An Vermeulen, Mieke Uyttendaele, Geert Gins, Anja De Loy...

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

An Vermeulen, Mieke Uyttendaele, Geert Gins, Anja De Loy-Hendrickx,

Hubert Paelinck, Jan Van Impe and Frank Devlieghere

LFMFP – Laboratory of Food Microbiology and Food Preservation, Ghent University, Belgium

BioTeC – Chemical and Biochemical Process Technology and Control, KULeuven, Belgium

KaHo – Sint-Lieven, Belgium

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

Introduction

L. monocytogenes in RTE-foods (EU-legislation N°2073/2005)

(i) RTE foods for infants and for special medical purposes

absence in 25 g

(iii) RTE foods unable to support growth, other than those intended for

infants and for special medical purposes

100 CFU/g (products placed on the market during their shelf-life)

(ii) RTE foods able to support growth, other than those intended for

infants and for special medical purposes

absence in 25 g (before the food has left the immediate control of the food producer)

100 CFU/g (products placed on the market during their shelf-life)

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

Introduction

Industrial project in cooperation with 30 manufacturers of processed meat and with interaction with the Belgian food safety authority

Need for: • Profound validation of existing models • Transfer of knowledge to the industry

Aim: • Reduction in the amount of challenge tests, needed to prove

the compliance with EU 2073/2005 • Stimulate product innovation in the companies

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

Scheme of the research

Overview of existing

models

Intrinsic and extrinsic

properties of meat products

Data collection on

model products

Validation in

industrial products

Validation

historical data

Challenge tests to

assess growth rate

Challenge tests to

assess growth

potential

Challenge tests to

assess growth potential

Listeria Meat Model

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

Scheme of the research

Overview of existing

models

Intrinsic and extrinsic

properties of meat products

Data collection on

model products

Validation in

industrial products

Validation

historical data

Listeria Meat Model

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

Intrinsic and extrinsic factors

Processed meat divided in 5 categories

1. Cooked meat products with meat structure (e.g. cooked ham)

2. Cooked meat products without meat structure (e.g. frankfurter, pâté)

3. Salted, cured meat products (e.g. bacon)

4. Fermented meat products (e.g. salami)

5. Aspic products

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

Intrinsic and extrinsic factors

Data collected from companies:

• Extrinsic properties: Shelf-life, storage temperature,

• Intrinsic properties: pH, lactic acid, acetic acid, nitrite

• Packaging: MAP (gas composition and gas/product ratio), vacuum

Data formed the basis for the recipe for model products (decided by the participating companies)

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

Scheme of the research

Overview of existing

models

Intrinsic and extrinsic

properties of meat products

Data collection on

model products

Validation in

industrial products

Validation

historical data

Listeria Meat Model

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

Data collection challenge testing

• Extensive challenge tests to assess growth rate

– Two monocultures of L. monocytogenes

– Constant temperature (Tref = 7°C )

– 15 data points (for each growth curve)

– Fysico chemical parameters at day 0 and end shelf-life

– Analysis of background flora

0

1

2

3

4

5

6

7

8

9

0 5 10 15 20 25 30

Log

CFU

/g

Time (d)

2

min

2

minmaxrefmax

µµTT

TT

ref

idµCFU/glog imax,

µmax,ref

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

0

1

2

3

4

5

6

7

8

0 5 10 15 20 25 30

Lo

g C

FU

/g

Time (days)

Results MAP cooked ham: growth rate

Batch 1

L. mono 1

L. mono 2

Batch 2

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

Results MAP cooked ham: growth rate

Linear regression

0

1

2

3

4

5

6

7

8

9

0 5 10 15 20 25

Lo

g C

FU

/g

Time (days)

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

Results MAP cooked ham: growth rate

Linear regression

COMBASE

0

1

2

3

4

5

6

7

8

9

0 5 10 15 20 25

Lo

g C

FU

/g

Time (days)

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

SSSP with background flora

Linear regression

COMBASE

0

1

2

3

4

5

6

7

8

9

0 5 10 15 20 25

Lo

g C

FU

/g

Time (days)

Results cooked ham: growth rate

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

SSSP with background flora

SSSP with nitrite

Linear regression

COMBASE

0

1

2

3

4

5

6

7

8

9

0 5 10 15 20 25

Lo

g C

FU

/g

Time (days)

Results cooked ham: growth rate

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

Results MAP cooked meat with adaptation factor

Category 1: cooked meat products with meat structure

Category 2: cooked meat products without meat structure

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

Results other categories

Category 3: salted, cured meat products: no growth of L. monocytogenes

Category 4: fermented meat products: no growth of L. monocytogenes

Category 5: aspic meat products: extra adaptation factor

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

Data collection challenge tests

• Challenge tests to assess growth potential

– Cocktail of strains

– Time-Temperature profile

– Analyses in threefold at day 0 and end of shelf-life

– Fysico chemical parameters at day 0 and end of shelf-life

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

Data collection challenge tests

N° Inoculum Preculturing

conditions

T-profile

1 Cocktail 4 days @ 7°C 7d@8°C+15d@12°C

2 Cocktail 4 days @ 7°C 14d@4°C+8d@8°C

3 Cocktail 4 days @ 7°C 24d@4°C+12d@8°C

EU (1) LFMFP (2-4)

Intern 8 4

Retail 12 4

Consumer 12 8

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

Results cooked ham: growth potential

N° Day 0 THT δ Models

LMM SSSP 1 SSSP 2

1 2.51

2.54

2.63

7.32

7.57

7.79 5.03

2 2.51

2.54

2.63

5.52

5.83

5.00 2.98

3 2.51

2.54

2.63

< 3.00

5.00

6.08 2.46

SSSP 1: with nitrite – without background flora

SSSP 2: without nitrite – with background flora

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

Results cooked ham: growth potential

N° Dag 0 THT δ Models

LMM SSSP 1 SSSP 2

1 2.51

2.54

2.63

7.32

7.57

7.79 5.03 5.99 6.61 3.72

3 2.51

2.54

2.63

5.52

5.83

5.00 2.98 3.88 2.53 3.25

4 2.51

2.54

2.63

< 3.00

5.00

6.08 2.46 5.73 3.92 2.95

SSSP 1: with nitrite – without background flora

SSSP 2: without nitrite – with background flora

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

Conclusions

Challenge tests are expensive for companies,

particularly if 3 x 3 tests are necessary

Predictive models are a very good alternative

Should be profoundly validated in products

- cooked ham no interaction with background flora

- aspic products interaction with background flora

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

Scheme of the research

Overview of existing

models

Intrinsic and extrinsic

properties of meat products

Data collection on

model products

Validation in

industrial products

Validation

historical data

Listeria Meat Model

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

Listeria Meat Model

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

Listeria Meat Model

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

Listeria Meat Model

Flemish Cluster Predictive Microbiology in Foods www.cpmf2.be

Acknowledgement

You, for your attention