Microbial spoilage of fresh meat -...
Transcript of Microbial spoilage of fresh meat -...
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Microbial spoilage of
fresh meat
Erik Smit
TI Food and Nutrition &
NIZO food research
The Netherlands
5th International Cold Chain Management Workshop
University Bonn, Germany
June 10-11, 2013
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Introduction
• We are busy developing a model for microbial growth on meat
including variance in the growth parameters
• What to expect
• Introduction TI Food and Nutrition & Project
• QMRA
• Preliminary results
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TI Food and Nutrition: Who we are
We are a public-private partnership
with a strategic portfolio of projects,
that are industry-led, and designed
and executed on basis of an
interdisciplinary scientific and
technological approach
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Theme cluster: Food Chain Sustainability
and Dynamics
“The chain approach is the
only way for the agrifood
industry to fulfill its
responsibilities in securing the
world’s food supply.”
Toine Timmermans, MSc.
Theme Director Food Chain Sustainability and
Dynamics
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Project (RE-002):
Reduction of spoilage of fresh
and chilled products
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Project Objective (FPP)
“The aim of the project is to
develop a decision support
system that can be used as a
management tool to analyze the
supply chain of perishable food
products and to evaluate logistical
and technological innovations for
their contribution to spoilage
reduction.”
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Supply Chain
consumer
Supply
Demand
Order
Shrinkage from a retail perspective
Scope and outline: Decision support
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Work package 1:
Microbial spoilage and quality
decay of fresh meat
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Objective work package (FPP)
“The objective of this work package is to develop
accurate shelf life prediction computer models for
fresh meat which can be used as building block of
the decision support system.”
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Outline work package
• Develop predictive shelf life models for fresh meat (pork
tenderloin and minced beef) for decision support system (DSS)
• Main quality parameters shelf life fresh meat:
• Microbial growth
• Focus of rest of this presentation
• Discoloration
• From red to brown color
• Color is very important buying argument
• (Sensorial quality - Lipid oxidation)
• Effect on taste, smell of fresh meat
• Not taken into account in project
Fe++
Fe++
Fe+++
Deoxy- Myoglobin
Oxy- Myoglobin
Met- Myoglobin
Melvin Hunt (USA) & Chris Raines (USA)
Reduction O2 cons.
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Quantitative Microbial Risk Assessment
• A combination of (relatively simple) microbial kinetics, processing
parameter, statistical techniques (Monte Carlo simulation) and
mathematical modeling
max,, awpHTawpHT
Influential parameters process step 1
Influential parameters process step 2
Influential parameters process step 3
Influential parameters process step 4
Source of contamination
Output: 1. Risk of
contamination 2. List of most
influential parameters
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Why QMRA?
• Food producers have an increasing responsibility on food safety
• Legal responsibility
• Supply authorities and clients with proof of safety
• Risks of collateral damage after safety issues
• Challenging risks by new developments
• Minimal processing
• Reducing salt and sugar
• Increasingly complex supply chains
• International sourcing
• Multiple trading steps
• Predict chances in case of low contamination risks
• Quantification of risks
• Chain approach
• Choose a specific part or the whole chain
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QMRA approach: Input
• Process & product
• Describe the process chain in a stepwise manner
• Identify process parameters and their variance
• Identify product properties and their variance
• Micro-organism(s) of interest
• Identify key micro-organisms
• Collect growth data under different circumstances (including their
variance)
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QMRA approach: Model & Output
• Modeling
• Monte Carlo simulation to quantify risk
• Sensitivity analysis to identify most influential parameter/process unit/etc.
• Evaluate “what-if” scenarios
• Output
• Science-based risk on certain occurrence
• Knowledge of critical process parameters
• Calculated effects on changes of process conditions or level of
contamination
• Availability of the model to perform your own calculations
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Growth kinetics of meat specific micro-
organisms (I)
• Meat specific spoilage micro-organisms:
• Brochothrix thermosphacta
• Pseudomonas spp.
• Lactic acid bacteria (LAB)
• Growth data from ComBase for different:
• Temperatures, pH, aw and (gas) conditions
• Kind of meats: Beef, pork, poultry, unknown type of meat and also for
culture medium
• Use growth data to fit primary model (Gompertz)
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Growth kinetics of meat specific micro-
organisms (II) • Primary model:
• Gompertz function (Gibson et al. (1987))
• Secondary model:
• Development in progress
• Response fitted parameter(s) to change in conditions
•
Nupper
Nlower tmaxµrel
µrel
Fitted parameters for one data record are: • Nlower • Log(Nupper/Nlower) • µrel • tmaxµrel E.g. for a certain temperature, aw,
pH, gas condition etc. combination
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Building block decision support system
• Implement primary and secondary model in DSS
• Including variance
• Supply chain of fresh meat (and others) can be analyzed
• Evaluate logistical and technological innovations for their contribution to
spoilage reduction
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Preliminary results (I)
• Growth curve data from ComBase fitted with Gompertz function
• Fitted parameters and input parameters like temperature have
variances
Multi effects play a role: • Type of meat • Temperature • pH • aw • Packaging • Etc.
Brochothrix thermosphacta
0
0,05
0,1
0,15
0,2
0,25
0,3
0,35
0 5 10 15
Gro
wth
rat
e (
h-1
) Temperature ( C)
?
?
Higher temperature higher growth rate (logical)
µrel=f(T,pH,….)
-3,5
-3
-2,5
-2
-1,5
-1
-0,5
0
0 5 10 15 20 25 30 35 40
log(
Gro
wth
rat
e (
h-1
))
Temperature ( C)
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Preliminary results (II)
• Comparison mean values between types of meat, micro-
organisms (primary model)
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Future research
• Develop and implement secondary model
• Including variance
• Implement in Decision support system
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Conclusions
• QMRA is a valuable addition to the QA toolbox
• Make use of probability distributions instead of point estimates
• QMRA enables:
• A focus on the most influential parameters in a complex chain of events
• A fact based discussion
• QMRA can be used as a tool for:
• Convincing the authorities
• Reducing the number of samples
• Evaluation of new scenarios
• Monitoring of temperature in food chain is added value for QMRA
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Questions and remarks?
TI Food and Nutrition, The Netherlands
&
NIZO food research, The Netherlands
Processing & Safety department
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Thanks for your attention