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The importance of Predictive
Microbiology in Food Safety
Jeanne-Marie Membré
13 June 2017
2
Food chain production and consumption
EFSA, Parma, June 2017
Formulation:
pH, water
activity
Ambient or
chilled
Storage
Level and
frequency of
mo in raw
materials
Supply-chain
Shelf-life
Process: Heat
treatment Preservatives
Packagin
g
Food Safety
EFSA, Parma, June 2017 3
Food safety is about handling, storing and preparing food to
prevent infection and help to make sure that our food keeps
enough nutrients for us to have a healthy diet
Food Safety Management System (FSMS) provides a
preventative approach to identify, prevent and reduce food-borne
hazards. • This is to minimize the risk of food poisoning and to make
food safe for consumption.
• A well designed FSMS with appropriate control measures can
help food establishments comply with food hygiene regulations
and ensure that food prepared for sale is hygienic and safe for
consumers.
Food and Agriculture organization (FAO)
GMPs / GHPs / GAPs
HACCP
Food business
level
Country level
Standards
Policy
Food Safety management
Risk Management:
– high level, generic
– policy bases guidance
– specific standards, criteria
Risk Management:
– specific
– at supply-chain level
EFSA, Parma, June 2017
Country level
Standards
Policy
Food Safety management
Risk Management:
– high level, generic
– policy bases guidance
– specific standards, criteria
Performance process Standards: Generally recognized processes that have
been established by consensus or by regulation, for example:
• Sterilization of ambient stable non acidic food
– 3 min at 121.1°C (F0 3 min) proteolytic C. botulinum
EFSA, Parma, June 2017 5
GMPs / GHPs / GAPs
HACCP
Food business
level
Food Safety management
Risk Management:
– specific
– at supply-chain level
HACCP consists of seven basic principles, which can be summarized
schematically into the three following steps:
• Conduct a hazard analysis.
• Determine the critical control points (CCPs), e.g. temperature, time,
moisture level, water activity, pH… and establish critical limit(s)
• Develop a HACCP plan form
EFSA, Parma, June 2017 6
7 EFSA, Parma, June 2017
Risk assessment and risk-based food safety
management
8
Microbial
Risk Assessment
Food Safety
Management
Risk-based food safety
management
Predictive Microbiology
in Food Safety
Predictive
Microbiology
EFSA, Parma, June 2017
Food chain production and consumption
9
Risk Analysis
Risk
Communication
Interactive exchange of information and opinions
concerning risks
Risk Assessment
Hazard Identification
Hazard Characterisation
Exposure Assessment
Risk Characterisation
Risk Management
o Risk Evaluation
o Option Assessment
o Option Implementation
o Monitoring & Review
Scientific-based Policy-based
EFSA, Parma, June 2017
10
Exposure assessment
Factors to take into account:
• Propagation of microorganisms (hazard) along the food
supply-chain (transformation/distribution/consumption)
• Quantity eaten (portion and frequency)
• Number of exposed people
EFSA, Parma, June 2017
Formulation: pH, water
activity
Ambient or chilled Storage
Level and frequency of moin raw materials Supply-chain
Shelf-lifeProcess: Heat
treatmentPreservatives
Packaging
11
Microbial
Risk Assessment
Food Safety
Management
Risk-based food safety
management
Predictive Microbiology
in Food Safety
Predictive
Microbiology
EFSA, Parma, June 2017
Food chain production and consumption
RISK ANALYSIS Decisions?
Risk < ALOP: e.g. no action needed
Risk = ALOP: e.g. no action needed
Risk > ALOP: e.g. risk reduction action
needed
Public Health Risk Level
ALOP1 or public
health goal
FSO1
1: ALOP, Appropriate Level Of Protection
FSO, Food Safety Objective
Risk-based food safety management
Risk
Communication
Risk
Assessment
Risk
Management
12 EFSA, Parma, June 2017
Risk-based food safety management
• Food Authority :
• Concerned by foodborne pathogens
• Set ALOP
• e.g.: 0.2 cases of Listeriosis per 100 000 people per year
• To achieve compliance with ALOP Set FSO and may set PO
• e.g. FSO as < 100cfu/g of Listeria at the point of consumption
• e.g. PO2 as < 10cfu/g of Listeria at the manufacture release
FSO
ALOP
PO2 PO1 PO3
13 EFSA, Parma, June 2017
ALOP: Appropriate Level Of Protection, FSO: Food Safety Objective, PO: Performance Objective
FSO
ALOP
PO2 PO1 PO3
14 EFSA, Parma, June 2017
• Food business operator : o set Performance Criteria (PC) to comply with PO/FSO
• e.g. : PC as < 1 log of growth during food preparation in plant
o Set Product criteria (PdC) and Process criteria (PrC) to achieve a PC
• PdC: e.g. pH=5, PrC: e.g. T<8°C in manufacturing area
PC
PdC PrC
Risk-based food safety management
ALOP: Appropriate Level Of Protection
FSO: Food Safety Objective, PO: Performance Objective
15
Microbial
Risk Assessment
Food Safety
Management
Risk-based food safety
management
Predictive Microbiology
in Food Safety
Predictive
Microbiology
EFSA, Parma, June 2017
Food chain production and consumption
16
Microbial
Risk Assessment
Food Safety
Management
Risk-based food safety
management
Predictive Microbiology
in Food Safety
Predictive
Microbiology
EFSA, Parma, June 2017
Food chain production and consumption
Predictive Microbiology
EFSA, Parma, June 2017 17
Predictive microbiology is a description of the responses of
microorganism's to particular environmental conditions such as
• Temperature: storage at cold or ambient conditions, but
also heat-treatment (during manufacture process)
• pH and organic acid (for example in dressings), but and
more generally preservatives (e.g. nitrite in pork meat)
• water activity (for example in bakery products)
Predictive microbiology utilises mathematical models (built with
data from laboratory testing) and computer software to graphically
describe these responses
Adapted from Food Safety Authority of Ireland
Predictive Microbiology
EFSA, Parma, June 2017 18
Three modelling steps
• Primary models: describing the microbial concentration
(behaviour: growth, inactivation, survival) as a function of time.
Estimate kinetic parameters
• Secondary models: describing kinetic parameters as a function
of environmental conditions (e.g. temperature, pH, water
activity)
• Tertiary models: integrate primary and secondary models in
software tools, with friendly interfaces for use by non-modellers
but Food Safety assessors, R&D managers, Quality
managers…
Predictive Microbiology
EFSA, Parma, June 2017 19
Primary models: describing the microbial concentration
(behaviour: growth, inactivation, survival) as a function
of time.
Growth rate
Predictive Microbiology
EFSA, Parma, June 2017 20
Secondary models: describing kinetic parameters as a
function of environmental conditions (e.g. temperature,
pH, water activity)
See also K. Koutsoumanis and F Perez talks
Often 2nd model has a Gamma
structure with cardinal values:
𝜇 = 𝜇 opt x γ(aw) x γ(T) x γ(pH)
21 EFSA, Parma, June 2017
Predictive Microbiology in Food Safety
Food Safety
EFSA, Parma, June 2017 22
Food safety is about handling, storing and preparing food to
prevent infection and help to make sure that our food keeps
enough nutrients for us to have a healthy diet
Food Safety Management System (FSMS) provides a
preventative approach to identify, prevent and reduce food-borne
hazards. • This is to minimize the risk of food poisoning and to make
food safe for consumption.
• A well designed FSMS with appropriate control measures can
help food establishments comply with food hygiene regulations
and ensure that food prepared for sale is hygienic and safe for
consumers.
Food and Agriculture organization (FAO)
Challenge tests aims at studying the growth potential … to assess
for instance the product shelf-life as a control measure.
• It is recommended to estimate the lag time and the maximum
growth rate by fitting a recognized and commonly accepted
primary model used to describe the microbial growth.
• Whenever possible, use strains for which the cardinal values have
been determined … to enable further predictive modelling using
the gamma-concept.
• Assessment of the temperature fluctuations on the microbial
growth…. ……use i) available scientific literature data regarding
temperature monitoring along the food chain, ii) practical monitoring
of storage temperatures in the studied cold chains, and iii)
predictive modelling (what-if scenarios)
One of the control measures: Shelf-life
23 EFSA, Parma, June 2017
Norm ISO/DIS 20976-1 (in preparation)
24 EFSA, Parma, June 2017
Example of Foie gras
Foie Gras
• Foie gras is a popular and well-known delicacy in French
cuisine, belongs to gastronomical heritage (Law n° 2006-11)
• Retorted fatty duck liver
• Ambient stable product
• Heat-treatment process Process criteria (PrC)
• F0 value (time equivalent at 121.1°C) between 0.5 min to 1
min for 80% of the manufacturers in France
• (Process Standards for ambient stable products: 3 min)
• Is it safe to apply a process criteria lower than F0 3 min?
EFSA, Parma, June 2017
PrC
25
Perform a Microbial Risk Assessment, France, current intake
Factors taken into account in the risk assessment
– Level and frequency in “raw” livers
– Effect of Heat Treatment (HT)
– Effect of nitrite
– Consumer habit – Probability illness / dose
Foie Gras
EFSA, Parma, June 2017 26
Log10 N = log10 N0 – t/D
Primary model
Foie Gras
EFSA, Parma, June 2017 27
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
100 105 110 115 120 125 130 135 140 145
Clostridium sporogenes PA 3679
Clostridium botulinum
log
D-v
alu
es
(min
)
Temperature (°C)
Secondary model:
Effet of heat-tretment temperature on inactivation kinetic parameter (D-value)
Foie Gras
EFSA, Parma, June 2017 28
Number of people ill per
year
0.0
0.2
0.4
0.6
0.8
1.0
100 150 200 250 300 350 400 450
Cu
mu
lati
ve
pro
ba
bil
ity d
istr
ibu
tio
n
Number of people ill due to C. botulinum in foie gras per year
F0 0.3 min
Foie Gras
EFSA, Parma, June 2017
Heat-Treatment
29
Number of people ill per
year
0.0
0.2
0.4
0.6
0.8
1.0
-2 0 2 4 6 8 10 12
Cu
mu
lati
ve
pro
ba
bil
ity d
istr
ibu
tio
n
Number of people ill due to C. botulinum in foie gras per year
F0 0.4 min
Foie Gras
EFSA, Parma, June 2017
Heat-Treatment
30
Number of people ill per
year
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
-1 0 1 2 3 4
Cu
mu
lati
ve
pro
ba
bil
ity d
istr
ibu
tio
n
Number of people ill due to C. botulinum in foie gras per year
At F0 0.5 min, 0 case per year
5th percentile: 0
95th percentile: 1
F0 0.5 min
Foie Gras
EFSA, Parma, June 2017
Heat-Treatment
31
• Predictive models implemented in a risk assessment model
• A farm to fork to human assessment
• Foie gras product
• Very low public health risk at F0 0.5 min
• Result in agreement with epidemiological data
• Power of predictive microbiology
• Enable to challenge Standards as F0 3 min for ambient stable product
• Enable to optimize/revisit heat-treatment settings of food product
• Enable to design Process criteria
J.-M. Membré, M. Diao, C. Thorin, G. Cordier, F. Zuber, S. André. 2015. Risk assessment of
proteolytic Clostridium botulinum in canned foie gras. International Journal of Food
Microbiology. 210 : 62–72
32 EFSA, Parma, June 2017
Foie Gras
33 EFSA, Parma, June 2017
Conclusion
34
Microbial
Risk Assessment
Food Safety
Management
Risk-based food safety
management
Predictive Microbiology
in Food Safety
Predictive
Microbiology
EFSA, Parma, June 2017
Food chain production and consumption
GMP, GHP, GAPs
HACCP
Critical limits, CL
Microbial Criteria
Food business
operators
Competent
authority
Public health goal
ALOP
Risk assessment
FSO/PO/PC
Product
Criteria,
PdC
Process
Criteria,
PrC
Adapted from Membré
2013. Elsevier.
Predictive Microbiology application in Food Safety
Shelf-life
EFSA, Parma, June 2017 35
36
• Shelf-life (SL) determination
in-silico determination
in addition of challenge-tests: run what-if scenario (ISO Norm)
• Exposure assessment
Predictive microbiology is included in exposure assessment
Impact of transformation – distribution on consumer exposure,
particularly used in process optimization and storage condition
(SL)
• Risk-based food safety management
Probability to achieve a PO or an FSO
Set process and product criteria to comply PO and FSO
EFSA, Parma, June 2017
Predictive Microbiology application in Food Safety
37
J.-M. Membré, G. Boué. 2017. Quantitative microbial risk assessment during food
processing. In V. Valdramidis, E. Cummin, J. V Impe (Eds). Quantitative Tools for Sustainable
Food and Energy in the food chain. Eurosis Publisher. Chap. 6.
J.-M. Membré. 2016. Microbiological risk assessments in food industry. In Kotzekidou, P.,
(Ed.), Food Hygiene and Toxicology in Ready-to-Eat Foods. Elsevier Inc., UK. ISBN:
9780128019160. Chap. 19. Pp.337-349.
Membré, J.-M., Dagnas, S. 2016. Modeling microbial responses: application to food
spoilage. In: Membré, J.-M., Valdramidis, V., (Eds.), Modeling in food microbiology. From
predictive microbiology to exposure assessment. ISTE Press Ltd and Elsevier Ltd, UK. 33-60.
J.M. Membré. 2013. HAZARD APPRAISAL (HACCP) | Establishment of performance criteria
(MS 155) In Encyclopedia of Food Microbiology, 2nd Edition. Elsevier
J.-M. Membré. 2012. Setting of thermal processes in a context of food safety objectives
(FSOs) and related concepts. In V. Valdramidis and J. F.M. Van Impe (Eds). Progress on
Quantitative Approaches of Thermal Food Processing. Series “Advances in Food Microbiology
and Food Safety”. ISBN: 978-1-62100-842-2. Chapter 12, pp 295-324. Nova Science Publishers
Talk based on
EFSA, Parma, June 2017
38
Thanks for your attention !
http://www6.angers-nantes.inra.fr/secalim
EFSA, Parma, June 2017
39 39
Example of bakery products
EFSA, Parma, June 2017
40
Brioche-type Product
• Best-by-date around 21 days
• Water activity (aw) around 0.86
• No preservative
• Slightly acidic (pH ca 5.2)
• In-factory mould contamination: possible
• Key formulation factor: aw
Combination of aw and shelf-life to avoid spoilage?
EFSA, Parma, June 2017
41
Predictive models for SL determination
• Time (primary model):
• the storage time varies with consumer’s habits.
• Formulation and environmental factor (secondary models):
• the formulation, and particularly aw, does not vary (for a
given product), it has limited acceptable changes, related
to technological feasibility, legislation, regional
requirements, consumer’s preference, etc.
• the storage temperature varies with region & season
EFSA, Parma, June 2017
42
0.27 15.7290.0%
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0 5 10 15 20 25 30
Pro
bab
ility
de
nsi
ty
Storage time (days)
Shelf-life Best before date (BBD) and Shelf-life (= storage time):
• Storage time varies with consumer’s habits (BBD is fixed)
• A general rule for modelling ha been recently suggested:
Time = Exp(Best-Before-Date / 4)
Roccato et al. 2017.
International Food
Research 96, 171-181.
Ex BBD 21
days
EFSA, Parma, June 2017
43
Probability of spoilage
43
• Spoilage rate = Pr Indλ ≤ Storage time
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0 5 10 15 20 25 30
Pro
bab
ility
de
nsi
ty
Storage time (days)
1Ind𝑚𝑒𝑎𝑛 = 1
λ𝑜𝑝𝑡 x γ(aw) x γ(T)
Aw: 0.85; 0.86; 0.87….. 0.90
0.00
0.05
0.10
0.15
0.20
0.25
16 18 20 22 24 26 28 30
Pro
bab
ility
de
nsi
ty
Temperature (°C)
BBD: 7, 14…. 35 days
EFSA, Parma, June 2017
• Spoilage: proba to achieve visible growth before shelf − life
44
Brioche-type Product
0.80
0.82
0.84
0.86
0.88
0.90
7 14 21 28
Wat
er
acti
vity
"Best-Before-Date" (duration expressed in days)
RR 0.7 RR 1 RR 1.1 RR 1.2
Combinations of aw and BBD having same spoilage rate • Iso-risk curve, expressed in Relative Risk (RR)
• RR=1 for current BBD and formulation
EFSA, Parma, June 2017
45
• Predictive modelling for shelf-life determination • Individual spore growth (germination + mycelium growth) as initially low
number of spore on product (post-process contamination)
• Secondary model aw and temperature effect on lag • Gamma structure
• Probabilistic inputs: Storage temperature
• Shelf-life set as small probability of growth before consumption • Pr(lag ≤Storage time)
• Probabilistic inputs: Storage time
• Power of predictive microbiology • Build combinations of aw and SL giving same probability
Highly valuable in formulation and shelf-life design (what-if scenarios)
Dagnas, S., Gougouli, M., Onno, B., Koutsoumanis, K.P., Membré, J.-M., 2017. Quantifying the effect of
water activity and storage temperature on single spore lag times of three moulds isolated from spoiled
bakery products. Int. J. Food Microbiol. 240, 75-84.
45 EFSA, Parma, June 2017
Brioche-type Product