine formation by both

M. psychrotolerans and M. morganii have been developed and validated

1 2

1.4 : Morganella psychrotolerans: Morganella morganii

1.0

1.2

x, h

-1)

0.6

0.8

Sqrt

(µm

ax

0.2

0.4

S

-5 0 5 10 15 20 25 30 35 40 450

DTU Food 10/32

Temperature (°C)

Emborg & Dalgaard (2008b)

Histamine in marine finfish Histamine formation by M psychrotolerans can be predicted for vacuum Histamine formation by M. psychrotolerans can be predicted for vacuum packed fresh tuna and it is markedly faster at 4.4˚C compared to 2.0˚C

DTU Food 11/32Emborg & Dalgaard (2008b) - sssp.dtuaqua.dk


• Combined model for M. psychrotolerans and M. morganii predicts

histamine formation for a wide range of storage temperaturesg g

• The model allows the effect of delayed chilling to be predicted

Delayed chilling: 25˚C for 17 h 25˚C for 22 hThen chilled storage at: 5 ˚C 5˚C

DTU Food 12/32Emborg & Dalgaard (2008b) – http://sssp.dtuaqua.dk













DTU Food 13/32

Listeria monocytogenes

• Present in low concentrations in many fresh and lightly preserved

aquatic foods

• Causes listeriosis with high mortality (20-30%)

• Is rapidly inactivated at > 70 75°C (cooking and hot smoking)• Is rapidly inactivated at > 70 - 75°C (cooking and hot smoking)

• Growth can be difficult to prevent in chilled foods

(Psychrotolerant and halotolerant)

• Listeriosis has been caused by various ready-to-eat (RTE) foods Listeriosis has been caused by various ready to eat (RTE) foods

DTU Food 14/32

Listeria monocytogenes - microbiological criteria

EU regulation (EC 2073/2005):

• RTE seafoods able or unable to support growth

• Predictive microbiology models can be used to document

control of Listeria monocytogenes in seafood

Ready-to-eat f d

Critical limit Comments foods

C t ca t Co e ts

Support growth None in 25 g - When produced

S t th 100 f / It t b d t d th t Support growth 100 cfu/g - It must be documented that 100 cfu/g is not exceeded within the storage period

bl f /Unable to support growth

100 cfu/g - Documentation - pH ≤ 4,4 or aw ≤ 0,92 - pH ≤ 5,0 and aw ≤ 0,94

DTU Food 15/32

- Shelf-life below 5 days

Quantitative microbiological risk assessment(QMRA)

Prevalence and conc. of Listeria

Product characteristics

Storage conditions

Storage time (shelf-life)

Predictive microbiology models

osu

ssm

ent

(Deterministic and stochastic)

Expo

a

sses Output: Predicted concentration in seafood at

the time of consumption

n

Consumption patterns and dose response models

zard

re

rizat

ion dose-response models

DTU Food 16/32

Haz

char

axr

Output: Cases per year FAO/WHO (2004)

Listeriosis QMRA for RTE seafood in Navarra, Spain

• Risk estimate: < 0.3 cases/year in Navarra due to smoked fish

• Risk can be markedly reduced by limiting storage time to 7 daysy y g g y

and storage temperature to 4.5°C

DTU Food 17/32Garrido et al. (2010)

Listeria monocytogenes – product validation of complex predictive growth rate model

0.50Line of perfect match

0.40

cted

0.30

e - p

redi

c

0.20

row

th ra

te

Meat (n = 390)Seafood (n = 145)Poultry (n 47)

0.10G

Bias/accuracy factors = 1.0/1.5 (n = 635)

Poultry (n = 47)Dairy (n = 53)

0.00 0.10 0.20 0.30 0.40 0.50

0.00

DTU Food 18/32

Growth rate - observedMejlholm et al. 2010

S ft di t th f L t i id f f d

Listeria monocytogenes in ready-to-eat seafood

Software can predict growth of L. monocytogenes in a wide range of seafood

DTU Food 19/32DIFRES


Software can predict growth of L. monocytogenes (and lactic acid bacteria, LAB) for constant and dynamic temperature storage conditions

The SSSP software is available for free at http://sssp.dtuaqua.dk

DTU Food 20/32


The SSSP software predicts combinations of product characteristics

that prevent growth of L. monocytogenes

d

MIC sorbic acid

p g y g

rbic

aci

d

ψ = 1

ψ = 2.0

No-growth

hase

so M

IC benzo

ψ = 0

ψ = 1.0

1.5ψ = 1.25

Wat

er p

ψ = 0.5

oic acid

0.75

% W Growth

DTU Food 21/32

% W ater phase benzoic acidMejlholm & Dalgaard 2009

SSSP v. 3.1

DTU Food 22/32


Product development Quality control

(Taget characteristics) (Acceptable variation)

Validated

3.54.0 % LactateTemperature:8°CpH:6.0Phenol:10.0ppmPr edicted growth boundaries - effect of w

Predictive model

Customers Authorities

(Documentation) (Documentation)

DTU Food 23/32













DTU Food 24/32

Growth of spoilage bacteria in fresh MAP cod fillets

910

: Total microfloraPh t b t i h h

78

)

: Photobacterium phosphoreum

56

g (c

fu/g

)

34Lo

g

12

0 2 4 6 8 10 12 14 16 18Storage period (days at 0 oC)

0

DTU Food 25/32

Storage period (days at 0 C)

Shelf-life prediction - models and software

SSO Product Freeware

H2S-producing Fresh seafood - Seafood Spoilage and Safety Predictor

Shewanella Fresh seafood - Seafood Spoilage and Safety Predictor

Pseudomonas spp. Fresh seafood - Combase Predictor - Fish Shelf Life Prediction Fish Shelf Life Prediction

Photobacterium phosphoreum

Fresh marine MAP fish and shell-fish - Seafood Spoilage and Safety Predictor

Lactic acid bacteria Fresh and lightly preserved products

- Seafood Spoilage and Safety Predictor

Brochothrix thermosphacta

Fresh and lightly preserved products

- Combase Predictor

• Seafood Spoilage and Safety Predictor (http://sssp.dtuaqua.dk )

• Combase Predictor (http://www.combase.cc)

DTU Food 26/32

• Fish Shelf Life Prediction (http://www.azti.es/...)

Shelf-life prediction –d l d ft

Software predicts the effect of

models and software Temperature p

measured or theoretical product temperature profiles on growth of

f

(°C)

specific spoilage organisms (SSO) and on the remaining shelf-life of products:products:

Example with Photobacterium phosphoreum in fresh MAP salmonphosphoreum in fresh MAP salmon

DTU Food 27/32

http://sssp.dtuaqua.dk

Time temperature integration (TTI) tags

The French company CRYOLOG produces the microbial TTIs TRACEO® and (eO)®

Predictive models for lactic acid bacteria inside the tags is used to set gtheir response time for different foods

DTU Food 28/32http://www.cryolog.com

Time temperature integration (TTI) tags

DTU Food 29/32http://www.cryolog.com

Conclusions and perspectives 1

• Predictive microbiology models are available to predict human health

risk and shelf-life for several seafood:micro-organisms combinations

• Predictive microbiology models can be used to:

- Evaluate and document the microbial growth in specific seafoodsEvaluate and document the microbial growth in specific seafoods

(Human health risk and shelf-life assessment)

Identify combinations of product characteristics that reduce - Identify combinations of product characteristics that reduce

growth of specific microorganisms

( d t d l t d t ti ) (product development, documentation)

DTU Food 30/32

Conclusions and perspectives 2

• The number successfully validated predictive models is increasing but

we are still lacking:we are still lacking:

- Shelf-life models for various lightly preserved seafoods

M d l f i bi l i l i d h i l h - Models for microbiological, enzymatic and chemical changes

- New/improved safety models (e.g. Clostridium, Vibrio and viruses)

• Software has stimulate the practical application predictive

models within the seafood sector

• The benefits of predictive models are far from being fully exploited

within the seafood sector (including both industry and authorities)

DTU Food 31/32

2nd Int. Congress on Quality of Fishery Products, 17-18 Nov., Bilbao

Use of predictive microbiology in the assessment of p gy


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