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